Performance Analysis of Machine Learning-Based Signal Processing in Electronic System

Abstract

Signal processing is a fundamental function of modern electronic systems, enabling the acquisition, analysis, and interpretation of signals in applications such as communications, biomedical devices, radar, and industrial automation. Recent advancements in machine learning have significantly enhanced traditional signal processing techniques by enabling adaptive, data-driven analysis. This paper presents a performance analysis of machine learning-based signal processing approaches in electronic systems. Various machine learning models used for signal classification, filtering, feature extraction, and noise reduction are discussed. Performance is evaluated in terms of accuracy, computational efficiency, robustness, and adaptability. The study highlights the advantages and limitations of integrating machine learning techniques into electronic signal processing systems and emphasizes their growing importance in intelligent electronic applications.