Wavelet Denoising with Adaptive Filtering Implementation

The program implementing wavelet denoising and adaptive filtering has been successfully developed in MATLAB and validated through comprehensive debugging. This implementation effectively processes noisy signals and enhances signal quality through sophisticated algorithmic approaches. The core functionality utilizes wavelet transform decomposition to separate signal components across different frequency bands, followed by thresholding techniques to remove noise while preserving important signal features. The adaptive filtering component employs algorithms like LMS (Least Mean Squares) or RLS (Recursive Least Squares) to dynamically adjust filter coefficients based on signal characteristics. Key MATLAB functions employed include wavedec for wavelet decomposition, wthresh for threshold application, and adaptfilt for implementing adaptive filters. The program offers flexible parameter configuration, allowing users to adjust wavelet types, decomposition levels, thresholding rules, and filter parameters to achieve optimal denoising performance for specific application requirements. This robust implementation provides reliable signal preprocessing capabilities, establishing a solid foundation for subsequent data analysis and practical applications. The modular design facilitates easy integration with existing signal processing workflows and supports further customization for specialized use cases.