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NUS-WIDE: a real-world web image database from National University of Singapore This paper introduces a web image dataset created by NUS's Lab for Media Search. The dataset includes: (1) 269,648 images and the associated tags from Flickr, with a total of 5,018 unique tags; (2) six types of low-level features extracted ... | 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 | 0.001873 | 0.001151 | 0.000767 | 0.00059 | 0.00049 | 0.00036 | 0.000277 | 0.000163 | 0.000082 | 0.000059 | 0.00005 | 0.000044 | 0.000043 |
An equational approach to theorem proving in first-order predicate calculus A new approach for proving theorems in first-order predicate calculus is developed based on term rewriting and polynomial simplification methods. A formula is translated into an equivalent set of formulae expressed in terms of 'true', 'false', ... | 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 | 0.002046 | 0.001258 | 0.000838 | 0.000644 | 0.000535 | 0.000394 | 0.000302 | 0.000178 | 0.000089 | 0.000065 | 0.000055 | 0.000048 | 0.000047 |
An Attack Surface Metric Measurement of software security is a long-standing challenge to the research community. At the same time, practical security metrics and measurements are essential for secure software development. Hence, the need for metrics is more pressing now due to a growing demand for secure software. 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 ... | 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 |
TCP Vegas: end to end congestion avoidance on a global Internet Vegas is an implementation of TCP that achieves between 37 and 71% better throughput on the Internet, with one-fifth to one-half the losses, as compared to the implementation of TCP in the Reno distribution of BSD Unix. This paper motivates and describes 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... | 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 timed Petri-net model for fine-grain loop scheduling Efficient execution of loops is one of the most important obstacles facing high-performance computer architectures. Loop scheduling involves handling a partially ordered set of operations which are to be performed repetitively over a number of iterations.In this pa... | 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 | 0.002143 | 0.001317 | 0.000877 | 0.000675 | 0.000561 | 0.000412 | 0.000316 | 0.000186 | 0.000094 | 0.000068 | 0.000057 | 0.000051 | 0.00005 |
The JPEG still picture compression standard A joint ISO/CCITT committee known as JPEG (Joint Photographic Experts Group) has been working to establish the first international compression standard for continuous-tone still images, both grayscale and color. JPEG's proposed standard aims to be generic, to support a wide 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 ... | 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 |
Proceedings of the 4th International Conference on Genetic Algorithms, San Diego, CA, USA, July 1991 | 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 |
Self-configuring crossover Crossover is a core genetic operator in many evolutionary algorithms (EAs). The performance of such EAs on a given problem is dependent on properly configuring crossover. A small set of common crossover operators is used in the vast majority of EAs, typically fixed for the entire evolutionary... | 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 introduction to kernel-based learning algorithms This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis theory 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... | 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 | 0.00122 | 0.000812 | 0.000625 | 0.000519 | 0.000382 | 0.000293 | 0.000172 | 0.000087 | 0.000063 | 0.000053 | 0.000047 | 0.000046 |
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions We present an algorithm for the c-approximate nearest neighbor problem in a d-dimensional Euclidean space, achieving query time of O\left( {dn^{1/c^2+ o(1)} } \right) and space O\left( {dn + n^{1 + 1/c^2+ o(1)} } \right). This almost ma... | 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 | 0.001965 | 0.001208 | 0.000804 | 0.000619 | 0.000514 | 0.000378 | 0.00029 | 0.000171 | 0.000086 | 0.000062 | 0.000052 | 0.000046 | 0.000045 |
The Gaussian wire-tap channel Wyner's results for discrete memoryless wire-tap channels are extended to the Gaussian wire-tap channel. It is shown that the secrecy capacity Cs is the difference between the capacities of the main and wire.tap channels. It is further shown that Rd= Cs is the upper boundary of the achieva... | 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 | 0.001873 | 0.001151 | 0.000767 | 0.00059 | 0.00049 | 0.00036 | 0.000277 | 0.000163 | 0.000082 | 0.000059 | 0.00005 | 0.000044 | 0.000043 |
Poisson Arrivals See Time Averages In many stochastic models, particularly in queueing theory, Poisson arrivals both observe see a stochastic process and interact with it. In particular cases and/or under restrictive assumptions it ... | 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 |
Smallest Enclosing Disks (Balls And Ellipsoids) A simple randomized algorithm is developed which computes the smallest enclosing disk of a finite set of points in the plane in expected linear time. The algorithm is based on Seidel's recent Linear Programming algorithm, and it can be generalized to computing smallest en... | 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 |
DAG-aware AIG rewriting: a fresh look at combinational logic synthesis This paper presents a technique for preprocessing combinational logic before technology mapping. The technique is based on the representation of combinational logic using And-Inverter Graphs (AIGs), a networks of two-input ANDs and inverters. The op... | 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 | 0.002006 | 0.001233 | 0.000821 | 0.000631 | 0.000525 | 0.000386 | 0.000296 | 0.000174 | 0.000088 | 0.000063 | 0.000053 | 0.000047 | 0.000046 |
