Explore MATLAB source code curated for "工具箱" with clean implementations, documentation, and examples.
The Rice University Compressive Sensing Sparse Learning Toolbox enables sparse signal representations, achieving compressive sensing at sampling rates significantly below the Nyquist-Shannon threshold through advanced optimization algorithms.
A comprehensive time-frequency analysis toolbox primarily featuring wavelet transform signal processing functions with MATLAB/Python implementation examples
Genetic Algorithm Toolbox for MATLAB, based on version 6.5. Support and adoption are highly encouraged. The toolbox is currently being evaluated for graph coloring applications. This implementation includes key evolutionary computing features such as selection, crossover, and mutation operations with customizable parameters.
An internationally developed nonlinear estimation toolbox featuring comprehensive implementations including particle filtering, unscented Kalman filter (UKF), extended Kalman filter (EKF), and other advanced algorithms.
Image registration performed using MATLAB toolbox requires feature point extraction to achieve alignment between images
classification_matlab_toolbox - A comprehensive classification methods toolbox featuring an intuitive graphical user interface for machine learning applications
A comprehensive MATLAB toolbox for RANdom SAmple Consensus (RANSAC) algorithm implementation, featuring robust parameter estimation with outlier rejection capabilities.
A comprehensive toolbox designed for speech signal analysis and processing, featuring various algorithms and functions for audio enhancement and recognition tasks.
A MATLAB-based toolbox implementation for multiscale image analysis using Non-Subsampled Contourlet Transform (NSCT), featuring applications in image compression, segmentation, fusion, and super-resolution. The implementation includes directional filter banks and multiscale decomposition algorithms to capture image geometric structures without downsampling artifacts.
Implementation of a spectral clustering algorithm with development of a comprehensive spectral clustering toolbox