Fuzzy C-Means Clustering: MATLAB Implementation with Complete Function Suite
A comprehensive MATLAB implementation of Fuzzy C-Means clustering algorithm featuring 10 specialized functions for complete clustering workflow
EZW: Wavelet Transform-Based Zerotree Coding Implementation
MATLAB implementation of EZW (Embedded Zerotree Wavelet) algorithm featuring wavelet decomposition and hierarchical tree encoding for efficient data compression
MATLAB Implementation of QRS Detection Algorithm with High Accuracy
Highly accurate QRS detection algorithm implemented in MATLAB, featuring robust signal processing techniques for electrocardiogram analysis.
AGV (Automated Guided Vehicle) Path Recognition System
MATLAB implementation of an AGV (Automated Guided Vehicle) path recognition system featuring grayscale conversion, threshold segmentation, binarization, morphological opening/closing operations, and edge detection algorithms
Viterbi Decoding Algorithm Implementation for (2,1,7) Tail-Biting Convolutional Code in MATLAB
MATLAB implementation of Viterbi decoding algorithm for (2,1,7) tail-biting convolutional codes, featuring dynamic programming path metrics comparison and zero-termination processing
Fuzzy C-Means Clustering Algorithm Implementation in MATLAB
MATLAB implementation of the Fuzzy C-Means clustering algorithm with detailed code explanations and practical applications.
MATLAB Implementation of Image Registration Using Powell's Algorithm
MATLAB implementation of Powell's method for image registration with optimization techniques and code structure explanation
MATLAB Implementation of the Cordic Algorithm
MATLAB implementation of the Cordic algorithm featuring programmatically generated data files - this is the basic, unoptimized version of the algorithm
Extracting Image Texture Features Using Gray-Level Co-occurrence Matrix
Implementation of gray-level co-occurrence matrix for image texture feature extraction using MATLAB, including fuzzy c-means clustering for classification with detailed code explanations
MATLAB Implementation of Support Vector Machine with Multi-Class Classification Support
This MATLAB implementation of Support Vector Machine (SVM) supports multi-class classification and features a user-friendly GUI interface for intuitive operation. The implementation includes comprehensive data input/output processing, utilizing MATLAB's built-in SVM functions and custom algorithms for efficient classification.