Multi-Level Signal Decomposition Using Harmonic Wavelets
Implementing multi-level decomposition of signals with harmonic wavelets to extract characteristic signal features for detailed analysis
Professional MATLAB source code with comprehensive documentation and examples
Implementing multi-level decomposition of signals with harmonic wavelets to extract characteristic signal features for detailed analysis
MATLAB implementation of matching pursuit algorithm - a signal sparse decomposition method using overcomplete dictionaries, featuring matchingpursuit.m function with iterative approximation approach for signal compression and denoising applications.
Adaptive filter design methodology featuring LMS algorithm source code and comprehensive MATLAB simulation implementation, including weight update mechanisms and performance verification.
waveletimfusion - MATLAB implementation for wavelet-based image fusion with objective analysis of fusion results, featuring wavelet decomposition, coefficient fusion algorithms, and quality assessment metrics
Comprehensive Guide to Adaptive Filter Design - Featuring Detailed Technical Analysis and Code Implementation Strategies by Bob Stewart
Signal time-frequency analysis using wavelet transform and windowing techniques (Hamming window, Rectangular window, Blackman window) with MATLAB code implementation approaches
By leveraging translation invariance principles, wavelet threshold denoising effectively eliminates pseudo-Gibbs phenomenon, facilitating subsequent feature point extraction through optimized signal processing algorithms.
Calculating convolution and cross-correlation functions between two sequences, or computing autocorrelation functions for individual sequences, with implementation insights
Application of the conjugate gradient method for synthesizing and optimizing cross-coupled resonator filters, with reference to the analytical gradient-based optimization technique presented in Smain Amari's paper "Synthesis of Cross-coupled Resonato
This comprehensive MATLAB program developed through extensive research features fully functional implementations of FIR (Finite Impulse Response) and IIR (Infinite Impulse Response) filters, including both low-pass and band-pass configurations. The c
Implementation of versatile M-functions for various filtering methods including arithmetic mean filter, geometric mean filter, harmonic mean filter, and contraharmonic mean filter with code optimization details
Implementation of particle filter algorithm for visual target tracking in MATLAB, featuring probabilistic state estimation through particle propagation and weight updating with complete source code.
This repository contains extensive simulation programs for various MUSIC algorithm implementations including 1D classical MUSIC, ESPRIT algorithm, Root-MUSIC algorithm, 2D planar array MUSIC, L-shaped array configuration, spatial smoothing MUSIC, pro
A CFAR simulation program for radar signal processing that compares four detection thresholds under identical false alarm rates: ideal detection threshold, cell averaging CFAR (CA-CFAR) detection threshold, censored CFAR detection threshold, and orde
5_3 wavelet transform in scalable video coding. Motion Compensated Temporal Filtering (MCTF) serves as an effective scalable video coding scheme, and implementing it with the 5/3 lifting wavelet enhances coding performance through efficient multi-res
This collection contains commonly used adaptive filtering algorithms including LMS, RLS, Fast RLS (FTF), and others. Featuring multiple implementations in MATLAB m-files, these resources provide valuable insights for understanding adaptive algorithm
A comprehensive approach for diagnosing weak fault signals in rolling bearings using five source files: Minimum Entropy Deconvolution algorithm implementation, adaptive noise cancellation method for bearing signal processing, Case Western Reserve Uni
Designing Low-pass, Band-pass, High-pass, and Band-stop FIR Digital Filters Using MATLAB with Low-pass Sampling
Wavelet Denoising Program based on Modulus Maxima Method - Ready-to-use implementation containing: P_gama.m (noise estimation), P_y.m (signal processing), Py_Pgama.m (threshold calculation), and wavedenoisemod3_a.m (main denoising algorithm). The pri
This demonstration illustrates the implementation of a generic Sequential Importance Resampling (SIR) filter - also known as particle, bootstrap, or Monte Carlo filter - for estimating hidden states in nonlinear, non-Gaussian state space models, comp