Self-Compiled MATLAB Code for Wavelet Moment Feature Extraction

In this documentation, we provide comprehensive guidance on developing custom MATLAB code for wavelet moment feature extraction. First, we introduce the fundamental concepts of wavelet transform, which aims to decompose time-domain signals into linear combinations of wavelet basis functions at different frequencies. As a time-frequency analysis method, wavelet transform enables superior understanding of signal characteristics across both temporal and spectral domains through multi-resolution analysis.

We then demonstrate practical implementation approaches for wavelet transform, detailing MATLAB programming techniques with code examples. Our implementation covers key functions such as wavelet decomposition using wavedec() function, coefficient extraction, and reconstruction algorithms. The code examples include parameter configuration for wavelet types (e.g., 'db4', 'sym8'), decomposition levels, and thresholding techniques for noise reduction.

Finally, we explore wavelet moment feature extraction methodology - a signal processing technique that converts signals into statistical feature sets. We explain the mathematical principles behind calculating statistical moments (mean, variance, skewness, kurtosis) from wavelet coefficients across different decomposition levels. The MATLAB implementation includes feature vector construction, dimension reduction techniques, and practical applications in pattern recognition systems. Complete code samples with inline comments demonstrate feature calculation algorithms and result validation methods.

This documentation aims to provide thorough understanding of wavelet transforms and feature extraction techniques, empowering users to successfully develop their own MATLAB implementations for signal processing applications.