Academic Editor
Author
Contributions by role
Author 6
Reviewer 1
Editor 1
Xue-Bo JIN
Beijing Technology and Business University
Summary
Edited Journals
IECE Contributions

Open Access | Editorial | 08 October 2024
Sensing, Communication, and Control: A New Transactions
IECE Transactions on Sensing, Communication, and Control | Volume 1, Issue 1: 1-2, 2024 | DOI:10.62762/TSCC.2024.287867
Abstract
On behalf of the Editorial Board, I am very pleased to announce the launch of our new transactions, IECE Transitions on Sensing, Communication, and Control. This publication aims to serve as a premier platform for researchers, engineers, and scholars to share cutting-edge discoveries, methodologies, and applications in the rapidly evolving fields of sensing, communication, and control. More >

Code (Data) Available | Research Article | Feature Paper | 09 August 2024
LI3D-BiLSTM: A Lightweight Inception-3D Networks with BiLSTM for Video Action Recognition
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 | 08 June 2024
GPS Tracking Based on Stacked-Serial LSTM Network
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

Free Access | Research Article | 29 May 2024 | Cited: 4
Parameter Adaptive Non-Model-Based State Estimation Combining Attention Mechanism and LSTM
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
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
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