The self-organizing map The self-organized map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications. The self-organizing map has the property of effectively creating spatially organized internal representations of various features of inp... | 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 |
Geometry theorem proving using Hilbert's Nullstellensatz | 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 | 0.001968 | 0.00121 | 0.000806 | 0.00062 | 0.000515 | 0.000379 | 0.000291 | 0.000171 | 0.000086 | 0.000062 | 0.000052 | 0.000047 | 0.000046 |
Tracking multiple humans in complex situations. Tracking multiple humans in complex situations is challenging. The difficulties are tackled with appropriate knowledge in the form of various models in our approach. Human motion is decomposed into its global motion and limb motion. In the first part, we show how multiple... | 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 | 0.002233 | 0.001373 | 0.000914 | 0.000703 | 0.000584 | 0.00043 | 0.00033 | 0.000194 | 0.000098 | 0.000071 | 0.00006 | 0.000053 | 0.000052 |
The compact genetic algorithm Introduces the compact genetic algorithm (cGA) which represents the population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA with uniform crossover. It processes each gene independently and requires less 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... | Multiple Chaos Embedded Gravitational Search Algorithm This paper proposes a novel multiple chaos embedded gravitational search algorithm (MCGSA) that simultaneously utilizes multiple different chaotic maps with a manner of local search. The embedded chaotic local search can exploit a small region to refine solutions o... | 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 Trigonometric Mutation Operation to Differential Evolution Previous studies have shown that differential evolution is an efficient, effective and robust evolutionary optimization method. However, the convergence rate of differential evolution in optimizing a computationally expensive objective function still does not... | 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 | 0.001849 | 0.001137 | 0.000757 | 0.000582 | 0.000484 | 0.000356 | 0.000273 | 0.000161 | 0.000081 | 0.000059 | 0.000049 | 0.000044 | 0.000043 |
SimpleScalar: An Infrastructure for Computer System Modeling Designers can execute programs on software models to validate a proposed hardware design's performance and correctness, while programmerscan use these models to develop and test software before the real hardwarebecomes available. Three critical requirements d... | 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 |
A genetic algorithm for the vehicle routing problem This study considers the application of a genetic algorithm (GA) to the basic vehicle routing problem (VRP), in which customers of known demand are supplied from a single depot. Vehicles are subject to a weight limit and, in some cases, to a limit on the distance trav... | 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 |
Genetic evolution of the topology and weight distribution of neural networks This paper proposes a system based on a parallel genetic algorithm with enhanced encoding and operational abilities. The system, used to evolve feedforward artificial neural networks, has been applied to two widely different problem areas: Boo... | 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... | 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 | 0.00185 | 0.001137 | 0.000757 | 0.000583 | 0.000484 | 0.000356 | 0.000273 | 0.000161 | 0.000081 | 0.000059 | 0.000049 | 0.000044 | 0.000043 |
Maximum ratio transmission This paper presents the concept, principles, and analysis of maximum ratio transmission for wireless communications, where multiple antennas are used for both transmission and reception. The principles and analysis are applicable to general cases, including maximum-ratio combining. Simulation... | 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... | The road to 6G: a comprehensive survey of deep learning applications in cell-free massive MIMO communications systems The fifth generation (5G) of telecommunications networks is currently commercially deployed. One of their core enabling technologies is cellular Massive Multiple-Input-Multiple-Output (M-MIMO) systems. ... | 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 | 0.00027 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
On-line bipartite matching made simple We examine the classic on-line bipartite matching problem studied by Karp, Vazirani, and Vazirani [8] and provide a simple proof of their result that the Ranking algorithm for this problem achieves a competitive ratio of 1 -- 1/e. | 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 | 0.001892 | 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 of Scheduling Rules In the past two decades researchers in the field of sequencing and scheduling have analyzed several priority dispatching rules through simulation techniques. This paper presents a summary of over 100 such rules, a list of many references that analyze them, and a classification scheme. | 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 |
Generalization in Reinforcement Learning: Safely Approximating the Value Function To appear in: G. Tesauro, D. S. Touretzky and T. K. Leen, eds., Advances in NeuralInformation Processing Systems 7, MIT Press, Cambridge MA, 1995.A straightforward approach to the curse of dimensionality in reinforcementlearning and dyna... | 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... | 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 |
