RPCA

time domain symptom extraction

wireless sensor network time synchronization based on TPSN and RBS

Compressive sampling is an emerging technique that promises to effectively recover a sparse signal from far fewer measurements than its dimension. The compressive sampling theory assures almost an exact recovery of a sparse signal if the signal is sensed randomly where the number of the measurements taken is proportional to the sparsity level and a log factor of the signal dimension. Encouraged by this emerging technique, this thesis briefly reviews the application of Compressive sampling in speech processing. It comprises the basic study of two necessary condition of compressive sensing theory: sparsity and incoherence. In this thesis, various sparsity domain and sensing matrix for speech signal and different pairs that satisfy incoherence condition has been compiled.

说明：  mat lab designed filter

pca face recognition

Matlab code for multiclass svm

说明：  hslogic算法仿真,基于MATLAB的TSP算法的优化仿真与性能分析(Hslogic algorithm simulation, optimization simulation and performance analysis of TSP algorithm based on MATLAB)

说明：  使用matlab语言编程的人工蚁群算法对测试函数进行测试(Using artificial fish swarm algorithm programmed by MATLAB to test the test function)

MATLABlearn MATLABlearn

应用matlab计算心理声学参数loudness的工具。(loudness calculation)