Prof. Pingyi Fan

Academician of the United States National Academy of Artificial Intelligence

Tsinghua University, China

Biography: Dr. Pingyi Fan is a professor and the director of open source data recognition innovation center,  Department of Electronic Engineering, Tsinghua University. He is member (Academician) of the united states national academy of artificial intelligence (NAAI) and Fellow of IET and IET Fellowship international Assessor.  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 600 papers （ORCID） including 171 IEEE journals and more than 10 ESI highly cited papers as well as 4 academic books. He also applied for more than 40 national invention patents, 7 international patents. He won 10 best paper awards of IEEE international conferences, including IEEE ICCCS2023 and 2024, 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), the most popular scholar award 2023 of AEIC, the second natural Prize of CIC (2023) and several international innovation exhibition medals, i.e. Gold Medal at the Russian Invention Exhibition-2024, Silver Medal at Geneva Invention Exhibition-2023, and Silver Medal at Paris Invention Exhibition-2023 etc. and served as the editorial board member of several Journals, including IEEE and MDPI.  He is currently an Associate Editor of IEEE Transactions on Cognitive Communications and Networking (TCCN), the editorial board member of Open Journal of Mathematical Sciences and IAES international journal of artificial intelligence, 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:Towards Digital Image Compressions-One Soft Compression Processing Approach

Abstract: As the concepts of  metaverse and digital twins were proposed, some related techniques and/or system designs have attracted more attentions, more and more publications have been presented currently. One key thing is that the images or videos may  consume the highest resources of the  communication and computing in the system implementations. In this talk,  we mainly focus on the digital image lossless compression and present a new method- soft compression of digital images.  We first introduce the key idea of it and then show some numerical results on several known databases of digital images.  To further investigate its principles from information theory, we proved that soft compression can approach the exact entropy of images under mild conditions. We also investigated its robustness to the interferences in the image storages and  in the image transmission, respectively.  Finally, we point out the research directions related in the future. 

Prof. Wanyang Dai

Nanjing University, China

Biography: Wanyang Dai is a Distinguished Professor in Nanjing University, Chief Scientist in Su Xia Control Technology. He is the President & CEO of International (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 Mathematics, Probability in the Engineering and Informational Sciences, 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 70 keynote/plenary speeches in IEEE/ACM, big data and cloud computing, artificial intelligence and robot, 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. He is the current Guest Editor of MDPI Mathematics Special Issue of Probability Statistics. 

Title: Aerial communication and low-altitude economy with AGI and quantum transformer via spatial diffusion

Abstract: We present the system architecture of aerial communication with artificial general intelligence (AGI) for low-altitude economy and introduce its supported big data flows. To conduct the online decision-making for AGI oriented technical and business model, we establish a generalized quantum transformer (called Q-Transformer) with the capability of prediction and adaptive feedback control interaction through big model regression. Our Q-Transformer consists of quantum encode-decode coupling processes, which corresponds to a forward-backward coupling spatial diffusion model whose drift parameter vectors can be mapped to different real-world attentions for AGI. This newly proposed Q-Transformer is integrated into our previously developed quantum cloud computing platform as its smart federated learning engine, which is supported by our recently designed and justified neutral atom quantum computer. The main purpose to develop such an integrated big model platform system is to conduct high-dimensional spatial AI online decision-making for real-world multiple smart bodies via IoT/IoV and 6G, which involve big data flow management and heavy numerical computation. Specific applications such as multiple objective based robot routing, resource allocation, and dynamic pricing will be given. Related optimization and equilibrium policies will be trained with numerical simulations. 

Prof. Jinming Wen

Jinan University, China

Biography: Professor Jinming Wen is a full professor in the College of Information Science and Technology, Jinan University, Guangzhou, China. His research interests are in the areas of green wireless communications, signal processing and machine learning. He has published around 60 papers in top journals such as IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing and IEEE Transactions on Wireless Communications. He is an Associate Editor of IET Quantum Communications and Alexandria Engineering Journal.

Title: Binary Sparse Signal Recovery with Binary Matching Pursuit

In numerous applications from communications and signal processing, we often need to acquire a $K$-sparse binary signal from sparse noisy linear measurements.

In this talk, we first develop an algorithm called Binary Matching Pursuit (BMP) to recover the $K$-sparse binary signal. According to whether the residual vector is explicitly formed or not at each iteration, we develop two implementations of BMP which are respectively called explicit BMP and implicit BMP. We then analyze their complexities and show that, compared to the Batch-OMP, which is the fastest implementation of OMP, the improvements of the explicit and implicit BMP}algorithms are respectively $n/(2K)$ and $K$ times when some quantities are pre-computed. Finally, we provide sharp sufficient conditions of stable recovery of the support of the sparse signal using mutual coherence and restricted isometry property of the sensing matrix.