Comparison of multiobjective evolutionary algorithms: empirical results. In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in 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... | 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 | 0.001863 | 0.001145 | 0.000762 | 0.000586 | 0.000487 | 0.000358 | 0.000275 | 0.000162 | 0.000081 | 0.000059 | 0.00005 | 0.000044 | 0.000043 |
Combining convergence and diversity in evolutionary multiobjective optimization. Over the past few years, the research on evolutionary algorithms has demonstrated their niche in solving multiobjective optimization problems, where the goal is to find a number of Pareto-optimal solutions in a single simulation run. Many ... | 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 | 0.00185 | 0.001137 | 0.000757 | 0.000582 | 0.000484 | 0.000356 | 0.000273 | 0.000161 | 0.000081 | 0.000059 | 0.000049 | 0.000044 | 0.000043 |
Dynamic Critical-Path Scheduling: An Effective Technique for Allocating Task Graphs to Multiprocessors In this paper, we propose a static scheduling algorithm for allocating task graphs to fully connected multiprocessors. We discuss six recently reported scheduling algorithms and show that they possess one drawback or ... | 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 |
Fault Injection Techniques and Tools Dependability evaluation involves the study of failures and errors. The destructive nature of a crash and long error latency make it difficult to identify the causes of failures in the operational environment. It is particularly hard to recreate a failure scenario for a large, compl... | 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 |
Clock skew optimization Improving the performance of a synchronous digital system by adjusting the path delays of the clock signal from the central clock source to individual flip-flops is investigated. Using a model to detect clocking hazards, two linear programs are investigated: (1) minimizing the clock period, whil... | 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 |
Argos: practical many-antenna base stations Multi-user multiple-input multiple-output theory predicts manyfold capacity gains by leveraging many antennas on wireless base stations to serve multiple clients simultaneously through multi-user beamforming (MUBF). However, realizing a base station with a large number antenn... | 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 | 0.002097 | 0.001289 | 0.000858 | 0.00066 | 0.000549 | 0.000403 | 0.00031 | 0.000182 | 0.000092 | 0.000066 | 0.000056 | 0.00005 | 0.000048 |
Interference Alignment and Degrees of Freedom of the K-User Interference Channel For the fully connected K user wireless interference channel where the channel coefficients are time-varying and are drawn from a continuous distribution, the sum capacity is characterized as C(SNR)=K/2log(SNR)+o(log(SNR)) . Thus, the K us... | 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 |
Constraint Logic Programming Applied to Model Checking We review and discuss here some of the existing approaches based on CLP (Constraint Logic Programming) for verifying properties of various kinds of state-transition systems. | 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 | 0.001842 | 0.001132 | 0.000754 | 0.00058 | 0.000482 | 0.000354 | 0.000272 | 0.00016 | 0.00008 | 0.000058 | 0.000049 | 0.000044 | 0.000043 |
Slack Matching Quasi Delay-Insensitive Circuits Slack matching is an optimization that determines the amount of buffering that must be added to each channel of a slack elastic asynchronous system in order to reduce its cycle time to a specified target. We present two methods of expressing the slack matching problem as ... | 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... | Simultaneous slack matching, gate sizing and repeater insertion for asynchronous circuits. Slack matching, gate sizing and repeater insertion are well known techniques applied to asynchronous circuits to improve their power and performance. Existing asynchronous optimization flows typically perform these optimizations ... | 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 | 0.001873 | 0.001151 | 0.000767 | 0.00059 | 0.00049 | 0.00036 | 0.000277 | 0.000163 | 0.000082 | 0.000059 | 0.00005 | 0.000044 | 0.000043 |
Proteus: An ASIC Flow for GHz Asynchronous Designs Editors' note:The high-performance benefits of asynchronous design have hitherto been obtained only using full-custom design. This article presents an industrial-strength asynchronous ASIC CAD flow that enables the automatic synthesis and physical design of high-level ... | 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 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Heuristic and Metaheuristic Approaches for a Class of Two-Dimensional Bin Packing Problems <P>Two-dimensional bin packing problems consist of allocating, without overlapping, a given set of small rectangles (items) to a minimum number of large identical rectangles (bins), with the edges of the items parallel to those 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... | 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 |
Particle swarm optimization A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlin... | 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 evolutionary algorithm that constructs recurrent neural networks Standard methods for simultaneously inducing the structure and weights of recurrent neural networks limit every task to an assumed class of architectures. Such a simplification is necessary since the interactions between network structure and 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... | 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 | 0.001936 | 0.00119 | 0.000792 | 0.00061 | 0.000507 | 0.000372 | 0.000286 | 0.000168 | 0.000085 | 0.000061 | 0.000052 | 0.000046 | 0.000045 |
