Explore MATLAB source code curated for "hht" with clean implementations, documentation, and examples.
This program implements HHT normalization energy spectrum (3D plot), marginal spectrum, and instantaneous energy diagram through EMD and Hilbert transform, including completeness validation to ensure result accuracy.
A practical MATLAB implementation of the Hilbert-Huang Transform (HHT), designed for time-frequency analysis and nonlinear vibration signal processing. This code can be easily modified for your specific research requirements, including algorithmic improvements or additional data processing steps.
This is an EMD program for decomposing IMF components. The Hilbert-Huang Transform (HHT) is a novel non-stationary signal processing technique comprising two stages: Empirical Mode Decomposition (EMD) and Hilbert spectral analysis. The implementation involves decomposing arbitrary non-stationary signals into Intrinsic Mode Functions (IMFs) with distinct characteristic scales, followed by Hilbert spectral analysis for each IMF component to construct the signal's full Hilbert spectrum.
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.
The Hilbert-Huang Transform (HHT) represents an innovative approach for analyzing non-stationary signals, combining Empirical Mode Decomposition (EMD) with Hilbert spectral analysis. In implementation, signals undergo EMD processing to decompose them into Intrinsic Mode Functions (IMFs) with distinct characteristic scales. Each IMF component then undergoes Hilbert spectral analysis to compute instantaneous frequency and energy distributions. The complete Hilbert spectrum reconstructed from all IMF components provides a time-frequency-energy representation of the original signal, effectively stabilizing non-stationary signals through multi-scale decomposition of fluctuations and trends.
Power analysis using self-developed HHT implementation with enhanced iterative method published in 2009, featuring optimized algorithm implementation