Prof. Yuming Fang

Member of the Standing Committee of the CPC Jiangxi University of Finance and Economics Committee, Vice President

National-Level Talent

Jiangxi University of Finance and Economics, China

Yuming Fang, born in October 1984, holds a PhD and is a second-level professor and doctoral supervisor. He currently serves as a member of the Standing Committee of the CPC Jiangxi University of Finance and Economics Committee and Vice President. He is a recipient of the Excellent Young Scientists Fund of the National Natural Science Foundation of China. He has previously served as Deputy Director of the Research Office and Dean of a teaching school. He currently serves as a council member of the Chinese Computer Society and the Chinese Society for Image and Graphics, and as an editorial board member of several domestic and international academic journals. His research focuses on artificial intelligence, intelligent processing of visual big data, and virtual reality technology. He has led multiple research projects funded by the National Natural Science Foundation of China and the Ministry of Science and Technology. He has published numerous academic papers in renowned domestic and international journals and conferences, including more than 50 papers indexed by SCI or EI, and nearly 20 papers in leading international journals such as IEEE Transactions on Image Processing. His research achievements have received the First Prize of the Jiangxi Province Natural Science Award, and he has been selected for the Elsevier China Highly Cited Researchers List for consecutive years. He has served as Secretary-General, Special Topic Chair, and Session Chair for international academic conferences, including HHME and IEEE VCIP. He has also served as a reviewer for international academic journals such as IEEE Transactions on Image Processing and IEEE Transactions on Circuits and Systems for Video Technology, as well as a program committee member or reviewer for international academic conferences including IEEE ICME and IEEE ICIP. He currently serves as a member of the Multimedia Professional Committee of the Chinese Computer Society and a core member of the International Multimedia Communication Quality Experience Group.

Title: Virtual-Real Fusion in Immersive Media: Perception, Reconstruction, and Evaluation

Abstract: TBD

Prof. Feifei Gao

National-Level Talent, IEEE Fellow

Tsinghua University, China

Feifei Gao (Fellow, IEEE) received the B.Eng. degree from Xi'an Jiaotong University, Xi'an, China in 2002, the M.Sc. degree from McMaster University, Hamilton, ON, Canada in 2004, and the Ph.D. degree from National University of Singapore, Singapore in 2007. Since 2011, he joined the Department of Automation, Tsinghua University, Beijing, China, where he is currently a tenured full professor. Prof. Gao's research interests include signal processing for communications, array signal processing, convex optimizations, and artificial intelligence assisted communications. He has authored/coauthored more than 200 refereed IEEE journal papers and more than150 IEEE conference proceeding papers that are cited more than 25000 times in Google Scholar. Prof. Gao has served as an Editor of IEEE Transactions on Wireless Communications, IEEE Journal of Selected Topics in Signal Processing (Lead Guest Editor), IEEE Transactions on Cognitive Communications and Networking, IEEE Signal Processing Letters (Senior Editor), IEEE Communications Letters (Senior Editor), IEEE Wireless Communications Letters, and China Communications. He has also served as the symposium co-chair for 2019 IEEE Conference on Communications (ICC), 2018 IEEE Vehicular Technology Conference Spring (VTC), 2015 IEEE Conference on Communications (ICC), 2014 IEEE Global Communications Conference (GLOBECOM), 2014 IEEE Vehicular Technology Conference Fall (VTC), as well as Technical Committee Members for more than 50 IEEE conferences.

Title: Key Technologies and Prototype Verification of Integrated Communication, Sensing, Computing and Intelligence for Unmanned Machine Scenarios