Traveling Salesman Problems with Profits Traveling salesman problems with profits (TSPs with profits) are a generalization of the traveling salesman problem (TSP), where it is not necessary to visit all vertices. A profit is associated with each vertex. The overall goal is the simultaneous optimization of the collected... | 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 |
Accurate, scalable in-network identification of p2p traffic using application signatures The ability to accurately identify the network traffic associated with different P2P applications is important to a broad range of network operations including application-specific traffic engineering, capacity planning, provisioni... | 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 application-specific protocol architecture for wireless microsensor networks Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication... | 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 |
Gradual distributed real-coded genetic algorithms A major problem in the use of genetic algorithms is premature convergence. One approach for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, several subpopulations that are processed by genetic algorithms, 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 ... | 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 | 0.002484 | 0.001527 | 0.001017 | 0.000782 | 0.00065 | 0.000478 | 0.000367 | 0.000216 | 0.000108 | 0.000079 | 0.000066 | 0.000059 | 0.000057 |
A survey on wireless mesh networks Wireless mesh networks (WMNs) have emerged as a key technology for next-generation wireless networking. Because of their advantages over other wireless networks, WMNs are undergoing rapid progress and inspiring numerous applications. However, many technical issues still exist in this ... | 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 |
Fast and exact simultaneous gate and wire sizing by Lagrangian relaxation This paper considers simultaneous gate and wire sizing for general very large scale integrated (VLSI) circuits under the Elmore delay model. We present a fast and exact algorithm which can minimize total area subject to maximum delay bound. The 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... | 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 | 0.001903 | 0.00117 | 0.000779 | 0.000599 | 0.000498 | 0.000366 | 0.000281 | 0.000165 | 0.000083 | 0.00006 | 0.000051 | 0.000045 | 0.000044 |
Activity and location recognition using wearable sensors Using measured acceleration and angular velocity data gathered through inexpensive, wearable sensors, this dead-reckoning method can determine a user's location, detect transitions between preselected locations, and recognize and classify sitting, standing, and w... | 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 optimal algorithm for on-line bipartite matching | 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 |
Reconfigurable computing: a survey of systems and software Due to its potential to greatly accelerate a wide variety of applications, reconfigurable computing has become a subject of a great deal of research. Its key feature is the ability to perform computations in hardware to increase performance, while retaining muc... | 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 | 0.002006 | 0.001233 | 0.000821 | 0.000631 | 0.000525 | 0.000386 | 0.000296 | 0.000174 | 0.000088 | 0.000063 | 0.000053 | 0.000047 | 0.000046 |
Higher-order correlation-based approach to modulation classification of digitally frequency-modulated signals A general framework that theoretically links the higher-order correlation (HOC) domain with statistical decision theory is explored. It is then applied to the problem of classification of M-ary frequency shift ... | 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 |
Predictive models for the breeder genetic algorithm i. continuous parameter optimization In this paper a new genetic algorithm called the Breeder Genetic Algorithm (BGA) is introduced. The BGA is based on artificial selection similar to that used by human breeders. A predictive model for the BGA is presented that is 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... | 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... | A new authenticated group key agreement in a mobile environment A group key agreement protocol enables a group of communicating parties over an untrusted, open network to come up with a
common secret key. It is designed to achieve secure group communication, which is an important research issue for mobile communicatio... | 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 | 0.001904 | 0.00117 | 0.000779 | 0.000599 | 0.000498 | 0.000366 | 0.000281 | 0.000165 | 0.000083 | 0.00006 | 0.000051 | 0.000045 | 0.000044 |
Efficient implementation of a BDD package Efficient manipulation of Boolean functions is an important component of many computer-aided design tasks. This paper describes a package for manipulating Boolean functions based on the reduced, ordered, binary decision diagram (ROBDD) representation. The package is based on 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 ... | 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 | 0.001951 | 0.001199 | 0.000799 | 0.000614 | 0.000511 | 0.000375 | 0.000288 | 0.000169 | 0.000085 | 0.000062 | 0.000052 | 0.000046 | 0.000045 |
Boolector: An Efficient SMT Solver for Bit-Vectors and Arrays Satisfiability Modulo Theories (SMT) is the problem of deciding satisfiability of a logical formula, expressed in a combination of first-order theories. We present the architecture and selected features of Boolector, which is an efficient SMT solver for the ... | 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 |
