Wavelet Packet Decomposition + Frequency Band Energy Calculation

This document introduces wavelet packet decomposition, a signal processing technique that decomposes signals into different frequency bands. By calculating the energy within each frequency band, we can extract valuable information about the signal's spectral characteristics. The implementation typically involves using wavelet functions (such as 'db4' or 'sym8') to create a complete binary tree decomposition structure, where each node represents a specific frequency sub-band. Energy calculation for each sub-band is performed by computing the squared sum of decomposition coefficients. Additionally, we present two classification algorithms: LibSVM (Support Vector Machine) and Artificial Neural Networks (ANN). These algorithms can classify and identify signals based on their frequency band energy features. In practice, LibSVM requires feature normalization and kernel function selection (like RBF kernel), while ANN implementation involves designing network architecture with hidden layers and activation functions. By combining wavelet packet decomposition, frequency band energy analysis, and these classification algorithms, we achieve more accurate and reliable signal processing and classification results. The typical workflow includes signal decomposition, energy feature extraction, classifier training with labeled data, and final classification prediction.