MATLAB Routines for Empirical Mode Decomposition (EMD)

The following MATLAB routines demonstrate Empirical Mode Decomposition (EMD) implementations suitable for signal processing in vibration analysis, medical diagnostics, and industrial monitoring applications. EMD represents a nonlinear signal processing technique that enables extraction and analysis of intrinsic frequency components from complex signals. These routines provide practical implementations for decomposing signals into Intrinsic Mode Functions (IMFs), reconstructing signals from IMF components, and performing joint time-frequency analysis. The code examples include algorithms for: - Signal decomposition using the sifting process to extract IMFs - Hilbert-Huang Transform implementation for instantaneous frequency calculation - Boundary condition handling and stopping criterion optimization - Mode mixing mitigation through ensemble approaches These MATLAB scripts demonstrate key functions such as: 1. IMF extraction through iterative sifting with cubic spline interpolation 2. Signal reconstruction validation by summing IMF components 3. Time-frequency spectrum generation using Hilbert transform on IMFs 4. End effect reduction techniques using mirror extension or prediction algorithms Whether you're learning signal processing fundamentals or developing advanced applications, these routines provide working examples for understanding EMD's implementation mechanics, including handling non-stationary signals, optimizing decomposition parameters, and interpreting multimodal signal characteristics. The code structure allows modification of sifting thresholds, IMF number limits, and boundary processing methods to adapt to specific application requirements in mechanical vibration analysis, biomedical signal processing, or industrial condition monitoring.