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Advances in Digital Signal and Image Processing, Techniques, and Computations...
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Advances in Digital Signal and Image Processing, Techniques, and Computations with Multidisciplinary Applications, 2nd Edition

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

Deadline for manuscript submissions: 30 October 2025 | Viewed by 623

Special Issue 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; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

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 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

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

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Related Special Issue

Published Papers (1 paper)

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Research

31 pages, 2150 KiB  
Article
A Self-Supervised Point Cloud Completion Method for Digital Twin Smart Factory Scenario Construction
by Yongjie Xu, Haihua Zhu and Barmak Honarvar Shakibaei Asli
Electronics 2025, 14(10), 1934; https://doi.org/10.3390/electronics14101934 - 9 May 2025
Viewed by 469
Abstract
In the development of digital twin (DT) workshops, constructing accurate DT models has become a key step toward enabling intelligent manufacturing. To address challenges such as incomplete data acquisition, noise sensitivity, and the heavy reliance on manual annotations in traditional modeling methods, this [...] Read more.
In the development of digital twin (DT) workshops, constructing accurate DT models has become a key step toward enabling intelligent manufacturing. To address challenges such as incomplete data acquisition, noise sensitivity, and the heavy reliance on manual annotations in traditional modeling methods, this paper proposes a self-supervised deep learning approach for point cloud completion. The proposed model integrates self-supervised learning strategies for inferring missing regions, a Feature Pyramid Network (FPN), and cross-attention mechanisms to extract critical geometric and structural features from incomplete point clouds, thereby reducing dependence on labeled data and improving robustness to noise and incompleteness. Building on this foundation, a point cloud-based DT workshop modeling framework is introduced, incorporating transfer learning techniques to enable domain adaptation from synthetic to real-world industrial datasets, which significantly reduces the reliance on high-quality industrial point cloud data. Experimental results demonstrate that the proposed method achieves superior completion and reconstruction performance on both public benchmarks and real-world workshop scenarios, achieving an average CD-ℓ2 score of 15.96 on the 3D-EPN dataset. Furthermore, the method produces high-fidelity models in practical applications, providing a solid foundation for the precise construction and deployment of virtual scenes in DT workshops. Full article
(This article belongs to the Special Issue Advances in Digital Signal and Image Processing, Techniques, and Computations with Multidisciplinary Applications, 2nd Edition)
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