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An EMD decomposition program featuring instantaneous frequency calculation, instantaneous amplitude extraction, marginal spectrum analysis, and Hilbert spectrum visualization with MATLAB implementation
Application Background: Empirical Mode Decomposition (EMD) decomposes signals into monocomponent signals called Intrinsic Mode Functions (IMFs), enabling instantaneous frequency calculation through Hilbert transform. The primary challenge in practical Hilbert-Huang transform applications is the endpoint effect. Our solution introduces an adaptive spurious IMF filtering algorithm using residue-to-original-signal correlation coefficient as threshold. Key Technology: Complex signal decomposition into monocomponent signals requires each IMF to satisfy two conditions: (1) Extremum and zero-crossing counts must be equal or differ by one throughout the data length; (2) The mean of upper and lower envelopes must be zero at any point. The implementation involves adaptive sifting with envelope interpolation and statistical boundary handling.
EMD method for nonlinear signal analysis within Hilbert-Huang Transform, focusing on fixed intrinsic mode functions and instantaneous frequency computation with algorithmic implementation insights.
Complete HHT implementation program featuring Empirical Mode Decomposition (EMD) and Ensemble EMD (EEMD) algorithms, instantaneous frequency calculation, and statistical significance testing for signal processing applications
Implementation of the phase difference method proposed by Wang Fenghua et al., primarily used for extracting instantaneous frequency in signal processing. Note that this method is only suitable for single-component signals. Additionally, another approach for determining instantaneous frequency uses the autocorrelation method.
MATLAB code implementations for extracting signal instantaneous phase and instantaneous frequency with detailed algorithm explanations and practical application examples
Extracting Instantaneous Amplitude, Phase, and Frequency of 2ASK Modulation Signals with Algorithm Implementation Details
Research on time-frequency characteristics of time series including Fourier transform and inverse transform, fast Mellin transform and inverse transform, short-time discrete Fourier transform for instantaneous frequency extraction. Implementation of time-frequency distributions: Born-Jordan distribution, Butterworth distribution, and Choi-Williams distribution for visualizing instantaneous frequency patterns.
A key function within a Chinese-developed HHT implementation that calculates instantaneous frequency and amplitude parameters
Empirical Mode Decomposition: An adaptive signal processing technique for decomposing complex signals into Intrinsic Mode Functions (IMFs) with time-frequency analysis capabilities.