Channel-Hopping Cognitive-Radio Wireless Networks for Internet of Things

Prof. Guu-Chang Yang (Fellow, IEEE)

National Chung Hsing University, Taiwan

ABSTRACT

The demand of spectral resources will grow tremendously in order to support the huge number of mobile devices in wireless networks. Cognitive radio (CR) becomes an attractive technology to alleviate the spectrum scarcity and efficiency problems. Channel hopping (CH) is a representative technique in the rendezvous processes that can enhance spectral efficiency and is robust against interference in cognitive radio networks. In this talk, the fundamentals of CH sequence designs in CR ad hoc networks are overviewed, including basic classifications, operating modes, and design criteria and performance metrics. The constructions of several novel families of asynchronous-symmetric CH sequences with desirable characteristics are presented. The talk is concluded by future research direction.

SPEAKER BIOGRAPHY

Guu-Chang Yang received the B.S. degree from National Taiwan University, Taipei, Taiwan, in 1985, and the M.S. and Ph.D. degrees from the University of Maryland, College Park, MD, in 1989 and 1992, respectively, all in electrical engineering. In 1992, he joined the faculty of the National Chung Hsing University, Taichung, Taiwan, where he is currently the Founding Dean of the College of Electrical Engineering and Computer Science, and a Chair Professor with the Department of Electrical Engineering and the Graduate Institute of Communication Engineering. His research interests include wireless and optical communication systems, sensor networks, modulation and signal processing techniques, and applications of CDMA.
Dr. Yang became an IEEE Fellow in 2012 for contributions to optical CDMA. He received the Distinguished Research Award from the National Science Council in 2004 and 2014; and the Outstanding Young Electrical Engineer Award and the Distinguished Electrical Engineering Professor Award from the Chinese Institute of Electrical Engineering in 2003 and 2012, respectively. He served as the Chairperson of the IEEE Communications Society (Taipei Chapter) from 2013 to 2014 and the IEEE Information Theory Society (Taipei Chapter) from 2003 to 2005. He also served as the Area Coordinator of the National Science Council’s Telecommunications Program from 2012 to 2014 and the Co-Coordinator of the National Science Council’s National Networked Communication Program from 2010 to 2014 and the Ministry of Science and Technology’s Development and Applications of Advanced Communications Networking Technologies Program from 2014 to 2018 and the Coordinator of the Ministry of Education’s A Talent Cultivation Program for 5G Mobile Broadband Technology from 2018 to 2023. He currently serves as the Co-Coordinator of the National Science and Technology Council’s Program for 6G Advanced Research from 2022 to 2027. He was an Associate Editor of the IEEE TRANSACTIONS ON COMMUNICATIONS from 2016 to 2021.


 

 

A Feature-based Model for Optimizing HVOF Spray Parameters

Prof. Yongsheng Ma

SUSTech in Shenzhen, China

ABSTRACT

 Industrial process modeling is currently undergoing a fundamental transformation, leading towards interconnected closed-loop twins of models, i.e., the parametrically-controlled real-world physics model, and its corresponding digitalized virtual system model. However, with the application of advanced manufacturing technologies to industrial processes, the digitalized modeling of the physics phenomena involved becomes the obstruction to realize this transformation. Thus, this research proposes a semantic conceptual framework for industrial process modeling in the context of digital twins. Based on a hierarchical structure of digital twins, this framework modularizes the modeling process in terms of the semantic information modules of physics in the real-world phenomena, and clarifies inter-module associations and near-real-time data processing so that the time-sensitive phenomenon information objects distributed on virtually-separated sub-level physics models can be supported for representing the real-world process comprehensively. Advanced feature concept is adopted to construct the digital models as the basic compositions of any virtual industrial process. The related feature definitions are extended in this work so that the common characteristics in the concept of digital twins could be generically and concisely represented.

SPEAKER BIOGRAPHY

Dr. Yongsheng Ma has joined Southern University of Science and Technology (SUSTech) in Shenzhen, China since July, 2021 as a full professor. Before that, Dr. Ma had been a full professor with the University of Alberta (UA) since 2007. He was an associate professor with Nanyang Technological University, Singapore during 2000-2007. Dr. Ma started his career as a polytechnic lecturer in Singapore (1993-1996); and then a research fellow, senior research fellow and group manager (1996-2000) at Singapore Institute of Manufacturing Technology. Dr. Ma received his B.Eng. from Tsinghua University, Beijing (1986), both M.Sc. (1990) and Ph.D. (1994) from UMIST, UK. Dr. Ma has had an established research profile with many research projects from different sources, and published more than 200 papers internationally in recognized top journals, conferences, and book chapters. Dr. Ma had served as an Editorial Board Member of Advanced Engineering Informatics (ADVEI, Elsevier) since 2012, and became an associate editor since 2020. Concurrently, he also serves as an associate editor for ASME Journal of Computer Information Science and Engineering (JCISE), and an Editorial Member of Scientific Reports (Springer Nature). Dr. Ma had also been an associate editor of IEEE Transaction of Automation Science and Engineering (2009-2013). Dr. Ma is a member of ASEE, SME, SPE, ASME, CSME and a Canada (Alberta) registered Professional Engineer (P.Eng.) since 2009. In 2012, he won the prestigious ASTech award sponsored by The Alberta Science and Technology Leadership Foundation together with Drader Manufacturing Ltd. Dr. Ma had also served as a senator of UA during 2014-2016.


 

 

Decentralized Cooperative Learning and Control in Swarm Intelligent Systems

Prof. Tao Li

East China Normal University, China

ABSTRACT

We consider several decentralized cooperative learning and control algorithms by a swarm of agents interacting in uncertain environments. We analyze convergence of decentralized parameter estimation algorithms with random observation matrices and communication graphs by a network of multiple nodes via information exchange. We established several “small capacity theorems” for cooperative consensus and control with finite communication data rate and several stochastic cooperatability theorem for stochastic dynamic networks with multiplicative noises. Besides, we propose a fully distributed economic dispatch algorithm for Energy Internet which can transit between the grid-connected and isolated operation modes smoothly. Partial results are evaluated as “made the first connection of consensus and communication theory”, “fundamental results ” , “provide an elegant method” and “opens new research directions” in publication, and have been applied to federated learning, differential privacy, multi-media scheduling and cooperative control of micro-grids.

SPEAKER BIOGRAPHY

Tao Li received the B.E. degree in automation from Nankai University, Tianjin, China, in 2004, and the Ph.D. degree in systems theory from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China, in 2009. Since January 2017, he has been with East China Normal University, Shanghai, China, where he is currently a Professor, the Director of the Department of Mathematical Intelligence Sciences, School of Mathematical Sciences, and an affiliated faculty of the Institute of Mathematical Sciences of New York University Shanghai. His current research interests include stochastic systems and control theory, Cyber-physical multiagent systems, distributed learning algorithms and network game.
Dr. Li was entitled Dongfang Distinguished Professor by Shanghai Municipality in 2012, received the Excellent Young Scholar Fund from NSFC in 2015, and was elected to the Chang Jiang Distinguished Professor, Ministry of Education, China in 2023. He was a recipient of the 28th “Zhang Siying” Outstanding Youth Paper Award in 2016, the Best Paper Award of the 7th Asian Control Conference in 2009. He received the 2009 Singapore Millennium Foundation Research Fellowship and the 2010 Australian Endeavor Research Fellowship. He now serves as Associate Editor for several journals, including Systems and Control Letters, IFAC Nonlinear Analysis: Hybrid Systems, and IEEE Control Systems Letters. He is a Member of the IFAC Technical Committee 1.5 on Networked Systems and a Senior Member of IEEE.