Indexed BDDs: algorithmic advances in techniques to represent and verify Boolean functions A new Boolean function representation scheme, the Indexed Binary Decision Diagram (IBDD), is proposed to provide a compact representation for functions whose Ordered Binary Decision Diagram (OBDD) representation is intractably la... | 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 | 0.00197 | 0.001211 | 0.000806 | 0.00062 | 0.000515 | 0.000379 | 0.000291 | 0.000171 | 0.000086 | 0.000062 | 0.000053 | 0.000047 | 0.000046 |
Verification of arithmetic circuits with binary moment diagrams | 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 | 0.002145 | 0.001318 | 0.000878 | 0.000675 | 0.000561 | 0.000413 | 0.000317 | 0.000186 | 0.000094 | 0.000068 | 0.000057 | 0.000051 | 0.00005 |
An optimal parallel algorithm for the minimum circle-cover problem Given a set of n circular arcs, the problem of finding a minimum number of circular arcs whose union covers the whole circle has been considered both in sequential and parallel computational models. Here we present a parallel algorithm in the EREW PRAM ... | 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 |
URBiVA: uniform reduction to bit-vector arithmetic We describe a system URBiVA for specifying and solving a range of problems by uniformly reducing them to bit-vector arithmetic (bva). A problem description is given in a C-like specification language and this high-level specification is transformed to a bva formula by ... | 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 |
Novelty detection: a review—part 1: statistical approaches Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes 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 ... | 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 | 0.002097 | 0.001289 | 0.000858 | 0.00066 | 0.000549 | 0.000403 | 0.00031 | 0.000182 | 0.000092 | 0.000066 | 0.000056 | 0.00005 | 0.000048 |
Hard examples for resolution Exponential lower bounds are proved for the length-of-resolution refutations of sets of disjunctions constructed from expander graphs, using the method of Tseitin. Since these sets of clauses encode biconditionals, they have short (polynomial-length) refutations in a standard axiomatic form... | 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 | 0.001981 | 0.001218 | 0.000811 | 0.000624 | 0.000518 | 0.000381 | 0.000293 | 0.000172 | 0.000086 | 0.000063 | 0.000053 | 0.000047 | 0.000046 |
On prediction using variable order Markov models This paper is concerned with algorithms for prediction of discrete sequences over a finite alphabet, using variable order Markov models. The class of such algorithms is large and in principle includes any lossless compression algorithm. We focus on six prominent predicti... | 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 | 0.001823 | 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 effective implementation of the Lin–Kernighan traveling salesman heuristic This paper describes an implementation of the Lin–Kernighan heuristic, one of the most successful methods for generating optimal or near-optimal solutions for the symmetric traveling salesman problem (TSP). Computational tests show that the i... | 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 |
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping This paper investigates conditions underwhich modifications to the reward functionof a Markov decision process preserve the optimalpolicy. It is shown that, besides thepositive linear transformation familiar fromutility theory, on... | 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... | 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 | 0.001823 | 0.001121 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.000269 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Identity-Based aggregate signatures An aggregate signature is a single short string that convinces any verifier that, for all 1 ≤ i ≤ n, signer Si signed message Mi, where the n signers and n messages may all be distinct. The main motivation of aggregate signatures is compactness. However, while the aggregate signature... | 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 | 0.001974 | 0.001213 | 0.000808 | 0.000621 | 0.000516 | 0.00038 | 0.000291 | 0.000171 | 0.000086 | 0.000062 | 0.000053 | 0.000047 | 0.000046 |
Logic emulation with virtual wires Logic emulation enables designers to functionally verify complex integrated circuits prior to chip fabrication. However, traditional FPGA-based logic emulators have poor inter-chip communication bandwidth, commonly limiting gate utilization to less than 20%. Global routing contention ... | 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 Pascal Visual Object Classes (VOC) Challenge The Pascal Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annu... | 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... | AlphaStar: An Evolutionary Computation Perspective. In January 2019, DeepMind revealed AlphaStar to the world---the first artificial intelligence (AI) system to beat a professional player at the game of StarCraft II---representing a milestone in the progress of AI. AlphaStar draws on many areas of AI research, includin... | 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 | 0.001848 | 0.001136 | 0.000756 | 0.000582 | 0.000484 | 0.000356 | 0.000273 | 0.00016 | 0.000081 | 0.000059 | 0.000049 | 0.000044 | 0.000043 |
The exploration/exploitation tradeoff in dynamic cellular genetic algorithms This paper studies static and dynamic decentralized versions of the search model known as cellular genetic algorithm (cGA), in which individuals are located in a specific topology and interact only with their neighbors. Making changes in the s... | 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... | 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 | 0.002159 | 0.001327 | 0.000884 | 0.00068 | 0.000565 | 0.000415 | 0.000319 | 0.000187 | 0.000094 | 0.000068 | 0.000058 | 0.000051 | 0.00005 |
