English | 繁體

Search in the results

UM

Browse/Search Results:  1-10 of 30 Help

Selected(0)Clear Items/Page:    Sort:
A 512-nW 0.003-mm2 Forward-Forward Black Box Trainer for an Analog Voice Activity Detector in 28-nm CMOS Journal article
LI JUNDE, XIN GUOQIANG, YU WEI HAN, UN KA FAI, RUI P MARTINS, MAK PUI IN. A 512-nW 0.003-mm2 Forward-Forward Black Box Trainer for an Analog Voice Activity Detector in 28-nm CMOS[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2024, 71(11), 4703-4707.
Authors:  LI JUNDE;  XIN GUOQIANG;  YU WEI HAN;  UN KA FAI;  RUI P MARTINS; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:4.0/3.7 | Submit date：2024/08/29
Voice Activity Detection  Convolution Neural Network  Edge Learning  Forward-forward Algorithm  Black Box Training  Back Propagation  
Joint Optimization of Model Partition and Resource Allocation for Split Federated Learning over Vehicular Edge Networks Journal article
Wu Maoqiang, Yang Ruibin, Huang Xumin, Wu Yuan, Kang Jiawen, Xie Shengli. Joint Optimization of Model Partition and Resource Allocation for Split Federated Learning over Vehicular Edge Networks[J]. IEEE Transactions on Vehicular Technology, 2024, 73(10), 15860-15865.
Authors:  Wu Maoqiang;  Yang Ruibin;  Huang Xumin;  Wu Yuan;  Kang Jiawen; et al.
Favorite | TC[WOS]:2 TC[Scopus]:3  IF:6.1/6.5 | Submit date：2024/06/05
Split Federated Learning  Model Partition  Resource Allocation  Vehicular Edge Networks  
A Two-stage Deep Reinforcement Learning Framework for MEC-enabled Adaptive 360-Degree Video Streaming Journal article
Bi Suzhi, Chen Haoguo, Li Xian, Wang Shuoyao, Wu Yuan, Qian Liping. A Two-stage Deep Reinforcement Learning Framework for MEC-enabled Adaptive 360-Degree Video Streaming[J]. IEEE Transactions on Mobile Computing, 2024, 1-17.
Authors:  Bi Suzhi;  Chen Haoguo;  Li Xian;  Wang Shuoyao;  Wu Yuan; et al.
Favorite | TC[Scopus]:1  IF:7.7/6.5 | Submit date：2024/08/26
Accuracy  Adaptive Streaming  Bit Rate  Deep Reinforcement Learning  Multi-access Edge Computing  Quality Of Experience  Quality Of Experience  Real-time Systems  Resists  Streaming Media  Wireless Communication  
Joint Participant and Learning Topology Selection for Federated Learning in Edge Clouds Journal article
Wei, Xinliang, Ye, Kejiang, Shi, Xinghua, Xu, Cheng Zhong, Wang, Yu. Joint Participant and Learning Topology Selection for Federated Learning in Edge Clouds[J]. IEEE Transactions on Parallel and Distributed Systems, 2024, 35(8), 1456-1468.
Authors:  Wei, Xinliang;  Ye, Kejiang;  Shi, Xinghua;  Xu, Cheng Zhong;  Wang, Yu
Favorite | TC[WOS]:0 TC[Scopus]:1  IF:5.6/4.5 | Submit date：2024/07/04
Edge Computing  Federated Learning  Learning Topology  Participant Selection  
A Delta-Sigma-Based Computing-In-Memory Macro Targeting Edge Computation Conference paper
ZHANG RAN, UN KA FAI, GUO MINGQIANG, QI LIANG, XU DENGKE, ZHAO WEIBING, RUI P. MARTINS, FRANCO MALOBERTI, SIN SAI WENG. A Delta-Sigma-Based Computing-In-Memory Macro Targeting Edge Computation[C]:IEEE, 2024.
Authors:  ZHANG RAN;  UN KA FAI;  GUO MINGQIANG;  QI LIANG;  XU DENGKE; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0 | Submit date：2024/08/19
