Implementation of Signal Compressed Sensing using Orthogonal Matching Pursuit Algorithm
Implementation of Signal Compressed Sensing with Orthogonal Matching Pursuit (OMP) Method - Algorithm Explanation and Code Implementation Details
Explore MATLAB source code curated for "实现" with clean implementations, documentation, and examples.
Implementation of Signal Compressed Sensing with Orthogonal Matching Pursuit (OMP) Method - Algorithm Explanation and Code Implementation Details
Implementation of Bayesian Matting - the most classic and fundamental matting algorithm in MATLAB, demonstrating excellent performance in both output quality and computational efficiency with robust probabilistic frameworks and optimized matrix operations.
Implementation of standard Cellular Neural Networks for image edge detection using MATLAB, featuring simplified code structure for better understanding
Implementation of the COST 207 channel model in MATLAB with code examples for simulating wireless communication environments
MATLAB code for Costas loop implementation, widely used in modern communication systems for carrier synchronization
This source code implements 2D Discrete Wavelet Transform based on the Mallat pyramid algorithm, providing multi-scale decomposition for signal and image processing applications.
MATLAB code for computing PSNR between two images, designed to compare peak signal-to-noise ratio with detailed algorithmic implementation.
A comprehensive MATLAB genetic algorithm example demonstrating complete implementation workflow, featuring selection, crossover, and mutation operations with practical code explanations
This post presents a MATLAB implementation of a second-order lattice filter with detailed code structure and reflection coefficient calculations. I'm seeking expert feedback and collaboration for extending this to higher-order lattice filters. Contact: zhoujoejx@163.net
This package implements a Fisher Linear Discriminant (FLD) based face recognition system, known as the Fisherface method. The implementation features heavily commented code with clear function descriptions, including data preprocessing routines, eigenvalue decomposition for optimal projection vectors, and classification algorithms for face matching.