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New directions in cryptography Two kinds of contemporary developments in cryptography are examined. Widening applications of teleprocessing have given rise to a need for new types of cryptographic systems, which minimize the need for secure key distribution channels and supply the equivalent of a written signature. Thi... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
A Comparative Analysis of Selection Schemes Used in Genetic Algorithms This paper considers a number of selection schemes commonly used in modern genetic algorithms. Specifically, proportionate reproduction, rank- ing selection, tournament selection, and Genitor (or «steady state") selec- tion are compared on the basis... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Memetic Algorithms for Continuous Optimisation Based on Local Search Chains Memetic algorithms with continuous local search methods have arisen as effective tools to address the difficulty of obtaining reliable solutions of high precision for complex continuous optimisation problems. There exists a group of continuous ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.00186 | 0.001143 | 0.000761 | 0.000586 | 0.000487 | 0.000358 | 0.000275 | 0.000162 | 0.000081 | 0.000059 | 0.00005 | 0.000044 | 0.000043 |
Graph-Based Algorithms for Boolean Function Manipulation In this paper we present a new data structure for representing Boolean functions and an associated set of manipulation algorithms. Functions are represented by directed, acyclic graphs in a manner similar to the representations introduced by Lee [1] and Akers [2]... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Dynamic program slicing Program slices are useful in debugging, testing, maintenance, and understanding of programs. The conventional notion of a program slice, the static slice, is the set of all statements that might affect the value of a given variable occurrence. In this paper, we investigate the concept of the dyn... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
The nature of statistical learning theory~. First Page of the Article | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
A model of saliency-based visual attention for rapid scene analysis A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001885 | 0.001159 | 0.000772 | 0.000593 | 0.000493 | 0.000363 | 0.000278 | 0.000164 | 0.000082 | 0.00006 | 0.00005 | 0.000045 | 0.000044 |
Threaded code The concept of “threaded code” is presented as an alternative to machine language code. Hardware and software realizations of it are given. In software it is realized as interpretive code not needing an interpreter. Extensions and optimizations are mentioned. | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | Towards Developing High Performance RISC-V Processors Using Agile Methodology While research has shown that the agile chip design methodology is promising to sustain the scaling of computing performance in a more efficient way, it is still of limited usage in actual applications due to two major obstacles: 1) Lack of t... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000575 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Signature Schemes and Anonymous Credentials from Bilinear Maps We propose a new and efficient signature scheme that is provably secure in the plain model. The security of our scheme is based on a discrete-logarithm-based assumption put forth by Lysyanskaya, Rivest, Sahai, and Wolf (LRSW) who also showed that it holds f... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002015 | 0.001238 | 0.000825 | 0.000634 | 0.000527 | 0.000388 | 0.000297 | 0.000175 | 0.000088 | 0.000064 | 0.000054 | 0.000048 | 0.000047 |
NP-complete scheduling problems We show that the problem of finding an optimal schedule for a set of jobs is NP-complete even in the following two restricted cases.o(1)All jobs require one time unit. (2)All jobs require one or two time units, and there are only two processor resolving (in the negative a conjecture of R... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
The Complexity of Flowshop and Jobshop Scheduling NP-complete problems form an extensive equivalence class of combinatorial problems for which no nonenumerative algorithms are known. Our first result shows that determining a shortest-length schedule in an m-machine flowshop is NP-complete for m ≥ 3. For m = 2, there is... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Deep Residual Learning for Image Recognition Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the l... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
A note on two problems in connexion with graphs We consider n points (nodes), some or all pairs of which are connected by a branch; the length of each branch is given. We restrict ourselves to the case where at least one path exists between any two nodes. We now consider two problems. Problem 1. Constrnct the tree of m... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
An analysis of stochastic shortest path problems We consider a stochastic version of the classical shortest path problem whereby for each node of a graph, we must choose a probability distribution over the set of successor nodes so as to reach a certain destination node with minimum expected cost. The costs of transiti... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Learning to Predict by the Methods of Temporal Differences This article introduces a class of incremental learning procedures specialized for prediction – that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit b... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001869 | 0.001149 | 0.000765 | 0.000588 | 0.000489 | 0.00036 | 0.000276 | 0.000162 | 0.000082 | 0.000059 | 0.00005 | 0.000044 | 0.000043 |
Cognitive radio: brain-empowered wireless communications Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001893 | 0.001163 | 0.000775 | 0.000596 | 0.000495 | 0.000364 | 0.000279 | 0.000164 | 0.000083 | 0.00006 | 0.00005 | 0.000045 | 0.000044 |
Authenticated Multi-Party Key Agreement . We examine key agreement protocols providing (i) key authentication(ii) key confirmation and (iii) forward secrecy. Attacks arepresented against previous two-party key agreement schemes and we subsequentlypresent a protocol providing the properties listed above.A generalizatio... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
The Random Oracle Methodology, Revisited. We take a critical look at the relationship between the security of cryptographic schemes in the Random Oracle Model, and the security of the schemes that result from implementing the random oracle by so called "cryptographic hash functions".The main result of this article is a... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | A Heuristic Model For Dynamic Flexible Job Shop Scheduling Problem Considering Variable Processing Times In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artific... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002118 | 0.001302 | 0.000867 | 0.000667 | 0.000554 | 0.000408 | 0.000313 | 0.000184 | 0.000092 | 0.000067 | 0.000056 | 0.00005 | 0.000049 |
