Frequency Spectrum

Frequency spectrum analysis examines the distribution of energy across different frequencies within a signal, aiming to extract meaningful information and patterns. Current research focuses on applying frequency spectrum analysis to diverse fields, including improving wireless network efficiency through protocol classification using machine learning algorithms (like SVM and KNN), enhancing image super-resolution techniques by modeling neural networks as low-pass filters, and detecting anomalies in various data types such as audio for dementia screening or images for GAN-generated face detection. These applications highlight the frequency spectrum's significance in signal processing, machine learning, and various scientific domains, enabling improved data analysis and more robust systems.