Signal Processing and Machine Learning in Real-Life Processes

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

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• 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