IECE - Institute of Emerging and Computer Engineers

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    Academic Editor
    Author
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    Author 1
    Reviewer 7
    Editor 7
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    You He
    Tsinghua University, China
    Summary
    Prof. You He received the Ph.D. degree from Tsinghua University, Beijing, China, in 1997. He was selected as the Chinese Academy of Engineering Academician in 2013. He is currently a Professor with the Department of Electronic Engineering, Tsinghua University, the director of the Institute of Information Fusion, Naval Aeronautical University, Yantai, China. His research interests include information fusion, computer vision, and Big Data technology. He was the recipient of four second prizes of national science and technology progress and seven first prizes of military science and technology progress.
    Edited Journals
    Chinese Journal of Information Fusion
    IECE Contributions
    Articles
    1
    Edited
    7
    Free Access | Review Article | 12 June 2024 | Cited: 2
    Bridging Modalities: A Survey of Cross-Modal Image-Text Retrieval
    by
    Tieying Li
    ,
    Lingdu Kong
    ,
    Xiaochun Yang
    ,
    Bin Wang
    ,
    Jiaxing Xu
    Chinese Journal of Information Fusion | Volume 1, Issue 1: 79-92, 2024 | DOI:10.62762/CJIF.2024.361895
    Abstract
    The rapid advancement of Internet technology, driven by social media and e-commerce platforms, has facilitated the generation and sharing of multimodal data, leading to increased interest in efficient cross-modal retrieval systems. Cross-modal image-text retrieval, encompassing tasks such as image query text (IqT) retrieval and text query image (TqI) retrieval, plays a crucial role in semantic searches across modalities. This paper presents a comprehensive survey of cross-modal image-text retrieval, addressing the limitations of previous studies that focused on single perspectives such as subspace learning or deep learning models. We categorize existing models into single-tower, dual-tower,... More >
    Graphical Abstract
    Bridging Modalities: A Survey of Cross-Modal Image-Text Retrieval
    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
    Free Access | Research Article | 08 June 2024
    GPS Tracking Based on Stacked-Serial LSTM Network
    by
    Xuebo Jin
    ,
    Songzheng Liu
    ,
    Jianlei Kong
    ,
    Yuting Bai
    ,
    Tingli Su
    ,
    Huijun Ma
    Chinese Journal of Information Fusion | Volume 1, Issue 1: 50-62, 2024 | DOI:10.62762/CJIF.2024.361889
    Abstract
    Maneuvering target tracking is widely used in unmanned vehicles, missile navigation, underwater ships, etc. Due to the uncertainty of the moving characteristics of maneuvering targets and the low sensor measurement accuracy, trajectory tracking has always been an open research problem and challenging work. This paper proposes a trajectory estimation method based on LSTM neural network for uncertain motion characteristics. The network consists of two LSTM networks with stacked serial relationships, one of which is used to predict the movement dynamics, and the other is used to update the track's state. Compared with the classical Kalman filter based on the maneuver model, the method proposed... More >
    Graphical Abstract
    GPS Tracking Based on Stacked-Serial LSTM Network
    Author's Talk | Free Access | Research Article | Feature Paper | 28 May 2024 | Cited: 7
    A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory
    by
    Xiaoming Guo
    ,
    Fengbao Yang
    ,
    Linna Ji
    Chinese Journal of Information Fusion | Volume 1, Issue 1: 33-49, 2024 | DOI:10.62762/CJIF.2024.361886
    Abstract
    In response to the current practical fusion requirements for infrared and visible videos, which often involve collaborative fusion of difference feature information, and model cannot dynamically adjust the fusion strategy according to the difference between videos, resulting in poor fusion performance, a mimic fusion algorithm for infrared and visible videos based on the possibility distribution synthesis theory is proposed. Firstly, quantitatively describe the various difference features and their attributes of the region of interest in each frame of the dual channel video sequence, and select the main difference features corresponding to each frame. Secondly, the pearson correlation coeffi... More >
    Graphical Abstract
    Author's Talk
    A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory
    A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory
    Free Access | Research Article | Feature Paper | 27 May 2024 | Cited: 5
    Simultaneous Spatiotemporal Bias Compensation and Data Fusion for Asynchronous Multisensor Systems
    by
    Gongjian Zhou
    ,
    Shizhe Bu
    ,
    Thiagalingam Kirubarajan
    Chinese Journal of Information Fusion | Volume 1, Issue 1: 16-32, 2024 | DOI:10.62762/CJIF.2024.361881
    Abstract
    Bias estimation of sensors is an essential prerequisite for accurate data fusion. Neglect of temporal bias in general real systems prevents the existing algorithms from successful application. In this paper, both spatial and temporal biases in asynchronous multisensor systems are investigated and two novel methods for simultaneous spatiotemporal bias compensation and data fusion are presented. The general situation that the sensors sample at different times with different and varying periods is explored, and unknown time delays may exist between the time stamps and the true measurement times. Due to the time delays, the time stamp interval of the measurements from different sensors may be di... More >
    Graphical Abstract
    Simultaneous Spatiotemporal Bias Compensation and Data Fusion for Asynchronous Multisensor Systems
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