IECE - Institute of Emerging and Computer Engineers

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    • shenglun@dei.unipd.it
    Academic Editor
    Author
    Contributions by role
    Author 1
    Reviewer 2
    Editor 2
    ORCID
    0009-0000-1079-2564
    Shenglun Yi
    Department of Information Engineering, University of Padua, Italy
    Summary
    Edited Journals
    IECE Transactions on Intelligent Systematics
    IECE Contributions
    Articles
    1
    Edited
    2
    Code (Data) Available | Free Access | Research Article | Feature Paper | 09 August 2024
    LI3D-BiLSTM: A Lightweight Inception-3D Networks with BiLSTM for Video Action Recognition
    by
    Fafa Wang
    ,
    Xuebo Jin
    ,
    Shenglun Yi
    IECE Transactions on Emerging Topics in Artificial Intelligence | Volume 1, Issue 1: 58-70, 2024 | DOI:10.62762/TETAI.2024.628205
    Abstract
    This paper proposes an improved video action recognition method, primarily consisting of three key components. Firstly, in the data preprocessing stage, we developed multi-temporal scale video frame extraction and multi-spatial scale video cropping techniques to enhance content information and standardize input formats. Secondly, we propose a lightweight Inception-3D networks (LI3D) network structure for spatio-temporal feature extraction and design a soft-association feature aggregation module to improve the recognition accuracy of key actions in videos. Lastly, we employ a bidirectional LSTM network to contextualize the feature sequences extracted by LI3D, enhancing the representation capa... More >
    Graphical Abstract
    LI3D-BiLSTM: A Lightweight Inception-3D Networks with BiLSTM for Video Action Recognition
    Free Access | Research Article | 29 May 2024 | Cited: 4
    Parameter Adaptive Non-Model-Based State Estimation Combining Attention Mechanism and LSTM
    by
    Xuebo Jin
    ,
    Tianxiao Sun
    ,
    Wei Chen
    ,
    Huijun Ma
    ,
    Yeqing Wang
    ,
    Yusen Zheng
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 1: 40-48, 2024 | DOI:10.62762/TIS.2024.137329
    Abstract
    Nowadays, state estimation is widely used in fields such as autonomous driving and drone navigation. However, in practical applications, it is difficult to obtain accurate target motion models and noise covariance.This leads to a decrease in the estimation accuracy of traditional Kalman filters. To address this issue, this paper proposes an adaptive model free state estimation method based on attention parameter learning module. This method combines Transformer's encoder with Long Short Term Memory Network (LSTM), and obtains the system's operational characteristics through offline learning of measurement data without modeling the system dynamics and measurement characteristics. In addition,... More >
    Graphical Abstract
    Parameter Adaptive Non-Model-Based State Estimation Combining Attention Mechanism and LSTM
    Free Access | Research Article | 27 May 2024 | Cited: 5
    YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image
    by
    Xuebo Jin
    ,
    Anshuo Tong
    ,
    Xudong Ge
    ,
    Huijun Ma
    ,
    Jiaxi Li
    ,
    Heran Fu
    ,
    Longfei Gao
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 1: 30-39, 2024 | DOI:10.62762/TIS.2024.137321
    Abstract
    In recent years, deep learning techniques have been increasingly applied to the detection of remote sensing images. However, the substantial size variation and dense distribution of objects in these images present significant challenges to detection algorithms. Current methods often suffer from low efficiency, missed detections, and inaccurate bounding boxes. To address these issues, this paper presents an improved YOLO algorithm, YOLOv7-bw, designed for efficient remote sensing image detection, thereby advancing object detection applications in the remote sensing industry. YOLOv7-bw enhances the original SPPCSPC pooling pyramid network by incorporating a Bi-level Routing Attention module, w... More >
    Graphical Abstract
    YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image