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  Jianwei Huang

  Vice President, z6首页; Presidential Chair Professor, CUHK-Shenzhen; Editor-in-Chief, IEEE TNSE; IEEE Fellow; AAIA Fellow

  Executive Chair

  Bio

  Abstract
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  Yang Yang

  Full Professor, ShanghaiTech University; IEEE Fellow; Founding Area Editor for Mobile Networks and Network Learning, IEEE TNSE

  Host

  Bio

  Prof. Yang Yang, IEEE Fellow, is currently a full professor with the School of Information Science and Technology, Master of Kedao College, and Director of Shanghai Institute of Fog Computing Technology (SHIFT), ShanghaiTech University, China. He is also an Adjunct Professor with the Research Center for Network Communication, Peng Cheng Laboratory, China, and a Senior Consultant for Shenzhen Smart City Technology Development Group, China. Before joining ShanghaiTech University, he held faculty positions at the Chinese University of Hong Kong, Brunel University, U.K., University College London (UCL), U.K. and CAS-SIMIT. 

  Prof. Yang's research interests include multi-tier computing networks, 5G/6G systems, AIoT technologies, intelligent services and applications, and advanced wireless testbeds. He has published more than 300 papers and filed more than 120 technical patents in these research areas. He has been the Chair of the Steering Committee of Asia-Pacific Conference on Communications (APCC) from 2019 to 2021. Currently, he is serving the IEEE Communications Society as the Chair for 5G Industry Community and Chair for Asia Region at Fog/Edge Industry Community. 

  Abstract
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  Shiwen Mao

  Professor and Earle C. Williams Eminent Scholar, Director, Wireless Engineering Research and Education Center, Dept. of Electrical & Computer Engineering, Auburn University; IEEE Fellow; Founding Area Editor for Computer Networks, IEEE TNSE

  TARF: Technology-agnostic RF Sensing for Human Activity Recognition

  Bio

  SHIWEN MAO is a professor and Earle C. Williams Eminent Scholar Chair, and Director of the Wireless Engineering Research and Education Center (WEREC) at Auburn University. His research interest includes wireless networks, multimedia communications, and smart grid. He is a Distinguished Lecturer of IEEE Communications Society and the IEEE Council of RFID, and is on the Editorial Board of IEEE TWC, IEEE TNSE, IEEE TMC, IEEE IoT, IEEE TCCN, IEEE OJ-ComSoc, IEEE/CIC China Communications, IEEE Multimedia, IEEE Network, IEEE Networking Letters, and ACM GetMobile. He received the IEEE ComSoc TC-CSR Distinguished Technical Achievement Award in 2019 and NSF CAREER Award in 2010. He is a co-recipient of the 2021 Best Paper Award of Elsevier/KeAi Digital Communications and Networks Journal, the 2021 IEEE Communications Society Outstanding Paper Award, the IEEE Vehicular Technology Society 2020 Jack Neubauer Memorial Award, the 2004 IEEE Communications Society Leonard G. Abraham Prize in the Field of Communications Systems, and several conference best paper/demo awards. He is a Fellow of the IEEE.

  Abstract

  In recent years, 3D human activity recognition (HAR) has become an important topic in human-computer interaction (HCI). To improve the privacy of users, there is considerable interest in techniques without using a video camera. Various radio-frequency (RF) sensing technologies, such as WiFi, Radio-Frequency Identification (RFID), and Frequency-Modulated Continuous Wave (FMCW) radar, have been utilized for non-invasive human activity recognition (HAR). It will be highly desirable to develop a HAR solution that can work with different types of RF technologies, such that the cost and the barrier of wide deployment can both be greatly reduced, and more robust performance can be achieved by utilizing the complementary RF sensory data. In this talk, we present a technology-agnostic approach for RF-based HAR, termed TARF, which works with several different RF sensing technologies. A novel data generalization technique is proposed to mitigate the disparity in measured data from different RF devices. A domain adversarial neural network is proposed to combat the interference from various RF sensing technologies. The performance of the proposed system is evaluated with experiments using four different RF sensing technologies. TARF is shown to outperform the state-of-the-art Convolutional Neural Network (CNN)-based solution with considerable gains.

Video Archive

毛世文教授分享基于多种射频传感技术合作的三维人类活动鉴别模型有关内容