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Advances in Digital Signal and Image Processing, Techniques, and Computations with Multidisciplinary Applications: Third Edition

A Special Issue of Electronics (ISSN 2079-9292) belonging to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 2285

Editor

Dr. Honarvar Shakibaei Asli

E-Mail Website
Guest Editor
Centre for Life-Cycle Engineering and Management, School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK
Interests: signals and systems; digital filter design; digital image processing; medical image processing; and pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce the Third Edition of this Special Issue of Electronics, "Advances in Digital Signal and Image Processing, Techniques, and Computations with Multidisciplinary Applications: Third Edition", following its prior success. Image processing is a rapidly evolving technique applied in several research fields. The processing digital signals, a major objective in many scientific domains, can be achieved through image-processing approaches. This path includes the analysis, classification, and manipulation of signals using operations such as filtering, compression, feature extraction, enhancement, and spectral analysis. 

This Special Issue aims to highlight innovative ideas and algorithms for treating different types of discrete signals using image-processing algorithms.

We welcome original and novel contributions, including research papers and extensive reviews, addressing the impact and relevance of electronic signal processing using image-processing applications.

We welcome submissions detailing new theories and evolutionary methods for digital signal processing using image processing approaches. A non-exhaustive list of topics is as follows:

  • Digital signal processing using machine learning;
  • Deep learning for digital signal processing;
  • Image restoration and noise reduction;
  • Image classification, segmentation, and clustering;
  • Object detection and tracking;
  • Medical imaging for EEG/ECG signal processing;
  • Feature selection, extraction, and learning;
  • Digital signal detection and recognition using image processing techniques;
  • Motion analysis of digital signals.

Dr. Honarvar Shakibaei Asli
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • digital signal processing
  • image processing
  • machine learning
  • deep learning

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Related Special Issues

Published Papers (4 papers)

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Research

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40 pages, 52553 KB  
Article
An Adaptive Low-Light Image Enhancement Framework via Metaheuristic-Optimized Inverted Dehazing and Gamma Correction with Global Limits
by Cheng-Hsiung Hsieh, Xin-Rui Lin, Chia-Hsin Cheng, Yung-Hoh Sheu and Yung-Fa Huang
Electronics 2026, 15(14), 3210; https://doi.org/10.3390/electronics15143210 - 21 Jul 2026
Viewed by 304
Abstract
Low-light image enhancement (LLIE) is a fundamental task in computer vision, required for restoring luminance, contrast, and structural fidelity in images captured under suboptimal lighting environments. This paper introduces an optimization-driven, scene-adaptive LLIE framework, designated as [...] Read more.
Low-light image enhancement (LLIE) is a fundamental task in computer vision, required for restoring luminance, contrast, and structural fidelity in images captured under suboptimal lighting environments. This paper introduces an optimization-driven, scene-adaptive LLIE framework, designated as OMIDCPGCGL, which exploits the optical duality between low-light inversion and atmospheric scattering. The proposed methodology transforms low-light inputs into quasi-haze representations through an optical inversion process, followed by structural restoration using an Improved Dark Channel Prior (MIDCP) baseline. To refine the restored output, a Gamma Correction with Global Limits (GCGL) module is integrated as a boundary constraint to mitigate localized over-exposure and preserve chromatic consistency. A core novelty of this framework lies in the deployment of metaheuristic optimization algorithms (MOAs)—specifically the Gray Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), and Marine Predators Algorithm (MPA)—to autonomously resolve optimal, image-specific parameter configurations. This search paradigm is guided by perception-driven fitness functions, namely the Patch-based Contrast Quality Index (PCQI) or the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Quantitative and qualitative evaluations across a comprehensive pool of 1092 benchmark images demonstrate that the proposed framework exhibits robust statistical resilience and cross-dataset generalization compared to four state-of-the-art deep learning methods. While data-driven deep learning architectures retain localized superiority under the extreme degradation boundaries of the DARK FACE dataset, the proposed physics-inspired optimization framework achieves the leading overall cross-dataset aggregate ranking (R¯=2.467) across diverse evaluation environments due to its per-image dynamic solution space mapping. Full article
(This article belongs to the Special Issue Advances in Digital Signal and Image Processing, Techniques, and Computations with Multidisciplinary Applications: Third Edition)
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25 pages, 1129 KB  
Article
Test-Time Adaptation for Personal Voice Activity Detection: VAD-Gated Test-Time Training and Speaker Embedding Adaptation
by Tai-You Chen, Chien-Chia Chiu, Jung-Shan Lin and Jeih-Weih Hung
Electronics 2026, 15(14), 3111; https://doi.org/10.3390/electronics15143111 - 15 Jul 2026
Viewed by 380
Abstract
Personal voice activity detection (PVAD) identifies whether each detected speech frame originates from a designated target speaker. Modern PVAD systems are typically trained offline and then deployed with frozen model parameters and a fixed, pre-enrolled speaker embedding, leaving them unable to adapt to [...] Read more.
Personal voice activity detection (PVAD) identifies whether each detected speech frame originates from a designated target speaker. Modern PVAD systems are typically trained offline and then deployed with frozen model parameters and a fixed, pre-enrolled speaker embedding, leaving them unable to adapt to distribution shifts at inference time such as unseen acoustic environments, changing speaking styles, or mismatches between enrollment and test conditions. Test-time training (TTT) and test-time adaptation have shown promise in language, vision, and several speech tasks, yet their behavior on PVAD has not been studied. In this work, we present an empirical study of two complementary test-time adaptation mechanisms built on top of the recently proposed FDE-Mamba backbone. The first is a VAD-gated TTT adapter, which instantiates the TTT-Linear formulation within the personalization pathway and augments it with a VAD-probability gate and exponential moving-average stabilization, adapting an internal weight matrix on the speaker-conditioned feature stream of each test utterance. The second is TEA (Test-time Embedding Adaptation), a scheme that keeps all model parameters frozen and instead adapts the target speaker d-vector itself via self-supervised objectives at inference time, directly targeting enrollment–test mismatch. We evaluate both mechanisms on the LibriSpeech PVAD benchmark across two backbones (LSTM-based FDE-RNN and Mamba-based FDE-Mamba), reporting category-wise average precision, mean average precision (mAP), accuracy, recall, precision, and real-time factor. We further isolate the effect of a post-hoc Gaussian smoothing step and report that, of all the components we examine, this task-agnostic smoothing accounts for the largest single accuracy gain on the FDE-Mamba backbone (accuracy 89.87%→90.47%); the test-time adaptation mechanisms contribute a separate, smaller gain that is concentrated on speaker-discrimination metrics (mAP, precision) rather than on accuracy. Overall, the proposed test-time adaptation yields a consistent but modest improvement over the FDE-Mamba baseline (mAP 0.9605→0.9641, precision 0.881→0.899), while slightly reducing recall and increasing inference cost when TEA is enabled. Through ablation studies, we quantify the independent and combined contribution of each component and characterize the recall–precision trade-off introduced by adaptation. These findings, together with a discussion of their limitations and cost–benefit profile, provide a measured baseline and design insights for future work on adaptive PVAD, particularly under stronger acoustic and enrollment mismatches than those captured by the LibriSpeech protocol. Full article
(This article belongs to the Special Issue Advances in Digital Signal and Image Processing, Techniques, and Computations with Multidisciplinary Applications: Third Edition)
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22 pages, 33285 KB  
Article
A High Speed Coprocessor Design for Sensorless PMSM Control
by Qingfeng Wang, Fei Wang, Hongji Huang and Jiangkangjian Chen
Electronics 2026, 15(11), 2464; https://doi.org/10.3390/electronics15112464 - 4 Jun 2026
Viewed by 400
Abstract
Permanent Magnet Synchronous Motors (PMSMs) are widely used in industries, and high-speed PMSMs are becoming common. Digital control systems are frequently used for PMSM control, which introduce control delays. These delays may reduce the bandwidth of the control loop and even cause the [...] Read more.
Permanent Magnet Synchronous Motors (PMSMs) are widely used in industries, and high-speed PMSMs are becoming common. Digital control systems are frequently used for PMSM control, which introduce control delays. These delays may reduce the bandwidth of the control loop and even cause the current regulator to lose stability. Complicated algorithms are often proposed to alleviate control delays. This paper attempts to address this problem from another perspective. A high-speed coprocessor working with a RISC-V processor core is proposed; both of them are implemented on an FPGA. The coprocessor not only implements the basic functions of FOC and SVPWM and integrates current-loop and speed-loop control, but more critically, it also implements a sliding mode observer for rotor position estimation for sensorless control of PMSMs. Simulations and experiments are conducted to verify the effectiveness of the proposed architecture, and a computational speedup of more than 20 times is achieved. Full article
(This article belongs to the Special Issue Advances in Digital Signal and Image Processing, Techniques, and Computations with Multidisciplinary Applications: Third Edition)
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Review

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37 pages, 12008 KB  
Review
Deep Learning Architectures for Pattern Recognition: A Comparative Review of Challenges, Applications, and the Path Toward XAI
by Georgia Koukiou
Electronics 2026, 15(11), 2402; https://doi.org/10.3390/electronics15112402 - 1 Jun 2026
Viewed by 937
Abstract
The recent rapid growth of deep learning has significantly reshaped the landscape of computer vision, establishing itself as the preferred paradigm for various tasks. Deep learning methods have demonstrated superior performance compared to previous state-of-the-art machine learning techniques across various fields. This review [...] Read more.
The recent rapid growth of deep learning has significantly reshaped the landscape of computer vision, establishing itself as the preferred paradigm for various tasks. Deep learning methods have demonstrated superior performance compared to previous state-of-the-art machine learning techniques across various fields. This review provides a concise overview of artificial neural networks (ANNs) and some of the most significant deep learning architectures, such as recurrent neural networks (RNNs), generative adversarial networks (GANs) and radial basis function networks (RBFNs). This review not only outlines the historical context and structures of these architectures but also provides a sophisticated understanding of their applications across different computer vision domains. A rigorous and comprehensive overview of these architectures is discussed throughout this review, and an essential systematic comparative analysis based on specific benchmarking criteria is provided. While individual deep learning frameworks excel in distinct domains, selecting the optimal architecture requires a balanced trade-off between algorithmic complexity, computational overhead, data dependencies, and structural interpretability. An intuitive and holistic benchmarking process synthesizes the core characteristics, technical configurations, operational constraints, and developmental pathways toward Explainable AI (XAI) and Green AI sustainability for the examined architectures (ANNs, RNNs, LSTMs, GANs, and RBFNs). Additionally, in this work the advantages and limitations of these architectures are discussed. Furthermore, an investigation of their applications in diverse computer vision tasks is carried out. Full article
(This article belongs to the Special Issue Advances in Digital Signal and Image Processing, Techniques, and Computations with Multidisciplinary Applications: Third Edition)
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