Speakers

Prof. Pingyi Fan

Tsinghua University, China

Dr. Pingyi Fan is a professor of the Department of Electronic Engineering of Tsinghua University. He received Ph.D. degree at the Department of Electronic Engineering of Tsinghua University in 1994. From 1997 to 1999, he visited the Hong Kong University of Science and Technology and the University of Delaware in the United States. He also visited many universities and research institutes in the United States, Europe, Japan, Hong Kong and Singapore. He has obtained many research grants, including national 973 Project, 863 Project, mobile special project and the key R&D program, national natural funds and international cooperation projects. He has published more than 190 SCI papers (more than 130 IEEE journals), and 4 academic books. He also applied for more than 30 national invention patents, 5 international patents and. He won seven best paper awards of international conferences, including IEEE ICC2020 and Globecom 2014, and received the best paper award of IEEE TAOS Technical Committee in 2020, the excellent editor award of IEEE TWC (2009), etc. He has served as the editorial board member of several Journals, including IEEE and MDPI. He is currently the editorial board member of Open Journal of Mathematical Sciences, the deputy director of China Information Theory society, the co-chair of China's 6G-ANA TG4, and the chairman of Network and Communication Technology Committee of IEEE ChinaSIP. His current research interests are in 6G wireless communication network and machine learning, semantic information theory and generalized information theory, big data processing theory, intelligent network and system detection, etc.

Title:Variational Auto-Encoder Technique and New Applications

Abstract: In this report, we first Introduce the inference problem with Variational auto-Encoder (VAE)  technology and massive data, and then present the theory of VAE as well as its possible application scenarios. Secondly, we introduce some recent new developments by using VAE in different areas. Later on, we present an new result by jointly using VAE and GAN (generative adversial Networks) --- AEGAN, for machine fault detection by using Mechanical sounds. Some interesting observation are obtained.  Finally, we give a summary and point out some promising research directions for combinations of VAE and other other Machine learning methods. 

Prof. Wendong Xiao

IEEE Senior Member

The University of Science and Technology Beijing, China

Wendong Xiao is currently a professor, doctoral supervisor, and director of the Collaborative Intelligent Sensing and Control Decision Laboratory at the School of Automation, University of Science and Technology Beijing.Professor Xiao serves as a senior member of IEEE, a member of the IEEE Interactive Wearable Technology and Equipment Expert Committee, the deputy director of the Intelligent IoT System Professional Committee of the China Simulation Federation, the secretary-general of the Health Engineering Branch of the Chinese Society of Biomedical Engineering, consultant for the direction of active health in the National 14th Five-Year Plan, consultant of National Major Project of Modernization Technology and Equipment of Traditional Chinese Medicine, reviewer of National Engineering Research Center of National Development and Reform Commission, reviewer of Intelligent Manufacturing Major Project of Ministry of Industry and Information Technology, reviewer of Key Laboratory of Ministry of Industry and Information Technology, responsible expert and reviewer of National Key R&D Program Project, and expert of Science and Technology of Military Commission Commissioned. 

Professor Xiao has long been committed to technological innovation, development and application promotion in wearable computing, health intelligent perception, wireless intelligent perception, big data processing, Internet of Things and information platform construction, etc; he chaired a number of landmark projects, such as key technology research on intelligent interactive services for healthy elderly care, user behavior analysis based on big data mining, application demonstration of the Internet of Things for smart elderly care by the Ministry of Civil Affairs, and optimization design of energy harvesting wireless sensor networks; his projects have been supported by a number of national projects and enterprise cooperation projects, such as the National Key Research and Development Program, the National Natural Science Foundation of China, and the Singapore Science and Technology Agency. He has published more than 200 academic papers in domestic and foreign journals and conferences, of which nearly 100 are included in SCI.

Title：Millimeter Wave Radar based Non-Contact Intelligent Health Monitoring Technology

Abstract:The intelligent monitoring technology of millimeter wave （mm-Wave） radar provides a new intelligent perception mode for health care, medical detection and intelligent elderly care. It can used for the perception of human physiological parameters, location estimation, behavior analysis and other information by analyzing the radar echo signal, without attaching any electronic equipment. It can not only be deployed in conventional indoor and outdoor environments, but also in dark, smoke and emergency situations, with good privacy protection. The talk will analyze the application status of millimeter wave radar in intelligent health monitoring, and introduce the non-contact millimeter wave radar intelligent health monitoring system and the related key technologies developed by the team, including the work on the monitoring of breath, heartbeat, blood pressure, sleep status, position estimation, behavior analysis, fall detection, etc.

Prof. Wanyang Dai

Nanjing University, China

Wanyang Dai is a Distinguished Professor in Nanjing University, Chief Scientist in Su Xia Control Technology. He is the current President & CEO of U.S. based (Blockchain & Quantum-Computing) SIR Forum, President of Jiangsu Probability & Statistical Society, Chairman of Jiangsu BigData-Blockchain and Smart Information Special Committee. He received his Ph.D. in mathematics and systems & industrial engineering from Georgia Institute of Technology in USA. He was an MTS and principal investigator in U.S. based AT&T Bell Labs (currently Nokia Bell Labs) with some project won “Technology Transfer” now called cloud system. He was the Chief Scientist in DepthsData Digital Economic Research Institute. He published numerous influential papers in big name journals including Quantum Information Processing, Operations Research, Operational Research, Queueing Systems, Computers & Mathematics with Applications, Communications in Mathematical Sciences, and Journal of Computational and Applied Mathematics. He received various academic awards and has presented over 50 keynote/plenary speeches in IEEE/ACM, big data and cloud computing, quantum computing and communication technology, computational and applied mathematics, biomedical engineering, mathematics & statistics, and other international conferences. He has been serving as IEEE/ACM conference chairs, editors-in-chief and editorial board members for various international journals ranging from artificial intelligence, machine learning, data science, wireless communication, pure mathematics & statistics to their applications.

Title:Quantum computer via neutral atom and quantum entanglement for big model with big data

Abstract:We study quantum computer via neutral atom and quantum entanglement for big model with big data via federated learning. Operational rules via quantum entanglements for quantum computers are established through deriving a general spherical coordinate formula for a quantum state of n-qubit register. The associated angle-based n-qubit operational rules on a (n+1)-manifold are established, which are simple and efficient in the sense that they reduce the complicated quantum multiplication and division operations to simple addition and subtraction operations just like those used in a conventional computer. The rules for n-qubit operations are realized through neutral atom & measurement-oriented feedback controls to reach quantum entanglements. The performance models are derived for n-qubit quantum computer-based quantum storage systems. The generative AI based decision-making big model with big data via federated learning is also established and simulated.