The 4th International Conference on Signal Processing, Computer Networks and Communications（SPCNC 2025）

	Prof. Lei Zhang Chongqing University, China IEEE Senior Member Lei Zhang is a Professor and Doctoral Supervisor at the School of Microelectronics and Communication Engineering, Chongqing University. He also serves as Director of the Chongqing Key Laboratory of "Biosensing andMultimodal Intelligent Information Processing" and Leader of the "Visual Intelligence and Learning" Team at Chongqing University. His academic honors include being a National High-Level Young Talent, a recipient of the 3rd Hong Kong Scholar Program funded by the Ministry of Human Resources and Social Security, a winner of the Chongqing Outstanding Youth Science Fund, and a fellow of the Chongqing High-Level Talent Special Support Program. He is listed in both the "Lifetime Scientific Impact" and "Annual Scientific Impact" rankings of the Top 2% of World's Scientists released by Stanford University. Research Interests: Multimodal Artificial Intelligence, Computer Vision, Robust Machine Learning, Efficient Transfer of Large Models, Vision-Language Large Models, etc. Speech Title: Unification of Robustness and Generalization in Visual Perception: Theory, Algorithms, and Applications Abstract: Robustness and generalization are two core challenges in visual perception—distinct yet interdependent. Historically, they have been studied in isolation, lacking a unified theoretical framework. This presentation aims to explore the coexistence and theoretical feasibility of robustness and generalization, enabling their mutual enhancement. Specifically, it will first address generalization and robustness issues in key visual perception tasks, then establish a unified theory integrating the two. Based on this theory, diverse algorithms and applications will be proposed, providing theoretical and methodological foundations for robust and generalizable large models.
	Prof. Lei Shen Hangzhou Dianzi University, China Shen Lei, Ph.D., is a professor and doctoral supervisor at Hangzhou Dianzi University. He graduated with a Bachelor's degree in Engineering from the Department of Information and Electronics at Zhejiang University in 2002, then pursued his Ph.D. in the same department, earning a Doctorate in Engineering in Telecommunications and Information Systems in 2007. Since June 2007, he has been working at Hangzhou Dianzi University, engaging in teaching and research. His current focus includes wireless communication and artificial intelligence in both education and research. In recent years, he has led numerous national, provincial, and defense-related research projects, as well as industry collaborations. His ongoing research primarily explores traditional and AI-based communication reconnaissance, protocol identification, modulation recognition, and fingerprint recognition algorithms. This includes studies on vein recognition, fingerprint recognition, smart pastures, and cattle face/back identification. He has published over 40 papers in prestigious academic journals and international conferences, with more than 20 indexed by SCI. His achievements include the Third Prize of Zhejiang Provincial Science and Technology Progress Award, the Second Prize of Hangzhou Natural Science Award, and the Third Prize of Zhejiang Provincial University Research Achievements Award. Speech Title: Signal detection, modulation recognition, and individual recognition based on machine vision Abstract: Based on artificial intelligence signal investigation, signal detection and parameter estimation using machine vision were studied by converting signals into images. Various signal modulation methods, protocol recognition, and individual recognition technologies and applications based on images were identified.
	Prof. Shiyuan Wang Southwest University, China IEEE Senior Member Shiyuan Wang, IEEE Senior Member, Southwest University "Signal and Intelligent Information Processing" research team leader, Senior Member of the Chinese Institute of Electronics, Committee Member of the University Electronic Technology Specialized Committee of the Chongqing Institute of Electronics. He was a Research Associate at The Hong Kong Polytechnic University in 2013 and a Research Fellow at The City University of Hong Kong in 2023, respectively. His research focuses on adaptive signal processing, integrated navigation, nonlinear dynamics, and simultaneous localization and mapping (SLAM). He has published one monograph and over 150 papers in leading journals and conference proceedings, including one ESI Highly Cited Paper. He has been awarded the Third Prize of the Chongqing Natural Science Award (ranked third) and has led over 10 research projects, including four funded by the National Natural Science Foundation of China. From 2018 to 2021, he served as an Associate Editor for IEEE TCAS II, a leading journal in circuits and systems, and currently serves as an Associate Editor for Symmetry. Speech Title: State Estimation-Based Intelligent Target Tracking Abstract: Intelligent target tracking is a fundamental capability in autonomous systems, such as robotics and surveillance. This report first provides a overview and comparison of two dominant paradigms in this field: model-based (MB) and data-driven (DD) tracking methods. As the representative model-based methods, the Kalman filter (KF) and its variants that rely on well-defined state-space models (SSMs) have been widely adopted for their low complexity and fast convergence. However, in complex and uncertain scenarios where accurate models are hard to build, the performance of these MB methods drops considerably. To this end, the model-agnostic nature of neural networks has spurred the development of data-driven methods for state estimation in uncertain dynamics by learning from data. These emerging DD methods still require large amounts of labeled data for effective training and lack robustness to SSM fluctuations. To address these challenges, hybrid methods have been proposed with the aim of leveraging the strengths of both MB and DD approaches while compensating for their respective weaknesses. By replacing the computation of intermediate values in MB framework which are affected by inaccurate SSM information with neural networks, these hybrid methods are capable of utilizing the strong nonlinear modeling capability of neural networks to handle highly nonlinear and inaccurate SSMs, while alleviating the need for extensive labeled data since networks are only trained to approximate intermediate values. Therefore, supported by extensive evidence, these hybrid methods position themselves as a premier strategy for future research in robust and data-efficient state estimation, addressing the long-standing challenges of complexity and uncertainty.
	Prof. Ming Jiang Sun Yat-sen University, China IEEE Senior Member Ming Jiang received the B.Eng. and M.Eng. degrees in electronic engineering from the South China University of Technology (SCUT), China, and the Ph.D. degree in electronic engineering from the University of Southampton, U.K. He has substantial international and industrial experience with Fortune 500 telecom companies. From 2006 to 2013, he had held key research/development or executive positions at Samsung Electronics Research Institute (SERI), U.K., Nortel Networks' research and development center, China, and the telecom equipment maker New Postcom, China, where he actively participated in numerous collaborative projects across the EU, North America and Asia, contributing to algorithm and system research and standardization, as well as radio access and core network product designs. Since June 2013, he has been a Full Professor and a Ph.D. Supervisor with Sun Yat-sen University, China, where he focuses on both fundamental research and technology transfer, and leads a number of national, provincial and industrial research projects. He has coauthored six books, 100+ articles, 120+ patents and 400+ 3GPP/IEEE standardization contributions. He received several Chinese local council awards in 2011 and 2022, including Innovative Leading Talents, Outstanding Experts, and Top Overseas Scholars. He is currently the Deputy Director of the State-Province Joint IoT Engineering Laboratory and the Director of Guangdong Province IoT Engineering Laboratory. He is also a Senior Member of the IEEE. Speech Title: Spectrum-Efficient Random Access for Aeronautical Communication Systems Abstract: Aeronautical communication (AC) is a key technology for future wireless networks, facing challenges like severe Doppler effects due to the high mobility of aerial user equipment (AUE) and large cell coverage causing significant round trip delay (RTD). These factors complicate reliable random access design. To tackle this, we propose a new preamble detection algorithm using short formats and a shifted partial combination mechanism, enhancing detection performance and spectral efficiency under high Doppler shifts. Additionally, a dual-end correlation-based RTD estimation method is introduced for robust delay estimation. Simulations and lab tests verify the scheme's effectiveness.