V
Victor C. M. Leung
Researcher at University of British Columbia
Publications - 1645
Citations - 50300
Victor C. M. Leung is an academic researcher from University of British Columbia. The author has contributed to research in topics: Wireless network & Wireless. The author has an hindex of 91, co-authored 1585 publications receiving 40397 citations. Previous affiliations of Victor C. M. Leung include Huazhong University of Science and Technology & University of Hong Kong.
Papers
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Journal ArticleDOI
Body Area Networks: A Survey
TL;DR: This paper provides a detailed investigation of sensor devices, physical layer, data link layer, and radio technology aspects of BAN research, and presents a taxonomy of B Ban projects that have been introduced/proposed to date.
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Cache in the air: exploiting content caching and delivery techniques for 5G systems
TL;DR: A novel edge caching scheme based on the concept of content-centric networking or information-centric networks is proposed and evaluated, using trace-driven simulations to evaluate the performance of the proposed scheme and validate the various advantages of the utilization of caching content in 5G mobile networks.
Book
Extreme Learning Machine
Erik Cambria,Guang-Bin Huang,Liyanaarachchi Lekamalage Chamara Kasun,Hongming Zhou,Chi-Man Vong,Jiarun Lin,Jianping Yin,Zhiping Cai,Qiang Liu,Kuan Li,Victor C. M. Leung,Liang Feng,Yew-Soon Ong,Meng-Hiot Lim,Anton Akusok,Amaury Lendasse,Francesco Corona,Rui Nian,Yoan Miche,Paolo Gastaldo,Rodolfo Zunino,Sergio Decherchi,Xuefeng Yang,Kezhi Mao,Beom-Seok Oh,Jehyoung Jeon,Kar-Ann Toh,Andrew Beng Jin Teoh,Jaihie Kim,Hanchao Yu,Yiqiang Chen,Junfa Liu +31 more
TL;DR: This special issue includes eight original works that detail the further developments of ELMs in theories, applications, and hardware implementation.
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Blockchain-Based Decentralized Trust Management in Vehicular Networks
TL;DR: Simulation results reveal that the proposed system is effective and feasible in collecting, calculating, and storing trust values in vehicular networks.
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Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
TL;DR: By consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge, i.e., Edge DL.