Endpoint Detection Using Hilbert-Huang Transform (HHT)

This document presents the original source code for Hilbert-Huang Transform (HHT), Fast Fourier Transform (FFT) implementations, and the author's customized modifications. While these elements provide substantial technical value, deeper exploration reveals additional insights into algorithmic implementations and applications. The HHT algorithm combines Empirical Mode Decomposition (EMD) for signal decomposition and Hilbert spectral analysis for instantaneous frequency calculation, particularly effective for non-stationary signal processing. The FFT implementation typically utilizes Cooley-Tukey algorithms with optimized windowing functions for frequency domain transformation. The author's modifications likely involve parameter optimizations, boundary condition handling, or integration techniques between these transforms. These algorithms find extensive applications across signal processing domains including vibration analysis, biomedical signal processing, image processing feature extraction, and machine learning preprocessing. Through comprehensive examination of these topics, engineers can better understand algorithmic trade-offs regarding computational complexity, resolution limitations, and adaptability to different data characteristics. Thus, while the current content provides foundational technical resources, extended investigation into implementation specifics and comparative performance analysis would yield deeper engineering insights for practical applications.