MATLAB Implementation of HHT with Wavelet Transform Analysis

This documentation explores the implementation of Hilbert-Huang Transform (HHT) program code and its integration with wavelet transform applications. We focus on generating comparative power spectral density function plots of signal components and leveraging these visualizations to identify optimal wavelet functions. Through MATLAB-based HHT code implementation for wavelet transformation, we can perform comprehensive signal analysis to detect local characteristics and distinguish high-frequency and low-frequency components within signals. The implementation typically involves using functions like hht() for empirical mode decomposition and cwt() for continuous wavelet transforms. The process includes learning systematic approaches for selecting appropriate wavelet functions (such as Daubechies, Morlet, or Haar wavelets) and evaluating their performance through power spectral density analysis using pwelch() or periodogram() functions. Ultimately, we establish an optimization methodology that yields an optimal wavelet function, which can be employed for effective signal decomposition and reconstruction using wavelet toolbox functions like wavedec() and waverec(), thereby achieving enhanced signal processing and analytical outcomes.