Wavelet Packet Decomposition and Power Spectrum Analysis of Signal Components

Perform wavelet packet decomposition on signals and calculate the power spectrum of each component. Wavelet packet decomposition is a signal processing technique that decomposes signals into sub-signals across different frequency ranges. Through power spectrum analysis of these sub-signals, we can gain detailed insights into the energy distribution across various frequency bands. This analytical method enables in-depth study of signal characteristics and finds wide applications in fields such as communications, image processing, and biomedical engineering. Implementation typically involves using wavelet packet decomposition functions like wpdec() to obtain node coefficients, followed by power spectral density estimation using methods such as pwelch() or periodogram() functions for each decomposed component. The algorithm allows flexible tree structure selection and supports both time-frequency analysis and feature extraction through energy distribution patterns across frequency bands.