Abstract: In the future, intelligent agents such as drones, unmanned vehicles, robots and quadruped robots will form an embodied intelligent terminal network with a scale ranging from millions to hundreds of millions. While undertaking communication functions, these terminals adopt multimodal sensors to perceive environmental dynamics in real time, and realize autonomous decision-making and precise control relying on large models. This will drive the evolution of 6G technology into an intelligent multidimensional system integrating communication, sensing, computing and control, and ultimately fulfill the vision of intelligent connection of all things. In terms of unmanned machine scenarios, our research group conducts research on key technologies of integrated communication, sensing, computing and intelligence. A full-process real-time perception architecture for dynamic targets sensing of low-altitude UAV is proposed to form a closed loop of multi-base station collaborative detection, tracking, imaging and recognition. In terms of environmental interaction of machine terminals, a static environment reconstruction system integrated with communication and vision modalities is developed. Meanwhile, a millimeter-wave integrated sensing and communication prototype system is built based on self-developed RFSoC-FPGA boards to realize low-altitude target perception. Unmanned vehicles are deployed to complete environment reconstruction and autonomous obstacle avoidance tasks. Furthermore, an embodied intelligent platform with collaboration between unmanned vehicles and robotic arms is developed combined with large language models, which can perform intelligent tasks including autonomous mapping and planning, automatic inspection, and dexterous dual-arm coordination. The research provides key technical support and prototype verification for the integrated development of communication, sensing, computing, intelligence and control in massive air-space-ground unmanned machine scenarios.

Prof. Weidong Min

Dean of the Metaverse Research Institute, National-Level Talent

Nanchang University, China

Professor Min Weidong is a tenured (Level II) professor at the School of Mathematics and Computer Science, Nanchang University, and national-level talent. He previously served as Dean of the School of Software at Nanchang University and currently the Dean of the Metaverse Research Institute at the university, and the Director of the Jiangxi Provincial Key Laboratory of Virtual Reality, and holds the position of Vice Chairman of the Jiangxi Provincial Digital Economy Society. Professor Min earned his B.E., M.E., and Ph.D. degrees in computer science from Tsinghua University. He is a standing director of the China Society of Image and Graphics and is widely recognized as a leading scholar in artificial intelligence, computer vision, virtual reality, embodied intelligence, and human-robot collaboration. He serves on dozens of national-level expert panels and frequently acts as a reviewer for national research funding agencies and talent selection committees. Prior to his current appointment, Professor Min spent 18 years in research and management roles at the University of Alberta in Canada and at several prominent multinational computer technology firms, with professional experience in four of the world's foremost technology hubs. Upon returning to China in 2015, he established both the Metaverse Research Institute at Nanchang University and the Jiangxi Provincial Key Laboratory of Virtual Reality. He has been in charge of more than 20 research initiatives, including seven funded at the national level. His publication record includes over 200 papers in leading journals such as IEEE and ACM Transactions. He has received four provincial- and ministerial-level science and technology awards and holds more than 40 authorized invention patents as primary inventor. The practical application and technology transfer of his work have generated notable commercial value.

Title: New Era of Artificial Intelligence: Development Logic, Frontier Directions, and Emerging Technologies

Abstract: Artificial intelligence is entering a new era of rapid development and fast iteration. New technologies in the intelligent age bring numerous innovations and development opportunities, while also facing many technical bottlenecks and challenges on various fronts. The report first presents the current state and core driving forces of AI development. Then, it analyzes the logic and directions of AI technology development. The report then discusses in detail new metaverse technologies in the era of large models and showcases some advances in these new metaverse technologies. Finally, the report discusses the challenges and reflections brought by the new era of artificial intelligence.

Prof. Jianbin Zhou

Chengdu Rongcheng Talent Entrepreneurship Leading Talent

Chengdu University of Technology, China

Zhou Jianbin is a Grade II Professor at Chengdu University of Technology, a doctoral supervisor, a Chengdu Leading Talent in Entrepreneurship, a contract researcher at Tianfu Jiangxi Laboratory, and a Standing Council Member of the Sichuan Society of Power Engineering. He is mainly engaged in research and development in nuclear radiation detection technology and nuclear instruments and equipment, graphical circuits, and signal and system simulation. He independently developed NESIM.A, a graphical circuit and system simulation software based on HDL-X, providing important support for the localization of circuit, signal, and system simulation software in China. He has presided over more than 20 projects, including a sub-project of the National "863" Program, a project of the National Key R&D Program, a sub-project of the National Science and Technology Major Project, and a General Program project of the National Natural Science Foundation of China. He has published more than 100 academic papers, published 4 academic monographs, been granted more than 10 patents, and received multiple provincial and ministerial awards. He participated in drafting one national standard, GB/T 31364-2015, "Main Performance Testing Methods for Energy-Dispersive X-ray Fluorescence Spectrometers."