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach Evolutionary algorithms (EA's) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to mul- tiobjective optimization have been develope... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO Machine learning (ML) has attracted a great research interest for physical layer design problems, such as channel estimation, thanks to its low complexity and robustness. Channel estimation via ML requires model training on a datase... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | Predicting Node failure in cloud service systems. In recent years, many traditional software systems have migrated to cloud computing platforms and are provided as online services. The service quality matters because system failures could seriously affect business and user experience. A cloud service system typically c... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001868 | 0.001148 | 0.000765 | 0.000588 | 0.000489 | 0.000359 | 0.000276 | 0.000162 | 0.000082 | 0.000059 | 0.00005 | 0.000044 | 0.000043 |
MOPSO: a proposal for multiple objective particle swarm optimization This paper introduces a proposal to extend the heuristic called "particle swarm optimization" (PSO) to deal with multiobjective optimization problems. Our approach uses the concept of Pareto dominance to determine the flight direction of a particle an... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
TCP-Illinois: A loss- and delay-based congestion control algorithm for high-speed networks We introduce a new congestion control algorithm, called TCP-Illinois, which has many desirable properties for implementation in (very) high-speed networks. TCP-Illinois is a sender side protocol, which modifies the AIMD algorithm... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002015 | 0.001238 | 0.000825 | 0.000634 | 0.000527 | 0.000388 | 0.000298 | 0.000175 | 0.000088 | 0.000064 | 0.000054 | 0.000048 | 0.000047 |
CUBIC: a new TCP-friendly high-speed TCP variant CUBIC is a congestion control protocol for TCP (transmission control protocol) and the current default TCP algorithm in Linux. The protocol modifies the linear window growth function of existing TCP standards to be a cubic function in order to improve the scalability of ... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Non-Strict Cache Coherence: Exploiting Data-Race Tolerance in Emerging Applications Software distributed shared memory (DSM) platforms on networks of workstations tolerate large network latencies by employing one of several weak memory consistency models. Data-race tolerant applications, such as Genetic Algorithms (GAs... | A Heuristic Model For Dynamic Flexible Job Shop Scheduling Problem Considering Variable Processing Times In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artific... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002565 | 0.001576 | 0.00105 | 0.000807 | 0.000671 | 0.000493 | 0.000379 | 0.000223 | 0.000112 | 0.000081 | 0.000068 | 0.000061 | 0.000059 |
FAST TCP: Motivation, Architecture, Algorithms, Performance We describe FAST TCP, a new TCP congestion con- trol algorithm for high-speed long-latency networks, from design to implementation. We highlight the approach taken by FAST TCP to address the four difficulties which the current TCP implementa- tion has at large... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | A Heuristic Model For Dynamic Flexible Job Shop Scheduling Problem Considering Variable Processing Times In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artific... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002155 | 0.001324 | 0.000882 | 0.000678 | 0.000564 | 0.000414 | 0.000318 | 0.000187 | 0.000094 | 0.000068 | 0.000057 | 0.000051 | 0.00005 |
Random early detection gateways for congestion avoidance This paper presents Random Early Detection (RED) gateways for congestion avoidance in packet-switched networks. The gateway detects incipient congestion by computing the av- erage queue size. The gateway could notify connections of con- gestion either by dropping... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Performance assessment of multiobjective optimizers: an analysis and review An important issue in multiobjective optimization is the quantitative comparison of the performance of different algorithms. In the case of multiobjective evolutionary algorithms, the outcome is usually an approximation of the Pareto-optimal se... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO Machine learning (ML) has attracted a great research interest for physical layer design problems, such as channel estimation, thanks to its low complexity and robustness. Channel estimation via ML requires model training on a datase... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | Predicting Node failure in cloud service systems. In recent years, many traditional software systems have migrated to cloud computing platforms and are provided as online services. The service quality matters because system failures could seriously affect business and user experience. A cloud service system typically c... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001942 | 0.001193 | 0.000795 | 0.000611 | 0.000508 | 0.000373 | 0.000287 | 0.000169 | 0.000085 | 0.000062 | 0.000052 | 0.000046 | 0.000045 |
Indicator-Based Selection in Multiobjective Search This paper discusses how preference information of the decision maker can in general be integrated into multiobjective search. The main idea is to first define the optimization goal in terms of a binary performance measure (indicator) and then to directly use this meas... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO Machine learning (ML) has attracted a great research interest for physical layer design problems, such as channel estimation, thanks to its low complexity and robustness. Channel estimation via ML requires model training on a datase... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001918 | 0.001179 | 0.000785 | 0.000604 | 0.000502 | 0.000369 | 0.000283 | 0.000167 | 0.000084 | 0.000061 | 0.000051 | 0.000045 | 0.000044 |
PVS: A Prototype Verification System Without Abstract | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Petri nets: Properties, analysis and applications Starts with a brief review of the history and the application areas considered in the literature. The author then proceeds with introductory modeling examples, behavioral and structural properties, three methods of analysis, subclasses of Petri nets and their analysis. ... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | A Heuristic Model For Dynamic Flexible Job Shop Scheduling Problem Considering Variable Processing Times In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artific... | Predicting Node failure in cloud service systems. In recent years, many traditional software systems have migrated to cloud computing platforms and are provided as online services. The service quality matters because system failures could seriously affect business and user experience. A cloud service system typically c... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002332 | 0.001433 | 0.000955 | 0.000734 | 0.00061 | 0.000449 | 0.000344 | 0.000203 | 0.000102 | 0.000074 | 0.000062 | 0.000055 | 0.000054 |
