Special Session 15  会议特别专题 15

Marine Intelligent Systems: Integration of Sensing, Communication, Computing and Large Language Models

Description: The ocean, covering 71% of the Earth’s surface, presents significant challenges for timely and accurate perception and understanding due to its vastness, complexity, and lack of infrastructure. We are entering a new era of the “Intelligent Ocean,” driven by advances in marine observation, the Internet of Things, and artificial intelligence (AI).

This special session aims to bring together researchers from academia and industry to explore solutions to these challenges through the deep integration of multiple technologies. We focus on building intrinsic intelligence within marine information systems. The core lies in the integrated design of sensing, communication, and computing, and the utilization of large-scale AI models (e.g., LLMs) to process and understand the growing volume of multi-modal maritime big data, ultimately enabling a closed loop from data to intelligent decision-making.

This session will showcase state-of-the-art research in this interdisciplinary frontier, covering novel architectures, efficient intelligent algorithms, and advanced system prototypes. We invite submissions on topics including intelligent marine perception, cross-domain information fusion, collaborative resource optimization, and the application and optimization of large models for ocean-specific scenarios. This session will provide a much-needed platform for interdisciplinary exchange among signal processing, communications, AI, and marine science
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Session organizers
Assoc. Prof. Yanglong Sun, Jimei University, China
Assoc. Prof. Zhiping Xu, Jimei University, China
Lecture Qiang Mei, Jimei University, China
Dr. Peng Wang, Institute of Computing Technology, Chinese Academy of Sciences
Lecture Qianfeng Lin, Zhejiang Ocean University, China

This session complements and deepens existing conference topics like “Underwater Vision Processing” and “AI-aided Signal Processing” by focusing on system-level integration and cognitive-level intelligence. Specific topics include, but are not limited to:
▪ Integrated Sensing, Communication, and Computing (ISAC) theories and technologies for marine environments
▪ Maritime big data management, analysis, and large model (LLM/Multimodal AI) applications
▪ Intelligent marine perception and autonomous cognition (target recognition, situation awareness, decision generation)
▪ Intelligent collaboration and resource management in underwater/surface heterogeneous networks
▪ Marine edge intelligence and lightweight model deployment
▪ Marine digital twins and intelligent simulation systems
▪ Marine cross-modal (acoustic, optical, electromagnetic) information fusion processing

Submission method
Submit your Full Paper (no less than 5 pages with two colums) or your paper abstract-without publication (200-400 words) via Online Submission System, then choose Special Session 15 (Marine Intelligent Systems: Integration of Sensing, Communication, Computing and Large Language Models)
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Introduction of Session organizers

Assoc. Prof. Yanglong Sun, Jimei University, China

Yanglong Sun received the Ph.D. degree from Xiamen University, China, in 2022, received the B.S. degree and M.S degree from Zhengzhou University, China, in 2011 and 2014 respectively. He is currently an Associate Professor at Navigation College, Jimei University. He was a Visiting Ph.D. Student at the Broadband Communications Research (BBCR) Laboratory, Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada, from 2018 to 2019. His research interests include resource allocation in mobile networks, the Internet of things, and intelligent transportation systems.



Assoc. Prof. Zhiping Xu, Jimei University, China

Zhiping Xu received the Ph.D. degree in information and communication engineering from Xiamen University, Xiamen, China, in 2021. He is currently an Associate Professor with the School of Ocean Information Engineering, Jimei University, Xiamen, China. He has authored or coauthored more than 40 journals and conference papers. His current research interests include joint source-channel coding, coded modulation, maritime\underwater object detection, the Internet of Things, and synthetic aperture sonar\radar system.



Lecture Qiang Mei, Jimei University, China

Qiang Mei received the Ph.D. degree in Shanghai Maritime University, and is now working as a lecturer at the School of Navigation at Jimei university, currently executing one national major science and technology project as the project executor, leading one Fujian Provincial Natural Science Foundation project and two Fujian Provincial Department of Education funds as the project leader, one open fund from the Digital Fujian Big Data Modeling and Intelligent Computing Research Institute; He has participated in the drafting and approval of the Deep Blue Plan, a key project of the Maritime Safety Administration of the Ministry of Transportation. His direction is GIS and maritime safety.



Dr. Peng Wang, Institute of Computing Technology, Chinese Academy of Sciences

Wang Peng received the Ph.D.in Engineering from Shanghai Maritime University, specializes in multimodal data analysis and processing. His research focuses on key technological breakthroughs, including knowledge construction methods for multimodal maritime information, anomaly detection algorithms, and intelligent decision-making models. He has also developed lightweight situational awareness systems that meet the demands of carrier-based scenarios requiring low latency, high real-time performance, network independence, and high reliability, enabling practical implementation.



Lecture Qianfeng Lin, Zhejiang Ocean University, China

Qianfeng Lin received the Ph.D. degree in Computer Engineering from National Korea Maritime and Ocean University. He is now working at the School of Naval Architecture and Maritime, Zhejiang Ocean University. His research interests include in-ship positioning, maritime intelligence, deep learning, machine learning, and data mining.