Machine Learning  Edge Computation  Computing-in-memory  Delta-sigma Converter  Floating Inverter Amplifier  
Broadband Digital Over-the-Air Computation for Wireless Federated Edge Learning Journal article
You, Lizhao, Zhao, Xinbo, Cao, Rui, Shao, Yulin, Fu, Liqun. Broadband Digital Over-the-Air Computation for Wireless Federated Edge Learning[J]. IEEE Transactions on Mobile Computing, 2024, 23(5), 5212 - 5228.
Authors:  You, Lizhao;  Zhao, Xinbo;  Cao, Rui;  Shao, Yulin;  Fu, Liqun
Favorite | TC[WOS]:4 TC[Scopus]:5  IF:7.7/6.5 | Submit date：2024/02/23
Atmospheric Modeling  Channel Codes  Data Models  Decoding  Federated Edge Learning  Multiple Access  Ofdm  Over-the-air Computation  Real-time Implementation  Real-time Systems  Servers  Symbols  
Joint Resource Overbooking and Container Scheduling in Edge Computing Journal article
Tang Zhiqing, Mou Fangyi, Lou Jiong, Jia Weijia, Wu Yuan, Zhao Wei. Joint Resource Overbooking and Container Scheduling in Edge Computing[J]. IEEE Transactions on Mobile Computing, 2024, 23(12), 10903 - 10917.
Authors:  Tang Zhiqing;  Mou Fangyi;  Lou Jiong;  Jia Weijia;  Wu Yuan; et al.
Favorite | TC[Scopus]:1  IF:7.7/6.5 | Submit date：2024/05/16
Container Scheduling  Edge Computing  Resource Overbooking  Soft Actor-critic Reinforcement Learning  
Joint Channel Estimation and Reinforcement-Learning-Based Resource Allocation of Intelligent-Reflecting-Surface-Aided Multicell Mobile Edge Computing Journal article
Xu Wenhan, Yu Jiadong, Wu Yuan, Tsang Danny Hin Kwok. Joint Channel Estimation and Reinforcement-Learning-Based Resource Allocation of Intelligent-Reflecting-Surface-Aided Multicell Mobile Edge Computing[J]. IEEE Internet of Things Journal, 2024, 11(7), 11862-11875.
Authors:  Xu Wenhan;  Yu Jiadong;  Wu Yuan;  Tsang Danny Hin Kwok
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:8.2/9.0 | Submit date：2024/05/02
Channel Estimate  Intelligent Reflecting Surface (Irs)  Mobile Edge Computation  Multicell Networks  Proximal Policy Optimization (Ppo)  Reinforcement Learning (Rl)  
Denoising Noisy Neural Networks: A Bayesian Approach with Compensation Journal article
Shao,Yulin, Liew,Soung Chang, Gunduz,Deniz. Denoising Noisy Neural Networks: A Bayesian Approach with Compensation[J]. IEEE Transactions on Signal Processing, 2023, 71, 2460 - 2474.
Authors:  Shao,Yulin;  Liew,Soung Chang;  Gunduz,Deniz
Favorite | TC[WOS]:2 TC[Scopus]:4  IF:4.6/5.2 | Submit date：2023/08/03
Denoiser  Estimation  Federated Edge Learning  Maximum Likelihood Estimation  Neural Networks  Noise Measurement  Noise Reduction  Noisy Neural Network  Training  Wireless Communication  Wireless Transmission Of Neural Networks  
AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices Conference paper
Li Peichun, Cheng Guoliang, Huang Xumin, Kang Jiawen, Yu Rong, Wu Yuan, Pan Miao. AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices[C]:Institute of Electrical and Electronics Engineers Inc., 2023.
Authors:  Li Peichun;  Cheng Guoliang;  Huang Xumin;  Kang Jiawen;  Yu Rong; et al.
Favorite | TC[Scopus]:6 | Submit date：2023/08/29
Federated Learning  Edge Intelligence  Mobile Computing  Resource Management