Image quality assessment: from error visibility to structural similarity. Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Un... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | A Heuristic Model For Dynamic Flexible Job Shop Scheduling Problem Considering Variable Processing Times In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artific... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002188 | 0.001345 | 0.000895 | 0.000689 | 0.000572 | 0.000421 | 0.000323 | 0.00019 | 0.000096 | 0.000069 | 0.000058 | 0.000052 | 0.000051 |
An Analysis of Temporal-Difference Learning with Function Approximation We discuss the temporal-difference learning algo- rithm, as applied to approximating the cost-to-go function of an infinite-horizon discounted Markov chain. The algorithm we analyze updates parameters of a linear function approximator on- line duri... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO Machine learning (ML) has attracted a great research interest for physical layer design problems, such as channel estimation, thanks to its low complexity and robustness. Channel estimation via ML requires model training on a datase... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002167 | 0.001332 | 0.000887 | 0.000682 | 0.000567 | 0.000417 | 0.00032 | 0.000188 | 0.000095 | 0.000069 | 0.000058 | 0.000051 | 0.00005 |
Prediction of silicon content in hot metal using support vector regression based on chaos particle swarm optimization The prediction of silicon content in hot metal has been a major study subject as one of the most important means for the monitoring state in ferrous metallurgy industry. A prediction model of silicon co... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001893 | 0.001164 | 0.000775 | 0.000596 | 0.000495 | 0.000365 | 0.00028 | 0.000164 | 0.000083 | 0.00006 | 0.00005 | 0.000045 | 0.000044 |
Neuronlike adaptive elements that can solve difficult learning control problems | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Empirically derived analytic models of wide-area TCP connections Analyzes 3 million TCP connections that occurred during 15 wide-area traffic traces. The traces were gathered at five “stub” networks and two internetwork gateways, providing a diverse look at wide-area traffic. The author derives analytic models describi... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | A Heuristic Model For Dynamic Flexible Job Shop Scheduling Problem Considering Variable Processing Times In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artific... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002118 | 0.001302 | 0.000867 | 0.000667 | 0.000554 | 0.000408 | 0.000313 | 0.000184 | 0.000092 | 0.000067 | 0.000056 | 0.00005 | 0.000049 |
Distinctive Image Features from Scale-Invariant Keypoints This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide r... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Object Detection with Deep Learning: A Review. Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Z3: An Efficient SMT Solver Satisfiability Modulo Theories (SMT) problem is a decision problem for logical first order formulas with respect to combinations of background theories such as: arithmetic, bit-vectors, arrays, and unin- terpreted functions. Z3 is a new and efficient SMT Solver freely available from Microsof... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Computing size-independent matrix problems on systolic array processors A methodology to transform dense to band matrices is presented in this paper. This transformation, is accomplished by triangular blocks partitioning, and allows the implementation of solutions to problems with any given size, by means of contraflow... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Why systolic architectures? First Page of the Article | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Simultaneously Applying Multiple Mutation Operators in Genetic Algorithms The mutation operation is critical to the success of genetic algorithms since it diversifies the search directions and avoids convergence to local optima. The earliest genetic algorithms use only one mutation operator in producing the next genera... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | A Heuristic Model For Dynamic Flexible Job Shop Scheduling Problem Considering Variable Processing Times In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artific... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002405 | 0.001478 | 0.000984 | 0.000757 | 0.000629 | 0.000463 | 0.000355 | 0.000209 | 0.000105 | 0.000076 | 0.000064 | 0.000057 | 0.000056 |
On the security of public key protocols Recently the use of public key encryption to provide secure network communication has received considerable attention. Such public key systems are usually effective against passive eavesdroppers, who merely tap the lines and try to decipher the message. It has been pointed out, h... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Wireless sensor networks: a survey This paper describes the concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and ... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction Cooperative coevolution decomposes a problem into subcomponents and employs evolutionary algorithms for solving them. Cooperative coevolution has been effective for evolving neural networks. Different problem decomposition met... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Design of cascade form FIR filters with discrete valued coefficients | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
The Recognition of Human Movement Using Temporal Templates A new view-based approach to the representation and recognition of human movement is presented. The basis of the representation is a temporal template驴a static vector-image where the vector value at each point is a function of the motion properties at the corre... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Basic concepts and taxonomy of dependable and secure computing This paper gives the main definitions relating to dependability, a generic concept including a special case of such attributes as reliability, availability, safety, integrity, maintainability, etc. Security brings in concerns for confidentiality, in additio... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Quick detection of difficult bugs for effective post-silicon validation We present a new technique for systematically creating postsilicon validation tests that quickly detect bugs in processor cores and uncore components (cache controllers, memory controllers, on-chip networks) of multi-core System on Chips (SoCs). Su... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | A Heuristic Model For Dynamic Flexible Job Shop Scheduling Problem Considering Variable Processing Times In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artific... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002331 | 0.001433 | 0.000954 | 0.000734 | 0.000614 | 0.000448 | 0.000344 | 0.000202 | 0.000102 | 0.000074 | 0.000062 | 0.000055 | 0.000054 |
