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Mathematical Modeling of Signal Processing and Analysis in Light of...
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Mathematical Modeling of Signal Processing and Analysis in Light of Deep Learning

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 4271

Special Issue Editors

Dr. Eleni Vrochidou

E-Mail Website
Guest Editor
Department of Computer Science, International Hellenic University, 65404 Kavala, Greece
Interests: signal processing; intelligent systems; pattern recognition; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vladan Papić

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia
Interests: computer vision; expert systems; robotics; motion analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. George A. Papakostas

E-Mail Website
Guest Editor
MLV Research Group, Department of Computer Science, International Hellenic University, 65404 Kavala, Greece
Interests: pattern recognition; computer/machine vision; computational intelligence; machine learning; feature extraction; evolutionary optimization; signal and image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

 Deep neural networks are gaining widespread attention due to their ability to provide performance gains in several real-world problems, largely those related to image data. Mathematical theory of deep learning networks would illuminate their mechanisms, allow the assessment of the strengths and weaknesses of different network architectures and lead to major improvements. In the most recent wave of technological advancement, deep learning gained traction in signal processing and analysis, and since then it has been adopted in speech processing, music and environmental sound processing, seismic signal processing and numerous fields of science, such as genomics, quantum chemistry, drug discovery, natural language processing and recommendation systems. Deep learning research in signal processing and analysis focuses not only on its applications, but also on the development of new mathematical methods, algorithms and models. The future state of the art in the field, if efficient and effective deep learning algorithms are developed, could be represented by several types of advanced signal processing methods.

The aim of this Special Issue is to introduce readers to the emerging concept of mathematical modeling deep learning algorithms for signal processing and analysis. In the expanded technical scope of signal processing, the signal input is not limited to traditional signal types such as audio, speech, image and video, but extends to additional sensory data that convey high-level, semantic information. Overcoming model overfitting, data augmentation techniques for high-quality training data, prediction results and the interpretability of deep models are of special interest. 

Dr. Eleni Vrochidou
Prof. Dr. Vladan Papić
Prof. Dr. George A. Papakostas
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. Axioms is an international peer-reviewed open access monthly 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

  • deep learning applications in signal processing and analysis
  • mathematical modeling of signal processing and analysis for deep learning applications
  • deep learning algorithms for time series prediction
  • deep learning algorithms for predictive maintenance
  • deep generative models for signal processing and analysis
  • physics-informed neural networks (PINNs) for solving PDEs
  • solving PDEs with deep learning
  • new datasets for deep learning
  • applications in speech, audio, image, video, text, language processing, information retrieval

Published Papers (2 papers)

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Research

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26 pages, 1211 KiB  
Article
A Novel Weld-Seam Defect Detection Algorithm Based on the S-YOLO Model
by Yi Zhang and Qingjian Ni
Axioms 2023, 12(7), 697; https://doi.org/10.3390/axioms12070697 - 17 Jul 2023
Cited by 5 | Viewed by 2074
Abstract
Detecting small targets and handling target occlusion and overlap are critical challenges in weld defect detection. In this paper, we propose the S-YOLO model, a novel weld defect detection method based on the YOLOv8-nano model and several mathematical techniques, specifically tailored to address [...] Read more.
Detecting small targets and handling target occlusion and overlap are critical challenges in weld defect detection. In this paper, we propose the S-YOLO model, a novel weld defect detection method based on the YOLOv8-nano model and several mathematical techniques, specifically tailored to address these issues. Our approach includes several key contributions. Firstly, we introduce omni-dimensional dynamic convolution, which is sensitive to small targets, for improved feature extraction. Secondly, the NAM attention mechanism enhances feature representation in the region of interest. NAM computes the channel-wise and spatial-wise attention weights by matrix multiplications and element-wise operations, and then applies them to the feature maps. Additionally, we replace the SPPF module with a context augmentation module to improve feature map resolution and quality. To minimize information loss, we utilize Carafe upsampling instead of the conventional upsampling operations. Furthermore, we use a loss function that combines IoU, binary cross-entropy, and focal loss to improve bounding box regression and object classification. We use stochastic gradient descent (SGD) with momentum and weight decay to update the parameters of our model. Through rigorous experimental validation, our S-YOLO model demonstrates outstanding accuracy and efficiency in weld defect detection. It effectively tackles the challenges of small target detection, target occlusion, and target overlap. Notably, the proposed model achieves an impressive 8.9% improvement in mean Average Precision (mAP) compared to the native model. Full article
(This article belongs to the Special Issue Mathematical Modeling of Signal Processing and Analysis in Light of Deep Learning)
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Review

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21 pages, 2666 KiB  
Review
Automatic Facial Palsy Detection—From Mathematical Modeling to Deep Learning
by Eleni Vrochidou, Vladan Papić, Theofanis Kalampokas and George A. Papakostas
Axioms 2023, 12(12), 1091; https://doi.org/10.3390/axioms12121091 - 28 Nov 2023
Viewed by 1249
Abstract
Automated solutions for medical diagnosis based on computer vision form an emerging field of science aiming to enhance diagnosis and early disease detection. The detection and quantification of facial asymmetries enable facial palsy evaluation. In this work, a detailed review of the quantification [...] Read more.
Automated solutions for medical diagnosis based on computer vision form an emerging field of science aiming to enhance diagnosis and early disease detection. The detection and quantification of facial asymmetries enable facial palsy evaluation. In this work, a detailed review of the quantification of facial palsy takes place, covering all methods ranging from traditional manual mathematical modeling to automated computer vision-based methods. Moreover, facial palsy quantification is defined in terms of facial asymmetry indices calculation for different image modalities. The aim is to introduce readers to the concept of mathematical modeling approaches for facial palsy detection and evaluation and present the process of the development of this separate application field over time. Facial landmark extraction, facial datasets, and palsy grading systems are included in this research. As a general conclusion, machine learning methods for the evaluation of facial palsy lead to limited performance due to the use of handcrafted features, combined with the scarcity of the available datasets. Deep learning methods allow the automatic learning of discriminative deep facial features, leading to comparatively higher performance accuracies. Datasets limitations, proposed solutions, and future research directions in the field are also presented. Full article
(This article belongs to the Special Issue Mathematical Modeling of Signal Processing and Analysis in Light of Deep Learning)
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