Explore MATLAB source code curated for "经验模态分解" with clean implementations, documentation, and examples.
Core Implementation of Magnetotelluric Signal Static Effect Removal Utilizing Wavelet Transform and Empirical Mode Decomposition Algorithms
This archive contains the standard EMD/EEMD/CEEMD analysis toolkit developed by the Data Analysis Method Research Center at Taiwan's National Central University, led by Academician Norden E. Huang (inventor of HHT-EMD). Authored in 2013 by senior researcher Yung-Hung Wang, the toolkit implements both the original EMD algorithm and its latest variants, delivering authoritative, fast, precise, and user-friendly performance. During my research visit at the center, I enhanced the documentation by: 1) Adding 5 key references cited in the code comments to facilitate algorithmic understanding; 2) Modifying line 111 in eemd.m by replacing getDefaultStream with getGlobalStream for MATLAB 2013+ compatibility.
A custom-developed feature extraction algorithm implementing Empirical Mode Decomposition (EMD) - a method that transforms non-stationary signals into combined stationary components through adaptive signal decomposition.
The EMD Toolbox and usage methodology for Empirical Mode Decomposition (EMD) is a signal analysis technique developed by Dr. Norden E. Huang at NASA. This method decomposes signals based on their intrinsic time-scale characteristics without requiring predefined basis functions. This represents a fundamental distinction from Fourier and wavelet decomposition methods that rely on predetermined harmonic and wavelet basis functions. Due to this characteristic, EMD method can theoretically be applied to decompose any type of signal, giving it significant advantages in processing non-stationary and nonlinear data. Upon its introduction, EMD gained rapid adoption across various engineering fields, with implementations typically involving sifting processes, envelope detection using cubic spline interpolation, and intrinsic mode function (IMF) extraction through iterative algorithms.
A comprehensive example of signal denoising using Empirical Mode Decomposition, demonstrating implementation approaches and practical applications
High-quality research paper with complete source code implementation. The Hilbert-Huang Transform (HHT) represents an advanced signal processing approach for non-stationary signals, comprising two key algorithmic components: Empirical Mode Decomposition (EMD) and Hilbert Spectral Analysis. The EMD algorithm recursively decomposes arbitrary non-stationary signals into Intrinsic Mode Functions (IMFs) representing different characteristic scales. Each IMF undergoes Hilbert transform analysis to extract instantaneous frequency characteristics, with combined spectral results generating comprehensive time-frequency representations. This method effectively stabilizes non-stationary signals by progressively separating intrinsic fluctuations and trends through algorithmic sifting processes.
Empirical Mode Decomposition (EMD) is an adaptive signal decomposition method primarily applied to nonlinear and non-stationary signals. Ensemble Empirical Mode Decomposition (EEMD) addresses the mode mixing problem inherent in standard EMD. Implementation typically involves iterative sifting processes using MATLAB's signal processing toolbox or Python libraries like PyEMD.
Extended applications of empirical mode decomposition which enables direct visualization of intrinsic mode function (IMF) components, featuring robust analytical capabilities including signal processing algorithms and graphical output functions.
Complete MATLAB program for Empirical Mode Decomposition with signal processing applications
Core MATLAB implementation of Empirical Mode Decomposition (EMD) algorithm for signal processing and time-frequency analysis