Wireless Information and Power Transfer: Architecture Design and Rate-Energy Tradeoff Simultaneous information and power transfer over the wireless channels potentially offers great convenience to mobile users. Yet practical receiver designs impose technical constraints on its hardware realization, as practical circuit... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001885 | 0.001159 | 0.000771 | 0.000593 | 0.000493 | 0.000363 | 0.000278 | 0.000164 | 0.000082 | 0.00006 | 0.00005 | 0.000045 | 0.000044 |
A fast and elitist multiobjective genetic algorithm: NSGA-II Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN3) computational complexity (where M is the number of objectives and N is the population size); (2) their non-elitism app... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO Machine learning (ML) has attracted a great research interest for physical layer design problems, such as channel estimation, thanks to its low complexity and robustness. Channel estimation via ML requires model training on a datase... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Predicting Node failure in cloud service systems. In recent years, many traditional software systems have migrated to cloud computing platforms and are provided as online services. The service quality matters because system failures could seriously affect business and user experience. A cloud service system typically c... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001854 | 0.001139 | 0.000759 | 0.000584 | 0.000485 | 0.000357 | 0.000274 | 0.000161 | 0.000081 | 0.000059 | 0.000049 | 0.000044 | 0.000043 |
S2CBench: Synthesizable SystemC Benchmark Suite for High-Level Synthesis. High-level synthesis (HLS) is being increasingly used for commercial VLSI designs. This has led to the proliferation of many HLS tools. In order to evaluate their performance and functionalities, a standard benchmark suite in a common language su... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Massive MIMO for next generation wireless systems Multi-user MIMO offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequen... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | A latent space-based estimation of distribution algorithm for large-scale global optimization Large-scale global optimization problems (LSGOs) have received considerable attention in the field of meta-heuristic algorithms. Estimation of distribution algorithms (EDAs) are a major branch of meta-heuristic algorithms. How... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002117 | 0.001301 | 0.000867 | 0.000667 | 0.000554 | 0.000407 | 0.000313 | 0.000184 | 0.000092 | 0.000067 | 0.000056 | 0.00005 | 0.000049 |
A Low-Complexity Analytical Modeling for Cross-Layer Adaptive Error Protection in Video Over WLAN We find a low-complicity and accurate model to solve the problem of optimizing MAC-layer transmission of real-time video over wireless local area networks (WLANs) using cross-layer techniques. The objective in this problem... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Vision meets robotics: The KITTI dataset We present a novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research. In total, we recorded 6 hours of traffic scenarios at 10-100 Hz using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO Machine learning (ML) has attracted a great research interest for physical layer design problems, such as channel estimation, thanks to its low complexity and robustness. Channel estimation via ML requires model training on a datase... | Deep Residual Learning for Image Recognition Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the l... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001861 | 0.001144 | 0.000762 | 0.000587 | 0.000487 | 0.000358 | 0.000275 | 0.000162 | 0.000081 | 0.000059 | 0.00005 | 0.000044 | 0.000043 |
Caffe: Convolutional Architecture for Fast Feature Embedding Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for tr... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | Predicting Node failure in cloud service systems. In recent years, many traditional software systems have migrated to cloud computing platforms and are provided as online services. The service quality matters because system failures could seriously affect business and user experience. A cloud service system typically c... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001838 | 0.001129 | 0.000752 | 0.000579 | 0.000481 | 0.000354 | 0.000271 | 0.00016 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | A latent space-based estimation of distribution algorithm for large-scale global optimization Large-scale global optimization problems (LSGOs) have received considerable attention in the field of meta-heuristic algorithms. Estimation of distribution algorithms (EDAs) are a major branch of meta-heuristic algorithms. How... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.0021 | 0.001291 | 0.00086 | 0.000661 | 0.000549 | 0.000404 | 0.00031 | 0.000182 | 0.000092 | 0.000066 | 0.000056 | 0.00005 | 0.000049 |
A Survey of Research on Cloud Robotics and Automation The Cloud infrastructure and its extensive set of Internet-accessible resources has potential to provide significant benefits to robots and automation systems. We consider robots and automation systems that rely on data or code from a network to support their operat... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO Machine learning (ML) has attracted a great research interest for physical layer design problems, such as channel estimation, thanks to its low complexity and robustness. Channel estimation via ML requires model training on a datase... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001869 | 0.001148 | 0.000765 | 0.000588 | 0.000489 | 0.000359 | 0.000276 | 0.000162 | 0.000082 | 0.000059 | 0.00005 | 0.000044 | 0.000043 |
Practical Issues in Temporal Difference Learning This paper examines whether temporal difference methods for training connectionist networks, such as Sutton's TD(λ) algorithm, can be successfully applied to complex real-world problems. A number of important practical issues are identified and discussed from a general t... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001886 | 0.001159 | 0.000772 | 0.000594 | 0.000493 | 0.000363 | 0.000278 | 0.000164 | 0.000082 | 0.00006 | 0.00005 | 0.000045 | 0.000044 |
Temporal difference learning and TD-Gammon Ever since the days of Shannon's proposal for a chess-playing algorithm [12] and Samuel's checkers-learning program [10] the domain of complex board games such as Go, chess, checkers, Othello, and backgammon has been widely regarded as an ideal testing ground for exploring a v... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001887 | 0.00116 | 0.000772 | 0.000594 | 0.000494 | 0.000363 | 0.000279 | 0.000164 | 0.000082 | 0.00006 | 0.00005 | 0.000045 | 0.000044 |
Breaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective Cognitive radios hold tremendous promise for increasing spectral efficiency in wireless systems. This paper surveys the fundamental capacity limits and associated transmission techniques for different wireless network design paradigm... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001963 | 0.001206 | 0.000803 | 0.000618 | 0.000513 | 0.000378 | 0.00029 | 0.00017 | 0.000086 | 0.000062 | 0.000052 | 0.000046 | 0.000045 |
W4: Real-Time Surveillance of People and Their Activities $W^4$ is a real time visual surveillance system for detecting and tracking multiple people and monitoring their activities in an outdoor environment. It operates on monocular gray-scale video imagery, or on video imagery from an infrared camera. $W^4$ employs a ... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
The wire-tap channel We consider the situation in which digital data is to be reliably transmitted over a discrete, memoryless channel (dmc) that is subjected to a wire-tap at the receiver. We assume that the wire-tapper views the channel output via a second dmc). Encoding by the transmitter and decoding by the receive... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
A survey on vehicular cloud computing Vehicular networking has become a significant research area due to its specific features and applications such as standardization, efficient traffic management, road safety and infotainment. Vehicles are expected to carry relatively more communication systems, on board computing fa... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002135 | 0.001312 | 0.000874 | 0.000672 | 0.000559 | 0.000411 | 0.000315 | 0.000185 | 0.000093 | 0.000068 | 0.000057 | 0.000051 | 0.000049 |
Overview and Evaluation of Bluetooth Low Energy: An Emerging Low-Power Wireless Technology Bluetooth Low Energy (BLE) is an emerging low-power wireless technology developed for short-range control and monitoring applications that is expected to be incorporated into billions of devices in the next few years. This paper ... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Wireless sensor network survey A wireless sensor network (WSN) has important applications such as remote environmental monitoring and target tracking. This has been enabled by the availability, particularly in recent years, of sensors that are smaller, cheaper, and intelligent. These sensors are equipped with wireless ... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001892 | 0.001163 | 0.000774 | 0.000596 | 0.000495 | 0.000364 | 0.000279 | 0.000164 | 0.000083 | 0.00006 | 0.00005 | 0.000045 | 0.000044 |
A survey on sensor networks The advancement in wireless communications and electronics has enabled the development of low-cost sensor networks. The sensor networks can be used for various application areas (e.g., health, military, home). For different application areas, there are different technical issues that researc... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces A new heuristic approach for minimizing possiblynonlinear and non-differentiable continuous spacefunctions is presented. By means of an extensivetestbed it is demonstrated that the new methodconverges faster and wit... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001851 | 0.001138 | 0.000758 | 0.000583 | 0.000484 | 0.000356 | 0.000273 | 0.000161 | 0.000081 | 0.000059 | 0.000049 | 0.000044 | 0.000043 |
Long short-term memory. Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-bas... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Comparison of Orthogonal vs. Union of Subspace Based Pilots for Multi-Cell Massive MIMO Systems In this paper, we analytically compare orthogonal pilot reuse (OPR) with union of subspace based pilots in terms of channel estimation error and achievable throughput. In OPR, due to the repetition of the same pilot sequence... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002006 | 0.001233 | 0.000821 | 0.000631 | 0.000525 | 0.000386 | 0.000296 | 0.000174 | 0.000088 | 0.000063 | 0.000053 | 0.000047 | 0.000046 |
Sequence to Sequence Learning with Neural Networks. Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we prese... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Comparison of Orthogonal vs. Union of Subspace Based Pilots for Multi-Cell Massive MIMO Systems In this paper, we analytically compare orthogonal pilot reuse (OPR) with union of subspace based pilots in terms of channel estimation error and achievable throughput. In OPR, due to the repetition of the same pilot sequence... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001931 | 0.001187 | 0.00079 | 0.000608 | 0.000505 | 0.000371 | 0.000285 | 0.000168 | 0.000084 | 0.000061 | 0.000051 | 0.000046 | 0.000045 |
Long-term Recurrent Convolutional Networks for Visual Recognition and Description. Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent are effective for tasks involving sequences, visual and otherwise. We describe a class o... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Non-Strict Cache Coherence: Exploiting Data-Race Tolerance in Emerging Applications Software distributed shared memory (DSM) platforms on networks of workstations tolerate large network latencies by employing one of several weak memory consistency models. Data-race tolerant applications, such as Genetic Algorithms (GAs... | A Heuristic Model For Dynamic Flexible Job Shop Scheduling Problem Considering Variable Processing Times In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artific... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002528 | 0.001553 | 0.001034 | 0.000796 | 0.000661 | 0.000486 | 0.000373 | 0.000219 | 0.00011 | 0.00008 | 0.000067 | 0.00006 | 0.000058 |
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe how we can train this model in a deterministic manner using st... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | A Heuristic Model For Dynamic Flexible Job Shop Scheduling Problem Considering Variable Processing Times In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artific... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002255 | 0.001386 | 0.000923 | 0.00071 | 0.00059 | 0.000434 | 0.000333 | 0.000196 | 0.000098 | 0.000071 | 0.00006 | 0.000053 | 0.000052 |
ImageNet Large Scale Visual Recognition Challenge. The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more th... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | Predicting Node failure in cloud service systems. In recent years, many traditional software systems have migrated to cloud computing platforms and are provided as online services. The service quality matters because system failures could seriously affect business and user experience. A cloud service system typically c... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002074 | 0.001275 | 0.000849 | 0.000653 | 0.000543 | 0.000399 | 0.000306 | 0.00018 | 0.000091 | 0.000066 | 0.000055 | 0.000049 | 0.000048 |
