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Advanced Signal and Image Processing for Applied Engineering
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Advanced Signal and Image Processing for Applied Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 7315

Special Issue Editor

Prof. Dr. Mouloud Adel

E-Mail Website
Guest Editor
CNRS, Centrale Marseille, Institut Fresnel UMR 7249, Aix-Marseille University, 13007 Marseille, France
Interests: biomedical signal and image processing; computer-aided diagnosis; pattern recognition; machine learning for medical applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancements in signal and image processing techniques are driving innovation across diverse engineering fields. These technologies enable the extraction of meaningful information from complex datasets, offering transformative solutions for challenges in sectors such as healthcare, communication, manufacturing, automotive, and energy systems.

This Special Issue will focus on the development and application of cutting-edge signal and image processing methods tailored to real-world engineering problems. It will gather contributions that demonstrate novel algorithmic approaches and computational frameworks, as well as their integration into applied systems, highlighting both theoretical advancements and practical implementations, including more recent artificial intelligence-based approaches.

This Special Issue will also include a selection of papers deemed as journal quality from the 14th Conference on Image Processing Tools, Theory and Applications (IPTA), https://ipta-conference.com/ipta25/.

Prof. Dr. Mouloud Adel
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences 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

  • signal processing
  • image processing
  • algorithmic approaches
  • computational frameworks
  • healthcare
  • communication
  • manufacturing
  • automotive
  • energy systems
  • biomedical image processing
  • machine learning
  • computer-aided diagnosis
  • pattern recognition

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Published Papers (2 papers)

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Research

33 pages, 24046 KB  
Article
CoDA: A Cognitive-Inspired Approach for Domain Adaptation
by Cavide Balkı Gemirter, Emin Erkan Korkmaz and Dionysis Goularas
Appl. Sci. 2026, 16(9), 4115; https://doi.org/10.3390/app16094115 - 23 Apr 2026
Viewed by 395
Abstract
Modern neural networks have achieved remarkable success in visual recognition; however, due to their sensitivity to domain shifts, Unsupervised Domain Adaptation (UDA) remains an open research problem. A key reason for this limitation is that source-trained models rely primarily on texture, lacking the [...] Read more.
Modern neural networks have achieved remarkable success in visual recognition; however, due to their sensitivity to domain shifts, Unsupervised Domain Adaptation (UDA) remains an open research problem. A key reason for this limitation is that source-trained models rely primarily on texture, lacking the explicit geometric information required for object recognition. To overcome this problem, we introduce CoDA, an object-centric learning framework inspired by infant cognitive development, specifically the process of object individuation. By introducing a geometric prior, our approach employs a physically grounded generation pipeline that uses a textureless “Sculpture Mode” and object isolation to complement textural information with 3D geometric features, capturing shape information that is often ignored during training. To enable robust training from scratch, we further integrate two control mechanisms: a Network Stability Scheduler to orchestrate training progression based on convergence stability, and a Dynamic Top-K Pseudo-Labeling strategy that adapts confidence thresholds for each individual class. Extensive evaluations on three real-world target datasets (VegFru, Fruits-262, and Open Images v7) demonstrate that CoDA, trained on a source dataset of just 12,000 synthetic images, achieves comparable results to (and in specific domains surpasses) ImageNet-pretrained models (leveraging 1.2 million images), significantly outperforming state-of-the-art adversarial and semi-supervised domain adaptation methods. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing for Applied Engineering)
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20 pages, 760 KB  
Article
Detecting AI-Generated Images Using a Hybrid ResNet-SE Attention Model
by Abhilash Reddy Gunukula, Himel Das Gupta and Victor S. Sheng
Appl. Sci. 2025, 15(13), 7421; https://doi.org/10.3390/app15137421 - 2 Jul 2025
Cited by 5 | Viewed by 6260
Abstract
The rapid advancements in generative artificial intelligence (AI), particularly through models like Generative Adversarial Networks (GANs) and diffusion-based architectures, have made it increasingly difficult to distinguish between real and synthetically generated images. While these technologies offer benefits in creative domains, they also pose [...] Read more.
The rapid advancements in generative artificial intelligence (AI), particularly through models like Generative Adversarial Networks (GANs) and diffusion-based architectures, have made it increasingly difficult to distinguish between real and synthetically generated images. While these technologies offer benefits in creative domains, they also pose serious risks in terms of misinformation, digital forgery, and identity manipulation. This paper presents a novel hybrid deep learning model for detecting AI-generated images by integrating the ResNet-50 architecture with Squeeze-and-Excitation (SE) attention blocks. The proposed SE-ResNet50 model enhances channel-wise feature recalibration and interpretability by integrating Squeeze-and-Excitation (SE) blocks into the ResNet-50 backbone, enabling dynamic emphasis on subtle generative artifacts such as unnatural textures and semantic inconsistencies, thereby improving classification fidelity. Experimental evaluation on the CIFAKE dataset demonstrates the model’s effectiveness, achieving a test accuracy of 96.12%, precision of 97.04%, recall of 88.94%, F1-score of 92.82%, and an AUC score of 0.9862. The model shows strong generalization, minimal overfitting, and superior performance compared with transformer-based models and standard architectures like ResNet-50, VGGNet, and DenseNet. These results confirm the hybrid model’s suitability for real-time and resource-constrained applications in media forensics, content authentication, and ethical AI governance. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing for Applied Engineering)
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