Hilbert-Huang Transform (HHT) Implementation with Pack_EMD Toolbox

This article introduces the Pack_EMD toolbox and methodologies for creating HHT 3D visualizations. The Pack_EMD toolbox serves as a comprehensive signal processing utility that implements empirical mode decomposition (EMD) algorithms for signal decomposition and reconstruction. Through practical code implementation, users can employ functions like emd() to decompose signals into intrinsic mode functions (IMFs), enabling deep analysis of signal characteristics and structural properties. The toolbox provides essential functions for signal feature extraction and information retrieval from complex datasets.

Furthermore, we explore the implementation of HHT 3D plotting techniques using the Pack_EMD toolbox. The HHT 3D visualization method combines Hilbert spectral analysis with time-frequency representations, creating interactive plots that display instantaneous frequency variations over time. Implementation typically involves computing the Hilbert transform for each IMF component, followed by surface plotting functions that map amplitude, frequency, and time dimensions. This visualization approach allows researchers to intuitively observe time-frequency characteristics and identify signal patterns through color-coded amplitude representations.

By integrating the Pack_EMD toolbox with HHT 3D plotting capabilities, researchers can perform comprehensive signal analysis through code sequences that chain decomposition, transformation, and visualization functions. This integrated approach facilitates accurate signal characterization and supports predictive modeling through quantitative feature extraction from the time-frequency domain.