Activity Recognition from Accelerometer Data on a Mobile Phone Real-time monitoring of human movements can be easily envisaged as a useful tool for many purposes and future applications. This paper presents the implementation of a real-time classification system for some basic human movements using a conventional mobil... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Machine learning methods for classifying human physical activity from on-body accelerometers. The use of on-body wearable sensors is widespread in several academic and industrial domains. Of great interest are their applications in ambulatory monitoring and pervasive computing systems; here, some quantitative analysis ... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001945 | 0.001195 | 0.000796 | 0.000612 | 0.000509 | 0.000374 | 0.000287 | 0.000169 | 0.000085 | 0.000062 | 0.000052 | 0.000046 | 0.000045 |
Activity recognition using cell phone accelerometers Mobile devices are becoming increasingly sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors. These sensors include GPS sensors, vision sensors (i.e., cameras), audio sensors (i.e., microphones), light senso... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | A Heuristic Model For Dynamic Flexible Job Shop Scheduling Problem Considering Variable Processing Times In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artific... | Predicting Node failure in cloud service systems. In recent years, many traditional software systems have migrated to cloud computing platforms and are provided as online services. The service quality matters because system failures could seriously affect business and user experience. A cloud service system typically c... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.00231 | 0.00142 | 0.000945 | 0.000727 | 0.000604 | 0.000444 | 0.000341 | 0.000201 | 0.000101 | 0.000073 | 0.000062 | 0.000055 | 0.000053 |
A Survey on Human Activity Recognition using Wearable Sensors Providing accurate and opportune information on people's activities and behaviors is one of the most important tasks in pervasive computing. Innumerable applications can be visualized, for instance, in medical, security, entertainment, and tactical scenarios... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002015 | 0.001238 | 0.000825 | 0.000634 | 0.000527 | 0.000388 | 0.000297 | 0.000175 | 0.000088 | 0.000064 | 0.000054 | 0.000048 | 0.000047 |
Extreme learning machine: Theory and applications. It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: (1) the slow gradient-based learning algorithms are exten... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
On how pachycondyla apicalis ants suggest a new search algorithm In this paper we present a new optimization algorithm based on a model of the foraging behavior of a population of primitive ants ( Pachycondyla apicalis ). These ants are characterized by a relatively simple but efficient strategy for prey search in whic... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001826 | 0.001123 | 0.000748 | 0.000575 | 0.000478 | 0.000351 | 0.00027 | 0.000159 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
An Automatic Method For Finding The Greatest Or Least Value Of A Function | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | A latent space-based estimation of distribution algorithm for large-scale global optimization Large-scale global optimization problems (LSGOs) have received considerable attention in the field of meta-heuristic algorithms. Estimation of distribution algorithms (EDAs) are a major branch of meta-heuristic algorithms. How... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Evolution strategies –A comprehensive introduction This article gives a comprehensive introduction into one of the main branches of evolutionary computation – the evolution strategies (ES) the history of which dates back to the 1960s in Germany. Starting from a survey of history the philosophical background is explaine... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO Machine learning (ML) has attracted a great research interest for physical layer design problems, such as channel estimation, thanks to its low complexity and robustness. Channel estimation via ML requires model training on a datase... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions This paper presents a variant of particle swarm optimizers (PSOs) that we call the comprehensive learning particle swarm optimizer (CLPSO), which uses a novel learning strategy whereby all other particles' historical best in... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001971 | 0.001212 | 0.000807 | 0.000621 | 0.000516 | 0.000379 | 0.000291 | 0.000171 | 0.000086 | 0.000062 | 0.000053 | 0.000047 | 0.000046 |
Two-Loop Real-Coded Genetic Algorithms with Adaptive Control of Mutation Step Sizes Genetic algorithms are adaptive methods based on natural evolution that may be used for search and optimization problems. They process a population of search space solutions with three operations: selection, crossover, and mutation. Und... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001886 | 0.001159 | 0.000772 | 0.000594 | 0.000493 | 0.000363 | 0.000278 | 0.000164 | 0.000082 | 0.00006 | 0.00005 | 0.000045 | 0.000044 |
Memetic Algorithms for Continuous Optimisation Based on Local Search Chains Memetic algorithms with continuous local search methods have arisen as effective tools to address the difficulty of obtaining reliable solutions of high precision for complex continuous optimisation problems. There exists a group of continuous ... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | A Heuristic Model For Dynamic Flexible Job Shop Scheduling Problem Considering Variable Processing Times In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artific... | Predicting Node failure in cloud service systems. In recent years, many traditional software systems have migrated to cloud computing platforms and are provided as online services. The service quality matters because system failures could seriously affect business and user experience. A cloud service system typically c... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002486 | 0.001528 | 0.001017 | 0.000783 | 0.00065 | 0.000478 | 0.000367 | 0.000216 | 0.000109 | 0.000079 | 0.000066 | 0.000059 | 0.000057 |
Tracking control for multi-agent consensus with an active leader and variable topology In this paper, we consider a multi-agent consensus problem with an active leader and variable interconnection topology. The state of the considered leader not only keeps changing but also may not be measured. To track such a leader, ... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