Parallelism and evolutionary algorithms This paper contains a modern vision of the parallelization techniques used for evolutionary algorithms (EAs). The work is motivated by two fundamental facts: 1) the different families of EAs have naturally converged in the last decade while parallel EAs (PEAs) are still lack of u... | 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... | 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 | 0.003122 | 0.001919 | 0.001278 | 0.000983 | 0.000817 | 0.000601 | 0.000461 | 0.000271 | 0.000136 | 0.000099 | 0.000083 | 0.000074 | 0.000072 |
Code generation via higher-order rewrite systems We present the meta-theory behind the code generation facilities of Isabelle/HOL. To bridge the gap between the source (higher-order logic with type classes) and the many possible targets (functional programming languages), we introduce an intermediate language, Mini-Has... | 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 |
Stuxnet: Dissecting a Cyberwarfare Weapon Ralph Langner, an expert in industrial control system security, explores the technical side of Stuxnet, dangerous malware that attacks SCADA systems. | 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 |
Metaheuristics in combinatorial optimization: Overview and conceptual comparison The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industria... | 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 | 0.002683 | 0.001649 | 0.001098 | 0.000845 | 0.000702 | 0.000516 | 0.000396 | 0.000233 | 0.000117 | 0.000085 | 0.000072 | 0.000063 | 0.000062 |
Genetic and Nelder-Mead algorithms hybridized for a more accurate global optimization of continuous multiminima functions A hybrid method combining two algorithms is proposed for the global optimization of multiminima functions. To localize a “promising area”, likely to contain a global minimum, it is necessary to well... | 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... | TensorDash: Exploiting Sparsity to Accelerate Deep Neural Network Training TensorDash is a hardware-based technique that enables data-parallel MAC units to take advantage of sparsity in their input operand streams. When used to compose a hardware accelerator for deep learning, TensorDash can speedup the training proces... | 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 | 0.002166 | 0.001331 | 0.000887 | 0.000682 | 0.000567 | 0.000417 | 0.00032 | 0.000188 | 0.000095 | 0.000069 | 0.000058 | 0.000051 | 0.00005 |
Reinforcement learning with replacing eligibility traces The eligibility trace is one of the basic mechanisms used in reinforcement learning to handle delayed reward. In this paper we introduce a new kind of eligibility trace, the trace, analyze it theoretically, and show that it results in faster, more reliable learni... | 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 | 0.001932 | 0.001188 | 0.000791 | 0.000608 | 0.000506 | 0.000372 | 0.000285 | 0.000168 | 0.000084 | 0.000061 | 0.000052 | 0.000046 | 0.000045 |
Metaheuristics: A bibliography Metaheuristics are the most exciting development in approximate optimization techniques of the last two decades. They have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas. This bibliography provides a clas... | 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... | Binarized Aggregated Network With Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO Systems Massive multiple-input multiple-output (MIMO) is one of the key techniques to achieve better spectrum and energy efficiency in 5G system. The channel state information (CSI) needs to be fed back fr... | Multiple Chaos Embedded Gravitational Search Algorithm This paper proposes a novel multiple chaos embedded gravitational search algorithm (MCGSA) that simultaneously utilizes multiple different chaotic maps with a manner of local search. The embedded chaotic local search can exploit a small region to refine solutions o... | 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 | 0.003868 | 0.002378 | 0.001583 | 0.001218 | 0.001012 | 0.000744 | 0.000571 | 0.000336 | 0.000168 | 0.000122 | 0.000103 | 0.000092 | 0.000089 |
Learning from delayed rewards | 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 |
Training recurrent networks by Evolino. In recent years, gradient-based LSTM recurrent neural networks (RNNs) solved many previously RNN-unlearnable tasks. Sometimes, however, gradient information is of little use for training RNNs, due to numerous local minima. For such cases, we present a novel method: EVOlution of s... | 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 | 0.001953 | 0.0012 | 0.000799 | 0.000615 | 0.000511 | 0.000376 | 0.000288 | 0.00017 | 0.000085 | 0.000062 | 0.000052 | 0.000046 | 0.000045 |
On the importance of initialization and momentum in deep learning. | 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 | 0.001992 | 0.001224 | 0.000815 | 0.000627 | 0.000521 | 0.000383 | 0.000294 | 0.000173 | 0.000087 | 0.000063 | 0.000053 | 0.000047 | 0.000046 |
A novel connectionist system for unconstrained handwriting recognition. Recognizing lines of unconstrained handwritten text is a challenging task. The difficulty of segmenting cursive or overlapping characters, combined with the need to exploit surrounding context, has led to low recognition rates for even the best cur... | 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 | 0.001992 | 0.001224 | 0.000815 | 0.000627 | 0.000521 | 0.000383 | 0.000294 | 0.000173 | 0.000087 | 0.000063 | 0.000053 | 0.000047 | 0.000046 |
