Frequency Domain

Frequency domain analysis examines signals and data by decomposing them into their constituent frequencies, offering insights into underlying patterns and structures often obscured in the time domain. Current research focuses on leveraging frequency domain representations within various machine learning models, including neural networks and state-space models, to improve efficiency, accuracy, and robustness in tasks such as image processing, time series analysis, and signal processing. This approach is proving valuable across diverse fields, enhancing applications ranging from private inference and physiological signal measurement to anomaly detection and medical image segmentation by enabling more efficient computations and improved feature extraction. The ability to analyze data across different frequency bands also facilitates a deeper understanding of causal relationships and complex system dynamics.