ImageNet Classification with Deep Convolutional Neural Networks. We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, resp... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO Machine learning (ML) has attracted a great research interest for physical layer design problems, such as channel estimation, thanks to its low complexity and robustness. Channel estimation via ML requires model training on a datase... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks. Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these real-world ap... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001873 | 0.001151 | 0.000767 | 0.00059 | 0.00049 | 0.00036 | 0.000277 | 0.000163 | 0.000082 | 0.000059 | 0.00005 | 0.000044 | 0.000043 |
Microsoft Coco: Common Objects In Context We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001984 | 0.001219 | 0.000812 | 0.000625 | 0.000519 | 0.000382 | 0.000293 | 0.000172 | 0.000087 | 0.000063 | 0.000053 | 0.000047 | 0.000046 |
Imagenet: A Large-Scale Hierarchical Image Database The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a cr... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | Predicting Node failure in cloud service systems. In recent years, many traditional software systems have migrated to cloud computing platforms and are provided as online services. The service quality matters because system failures could seriously affect business and user experience. A cloud service system typically c... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001848 | 0.001136 | 0.000756 | 0.000582 | 0.000484 | 0.000356 | 0.000273 | 0.00016 | 0.000081 | 0.000059 | 0.000049 | 0.000044 | 0.000043 |
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition Recent results indicate that the generic descriptors extracted from the convolutional neural networks are very powerful. This paper adds to the mounting evidence that this is indeed the case. We report on a series of experiments conducted for different ... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001874 | 0.001152 | 0.000767 | 0.00059 | 0.00049 | 0.00036 | 0.000277 | 0.000163 | 0.000082 | 0.000059 | 0.00005 | 0.000044 | 0.000043 |
Handover schemes in satellite networks: state-of-the-art and future research directions First Page of the Article | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Evolutionary computation: comments on the history and current state Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950's. This article surveys the history as well as the current state of this rapidly growing field. We de... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Non-Strict Cache Coherence: Exploiting Data-Race Tolerance in Emerging Applications Software distributed shared memory (DSM) platforms on networks of workstations tolerate large network latencies by employing one of several weak memory consistency models. Data-race tolerant applications, such as Genetic Algorithms (GAs... | A Heuristic Model For Dynamic Flexible Job Shop Scheduling Problem Considering Variable Processing Times In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artific... | Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction Cooperative coevolution decomposes a problem into subcomponents and employs evolutionary algorithms for solving them. Cooperative coevolution has been effective for evolving neural networks. Different problem decomposition met... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.00261 | 0.001604 | 0.001068 | 0.000822 | 0.000683 | 0.000502 | 0.000385 | 0.000227 | 0.000114 | 0.000083 | 0.00007 | 0.000062 | 0.00006 |
A new GA-Local Search Hybrid for Continuous Optimization Based on Multi-Level Single Linkage Clustering Hybrid algorithms formed by the combination of Genetic Algorithms with Local Search methods provide increased performances when compared to real coded GA or Local Search alone. However, the cost of Local Search can b... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | A Heuristic Model For Dynamic Flexible Job Shop Scheduling Problem Considering Variable Processing Times In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artific... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002429 | 0.001493 | 0.000994 | 0.000765 | 0.000635 | 0.000467 | 0.000359 | 0.000211 | 0.000106 | 0.000077 | 0.000065 | 0.000057 | 0.000056 |
Evolutionary Modeling of Systems of Ordinary Differential Equations with Genetic Programming This paper describes an approach to the evolutionary modeling problem of ordinary differential equations including systems of ordinary differential equations and higher-order differential equations. Hybrid evolutionary modeling... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001893 | 0.001163 | 0.000775 | 0.000596 | 0.000495 | 0.000364 | 0.000279 | 0.000164 | 0.000083 | 0.00006 | 0.00005 | 0.000045 | 0.000044 |
Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach Existing traffic flow forecasting approaches by deep learning models achieve excellent success based on a large volume of data sets gathered by governments and organizations. However, these data sets may contain lots of user's private data, which... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | A Heuristic Model For Dynamic Flexible Job Shop Scheduling Problem Considering Variable Processing Times In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artific... | Predicting Node failure in cloud service systems. In recent years, many traditional software systems have migrated to cloud computing platforms and are provided as online services. The service quality matters because system failures could seriously affect business and user experience. A cloud service system typically c... | Application Task Allocation in Cognitive IoT: A Reward-Driven Game Theoretical Approach In this study we consider the scenario of sensors belonging to different platforms and owned by different owners that join the efforts in an opportunistic way to improve the overall sensing capabilities in a given geographical area ... | 1 | 0.002144 | 0.001318 | 0.000878 | 0.000675 | 0.000561 | 0.000412 | 0.000317 | 0.000186 | 0.000094 | 0.000068 | 0.000057 | 0.000051 | 0.00005 |
Gradient-Based Learning Applied to Document Recognition Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex ... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001869 | 0.001149 | 0.000765 | 0.000588 | 0.000489 | 0.00036 | 0.000276 | 0.000162 | 0.000082 | 0.000059 | 0.00005 | 0.000044 | 0.000043 |
Continual Lifelong Learning with Neural Networks: A Review. Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute ... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | A Heuristic Model For Dynamic Flexible Job Shop Scheduling Problem Considering Variable Processing Times In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artific... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002418 | 0.001486 | 0.00099 | 0.000761 | 0.000633 | 0.000465 | 0.000357 | 0.00021 | 0.000106 | 0.000077 | 0.000064 | 0.000057 | 0.000056 |
