IECE - Institute of Emerging and Computer Engineers

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    Author 1
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    ORCID
    0000-0002-9990-9163
    Hongqi Fan
    National Key Laboratory of Automatic Target Recognition (ATR), National University of Defense Technology, Changsha 410073, China
    Summary
    Hongqi Fan received his B.S. degree in mechanical engineering and automation from Tsinghua University, Beijing, P.R. China, in 2001, and his Ph.D. degree in information and communication engineering from the National University of Defense Technology, Changsha, Hunan, P. R. China, in 2008. He is currently a professor at the National University of Defense Technology. His research interests include information fusion, target tracking, signal processing, Intelligent Guidance Systems.
    Edited Journals
    Chinese Journal of Information Fusion
    IECE Contributions
    Articles
    1
    Edited
    1
    Code (Data) Available | Free Access | Review Article | 15 December 2024
    A Comprehensive Survey on Emerging Techniques and Technologies in Spatio-Temporal EEG Data Analysis
    by
    Pengfei Wang
    ,
    Huanran Zheng
    ,
    Silong Dai
    ,
    Yiqiao Wang
    ,
    Xiaotian Gu
    ,
    Yuanbin Wu
    ,
    Xiaoling Wang
    Chinese Journal of Information Fusion | Volume 1, Issue 3: 183-211, 2024 | DOI:10.62762/CJIF.2024.876830
    Abstract
    In recent years, the field of electroencephalography (EEG) analysis has witnessed remarkable advancements, driven by the integration of machine learning and artificial intelligence. This survey aims to encapsulate the latest developments, focusing on emerging methods and technologies that are poised to transform our comprehension and interpretation of brain activity. The structure of this paper is organized according to the categorization within the machine learning community, with representation learning as the foundational concept that encompasses both discriminative and generative approaches. We delve into self-supervised learning methods that enable the robust representation of brain sig... More >
    Graphical Abstract
    A Comprehensive Survey on Emerging Techniques and Technologies in Spatio-Temporal EEG Data Analysis
    Free Access | Research Article | Feature Paper | 10 June 2024 | Cited: 2
    Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform
    by
    Hongqi Fan
    ,
    Dawei Lu
    ,
    Yanwen Jiang
    ,
    Achim J. Lilienthal
    Chinese Journal of Information Fusion | Volume 1, Issue 1: 63-78, 2024 | DOI:10.62762/CJIF.2024.361892
    Abstract
    Considering the tractability of OGM (Occupancy Grid Map) and its wide use in the dynamic environment representation of mobile robotics, the extraction of motion information from successive OGMs are very important for many tasks, such as SLAM (Simultaneously Localization And Mapping) and DATMO (Detection and Tracking of Moving Object). In this paper, we propose a novel motion extraction method based on the signal transform, called as S-KST (Spatial Keystone Transform), for the motion detection and estimation from successive noisy OGMs. It extends the KST in radar imaging or motion compensation to 1D spatial case (1DS-KST) and 2D spatial case (2DS-KST) combined multiple hypotheses about poss... More >
    Graphical Abstract
    Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform