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Electronics
Special Issues
Intelligent Signal Processing and Its Applications
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Intelligent Signal Processing and Its Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: 15 January 2026 | Viewed by 1186

Special Issue Editors

Prof. Dr. Shuai Li

E-Mail Website
Guest Editor
School of Control Science and Engineering, Shandong University, Jinan 250061, China
Interests: 3D vision; image/video coding and processing; IndRNN
Special Issues, Collections and Topics in MDPI journals
Dr. Chuankun Li

E-Mail Website
Guest Editor
School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
Interests: action recognition; sequence processing
Special Issues, Collections and Topics in MDPI journals
Dr. Jian Li

E-Mail Website
Guest Editor
School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
Interests: artificial intelligence; signal processing; array signal
Dr. Yanbo Gao

E-Mail Website
Guest Editor
School of Software, Shandong University, Jinan 250101, China
Interests: video coding; computer vision; 3D video processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of technologies such as 5G and the Internet of Things (IoT), intelligent signal processing has become increasingly widespread in numerous fields, including communications, healthcare, and industry. The complexity and ever-changing nature of signal environments, along with their diverse application requirements, place greater demands on intelligent signal processing techniques. Meanwhile, the continuous innovation in artificial intelligence, machine learning, and other cutting-edge technologies provides continued impetus for its development.​

On the other hand, the emergence of novel signal processing algorithms, especially in the context of deep learning and reinforcement learning, has revolutionized many traditional signal processing methods. These new algorithms, with their powerful feature extraction and pattern recognition capabilities, unlock new possibilities for solving complex signal processing problems, revealing a new chapter in intelligent signal processing and its applications.​

This Special Issue explores the latest trends in intelligent signal processing and its applications, covering new signal processing algorithms, innovative application scenarios, and novel datasets. Prospective authors are invited to submit high-quality original research papers and comprehensive reviews to this Special Issue, with topics of interest including, but not limited to, the following:

  1. Signal processing algorithms based on deep learning, including convolutional neural networks, recurrent neural networks, and their variants​;
  2. Adaptive signal processing techniques for complex environments, such as interference suppression and noise reduction​;
  3. Intelligent signal processing in communication systems, including 5G/6G signal modulation and demodulation, as well as channel estimation​;
  4. Signal processing applications in healthcare, such as electrocardiogram (ECG) and brain–computer interface (BCI) analyses;
  5. Signal processing for the industrial Internet of Things (IIoT), including fault diagnosis of mechanical equipment and condition monitoring​;
  6. New representation and feature extraction methods for signals, including time-frequency analysis and sparse representation​.

Prof. Dr. Shuai Li
Dr. Chuankun Li
Dr. Jian Li
Dr. Yanbo Gao
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 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-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

  • intelligent signal processing
  • signal feature extraction
  • deep learning
  • industrial IoT signal processing
  • signal processing tools and frameworks

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

16 pages, 3547 KB  
Article
Frequency-Aware Multi-Rate Resampling with Multi-Band Deep Supervision for Modular Speech Denoising
by Seon Man Kim
Electronics 2025, 14(22), 4523; https://doi.org/10.3390/electronics14224523 - 19 Nov 2025
Viewed by 455
Abstract
Conventional waveform-based speech enhancement models prioritize temporal modeling, often neglecting the irreversible spectral information loss triggered by standard downsampling. Consequently, this study introduces a novel frequency-aware framework. The proposed approach incorporates a modular, multi-rate resampling module with principled anti-aliasing to precisely control each [...] Read more.
Conventional waveform-based speech enhancement models prioritize temporal modeling, often neglecting the irreversible spectral information loss triggered by standard downsampling. Consequently, this study introduces a novel frequency-aware framework. The proposed approach incorporates a modular, multi-rate resampling module with principled anti-aliasing to precisely control each layer’s effective frequency band, complemented by a multi-band loss function for deep supervision. Integrating this module into a standard Wave-U-Net and an attention-enhanced variant confirmed its effectiveness. The findings show a significant improvement over the baseline, yielding an average Perceptual Evaluation of Speech Quality gain of 0.40, with further benefits when paired with an advanced temporal model at a permissible increase in computational complexity. Furthermore, tests on novel noise types validate the generalizability of the proposed principles, establishing structured frequency band allocation as a fundamental, modular design strategy for improving end-to-end models. Full article
(This article belongs to the Special Issue Intelligent Signal Processing and Its Applications)
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18 pages, 24463 KB  
Article
Multi-Scale Adaptive Modulation Network for Efficient Image Super-Resolution
by Zepeng Liu, Guodong Zhang, Jiya Tian and Ruimin Qi
Electronics 2025, 14(22), 4404; https://doi.org/10.3390/electronics14224404 - 12 Nov 2025
Viewed by 401
Abstract
As convolutional neural networks (CNNs) become gradually larger and deeper, their applicability in real-time and resource-constrained environments is significantly limited. Furthermore, while self-attention (SA) mechanisms excel at capturing global dependencies, they often emphasize low-frequency information and struggle to represent fine local details. To [...] Read more.
As convolutional neural networks (CNNs) become gradually larger and deeper, their applicability in real-time and resource-constrained environments is significantly limited. Furthermore, while self-attention (SA) mechanisms excel at capturing global dependencies, they often emphasize low-frequency information and struggle to represent fine local details. To overcome these limitations, we propose a multi-scale adaptive modulation network (MAMN) for image super-resolution. The MAMN mainly consists of a series of multi-scale adaptive modulation blocks (MAMBs), each of which incorporates a multi-scale adaptive modulation layer (MAML), a local detail extraction layer (LDEL), and two Swin Transformer Layers (STLs). The MAML is designed to capture multi-scale non-local representations, while the LDEL complements this by extracting high-frequency local features. Additionally, the STLs enhance long-range dependency modeling, effectively expanding the receptive field and integrating global contextual information. Extensive experiments demonstrate that the proposed method achieves an optimal trade-off between computational efficiency and reconstruction performance across five benchmark datasets. Full article
(This article belongs to the Special Issue Intelligent Signal Processing and Its Applications)
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