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Mathematics
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Signal Processing and Machine Learning in Real-Life Processes
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Signal Processing and Machine Learning in Real-Life Processes

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 20 January 2026 | Viewed by 2687

Special Issue Editors

Dr. Miguel Enrique Iglesias Martínez

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Guest Editor
1. Grupo de Modelización Interdisciplinar, InterTech, Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València, Camino de Vera, 46022 Valencia, Spain
2. Grupo de Ingeniería Física, Escuela de Ingeniería Aeronáutica y del Espacio, Universidad de Vigo, Edif. Manuel Martínez Risco, Campus de As Lagoas, 32004 Ourense, Spain
Interests: statistical signal processing; automated pattern recognition; electronics and communication
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pedro José Fernández de Córdoba Castellá

E-Mail Website
Guest Editor
Grupo de Modelización Interdisciplinar, InterTech, Instituto Universitario de Matemática Pura y Aplicada, Universitat Politècnica de València, Camino de Vera, 46022 Valencia, Spain
Interests: mathematical modeling; numerical simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In an era where data are as vital as currency, the fusion of signal processing with machine learning has become a cornerstone of innovation, driving advancements across a multitude of real-life applications. The interplay between these two disciplines is unlocking unprecedented potential in interpreting, analyzing, and acting on the vast torrents of data generated by today's digital ecosystems.

This Special Issue seeks to explore the dynamic interface of signal processing algorithms and machine learning, highlighting their synergistic role in transforming theoretical models into practical solutions. With a spotlight on real-world processes, from the intricacies of communication systems to the complexities of interpreting biological data, we aim to showcase groundbreaking research and developments that are setting new benchmarks in technological progress.

We invite contributions that not only push the boundaries of signal processing and machine learning as individual fields but also exemplify their convergence in addressing the practical challenges of the modern world. Through rigorous research, case studies, and comprehensive reviews, we aim for this Special Issue to serve as a platform for academics and practitioners to present their innovative work, discuss the implications of their findings, and chart the course for future exploration in these pivotal areas of study.

Topics of Interest:

  • Deep Learning for Image and Video Signal Processing: Advanced techniques in processing visual data for applications in security, medicine, and entertainment.
  • General Time-Series Analysis: The use of signal processing and machine learning to predict market trends and automate trading strategies.
  • Signal Processing in Genomics: Machine learning applications for genomic data interpretation and disease prediction.
  • Natural Language Processing for Real-Time Communications: Enhancing machine translation and speech recognition systems through signal processing.
  • IoT Sensor Data Analysis: Utilizing signal processing and machine learning to interpret vast data from smart devices in real-time.
  • Machine Learning in Acoustic Signal Processing: Applications in noise reduction, echo cancellation, and audio enhancement for better sound quality.
  • Fault Diagnosis and Predictive Maintenance through Signal Analysis: Signal processing techniques in detecting machinery faults before they occur.
  • Biomedical Signal Processing: Machine learning algorithms for analyzing physiological signals for health monitoring and diagnostics.
  • Machine Learning for Enhancing Wireless Communication Signals: Improving bandwidth efficiency and reducing interference in wireless networks.
  • Real-time Traffic Signal Analysis for Smart Cities: Using signal processing and machine learning to optimize traffic flow and enhance urban mobility.

Dr. Miguel Enrique Iglesias Martínez
Prof. Dr. Pedro José Fernández de Córdoba Castellá
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. Mathematics 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 2600 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 for image and video signal processing
  • general time-series analysis
  • signal processing in genomics
  • natural language processing for real-time communications
  • IoT sensor data analysis
  • machine learning in acoustic signal processing
  • fault diagnosis and predictive maintenance through signal analysis
  • biomedical signal processing
  • machine learning for enhancing wireless communication signals
  • real-time traffic signal analysis for smart cities

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Published Papers (2 papers)

