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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 equivalenc(...TRUNCATED) | "Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicl(...TRUNCATED) | "A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks The recent breakthr(...TRUNCATED) | "A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges The lat(...TRUNCATED) | "A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection Large-scale (...TRUNCATED) | "RemembERR: Leveraging Microprocessor Errata for Design Testing and Validation Microprocessors are c(...TRUNCATED) | "Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Effic(...TRUNCATED) | "SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heteroge(...TRUNCATED) | "An analytical framework for URLLC in hybrid MEC environments The conventional mobile architecture i(...TRUNCATED) | "Collaboration as a Service: Digital-Twin-Enabled Collaborative and Distributed Autonomous Driving C(...TRUNCATED) | "Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning. •A car-following mode(...TRUNCATED) | "Deep Residual Learning for Image Recognition Deeper neural networks are more difficult to train. We(...TRUNCATED) | "Tetris: re-architecting convolutional neural network computation for machine learning accelerators (...TRUNCATED) | "Real-Time Estimation of Drivers' Trust in Automated Driving Systems Trust miscalibration issues, re(...TRUNCATED) | 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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