HMM-Based Recognition Program for Digits 0-9
An HMM-based recognition program that achieves 100% accuracy in recognizing digits 0-9, featuring robust pattern analysis and machine learning implementation.
DLDA: Complete Implementation with Training and Recognition Processes
This is my graduation project implementation of DLDA featuring both training and recognition modules. The algorithm demonstrates exceptional speed and achieves high recognition accuracy through optimized deep learning architecture.
Artificial Neural Network-Based Digital Signal Modulation Recognition
Implementation of common digital signal modulation recognition using artificial neural networks with recognition rate analysis and optimization strategies
MATLAB Implementation of ICA-Based Face Recognition
A MATLAB-based face recognition program utilizing Independent Component Analysis (ICA) algorithm, achieving high recognition accuracy with optimized preprocessing and computational efficiency.
Compact Gesture Recognition Tool
A lightweight gesture recognition tool developed as a Master's degree assignment, implementing block FFT algorithm with 70-80% recognition accuracy.
MATLAB Implementation of Linear Discriminant Analysis (LDA) for Face Recognition
This LDA-based program demonstrates facial recognition capabilities with promising accuracy. Tested on the ORCL face database, it achieves high recognition rates and is expected to perform well on other datasets. The implementation includes key LDA components like scatter matrix computation and eigenvalue decomposition for dimensionality reduction.
Integration of PCA and LDA Algorithms with Code Implementation for Face Recognition
This project combines Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for image training and recognition processes. Implemented using the ORL face database, the system achieves high recognition accuracy through optimized feature extraction and classification techniques. The implementation includes complete pipelines for data preprocessing, dimensionality reduction, and pattern matching.
Modulation Recognition of MPSK Signals Using Higher-Order Cumulants
Modulation recognition of MPSK signals using higher-order cumulants demonstrates exceptional performance, achieving remarkably high recognition rates. After extensive validation and rigorous analysis, I have confirmed the reliability of this method and its consistently high accuracy rates in practical applications.
Face Recognition Using Two-Dimensional Principal Component Analysis (2DPCA)
Face recognition based on 2DPCA provides superior image dimensionality reduction while achieving higher recognition accuracy compared to traditional PCA methods. This implementation preserves critical image features through direct matrix-to-matrix projection without vectorization.
Classification of Extracted Feature Vectors Using Fuzzy Support Vector Machines
This project demonstrates dimensionality reduction of ORA face images using Principal Component Analysis (PCA), followed by high-accuracy classification of extracted feature vectors through Fuzzy Support Vector Machines (FSVM).