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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) belonging to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: closed (31 July 2026) | Viewed by 8218

Editors

Dr. Miguel Enrique Iglesias Martínez

E-Mail Website
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, s/n, E-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 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-anonymized 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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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16 pages, 445 KB  
Article
Signal Processing and Machine Learning for the Sustainability of the Italian Social Security System: Evidence from ISTAT Pension Data
by Gianfranco Piscopo, Chiara Marciano, Maria Longobardi and Massimiliano Giacalone
Mathematics 2026, 14(4), 690; https://doi.org/10.3390/math14040690 - 15 Feb 2026
Cited by 2 | Viewed by 763
Abstract
The long-run sustainability of pay-as-you-go pension systems crucially depends on the dynamic balance between social-security contributions paid by the working population and benefits paid to retirees. In Italy, the National Social Security Institute (INPS) manages the core of the public system, whose financial [...] Read more.
The long-run sustainability of pay-as-you-go pension systems crucially depends on the dynamic balance between social-security contributions paid by the working population and benefits paid to retirees. In Italy, the National Social Security Institute (INPS) manages the core of the public system, whose financial equilibrium is increasingly challenged by demographic aging, labor market fragility, and macroeconomic shocks. In this paper, in line with the aims of the Special Issue “Signal Processing and Machine Learning in Real-Life Processes”, we reinterpret the Italian pension system as a complex stochastic signal-processing problem. Using the most recent data published in the Annuario Statistico Italiano 2024 highlighting by ISTAT—with a focus on Protection and Social Security—we construct a set of time series describing contributions, benefits, coverage ratios and pension amounts, both at the national and territorial level. On this basis, we compare classical time-series models and a recurrent neural network with Long Short-Term Memory (LSTM) architecture for multi-step forecasting of the main aggregates. The signal-processing perspective allows us to disentangle trend, cyclical and shock components, while machine learning provides flexible nonlinear forecasting tools capable of capturing structural breaks such as the COVID-19 crisis. Our empirical results suggest that (i) pension expenditure remains high and persistent as a share of GDP; (ii) the contribution coverage ratio improved in 2022 but remains below the pre-pandemic level; and (iii) regional heterogeneity in the per-capita pension deficit is substantial and stable over time, with persistent imbalances in Southern regions and Islands. Finally, we perform a scenario analysis combining LSTM-based forecasts with demographic and labor market hypotheses, and we quantify the impact of alternative policy measures on the future pension deficit signal. The proposed framework, which integrates permutation-based inference, signal decomposition and deep learning, provides a reproducible template for the real-time monitoring of pension sustainability using official open data. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning in Real-Life Processes)
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26 pages, 1731 KB  
Article
Time-Varying Linkages Between Survey-Based Financial Risk Tolerance and Stock Market Dynamics: Signal Decomposition and Regime-Switching Evidence
by Wookjae Heo
Mathematics 2026, 14(4), 667; https://doi.org/10.3390/math14040667 - 13 Feb 2026
Viewed by 731
Abstract
This study examines how aggregate financial risk tolerance (FRT), measured from repeated survey responses, co-evolves with stock-market dynamics over time. The observed FRT index is treated as a noisy preference signal containing both gradual drift and episodic deviations, and its market relevance is [...] Read more.
This study examines how aggregate financial risk tolerance (FRT), measured from repeated survey responses, co-evolves with stock-market dynamics over time. The observed FRT index is treated as a noisy preference signal containing both gradual drift and episodic deviations, and its market relevance is evaluated under time variation, frequency components, and stress regimes. Using monthly data that align the survey-based FRT index with market returns and risk measures, a three-part econometric design is implemented. First, a time-varying parameter VAR (TVP-VAR) characterizes bidirectional, non-constant linkages between FRT and market outcomes. Second, signal-extraction methods decompose FRT into a smooth “normal” component and a high-frequency “abnormal” component (with robustness to alternative filters) to test whether short-run deviations contain distinct information for volatility and downside risk. Third, a Markov-switching specification assesses state dependence by testing whether the FRT–market relationship differs between low-stress and high-stress regimes. Across specifications, the FRT–market linkage is strongly state dependent: the sign and magnitude of FRT effects drift over time and differ across regimes, with high-frequency FRT deviations aligning more closely with risk dynamics than the smooth component. Predictive validation is provided via out-of-sample forecasting of next-month market risk using elastic net and gradient boosting relative to an AR(1) benchmark; explainability analysis (SHAP) indicates that abnormal FRT contributes incremental predictive content beyond standard market-state variables. Overall, the framework offers a mathematically transparent approach to modeling survey-based preference signals in markets and supports regime-aware forecasting and risk-management applications. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning in Real-Life Processes)
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20 pages, 5484 KB  
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
Cited by 3 | Viewed by 2175
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 KB  
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 11 | Viewed by 3168
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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