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Electronics
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AI in Signal and Image Processing
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AI in Signal and Image Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 3089

Special Issue Editors

Dr. Haijian Zhang

E-Mail Website1 Website2
Guest Editor
School of Electronic Information, Wuhan University, Wuhan 430072, China
Interests: signal processing; artificial intelligence; multimedia forensics and security
Dr. Xing Tang

E-Mail Website
Guest Editor
School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China
Interests: artificial intelligence; digital communications; mobile communication system; wireless communications
Dr. Jinsheng Xiao

E-Mail Website
Guest Editor
School of Electronic Information, Wuhan University, Wuhan 430072, China
Interests: video and image processing; computer vision; artificial intelligence; swarm intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, entitled "AI in Signal and Image Processing", is dedicated to exploring the intersection of artificial intelligence (AI) techniques and signal/image processing methods. Its aim is to offer a platform for researchers and practitioners to showcase their most recent findings, methodologies, and innovations. By gathering contributions from experts in the field of AI-driven signal and image processing, this Special Issue strives to advance state-of-the-art research, foster interdisciplinary collaborations, address key challenges, and bridge the gap between theoretical research and practical applications by highlighting real-world implementations and experiments.

This Special Issue seeks to investigate how AI can enhance various aspects of signal and image processing tasks, ranging from basic preprocessing steps to sophisticated analyses and interpretation. Its scope may encompass a broad array of topics, including, but not limited to, the following:

  1. AI-based signal processing algorithms: This could involve using machine learning, deep learning, or other AI techniques to augment traditional signal processing tasks, such as filtering, noise reduction, compression, and feature extraction.
  2. Image processing and computer vision: This Special Issue may cover advancements in AI-driven techniques for image analysis, object detection and recognition, image segmentation, image registration, and other related tasks.
  3. Pattern recognition and classification: This Special Issue may also delve into how AI algorithms can improve pattern recognition and classification tasks within signal and image processing applications, such as biometrics, medical imaging, remote sensing, and surveillance.
  4. AI-enabled signal/image enhancement: This could include methods for enhancing the quality, resolution, or clarity of signals or images using AI approaches.
  5. Applications of AI in signal/image processing: This Special Issue may also highlight real-world applications of AI techniques across diverse domains, including healthcare, astronomy, multimedia, robotics, and industrial automation.

Dr. Haijian Zhang
Dr. Xing Tang
Dr. Jinsheng Xiao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • pattern recognition
  • computer vision
  • machine learning
  • applications of AI

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Published Papers (4 papers)

