Small-Signal Stability Analysis for Multi-Machine Power Systems
Small-signal stability analysis for multi-machine power systems involves eigenvalue and eigenvector computation to assess system dynamic behavior under small disturbances
Jacobi Method for Computing Matrix Eigenvalues and Eigenvectors
Implementation of Jacobi method for calculating matrix eigenvalues and eigenvectors using MATLAB, thoroughly debugged and ready for use
A Program to Compute 14 Haralick Features from Gray-Level Co-occurrence Matrix
This program calculates 14 Haralick texture features from gray-level co-occurrence matrices (GLCM) for image analysis applications, with implementation using OpenCV and NumPy libraries.
MATLAB Code Implementation of PCA Steps
PCA Implementation Steps: 1. Center the data (mean normalization); 2. Compute the covariance matrix; 3. Calculate eigenvalues and eigenvectors of the covariance matrix; 4. Sort eigenvalues and corresponding eigenvectors; 5. Determine projection direction based on target dimensionality d'; 6. Compute dimensionally reduced data
PCA Algorithm Programming Design Steps
PCA Algorithm Programming Design Steps: 1. Mean Centering 2. Compute Covariance Matrix and its Eigenvalues/Eigenvectors 3. Count Eigenvalues Exceeding Threshold 4. Sort Eigenvalues in Descending Order 5. Remove Small Eigenvalues 6. Remove Large Eigenvalues (Typically Omitted) 7. Combine Selected Eigenvalues 8. Select Corresponding Eigenvalues/Eigenvectors 9. Compute Whitening Matrix 10. Extract Principal Components
Extraction of Eigenvalues and Eigenvectors, Training Samples, and Final Recognition
This implementation features eigenvalue/eigenvector extraction, training sample processing, and final recognition stages. The program achieves high-performance levels capable of handling classification and regression tasks in pattern recognition domains.
PCA-Based Face Recognition Using Eigenface Methodology
Implementation of PCA-based face recognition (Eigenface) involving reading 20 training images, computing mean vectors, covariance matrices, eigenvalues and eigenvectors with timing measurements, displaying Eigenfaces in figure(i). Processing 4 test images with reconstruction in feature space, displaying original vs reconstructed images in figure(i) and figure(10+i) respectively. Calculating reconstruction errors for test images with comparative analysis of differences and underlying causes.
Texture Feature Extraction Based on Gray-Level Co-occurrence Matrix
Texture feature extraction using Gray-Level Co-occurrence Matrix (GLCM) that outputs sixteen feature values across four directional orientations of the image
Wavelet Transform-Based Texture Feature Extraction Method
This wavelet transform-based approach extracts texture eigenvalues and can be modified to extract other feature vectors, featuring MATLAB implementations of multi-level decomposition and energy calculation algorithms suitable for beginners.
Adaptive Eigenvalue-Based Time Delay Estimation Algorithm
MATLAB implementation of an adaptive eigenvalue-based time delay estimation algorithm with comprehensive code examples and technical explanations