Wavelet Packet Energy Feature Vector Extraction Program

In this document, we explore the methodology for extracting wavelet packet energy feature vectors. The process begins with wavelet packet decomposition of the input data, which involves recursively applying high-pass and low-pass filters to achieve multi-resolution analysis of signals. Following successful data decomposition, we can extract characteristic feature vectors from the wavelet packet coefficients. These feature vectors typically involve calculating the energy content at different frequency bands using mathematical operations like squaring and summing the coefficients. The extracted vectors serve as valuable inputs for subsequent analysis and processing tasks, enabling deeper insights into data patterns and facilitating applications across various domains such as signal processing, pattern recognition, and machine learning. Therefore, the wavelet packet energy feature vector extraction procedure proves highly beneficial for enhanced data understanding and practical problem-solving scenarios.