Generally, each variable that can be measured in the spatial or frequency domain is a signal. For instance, speed is a variable in time and its value can be measured. Other examples include quantities like acceleration, temperature, humidity, and sound. Therefore, by sampling these quantities in different time units, a signal can be formed. Signal processing is a science that analyzes these kinds of signals.

In the past few decades, deep learning and optimization approaches have become popular methods for solving complicated problems, especially those that have no known algorithms for solving their exact resolution in polynomial time. Unlike the hard computational (classical) methods that focus their entire determination and ability to be precise, and to perfectly model the truth, deep learning and optimization approaches are based on tolerance of inequities, partial and incomplete truths, and lack of certainty. In simple scientific terms, hard methods are driven by nature and how things behave, while deep learning and optimization practices are directed towards humans and the measures taken by their minds to resolve issues. Using "deep learning and optimization" computational methods, one can study, model, and analyze very complex phenomena in biology, medicine, engineering, humanities, and management. The most important branches of these calculations are fuzzy logic, artificial neural networks, and metaheuristic algorithms.

This Special Issue aims to focus on the applications of deep learning and optimization in signal, image, and video processing techniques. We welcome original research and review articles.

Potential topics include but are not limited to the following:

• • Applications of fuzzy logic in signal processing
• • Applications of deep learning methods in signal processing
• • Applications of artificial neural networks in signal processing
• • Applications of optimization algorithms in signal processing
• • Signal processing and machine vision
• • Signal processing and image processing
• • Sound processing
• • Audio/video complex surveillance systems
• • Adaptive signal processing
• • Data mining and signal processing
• • Metaheuristics and signal processing
• • Applications of deep learning approaches in biomedical signal processing