| Prof. Badong Chen, Xi'an Jiaotong University, China IEEE Senior Member Badong Chen is a professor at the Institute of Artificial Intelligence and Robotics at Xi'an Jiaotong University and a distinguished professor of the Yangtze River Scholar Program of the Ministry of Education. His research interests mainly include machine learning, brain-computer interfaces, and brain-inspired intelligence. He has published over 300 academic papers in internationally renowned journals and conferences, with his papers being cited over 19,000 times. He has been granted over 30 national invention patents and published 7 academic monographs. He has been selected as a highly cited researcher by Clarivate Analytics, a highly cited scholar in China by Elsevier, and one of the top 2% of the world's top scientists. He has received the First Prize of Natural Science Award from the Ministry of Education, the First Prize of Natural Science Award from Shaanxi Province, the First Prize of Natural Science Award from the Chinese Association of Automation, and the Young Scientist Award from the Chinese Association of Automation. He serves as an executive director of the Chinese Society for Cognitive Science, a chairman of the Computational Neuroengineering Specialty Committee of the Chinese Society for Cognitive Science. Title: Brain Machine Interfaces: Decoding and Regulation Abstract: Brain machine interface, as an important direction in the field of neuroscience research, mainly explores innovative technologies for direct interaction between brain and external devices. It has significant application prospects in fields such as healthcare, rehabilitation, education, and entertainment. Accurately decoding cognitive states such as perception, intention, and emotion in the brain, as well as accurately diagnosing and regulating brain disease states, is currently a research hotspot and a huge challenge in the field of brain machine interfaces. This report introduces the basic concepts, key technologies, and applications of brain machine interfaces, as well as the research progress of the reporter's team in areas such as brain signal decoding, brain disease diagnosis, and neural regulation. |
 | Prof. Feifei Gao, Tsinghua University, China IEEE Fellow Feifei Gao is a Tenured Professor in the Department of Automation, Tsinghua University, a National Outstanding Young Investigator (NSFC), and an IEEE Fellow. His research interests include ultra-massive MIMO communications, integrated sensing and communication (ISAC), multi-modal intelligent communications, and embodied intelligence. The team has independently developed a 256-channel broadband RF direct sampling software-defined radio (SDR) platform, an anti-jamming ad hoc network communication module with 10,000 hops/second, and an FPGA+GPU heterogeneous integrated sensing, computing, control, and embodied intelligence unit. He has published over 200 papers in IEEE journals, with more than 22,000 Google Scholar citations. He has served as an Editorial Board Member for journals such as IEEE Transactions on Communications (TCOM), IEEE Transactions on Wireless Communications (TWC), IEEE Journal of Selected Topics in Signal Processing (JSTSP), IEEE Transactions on Cognitive Communications and Networking (TCCN), IEEE Computational Intelligence Magazine (CML), IEEE Signal Processing Letters (SPL), IEEE Wireless Communications Letters (WCL), and China Communications. He has also acted as Co-Chair of Technical Sessions for over 10 IEEE flagship international conferences including IEEE ICC, IEEE GLOBECOM, and IEEE VTC, and as a Program Committee (PC) Member for more than 50 IEEE international conferences. His honors include the First-Class Technical Invention Award of the Chinese Association of Automation (2024), the First-Class Natural Science Award of the China Communications Society (2022), the First-Class Natural Science Award of Jiangsu Province (2022), and over 10 Best Paper Awards from IEEE flagship international conferences. Title: Key Technologies and Prototype Verification for Integrated Sensing, Computing, and Intelligence in Unmanned Machine Scenarios Abstract: In the future, intelligent agents such as drones, autonomous vehicles, robots, and robotic dogs will form an embodied intelligent terminal network with a scale ranging from millions to billions. These terminals, while serving communication functions, will perceive environmental dynamics in real-time through multimodal sensors and achieve autonomous decision-making and precise control via large models, driving the evolution of 6G technology toward an intelligent multidimensional system that integrates communication, sensing, computing, and control functions, ultimately realizing the vision of universal intelligent connectivity. To this end, our research team has conducted key research on integrated communication-sensing-computing-intelligence technologies for unmanned machine scenarios. In the field of low-altitude drone sensing, we proposed a dynamic target real-time perception architecture covering the entire process, achieving a closed-loop system of multi-base station collaborative detection, tracking, imaging, and recognition. In the realm of machine-terminal environmental interaction, we developed a static environment reconstruction system based on the fusion of communication and visual modalities. Additionally, we built a millimeter-wave integrated sensing and communication prototype system using self-developed RFSoC-FPGA boards to achieve low-altitude target sensing. An unmanned vehicle was also deployed to complete environmental reconstruction and autonomous obstacle avoidance tasks. Furthermore, by integrating large language models, we developed an embodied intelligent platform for unmanned vehicles and robotic arms, capable of performing tasks such as autonomous mapping, autonomous inspection, and dual-arm fine coordination, providing critical technical support and prototype validation for the integrated communication-sensing-computing-intelligence-control research of massive unmanned machine scenarios across land, sea, air, and space. |
| Prof. Xiaodong Xu, Beijing University of Posts and Telecommunications, China IEEE Senior Member Xiaodong XU is currently a tenure professor of Beijing University of Posts and Telecommunications. His research area covers semantic communication, intellicise network and satellite communication. He accomplished the 4G and 5G trial networks with several awards, such as the first-class S&T award of China Institute of Communications. The 6G prototype he developed in 2021 was recognized as the “industry-leading” platform. He is an expert of IMT-2030 promotion group, vice chair of CCSA TC630 and CIC Fellow. He is also the vice dean of ZGC Institute of X-NET. Title: Semantic Communications empower 6G Native AI networks Abstract: Traditional communication involves the transmission of “bit” as a carrier to convey information. In the future, a significant advancement from semantic communications will be the transmission of “model” which carry intelligence. The model-driven semantic communication will bring native AI solutions to the effective integration of AI, communication, and networks. Based on the semantic communication, the system design and air-interface technology evolution will be introduced. Especially, the semantic communication standard progress and 6G typical user-case experimental results will be presented. |
| Prof. Xianpeng Wang, Hainan University, China IEEE Member He is a National Young Talent and currently serves as Dean of the School of Information and Communication Engineering. His research focuses on precision detection and intelligent information processing, array and radar signal processing, and integrated sensing and communication (ISAC). He has presided over projects including Key Special Project of the National Key R&D Program, National Natural Science Foundation of China (NSFC) projects, and Key R&D Special Project of Hainan Province. He has been selected into the China Association for Science and Technology (CAST) "Young Talent Support Program", honored as Outstanding Science and Technology Worker of the Chinese Institute of Electronics (CIE), and recognized as a recipient of Hainan Province "Hundred Talents Program", Hainan Province "Leading Talent", Hainan Province "May Fourth Youth Medal", and Hainan Provincial Youth Science and Technology Award. He is also listed in the Top 2% Scientists Worldwide. His awards include one First-Class and one Second-Class Hainan Provincial Science and Technology Progress Award, Baosteel Outstanding Teacher Award, one Second-Class Hainan Provincial Teaching Achievement Award in Higher Education, and one First-Class Natural Science Award of the Hainan Institute of Electronics. He has obtained more than 10 authorized invention patents, published 2 academic monographs, and authored over 150 academic papers. Title: Deep learning framework for array signal parameter estimation Abstract: Analyze the challenges faced by traditional model-driven array signal parameter estimation methods. Introduce several deep learning-based array signal parameter estimation frameworks, and discuss how to achieve fast and high-accuracy Direction of Arrival (DOA) estimation. Finally, provide an outlook on future developments. |