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20 pages, 5484 KiB  
Article
LMPSeizNet: A Lightweight Multiscale Pyramid Convolutional Neural Network for Epileptic Seizure Detection on EEG Brain Signals
by Arwa Alsaadan, Mai Alzamel and Muhammad Hussain
Mathematics 2024, 12(23), 3648; https://doi.org/10.3390/math12233648 - 21 Nov 2024
Viewed by 975
Abstract
Epilepsy is a chronic disease and one of the most common neurological disorders worldwide. Electroencephalogram (EEG) signals are widely used to detect epileptic seizures, which provide specialists with essential information about the brain’s functioning. However, manual screening of EEG signals is laborious, time-consuming, [...] Read more.
Epilepsy is a chronic disease and one of the most common neurological disorders worldwide. Electroencephalogram (EEG) signals are widely used to detect epileptic seizures, which provide specialists with essential information about the brain’s functioning. However, manual screening of EEG signals is laborious, time-consuming, and subjective. The rapid detection of epilepsy seizures is important to reduce the risk of seizure-related implications. The existing automatic machine learning techniques based on deep learning techniques are characterized by automatic extraction and selection of the features, leading to better performance and increasing the robustness of the systems. These methods do not consider the multiscale nature of EEG signals, eventually resulting in poor sensitivity. In addition, the complexity of deep models is relatively high, leading to overfitting issues. To overcome these problems, we proposed an efficient and lightweight multiscale convolutional neural network model (LMPSeizNet), which performs multiscale temporal and spatial analysis of an EEG trial to learn discriminative features relevant to epileptic seizure detection. To evaluate the proposed method, we employed 10-fold cross-validation and three evaluation metrics: accuracy, sensitivity, and specificity. The method achieved an accuracy of 97.42%, a sensitivity of 99.33%, and a specificity of 96.51% for inter-ictal and ictal classes outperforming the state-of-the-art methods. The analysis of the features and the decision-making of the method shows that it learns the features that clearly discriminate the two classes. It will serve as a useful tool for helping neurologists and epilepsy patients. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning in Real-Life Processes)
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Review

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23 pages, 3832 KiB  
Review
Higher-Order Spectral Analysis and Artificial Intelligence for Diagnosing Faults in Electrical Machines: An Overview
by Miguel Enrique Iglesias Martínez, Jose A. Antonino-Daviu, Larisa Dunai, J. Alberto Conejero and Pedro Fernández de Córdoba
Mathematics 2024, 12(24), 4032; https://doi.org/10.3390/math12244032 - 23 Dec 2024
Cited by 1 | Viewed by 1074
Abstract
Fault diagnosis in electrical machines is a cornerstone of operational reliability and cost-effective maintenance strategies. This review provides a comprehensive exploration of the integration of higher-order spectral analysis (HOSA) techniques—such as a bispectrum, spectral kurtosis, and multifractal wavelet analysis—with advanced artificial intelligence (AI) [...] Read more.
Fault diagnosis in electrical machines is a cornerstone of operational reliability and cost-effective maintenance strategies. This review provides a comprehensive exploration of the integration of higher-order spectral analysis (HOSA) techniques—such as a bispectrum, spectral kurtosis, and multifractal wavelet analysis—with advanced artificial intelligence (AI) methodologies, including deep learning, clustering algorithms, Transformer models, and transfer learning. The synergy between HOSA’s robustness in noisy and transient environments and AI’s automation of complex classifications has significantly advanced fault diagnosis in synchronous and DC motors. The novelty of this work lies in its detailed examination of the latest AI advancements, and the hybrid framework combining HOSA-derived features with AI techniques. The proposed approaches address challenges such as computational efficiency and scalability for industrial-scale applications, while offering innovative solutions for predictive maintenance. By leveraging these hybrid methodologies, the work charts a transformative path for improving the reliability and adaptability of industrial-grade electrical machine systems. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning in Real-Life Processes)
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