Learning without Forgetting. When building a unified vision system or gradually adding new apabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we a... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO Machine learning (ML) has attracted a great research interest for physical layer design problems, such as channel estimation, thanks to its low complexity and robustness. Channel estimation via ML requires model training on a datase... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001957 | 0.001203 | 0.000801 | 0.000616 | 0.000512 | 0.000376 | 0.000289 | 0.00017 | 0.000085 | 0.000062 | 0.000052 | 0.000046 | 0.000045 |
Deep Residual Learning for Image Recognition Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the l... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002103 | 0.001292 | 0.000861 | 0.000662 | 0.00055 | 0.000404 | 0.00031 | 0.000183 | 0.000092 | 0.000067 | 0.000056 | 0.00005 | 0.000049 |
Highly dynamic Destination-Sequenced Distance-Vector routing (DSDV) for mobile computers An ad-hoc network is the cooperative engagement of a collection of Mobile Hosts without the required intervention of any centralized Access Point. In this paper we present an innovative design for the operation of such ad-hoc netwo... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
GRASP: A Search Algorithm for Propositional Satisfiability This paper introduces GRASP (Generic seaRch Algorithm for the Satisfiability Problem), a new search algorithm for Propositional Satisfiability (SAT). GRASP incorporates several search-pruning techniques that proved to be quite powerful on a wide variety of SAT ... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Security and Privacy Issues in Autonomous Vehicles: A Layer-Based Survey Artificial Intelligence (AI) is changing every technology we are used to deal with. Autonomy has long been a sought-after goal in vehicles, and now more than ever we are very close to that goal. Big auto manufacturers as well are investing billion... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Which model to use for cortical spiking neurons? We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks. | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.00186 | 0.001143 | 0.000761 | 0.000585 | 0.000487 | 0.000358 | 0.000275 | 0.000161 | 0.000081 | 0.000059 | 0.00005 | 0.000044 | 0.000043 |
Simple model of spiking neurons A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons. The model combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using this model, one can simulate tens ... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | A survey of dynamic spectrum allocation based on reinforcement learning algorithms in cognitive radio networks Cognitive radio is an emerging technology that is considered to be an evolution for software device radio in which cognition and decision-making components are included. The main function of cognitive radio is... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
A tutorial on support vector regression In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced ... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001914 | 0.001177 | 0.000784 | 0.000603 | 0.000501 | 0.000368 | 0.000283 | 0.000166 | 0.000084 | 0.000061 | 0.000051 | 0.000045 | 0.000044 |
Optimization Of Radio And Computational Resources For Energy Efficiency In Latency-Constrained Application Offloading Providing femto access points (FAPs) with computational capabilities will allow (either total or partial) offloading of highly demanding applications from smartphones to the so-called femto-cloud. Such ... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Relay-Assisted Cooperative Federated Learning Federated learning (FL) has recently emerged as a promising technology to enable artificial intelligence (AI) at the network edge, where distributed mobile devices collaboratively train a shared AI model under the coordination of an edge server. To significantly improve the... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Dynamic Computation Offloading for Mobile-Edge Computing with Energy Harvesting Devices. Mobile-edge computing (MEC) is an emerging paradigm to meet the ever-increasing computation demands from mobile applications. By offloading the computationally intensive workloads to the MEC server, the quality of computation exper... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | A Heuristic Model For Dynamic Flexible Job Shop Scheduling Problem Considering Variable Processing Times In real scheduling problems, unexpected changes may occur frequently such as changes in task features. These changes cause deviation from primary scheduling. In this article, a heuristic model, inspired from Artific... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002153 | 0.001323 | 0.000881 | 0.000678 | 0.000563 | 0.000414 | 0.000318 | 0.000187 | 0.000094 | 0.000068 | 0.000057 | 0.000051 | 0.00005 |
On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration. Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network. MEC offers ... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | DMM: fast map matching for cellular data Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are ... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.002083 | 0.00128 | 0.000853 | 0.000656 | 0.000545 | 0.000401 | 0.000308 | 0.000181 | 0.000091 | 0.000066 | 0.000056 | 0.000049 | 0.000048 |
General Inner Approximation Algorithm For Non-Convex Mathematical Programs Inner approximation algorithms have had two major roles in the mathematical programming literature. Their first role was in the construction of algorithms for the decomposition of large-scale mathe... | Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the des... | A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of stud... | A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial... | A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the appl... | RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are constantly increasing in complexity, but to remain competitive, their design and testing cycles must be kept as short as possible. This trend inevitably leads to design errors that eventually make their way into commercial... | Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process A key energy consumption in steel metallurgy comes from an iron ore sintering process. Enhancing carbon utilization in this process is important for green manufacturing and energy savin... | SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to... | An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV... | Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges bet... | Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model ... | Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving. Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving sim... | Tetris: re-architecting convolutional neural network computation for machine learning accelerators Inference efficiency is the predominant consideration in designing deep learning accelerators. Previous work mainly focuses on skipping zero values to deal with remarkable ineffectual computation, while zero bits in non-z... | Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of... | 1 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
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