Explore MATLAB source code curated for "端点效应" with clean implementations, documentation, and examples.
Mirror extension of signal endpoints can be used to eliminate edge effects caused by Hilbert transform. This technique involves replicating signal segments symmetrically at both ends to create seamless transitions for improved spectral analysis.
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.
The mirror extension method handles end-point effects by leveraging information from signal extrema to infer extreme points beyond the signal boundaries.
Reference code for EMD endpoint effects featuring six distinct algorithms: Extreme Value Averaging, Boundary Characteristic Scale Extension, Extreme Value Extension, ISBM-based Extension, Parallel Extension, and Mirror Extension methods with practical implementation details.
Enhanced envelope technique addressing Empirical Mode Decomposition endpoint artifacts through algorithmic optimization and signal extension approaches.
Application Background: Designed for fault diagnosis applications and endpoint effect processing, this implementation provides a robust Hilbert-Huang Transform (HHT) program with practical utility. The code implements Empirical Mode Decomposition (EMD) for signal analysis, incorporates boundary extension techniques to minimize endpoint effects, and enables Hilbert spectral analysis for time-frequency characterization. Key Technologies: Hilbert-Huang Transform (HHT), Empirical Mode Decomposition (EMD), Signal Extension Methods