Prioritized Sweeping: Reinforcement Learning with Less Data and Less Time We present a new algorithm, prioritized sweeping, for efficient prediction and control of stochastic Markov systems. Incremental learning methods such as temporal differencing and Q-learning have real-time performance. Classical methods are slowe... | 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 |
Are we ready for autonomous driving? The KITTI vision benchmark suite Today, visual recognition systems are still rarely employed in robotics applications. Perhaps one of the main reasons for this is the lack of demanding benchmarks that mimic such scenarios. In this paper, we take advantage of our autonomous driving p... | 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... | 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 | 0.001924 | 0.001183 | 0.000787 | 0.000606 | 0.000503 | 0.00037 | 0.000284 | 0.000167 | 0.000084 | 0.000061 | 0.000051 | 0.000046 | 0.000044 |
Creating Advice-Taking Reinforcement Learners Learning from reinforcements is a promising approach for creating intelligent agents. However, reinforcement learning usually requires a large number of training episodes. We present and evaluate a design that addresses this shortcoming by allowing a connectionist Q-learner... | 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 | 0.002268 | 0.001394 | 0.000928 | 0.000714 | 0.000593 | 0.000436 | 0.000335 | 0.000197 | 0.000099 | 0.000072 | 0.00006 | 0.000054 | 0.000052 |
A vector-perturbation technique for near-capacity multiantenna multiuser communication-part I: channel inversion and regularization Recent theoretical results describing the sum capacity when using multiple antennas to communicate with multiple users in a known rich scattering environment have not yet been followed 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 ... | 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 |
Analog-to-digital converter survey and analysis Analog-to-digital converters (ADCs) are ubiquitous, critical components of software radio and other signal processing systems. This paper surveys the state-of-the-art of ADCs, including experimental converters and commercially available parts. The distribution of resoluti... | 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 |
Securing vehicular ad hoc networks Vehicular networks are very likely to be deployed in the coming years and thus become the most relevant form of mobile ad hoc networks. In this paper, we address the security of these networks. We provide a detailed threat analysis and devise an appropriate security architecture. 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... | 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 | 0.001823 | 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 Efficient Identity-Based Batch Verification Scheme for Vehicular Sensor Networks With the adoption of state-of-the-art telecommunication technologies for sensing and collecting traffic related information, Vehicular Sensor Networks (VSNs) have emerged as a new application scenario that is envisioned to revolutionize... | 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 | 0.002088 | 0.001283 | 0.000855 | 0.000657 | 0.000546 | 0.000402 | 0.000308 | 0.000181 | 0.000091 | 0.000066 | 0.000056 | 0.000049 | 0.000048 |
A Scalable Robust Authentication Protocol for Secure Vehicular Communications Existing authentication protocols to secure vehicular ad hoc networks (VANETs) raise challenges such as certificate distribution and revocation, avoidance of computation and communication bottlenecks, and reduction of the strong reliance on 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 ... | 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... | 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 | 0.002048 | 0.001259 | 0.000838 | 0.000645 | 0.000536 | 0.000394 | 0.000302 | 0.000178 | 0.000089 | 0.000065 | 0.000055 | 0.000048 | 0.000047 |
An Efficient k-Means Clustering Algorithm: Analysis and Implementation In k\hbox{-}{\rm{means}} clustering, we are given a set of n data points in d\hbox{-}{\rm{dimensional}} space {\bf{R}}^d and an integer k and the problem is to determine a set of k points in {\bf{R}}^d, called centers, so as to minimize the mean squ... | 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... | AoI Minimization Charging at Wireless-Powered Network Edge Age of Information (AoI) has emerged as a new metric to measure data freshness from the destination’s perspective. The problem of optimizing AoI has been attracting extensive interests recently. However, existing works mainly focused on scheduling data transmis... | 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 | 0.001122 | 0.000746 | 0.000574 | 0.000477 | 0.000351 | 0.00027 | 0.000158 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Wireless Information-Theoretic Security This paper considers the transmission of confidential data over wireless channels. Based on an information-theoretic formulation of the problem, in which two legitimates partners communicate over a quasi-static fading channel and an eavesdropper observes their transmissions throu... | 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 | 0.002145 | 0.001318 | 0.000878 | 0.000675 | 0.000561 | 0.000413 | 0.000317 | 0.000186 | 0.000094 | 0.000068 | 0.000057 | 0.000051 | 0.00005 |
GORDIAN: VLSI placement by quadratic programming and slicing optimization The authors present a placement method for cell-based layout styles. It is composed of alternating and interacting global optimization and partitioning steps that are followed by an optimization of the area utilization. Methods using the divide-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 |
