PCA Dimensionality Reduction Code - MATLAB Implementation

This MATLAB implementation performs dimensionality reduction for M*N large datasets with clear procedural steps. The function y = pca(mixedsig) processes an n*T mixed signal matrix where n represents the number of signals and T denotes sampling points, returning an m*T principal component matrix. Algorithm workflow includes: 1) Mean centering, 2) Covariance matrix computation with eigenvalues/eigenvectors, 3) Threshold-based eigenvalue selection, 4) Descending eigenvalue sorting, 5) Small eigenvalue removal, 6) Large eigenvalue removal (typically skipped), 7) Selected eigenvalue merging, 8) Corresponding eigenvector selection, 9) Whitening matrix calculation, 10) Principal component extraction.

Collaborative Spectrum Sensing Based on Energy Detection in Cognitive Radio

Implementation of collaborative spectrum sensing using energy detection in cognitive radio systems, featuring detection probability analysis under varying SNR conditions, sampling points, and cooperative nodes with MATLAB/Python code examples for practical reference.

Wavelet Transform-Based Image Compressive Sensing with Performance Analysis

Implementation of image compressive sensing using wavelet transforms with OMP reconstruction, featuring performance analysis and comparison across different sampling rates for evaluating compressive sensing efficiency.

HHT Transformation MATLAB Implementation

This MATLAB program for HHT transformation can be executed directly in the MATLAB environment with customizable parameters and sampling points. The program generates various power quality signals (such as voltage sag, harmonics, temporary swell, and interruption) that can be superimposed for analysis. The provided example demonstrates voltage sag analysis with configurable signal parameters through script modification.