Symmetry in Digital Signal Processing: Toward Smart Embedded Applications

Dear Colleagues,

The concept of symmetry plays a pivotal role in signal processing and the sciences, with far-reaching consequences. Signal processing has become an indispensable tool in both theoretical research and practical applications, particularly in fields that require real-time data analysis and decision-making in which the concept of symmetry plays an essential role. As technology progresses, the need for embedded systems that can handle complex, noisy, and non-stationary data is becoming ever more pressing. These systems rely on advanced signal processing techniques such as noise removal, time–frequency analysis, and classification algorithms that not only improve the accuracy of data but also enhance the overall decision-making process. Moreover, the integration of advanced signal processing algorithms with embedded hardware and software platforms has enabled more efficient data processing, leading to new possibilities in fields such as real-time health monitoring, precision agriculture, and autonomous systems.

This Special Issue will focus on the latest advancements in embedded signal processing methods, with a particular emphasis on their theoretical foundations, research applications, and practical implementations. It seeks to highlight the most innovative developments in this field, from novel algorithms to hybrid models combining traditional techniques with machine learning and deep learning. This issue will also explore how embedded systems, whether in low-power, resource-constrained devices or larger-scale systems, can be optimized using state-of-the-art signal processing techniques to meet the demands of cutting-edge research. These systems are crucial for real-time analysis in a wide range of domains, from healthcare to environmental monitoring, agriculture, and sustainable applications.

This Special Issue aims to collect contributions that offer original insights and methods in the following domains:

• Advanced Signal Processing Techniques:

Innovative methods for noise removal, time–frequency analysis, and feature extraction to improve data accuracy and reliability.

• Embedded Signal Processing in Research:

Development of low-power, real-time embedded systems for noise removal and time–frequency analysis, focusing on efficient data processing in research applications.

• Optimizing Embedded Algorithms:

Techniques to balance computational efficiency and real-time performance for embedded systems in resource-constrained environments like wearable devices and remote sensors.

• Machine Learning Integration:

Incorporation of deep learning and hybrid models combining signal processing with AI to improve classification, pattern recognition, and predictive modeling in real-time embedded applications.

• Adaptive Learning for Embedded Systems:

Development of adaptive learning algorithms that enhance data accuracy and reliability in dynamic, real-time environments.

• Biomedical Signal Processing:

Innovative and enhanced biomedical signal processing methods as well as real-time processing of biomedical signals (ECG, EEG, EMG) using embedded systems for health monitoring, diagnostics, and personalized medicine.

• Environmental Monitoring and Sustainability:

Signal processing applications for monitoring and analyzing environmental parameters with embedded systems for environmental monitoring (air/water quality, pollution detection) and sustainability research, using low-power systems for long-term data collection.

• Time–Frequency Analysis in Environmental Systems:

Application of time–frequency analysis and machine learning to detect subtle environmental shifts and support climate change studies.

• Precision Agriculture:

Advances in signal processing and sensor networks to monitor and optimize farming practices, resource management, and crop health for sustainable agriculture.

• Geophysics and Earthquake Detection:

Innovations in seismic signal processing, including noise removal, microseismic analysis, and early warning systems for real-time earthquake detection and monitoring.

• Robotics, Audio, and Image Processing:

Real-time signal processing for embedded robotics, autonomous systems, and smart sensors, as well as audio/image analysis for applications like surveillance and medical imaging.

We welcome theoretical work, algorithmic methods, applied studies, and case studies, all unified by the common thread of advanced signal processing for noise removal and time–frequency classification across the fields of medicine, artificial intelligence, and seismology.

Dr. Wissam Jenkal
Dr. Samir Elouaham
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry 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.

• embedded signal processing
• machine learning in embedded systems
• real-time data acquisition
• biomedical signal processing
• time–frequency analysis
• environmental monitoring
• sustainability applications
• precision agriculture
• seismic signal processing
• autonomous systems