Optimization of Signal Representation in Real-Time Systems Using Meta-Heuristic Techniques

Systems operating in real-time must be able to receive, process, and output vast amounts of data quickly enough to satisfy rigid timing constraints. Signal Representation Signal representation is an important issue for real-time systems since it influences the entire process, from data acquisition through evaluation to result processing. Nonetheless, common signal representation techniques usually need to be more suitable for real-time systems due to their inherent complexity and dynamics. Meta-heuristic techniques are an appealing solution for optimizing the signal representation in real-time, Meta techniques where heuristic algorithms are used to solve complex problems and find near-optimal solutions. Through meta-heuristic algorithms, real-time systems can find a signal representation method tailored to their needs, which leads directly to better accuracy and performance. Several meta-heuristic algorithms, such as genetic algorithm, particle swarm optimization, and simulated annealing, have been exploited for signal representation in the context of real-time systems with satisfactory results. Such methods often adapt well to the varying surroundings and restrictions, making real-time systems able to manage interactive data effectively. Signal representation optimization using meta-heuristic techniques increases the performance and reliability of these real-time systems, which is a key factor in determining their suitability for critical applications, including military command control, autonomous driving, and future healthcare systems.