Explore MATLAB source code curated for "信号去噪" with clean implementations, documentation, and examples.
A MATLAB-based implementation of classic wavelet modulus maxima method for signal denoising, with output saved in TXT format. The program demonstrates multi-scale wavelet decomposition, modulus maxima detection, and threshold-based noise removal.
Application Background: Empirical Mode Decomposition (EMD) is a time-frequency analysis method for processing nonlinear and non-stationary signals. This method adaptively decomposes input signals into several Intrinsic Mode Functions (IMFs) based on their inherent characteristics without requiring prior knowledge. It is widely used in signal denoising and non-stationary time series prediction. Key Technology: The EMD algorithm enables denoising, analysis, and prediction of high-frequency signals through decomposition and trend analysis. The MATLAB implementation typically involves iterative sifting processes, envelope detection using cubic spline interpolation, and stopping criteria based on standard deviation thresholds.
Custom MATLAB source code for LMS adaptive algorithm implementation. This program generates a signal combined with white noise sequence and utilizes the LMS algorithm for adaptive noise cancellation. lmsx.m serves as the adaptive filter function, while task2.m acts as the main function that calls lmsx.m.
CS compressed sensing serves as a highly effective and advanced technique for signal estimation and denoising, leveraging sparse signal representations and optimization algorithms for superior performance.
The lifting scheme algorithm is utilized to implement second-generation wavelet transform and apply it to signal denoising. By implementing symmetric boundary extension for data, the method achieves effective noise reduction performance with robust handling of signal boundaries.
Application of EMD and EEMD Transform in Signal Denoising (Includes EEMD Implementation Code)
The classical K-SVD dictionary learning algorithm, widely applicable for signal denoising, image reconstruction, and other sparse representation tasks, employs an iterative optimization approach combining sparse coding and dictionary updating stages
The Mallat pyramid multi-level decomposition and reconstruction algorithm in wavelet transform is an effective method for signal denoising, involving hierarchical frequency decomposition and reconstruction processes.
Particle Swarm Optimization Algorithm | Artificial Intelligence | Optimization Technique | Signal Denoising | Highly Efficient Implementation