Explore MATLAB source code curated for "Matlab" with clean implementations, documentation, and examples.
This is a MATLAB source code implementation for template matching using the Sum of Squared Differences (SSD) algorithm, featuring comprehensive code documentation with explanations of key computational steps and image processing functions.
This article presents PWM rectifier PI parameter design methodology with comprehensive MATLAB simulation implementation, demonstrating robust performance and practical application value.
A MATLAB-based power flow calculation program utilizing Newton-Raphson method in rectangular coordinate system, featuring simple implementation and strong versatility with clear algorithm structure
Implementation of Newton's Iteration Method for Solving Nonlinear Equation Systems - Adapted from "Mastering MATLAB Scientific Computing"
Implementation of Broyden's iteration method for solving nonlinear equation systems in MATLAB development environment, including M-file functionality and algorithmic approach
Implementation of probability density function nonparametric estimation using MATLAB, primarily focusing on Parzen window method and k-nearest neighbors approach. Includes simulation demonstrations for both uniform distribution and normal distribution cases with code implementation details.
DACE: A MATLAB Kriging Toolbox Version 2.0, released August 1, 2002 (by Søren N. Lophaven, Hans Bruun Nielsen, Jacob Søndergaard). This comprehensive software package provides advanced tools for designing and analyzing computer experiments using kriging approximation models, featuring covariance function estimation and surrogate model validation capabilities.
This MATLAB-based code efficiently converts grayscale images into pseudo-color images using advanced color mapping algorithms, providing enhanced visual representation of image details.
This example demonstrates effective 3D mesh reconstruction using MATLAB, implementing triangulation-based surface reconstruction from point cloud data
A custom MATLAB implementation for reading YUV files and saving frames separately, generating an N-dimensional matrix (where N is the total frame count) where each dimension contains one frame's data, with Y, U, and V components stored independently along with their corresponding metadata.