Learning to predict where humans look For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene. Where eye tracking devices are not a viable option, models of saliency can be used to pre- dict fixation locations. Most saliency approaches are ba... | 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 |
Certificateless Public Key Cryptography. This paper introduces and makes concrete the concept of certificateless public key cryptography (CL-PKC), a model for the use of public key cryptography which avoids the inherent escrow of identity-based cryptography and yet which does not require certificates to guarantee the 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 |
Asymmetric group key agreement A group key agreement (GKA) protocol allows a set of users to establish a common secret via open networks. Observing that a major goal of GKAs for most applications is to establish a confiDential channel among group members, we revisit the group key agreement definition and distinguish 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 ... | 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 | 0.001867 | 0.001148 | 0.000764 | 0.000588 | 0.000488 | 0.000359 | 0.000276 | 0.000162 | 0.000082 | 0.000059 | 0.00005 | 0.000044 | 0.000043 |
Image information and visual quality Measurement of visual quality is of fundamental importance to numerous image and video processing applications. The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images or videos in a perceptually consistent manner. Ima... | 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 | 0.002226 | 0.001368 | 0.000911 | 0.000701 | 0.000582 | 0.000428 | 0.000329 | 0.000193 | 0.000097 | 0.00007 | 0.000059 | 0.000053 | 0.000051 |
Formal verification in hardware design: a survey In recent years, formal methods have emerged as an alternative approach to ensuring the quality and correctness of hardware designs, overcoming some of the limitations of traditional validation techniques such as simulation and testing.There are two main aspects to the 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... | 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... | 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 | 0.00279 | 0.001715 | 0.001142 | 0.000878 | 0.00073 | 0.000537 | 0.000412 | 0.000242 | 0.000122 | 0.000088 | 0.000074 | 0.000066 | 0.000065 |
GSA: A Gravitational Search Algorithm In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm based on the law of gravity and mass interactions is introduced. In the proposed algorithm, the ... | 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 | 0.002031 | 0.001248 | 0.000831 | 0.000639 | 0.000531 | 0.000391 | 0.0003 | 0.000176 | 0.000089 | 0.000064 | 0.000054 | 0.000048 | 0.000047 |
Gradient tabu search. This paper presents a modification of the tabu search called gradient tabu search (GTS). It uses analytical gradients for a fast minimization to the next local minimum and analytical diagonal elements of the Hessian to escape local minima. For an efficient blocking of already visited areas tabu re... | 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 | 0.001878 | 0.001155 | 0.000769 | 0.000591 | 0.000491 | 0.000361 | 0.000277 | 0.000163 | 0.000082 | 0.000059 | 0.00005 | 0.000044 | 0.000043 |
The parallel genetic algorithm as function optimizer In this paper, the parallel genetic algorithm PGA is applied to the optimization of continuous functions. The PGA uses a mixed strategy. Subpopulations try to locate good local minima. If a subpopulation does not progress after a number of generations, hillclimbing i... | 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... | 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 | 0.003152 | 0.001937 | 0.00129 | 0.000992 | 0.000825 | 0.000606 | 0.000465 | 0.000274 | 0.000138 | 0.0001 | 0.000084 | 0.000075 | 0.000073 |
Genetic Set Recombination and Its Application to Neural Network Topology Optimisation Forma analysis is applied to the task of op- timising the connectivity of a feed-forward neural network with a single layer of hidden units. This problem is reformulated as a mul- tiset optimisation problem and techniques are 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... | 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 |
College Admissions and the Stability of Marriage. | 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 |
Bias and Variance Approximation in Value Function Estimates We consider a finite-state, finite-action, infinite-horizon, discounted reward Markov decision process and study the bias and variance in the value function estimates that result from empirical estimates of the model parameters. We provide closed-form approxim... | 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 |
Reciprocal N-body Collision Avoidance In this paper, we present a formal approach to reciprocal n-body collision avoidance, where multiple mobile robots need to avoid collisions with each other while moving in a common workspace. In our formulation, each robot acts fully in- dependently, and does not communicate with 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... | Recombination and Novelty in Neuroevolution: A Visual Analysis Neuroevolution has re-emerged as an active topic in the last few years. However, there is a lack of accessible tools to analyse, contrast and visualise the behaviour of neuroevolution systems. A variety of search strategies have been proposed such as Novelt... | 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 | 0.000161 | 0.00008 | 0.000058 | 0.000049 | 0.000043 | 0.000042 |
Grey Wolf Optimizer. This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulati... | 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 | 0.001949 | 0.001198 | 0.000798 | 0.000614 | 0.00051 | 0.000375 | 0.000288 | 0.000169 | 0.000085 | 0.000062 | 0.000052 | 0.000046 | 0.000045 |
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