Keynote Speakers
A. Prof. Lisu Yu, Nanchang University, China
Research field：
Next generation communication/information system (5G/6G); Artificial intelligence (AI); Internet of Things (IoT)/Blockchain; Signal processing/Reconfigurable Intelligence Surface (RIS)
Dr. Lisu Yu has published lots of top international journals or flagship conferences papers as the first author or corresponding author, including IEEE Vehicular Technology Magazine, IEEE Communications Standards Magazine, IEEE Internet of Things Journal, IEEE Transactions on Vehicular Technology, and so on. He has served as the student activities chair of IEEE Communication Society Chengdu Chapter and several international conferences technical program committee (TPC) Chair/member, Section Chair and Special Track Chair, including IEEE BigData, IEEE ICMLA, IEEE WCSP, and so on. He is now serving as an Area Editor of the Elsevier Physical Communication, Editors of the Elsevier Computer Communications, PeerJ Computer Science, PLOS ONE, and Frontiers in Signal Processing for Communications, and a Managing Guest Editor of Elsevier Internet of Things and Cyber-Physical Systems, and IEEE Communications Society Technical Committee on Green Communications and Computing (TCGCC) and Signal Processing and Computing for Communications (SPCC) Members. He is a Senior Member of CIC, Life Member of CAAI, Member of IEEE, Member of CCF.
Title: A Novel Multiple Access Scheme for Visible Light Communication Networks in 6G
Abstract: As 5G networks are being rolled out in many different countries nowadays, the time has come to investigate how to upgrade and expand them toward 6G, where the latter is expected to realize the interconnection of everything as well as the development of a ubiquitous intelligent mobile world for intelligent life. To enable this epic leap in communications, this article provides an overview and outlook on the application of sparse code multiple access (SCMA) for 6G wireless communication systems, which is an emerging disruptive non-orthogonal multiple access (NOMA) scheme for the enabling of massive connectivity. We propose to apply SCMA to a massively distributed access system whose architecture is based on fiber-based visible light communication, ultra-dense networks, and NOMA. Under this framework, we consider the interactions between optical fronthauls and wireless access links. In order to stimulate more upcoming research in this area, we outline a number of promising directions associated with SCMA for faster, more reliable, and more efficient multiple access in future 6G communication networks.
Prof. Guangwei Bai, Nanjing Tech University, China
Research field：
Internet of Things (IoT), cloud computing, Communication security, 5G communication network and application
Guangwei Bai received the B.Eng. and M.Eng. degrees in computer engineering from Xi’an Jiaotong University, Xi’an, China, in 1983 and 1986, respectively,and the Ph.D. degree in computer science from the University of Hamburg, Hamburg, Germany, in 1999. From 1999 to 2001, he was a Research Scientist with the German National Research Center for Information Technology, Germany. In 2001, he joined the University of Calgary, Calgary, AB, Canada, as a Research Associate. Since 2005, he has been a Professor of computer science with Nanjing Tech University, Nanjing, China. From October to December 2010, he was a Visiting Professor with the Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada. His research interests include Internet of Things (IoT), cloud computing, Communication security, 5G communication network and application. He is a Member of the ACM and a Distinguished Member of CCF.
Title: Resource Optimization for Scalable Video Coding Multicast in UAV-assisted Radio-Access Networks
Abstract: A joint resource-optimization scheme is investigated for nonorthogonal multiple access (NOMA)-enhanced scalable video coding (SVC) multicast in unmanned aerial vehicle (UAV)-assisted radio-access networks (RANs). This scheme allows a ground base station and UAVs to simultaneously multicast successive video layers in SVC with successive interference cancellation in NOMA. A video quality-maximization problem is formulated as a mixedinteger nonlinear programming problem to determine the UAV deployment and association, RAN spectrum allocation for multicast groups, and UAV transmit power. The optimization problem is decoupled into the UAV deployment–association, spectrum-partition, and UAV transmit-power–control subproblems. A heuristic strategy is designed to determine the UAV deployment and association patterns. An upgraded knapsack algorithm is developed to solve spectrum partition, followed by fast UAV power fine-tuning to further boost the performance. The simulation results confirm that the proposed scheme improves the average peak signal-to-noise ratio, aggregate videoreception rate, and spectrum utilization over various baselines.
Prof. Johan DEBAYLE, Ecole Nationale Supérieure des Mines de Saint-Etienne (ENSM-SE), France
Research field：
Image processing, pattern recognition, stochastic geometry
Title: Digital twins for image and video analysis of granular media
Abstract: Granular media are widely used in many industrial applications and fields of science from physics to chemistry, biology or agronomy. In energy, power and chemical engineering systems, in particular, it is generally desired to extract information on geometrical characteristics and on spatial distribution from 2D images of the population of particles/grains involved in the process for a better control and optimization. For example in pharmaceutics, the size and the shape of crystals of active ingredients are known to have a considerable impact on the final quality of products, such as drugs. As another example, the performance of fuel cells (SOFC/SOEC) is mainly related to the electrode microstructure (size and spatial distribution of the solid and porous phase).The purpose of this talk is then to show different ways (deterministic and stochastic methods using digital twins) of image processing, analysis and modeling to geometrically characterize such granular media from 2-D or 3-D images/videos. The developed methods will be presented by addressing different issues: overlapping, projection, blur... The methods are mainly based on image enhancement, restoration, segmentation, tracking, modeling, feature detection, stereology, stochastic geometry, pattern recognition and machine learning.The methods will be particularly illustrated on real applications of crystallization processes (for pharmaceutics industry), multiphase flow processes (for nuclear industry) and fuel cell power systems (for energy industry).
A. Prof. Shuangming Yang, School of electrical and information engineering, Tianjin University, China
Research field：
Brain-inspired intelligence, artificial general intelligence, computer vision
Title: Neuroscience-inspired neuromorphic intelligence
Abstract: In recent years, with the rapid development of artificial intelligence, it has become an important research hotspot in both academic and Internet fields. Although the emergence of deep learning and big data in recent years makes artificial intelligence surpass human beings in some tasks, it is powerless to deal with complex problems that human brain can deal with. At the same time, it needs a lot of computing resources and data resources as support. Brain like computing is a new brain inspired computing model, which is expected to break through the constraints of traditional computing framework and realize high-performance and strong cognitive computing
