IEEE Macau 20th Anniversary Seminar "Digital and AI Era: opportunities and challenges for industrial development""
In order to celebrate the 20th anniversary of IEEE Macau, a seminar was held in the banquet Room of Banyan Tree Hotel, Macau on No.14 2023. A banquet was held at 6pm of the same day.
Date and Time
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- Date: 14 Nov 2023
- Time: 06:00 AM UTC to 01:00 PM UTC
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- The Banquet Room
- Banyan Tree Hotel
- Taipa, Macau
- Macau
Speakers
Jingdong Chen of Northwestern Polytechnical University (NWPU) in Xi'an, China
Array design and processing for acoustic and speech signal acquisition and reproduction
Voice communication and human-machine interaction systems are facing more and more challenging application environments where there exist not only strong noise, but reverberation, echo, and competing sources as well. How to acquire and deliver high-fidelity speech signals in such complicated acoustic environments has become a very challenging problem, which involves the use of microphone and loudspeaker arrays and many acoustic signal processing technologies. In this talk, I will present a brief overview of the basic principles of sensing and processing of speech signals as well as the state-of-the-art in the field. I will then focus on discussing important problems faced by teleconferencing and audio-bridging systems such as high gain beamforming with small microphone and loudspeaker arrays.
Biography:
Jingdong Chen He is currently a professor at the Northwestern Polytechnical University (NWPU) in Xi'an, China. Before joining NWPU in January 2011, he served as the Chief Scientist of WeVoice Inc. in New Jersey for one year. Prior to this position, he was with Bell Labs in New Jersey for nine years. Before joining Bell Labs, he held positions at the Griffith University in Brisbane, Australia and the Advanced Telecommunications Research Institute International (ATR) in Kyoto, Japan. Dr. Chen has long been working on the problems of acoustic signal processing, speech communication, microphone array processing, and artificial intelligence. He has authored and co-authored 14 monograph books and published over 300 papers in peer reviewed journals and conferences. He has been serving in various capacities in the global research community: as the Chair of IEEE Xi’an Section, as an Associate Editor to the IEEE Transactions on Audio, Speech and Language Processing and as a Member of the Editorial Board of several journals. He was the general chair of the IWAENC 2016, the technical program co-chair of the IEEE WASPAA 2009, IEEE TENCON 2013, ChinaSIP 2014, and helped organize many other conferences. Dr. Chen received the IEEE Signal Processing Society Best Paper Award in 2009, the best paper award from the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) in 2011, the Bell Labs Role Model Teamwork Award twice, respectively, in 2009 and 2007, the NASA Tech Brief Award twice, respectively, in 2010 and 2009, the Japan Trust International Research Grant from the Japan Key Technology Center in 1998, the “Distinguished Young Scientists Fund” from the National Nature Science Foundation of China (NSFC) in 2014, and the Young Author Best Paper Award from the National Conference on Man-Machine Speech Communications in 1998. He is also the co-author of a journal paper for which his PhD student, Chao Pan, received the IEEE Region 10 (Asia-Pacific) 2016 Distinguished Student Paper Award (First Prize). Dr. Chen is Fellow of IEEE.
Hong Ki Tsang of Department of Electronic Engineering, The Chinese University of Hong Kong
Beyond Moore’s Law: Silicon photonics for advancing the performance of Systems-on-Chips
Moore’s Law, which describes the approximate doubling every two years in the number of transistors that can be fabricated within an integrated circuit by developing and advancing the photolithography and chip fabrication technologies, has underpinned the advances in microelectronics and computing technology and enabled the rise of Artificial Intelligence systems in recent years. To further reduce the size of transistors beyond the 4nm technology node that is in large volume production for todays’ computers requires the use of Extreme Ultraviolet (EUV) photolithography systems, costing over US$300 million, and the prospect of yet higher costs for the next-generation EUV photolithography systems needed to further shrink the size of transistors. It is becoming apparent that continuing the strategy of reducing transistor size to continue Moore’s Law will eventually no longer be the best approach to improve the performance of integrated circuits. Recognizing the approaching end of Moore’s Law major microelectronic foundries such as Global Foundries, have been pursuing alternative strategies to advance integrated circuit technologies. Silicon photonics offers one such approach as it can provide new functional capabilities and has the potential to realize advances in system performances beyond what is possible using purely conventional state-of the-art microelectronics. Silicon photonics use photons instead of electrons to transmit and process signals in silicon chips which are manufactured with the same mature, high-yield, large-volume manufacturing processes developed for the previous generations (e.g., 65 nm technology node) of silicon microelectronics. Companies developing silicon photonics have already attracted billions of dollars in market valuations in recent years because of the potential applications of silicon photonics in data center transceivers, energy-efficient artificial intelligence accelerators and error-tolerant quantum computers. In this presentation we introduce some of the recent research at The Chinese University of Hong Kong on silicon photonics for high-capacity optical interconnects in data centers using polarization and mode-division- multiplexing, silicon photonics for high-speed dynamic optical coherence tomography, silicon photonics for enabling multimode fibers based endoscopic imaging systems and silicon photonic integrated circuits for energy-efficient low-latency optical matrix processors for signal processing applications.
Biography:
Hon Ki Tsang is the Interim Dean of Engineering and Wei Lun Professor of Electronic Engineering at the Chinese University of Hong Kong. He is a Fellow of IEEE, a Fellow of Optica, and the Editor-in-Chief of IEEE Journal of Quantum Electronics. His research on silicon photonics has spanned over two decades and include early work that led to the first field-deployed silicon photonics products to carry live data-traffic in telecommunication systems in 2002. He received the IEEE Hong Kong Section 50th Anniversary Distinguished Contribution Award in 2022. His current research interests remain focussed on silicon photonics and include advancing the basic device technologies, such as for advanced waveguide grating couplers, high-speed modulators and 100dB optical filter technologies for integrated quantum photonics, as well as system-level research for field programmable silicon photonics and photonic system-on- chips using silicon photonics.
Xizhao Wang of Shenzhen University
Uncertainty Modeling in Adversarial Machine Learning
Uncertainty modeling is a very old topic, but its integration into deep learning just starts only in the past several years. This presentation focuses on a class of deep learning, i.e., adversarial robustness, and discusses the uncertainties involved in the learning process, from the perspectives of data, model, and prediction respectively. It analyzes the relationship between uncertainty representation and adversarial robustness, emphasizing that the quantification of uncertainty can be adjusted by optimizing model parameters or improving the loss function, which can lead to a significant improvement in adversarial robustness.
Biography:
Dr. Xizhao Wang, a professor at Shenzhen University, IEEE Fellow, CAAI Fellow. He serves as the Chair of IEEE-SMC Society Technical Committee on Computational Intelligence and is the Chief Editor of Springer journal "Machine Learning and Cybernetics". Dr. Wang is an Executive Member of Chinese Association for Artificial Intelligence (CAAI) and the Chair of the CAAI Knowledge Engineering Technical Committee. Dr. Wang has received first-class awards at the provincial and ministerial levels, as well as the first prize of Wu Wenjun Artificial Intelligence Natural Science Award. His research interests include uncertainty modeling and machine learning for big data, with a substantial publication record in this domain. He has authored such books as "Induction of Decision Trees Based on Uncertainty" and "Learning with Uncertainty." Dr. Wang has successfully completed many research projects and has supervised over 200 doctoral and master's degree candidates. Dr. Wang has served as the general or program chair for multiple international conferences.