HHT Transformation Program: Algorithm Implementation and Applications

The Hilbert-Huang Transform (HHT) represents an advanced analytical method specifically designed for non-stationary and nonlinear signal processing, with extensive applications across various signal processing domains. The HHT transformation program developed by National Central University (Taiwan) has gained significant popularity among researchers due to its remarkable stability and user-friendly implementation.

The HHT algorithm comprises two core computational components: Empirical Mode Decomposition (EMD) and Hilbert Spectral Analysis. - EMD employs an adaptive sifting process to decompose complex signals into multiple Intrinsic Mode Functions (IMFs) - Each IMF undergoes Hilbert transformation to extract instantaneous frequency and amplitude characteristics - The final output generates a Hilbert spectrum that vividly illustrates the time-frequency energy distribution of the original signal

This program demonstrates particular effectiveness in processing non-stationary data such as seismic waves and biomedical signals. Key algorithmic advantages include: - No requirement for predefined basis functions - Superior adaptability to nonlinear systems - High time-frequency resolution capabilities Implementation considerations: Users should address boundary effects through techniques like mirror extension to optimize decomposition results. The code typically incorporates spline interpolation for envelope construction and employs stopping criteria to ensure IMF convergence during the sifting process.