EMD Decomposition in Hilbert-Huang Transform

This program implements Empirical Mode Decomposition (EMD) for extracting Intrinsic Mode Functions (IMF). The Hilbert-Huang Transform (HHT) represents an advanced non-stationary signal processing methodology structured in two phases: Empirical Mode Decomposition (EMD) and Hilbert spectral analysis. Through iterative sifting processes, the algorithm decomposes any non-stationary signal into multiple IMF components exhibiting distinct temporal scales, where each IMF must satisfy specific mathematical criteria including symmetric envelopes and zero-crossing count matching. The implementation typically involves envelope detection using cubic spline interpolation and stopping criteria based on standard deviation thresholds. Following decomposition, Hilbert spectral analysis is applied to each IMF to extract instantaneous frequency characteristics, with the composite Hilbert spectrum revealing the signal's time-frequency distribution. The code provides parameter control for IMF quantity specification, enabling enhanced characterization of non-stationary features through increased decomposition levels. Additional preprocessing modules can be integrated for signal denoising using wavelet thresholds or frequency filtering techniques to improve analysis accuracy. This EMD implementation serves as a robust analytical tool for researchers to investigate complex phenomena in non-stationary signals through adaptive multiscale decomposition and time-frequency analysis capabilities.