Wavelet Decomposition

Wavelet decomposition is a signal processing technique that breaks down complex signals into simpler components at different frequency scales, revealing underlying patterns and features often hidden in raw data. Current research focuses on applying wavelet decomposition within various machine learning models, such as neural networks (including CNNs, GANs, and diffusion models) and hybrid models combining wavelets with other methods (e.g., AR models, SVM). This approach enhances the performance of these models in diverse applications, including image denoising, remote sensing, time series prediction, and fault detection, ultimately leading to improved accuracy and efficiency in various scientific and engineering domains.