Title: NESIM-A Circuit and System Simulation Software

Abstract: To address the ecosystem blockade imposed by foreign circuit and system simulation software, as well as problems such as the fixed device models and insufficient flexibility of software like Multisim; the difficulty of real-time debugging in static simulations that combine Verilog/VHDL with ModelSim; the fact that signal sources in PSpice, Multisim, and similar tools are mostly periodic signals, making it difficult to directly simulate nuclear signals; and their incompatibility with domestic operating systems such as Kylin, UOS, and HarmonyOS, the team developed NESIM-A circuit and system simulation software. Based on the graphical hardware description language HDL-X, this software builds from the ground up a unified description system that can simultaneously model circuits, microcontrollers, and signals and systems. It is China's first simulation software that simultaneously supports analog, digital, mixed analog-digital, complex digital system design, signals and systems, computer organization principles, and graphical programming for MCUs and FPGAs.

Prof. MAZHAR SULEMAN

IEEE Senior Member

Harbin Engineering University, China

Prof. Suleman received his Ph.D. from the University of Tokyo, Japan, and completed his postdoctoral research at Georgetown University, USA. He is currently a Distinguished Lecturer (DL) of the IEEE Oceanic Engineering Society (IEEE-OES) and Chair of the Technical Committee on Living Resources (TC-Living Resources), and serves as a Professor in the Information and Communication Engineering Program at the College of Underwater Acoustic Engineering, Harbin Engineering University, China. His research interests include underwater robotics, bioacoustics, and the applications of deep learning and signal processing to environmental and ubiquitous monitoring, with particular emphasis on underwater pollution monitoring and marine mammal conservation. He also teaches courses in artificial intelligence, underwater acoustics, and environmental acoustic engineering. He has received research grants from international organizations including DAAD, ICIMOD, the Higher Education Commission (HEC) of Pakistan, and the World Wide Fund for Nature (WWF), and has served as an editor or reviewer for international journals including the IEEE Journal of Oceanic Engineering.

Title: Data-driven End-to-End Underwater OFDM Communication under Channel Mismatch Conditions

Abstract: Underwater acoustic (UWA) communication system faces challenges due to environmental factors, extensive multi-path spread, and rapidly changing propagation conditions. Deep learning-based solutions, especially for Orthogonal Frequency Division Multiplexing (OFDM) receivers, have been shown to improve performance. However, the UWA channel characteristics are highly dynamic and depend on the specific underwater conditions. Therefore, these models suffer from model mismatch when deployed in environments different from those used for training, leading to performance degradation and requiring costly, time-consuming retraining. In this talk, we discuss OFDM underwater communication using different deep learning architectures to address these issues and review autonomy/control that is offered by these different architectures/approaches. We shall also look into the impact of channel properties on the performance of machine/deep learning algorithms for underwater OFDM communication and analyze transfer learning as an appropriate solution to ensure robust data-driven end-to-end underwater communication.

Prof. Xian'en Zhou

Deputy Dean of the Research Institute

Jiangxi Communication Terminal Industry Technology Research Institute, China

Xian'en Zhou, Ph.D., is a Specially Appointed Researcher at the National Engineering Research Center for Robot Visual Perception and Control Technology at Hunan University (and a key member of Academician Wang Yaonan’s team). He serves as the Vice President and Senior Engineer of the Jiangxi Communication Terminal Industry Technology Research Institute. He is recognized as a "High-Level Talent" of Jiangxi Province and serves as an Industrial Professor at Jinggangshan University and a Master’s Supervisor at East China University of Technology. Dedicated to the research and industrial application of machine vision and high-end equipment, he has presided over 2 national-level projects and 4 provincial/municipal projects, including the Jiangxi Provincial Natural Science Foundation and "Open Competition" (揭榜挂帅) projects in Ji'an and Fuzhou. Having managed project funding totaling nearly 10 million RMB and completed over 10 major industrial demand projects, he has solved critical industry challenges regarding manual labor reliance in quality inspection. His technical achievements include over 20 high-level papers in journals such as IEEE TII and IEEE TIM, and over 30 authorized invention patents and 20 software copyrights.

Title: Key Technologies and Applications of Machine Vision

Abstract: TBD