Prof. Rui Zhang, IEEE Fellow
National University of Singapore, Singapore
Dr. Rui Zhang (Fellow of IEEE, Fellow of the Academy of Engineering Singapore) received the Ph.D. degree from Stanford University in electrical engineering. He is now a Provost’s Chair Professor in the Department of Electrical and Computer Engineering, National University of Singapore. His current research interests include wireless information and power transfer, UAV/satellite communication, and reconfigurable MIMO. He has published over 450 papers, which have been cited more than 53,000 times with the h-index over 115. He has been listed as a Highly Cited Researcher by Thomson Reuters / Clarivate Analytics since 2015. He was the recipient of the IEEE Communications Society Asia-Pacific Region Best Young Researcher Award in 2011, the Young Researcher Award of National University of Singapore in 2015, the Wireless Communications Technical Committee Recognition Award in 2020, and the IEEE Signal Processing and Computing for Communications (SPCC) Technical Recognition Award in 2021. He received 11 IEEE Best Paper Awards, including the IEEE Marconi Prize Paper Award in Wireless Communications (twice), the IEEE Communications Society Heinrich Hertz Prize Paper Award (twice), the IEEE Communications Society Stephen O. Rice Prize, the IEEE Signal Processing Society Best Paper Award and Donald G. Fink Overview Paper Award, etc. He has served as an Editor for several IEEE journals, including TWC, TCOM, JSAC, TSP, TGCN, etc., and as TPC co-chair or organizing committee member for over 30 international conferences. He served as an IEEE Distinguished Lecturer of IEEE Communications Society and IEEE Signal Processing Society in 2019-2020.
Speech Title: "Intelligent Reflecting Surface (IRS) Empowered 6G: Fundamentals, Applications and Challenges"
Abstract: In this talk, we introduce a new promising paradigm for future wireless communications by leveraging a massive number of low-cost passive elements with independently controllable reflection amplitude and/or phase, named Intelligent Reflecting Surface (IRS), which can be densely deployed in wireless networks to smartly reconfigure wireless channels for enhancing the communication performance. We present the signal and channel models of IRS by taking into account its hardware constraints in practice. We then illustrate the main functions and applications of IRS in achieving spectral and energy efficient wireless networks, and highlight its cost and performance advantages as compared to existing wireless technologies. Next, we focus on the main design challenges in efficiently integrating IRSs to future wireless systems such as 6G, including passive reflection optimization, IRS channel acquisition and IRS deployment, and overview their state-of-the-art solutions. Finally, we point out directions worthy of further investigation in future work.
Prof. Edmund Lai, IET Fellow
Auckland University of Technology, New Zealand
Edmund Lai received his BE(Hons) and PhD from The University of Western Australia, both in Electrical Engineering, in 1982 and 1991 respectively. He is currently a Professor of Information Engineering at the Auckland University of Technology, New Zealand. He has over 30 years of academic experience, having previously held faculty positions at universities in Australia, Hong Kong, and Singapore. He has published over 120 international refereed journal and conference papers in signal processing, intelligent control, computational and swarm intelligence, and artificial neural networks. Dr Lai is a Fellow of the Institution of Engineering and Technology (FIET) and a Fellow of Engineers Australia (FIEAust).
Speech Title: "Towards Handling More Generalized Dataset Shifts in Machine Learning"
Abstract: Dataset shift is a well-known problem for machine learning models that are trained in a supervised manner. In classification problems, it refers to the case where the underlying class distribution of the test data is different from that of the training dataset. Several methods have been proposed to tackle this problem. This talk presents an overview of the classify-and-count and the expectation maximization methods and their rather strong assumptions on the nature of the data shift. A more recent method that can be applied under more general conditions is then discussed.
Prof. Zhihua Wang, IEEE Fellow
Tsinghua University, China
Zhihua Wang (M’99-SM’04-F’17) received the B.S., M.S., and Ph.D. degrees in Electronic Engineering in 1983, 1985 and 1990, respectively, from Tsinghua University, Beijing, China, where he has served as full professor and Deputy Director of the Institute of Microelectronics since 1997 and 2000. He was a visiting scholar at CMU (1992-1993) and KU Leuven (1993-1994), and was a visiting professor at HKUST (2014.9-2015.3). His current research mainly focuses on CMOS RFIC and biomedical applications, involving RFID, PLL, low-power wireless transceivers, and smart clinic equipment combined with leading edge RFIC and digital signal processing techniques. He has co-authored 13 books/chapters, over 209 (537) papers in international journals (conferences), over 249 (29) papers in Chinese journals (conferences) and holds 122 Chinese and 9 US patents. Prof. Wang has served as the chairman of IEEE SSCS Beijing Chapter (1999-2009), an AdCom Member of the IEEE SSCS (2016-2019), a technology program committee member of the IEEE ISSCC (2005-2011), a steering committee member of the IEEE A-SSCC (2005-), the technical program chair for A-SSCC 2013, a guest editor for IEEE JSSC Special Issues (2006.12, 2009.12 and 2014.11), IEEE SSCS Distinguished Lecturer(2018-2019), Associate Editors in Chief, IEEE Open Journal of Circuits and Systems (2019~), associate editor of IEEE Trans on CAS-I(2016-2020), IEEE Trans on CAS-II(2010-2013) and IEEE Trans on BioCAS(2008-2015), TPC Member of IEEE International Solid State Circuit Conference (ISSCC, 2005-2011), IEEE CASS Technical Committee member for Biomedical and Life Science Circuits and Systems (2016~2019), Steering Committee Member of IEEE Asian Solid-State Circuits Conference(A-SSCC, 2004-2025) and other administrative/expert committee positions in China’s national science and technology projects.
Speech Title: "Binaural Hearing Aid and Artificial Intelligence in Fitting and Signal Processing"
Abstract: Nowadays, the hearing aid technology is facing a new horizon based on advanced digital signal processing, wireless communication and artificial intelligence. In this lecture, new methodology with a systematic solution covering both the auditory periphery and the cognitive system is given. The up to date and rapidly evolves technologies for the binaural hearing aid system are well addressed. The multi-channel wide dynamic range compression, active noise reduction, self-adaptable directivity, acoustic scene analysis, and the wireless linking with other audio or communication systems are presented. The key technologies including the ultra-low power chip design, the advanced digital signal processing (DSP), and the wireless system integration and connectivity are discussed. The micromechanics and high performance electro-acoustics, the miniaturized antenna, the user interface, and the fitting system development are also the important aspects for the hearing aid technologies are shown in this lecture. A smart binaural hearing aid technology, which simultaneously processes the acoustic signals from the four microphones in both ears are indicated which utilizing the computing power from the smart phone to finish the advanced binaural DSP algorithms.