Wavelet Analysis

Wavelet analysis is a signal processing technique used to decompose complex signals into simpler components across different frequency bands, revealing hidden patterns and features. Current research focuses on integrating wavelet transforms with machine learning models, such as neural operators and transformer networks, to improve performance in diverse applications including medical image analysis, time series forecasting, and fault detection in complex systems. This interdisciplinary approach enhances the ability to extract meaningful information from noisy or high-dimensional data, leading to improved accuracy and efficiency in various scientific and engineering domains. The resulting advancements are impacting fields ranging from medical diagnostics to remote sensing and industrial process monitoring.