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Research

11 pages, 3572 KiB  
Article
FCT: An Adaptive Model for Classification of Mixed Radio Signals
by Mingxue Liao, Yuanyuan Liang and Pin Lv
Electronics 2025, 14(10), 2028; https://doi.org/10.3390/electronics14102028 - 16 May 2025
Viewed by 18
Abstract
In recent years, radio signal classification has become a hot topic in the field of wireless communication. However, current algorithms have low classification accuracy at low signal-to-noise radio (SNR) signals, and under this condition, they cannot achieve good classification results of mixed radio [...] Read more.
In recent years, radio signal classification has become a hot topic in the field of wireless communication. However, current algorithms have low classification accuracy at low signal-to-noise radio (SNR) signals, and under this condition, they cannot achieve good classification results of mixed radio signals either. In this paper, we proposed an adaptive model based on feedforward neural network (FNN), convolutional neural network (CNN), and Transformer, named FCT. FCT is proposed to achieve better classification performance on mixed radio signals by leveraging the classification advantages of CNNs and Transformer networks for different SNR ratios. The parameters of FCT will be adjusted dynamically to achieve lower loss or better classification accuracy during the training process. The FCT model is verified on a public dataset, showing better performance than current state-of-the-art (SOTA) models of the mixed radio signals, especially at low SNR signals. The best classification accuracy of the FCT can reach 95.70% when the signals are at high SNR. The overall classification accuracy of FCT can reach 84.04%, which is higher than current SOTA models by 26.12%. Theoretical analysis and simulation experiments show that the proposed FCT model provides a new research direction in the classification of mixed radio signals. Full article
(This article belongs to the Special Issue AI in Signal and Image Processing)
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25 pages, 6066 KiB  
Article
CNN-Based Fault Classification in Induction Motors Using Feature Vector Images of Symmetrical Components
by Tae-Hong Min, Joong-Hyeok Lee and Byeong-Keun Choi
Electronics 2025, 14(8), 1679; https://doi.org/10.3390/electronics14081679 - 21 Apr 2025
Viewed by 467
Abstract
Motor Current Signature Analysis (MCSA) is a commonly used non-invasive method for diagnosing faults in electric motors. Although MCSA provides significant advantages—current signals are easy to acquire and inherently robust against noise—this study aims to further enhance its diagnostic capabilities by focusing on [...] Read more.
Motor Current Signature Analysis (MCSA) is a commonly used non-invasive method for diagnosing faults in electric motors. Although MCSA provides significant advantages—current signals are easy to acquire and inherently robust against noise—this study aims to further enhance its diagnostic capabilities by focusing on symmetrical components. Three-phase stator current signals are converted into zero, positive, and negative sequence components, and their time-domain feature vectors are systematically integrated into a single image representation. A Convolutional Neural Network (CNN) is then employed for fault classification. The proposed method is model-free, requiring no explicit motor model, which offers greater flexibility compared to model-based techniques. Validation experiments were conducted on a rotor kit test bench under seven different conditions (one healthy condition and six mechanical/electrical fault conditions), with fault severities chosen to reflect practical scenarios. The symmetrical components-based image classification method demonstrated superior performance, achieving 99.76% classification accuracy and outperforming a widely used Short-Time Fourier Transform (STFT)-based spectrogram approach. These findings highlight that integrating all symmetrical component information into one image effectively captures each fault’s distinct behavior, enabling reliable diagnostic outcomes. By leveraging the distinct variations in zero, positive, and negative components under fault conditions, the proposed method offers a powerful, accurate, and non-invasive framework for real-time motor fault diagnosis in industrial applications. Full article
(This article belongs to the Special Issue AI in Signal and Image Processing)
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34 pages, 3129 KiB  
Article
Social-Aware Link Reliability Prediction Model Based Minimum Delay Routing for CR-VANETs
by Jing Wang, Wenshi Dan, Hong Li, Lingyu Yan, Aoxue Mei and Xing Tang
Electronics 2025, 14(3), 627; https://doi.org/10.3390/electronics14030627 - 5 Feb 2025
Viewed by 642
Abstract
Cognitive radio vehicle ad hoc networks (CR-VANETs) can utilize spectrum resources flexibly and efficiently and mitigate the conflict between limited spectrum resources and the ever-increasing demand for vehicular communication services. However, in CR-VANETs, the mobility characteristics of vehicles as well as the dynamic [...] Read more.
Cognitive radio vehicle ad hoc networks (CR-VANETs) can utilize spectrum resources flexibly and efficiently and mitigate the conflict between limited spectrum resources and the ever-increasing demand for vehicular communication services. However, in CR-VANETs, the mobility characteristics of vehicles as well as the dynamic topology changes and frequent disruptions of links can lead to large end-to-end delays. To address this issue, we propose the social-based minimum end-to-end delay routing (SMED) algorithm, which leverages the social attributes of both primary and secondary users to reduce end-to-end delay and packet loss. We analyze the influencing factors of vehicle communication in urban road segments and at intersections, formulate the end-to-end delay minimization problem as a nonlinear integer programming problem, and utilize two sub-algorithms to solve this problem. Simulation results show that, compared to the intersection delay-aware routing algorithm (IDRA) and the expected path duration maximization routing algorithm (EPDMR), our method demonstrates significant improvements in both end-to-end delay and packet loss rate. Specifically, the SMED routing algorithm achieved an average reduction of 11.7% in end-to-end delay compared to EPDMR and 25.0% compared to IDRA. Additionally, it lowered the packet loss rate by 24.9% on average compared to EPDMR and 32.5% compared to IDRA. Full article
(This article belongs to the Special Issue AI in Signal and Image Processing)
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26 pages, 2860 KiB  
Article
Meta-YOLOv8: Meta-Learning-Enhanced YOLOv8 for Precise Traffic Light Color Detection in ADAS
by Vasu Tammisetti, Georg Stettinger, Manuel Pegalajar Cuellar and Miguel Molina-Solana
Electronics 2025, 14(3), 468; https://doi.org/10.3390/electronics14030468 - 24 Jan 2025
Cited by 1 | Viewed by 1346
Abstract
The ability to accurately detect traffic light color is critical for the functioning of Advanced Driver Assistance Systems (ADAS), as it directly impacts a vehicle’s safety and operational efficiency. This paper introduces Meta-YOLOv8, an improvement over YOLOv8 based on meta-learning, designed explicitly for [...] Read more.
The ability to accurately detect traffic light color is critical for the functioning of Advanced Driver Assistance Systems (ADAS), as it directly impacts a vehicle’s safety and operational efficiency. This paper introduces Meta-YOLOv8, an improvement over YOLOv8 based on meta-learning, designed explicitly for traffic light color detection focusing on color recognition. In contrast to conventional models, Meta-YOLOv8 focuses on the illuminated portion of traffic signals, enhancing accuracy and extending the detection range in challenging conditions. Furthermore, this approach reduces the computational load by filtering out irrelevant data. An innovative labeling technique has been implemented to address real-time weather-related detection issues, although other bright objects may occasionally confound it. Our model employs meta-learning principles to mitigate confusion and boost confidence in detections. Leveraging task similarity and prior knowledge enhances detection performance across diverse lighting and weather conditions. Meta-learning also reduces the necessity for extensive datasets while maintaining consistent performance and adaptability to novel categories. The optimized feature weighting for precise color differentiation, coupled with reduced latency and computational demands, enables a faster response from the driver and reduces the risk of accidents. This represents a significant advancement for resource-constrained ADAS. A comparative assessment of Meta-YOLOv8 with traditional models, including SSD, Faster R-CNN, and Detection Transformers (DETR), reveals that it outperforms these models, achieving an F1 score, accuracy of 93% and a precision rate of 97%. Full article
(This article belongs to the Special Issue AI in Signal and Image Processing)
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