Presentation

One of Biometrics fields is face recognition & face expression recognition ... 1- In face recognition .. we need to design authentication program by training a neural network ,there are two source codes..one of them is based on Discrete Wavelet Transform with Perceptron Neural Network.. and the other based on Discrete Cosine Transform with Perceptron Neural Network ... 2- In face expression recognition .. we defined the condition of the person (nature,happiness,disgust or anger) this source code is based on Principle component analysis(PCA) .. * we need to now about digital image processing ,neural network and PCA...

backstepping Controller application

随机矩阵为小波变换的压缩传感，包含有系数采样、随机矩阵、重构算法程序(compressive sensing)

This toolbox is not a general-purpose toolbox or manipulating polygons. It just provides a inimal set of functions dedicated to closed polygons to support the Active contour toolbox (by the same author). Only the plotting function also supports open polygons.

state system for DFIG model

信号与信息处理——阵列信号处理DOA估计的matlab算法，这是线性调频信号的稀疏分解算法，很有用(Signal and information processing- array signal processing matlab algorithm for DOA estimation, which is a linear FM signal sparse decomposition algorithm, useful)

图像自动识别程序（三角形，圆形，正方形）(Image Auto-recognition program (triangle, circle, square))

OFDM符号定时同步算法，几种经典算法的仿真和比较(OFDM symbol timing synchronization algorithm, simulation and comparison of several classical algorithm)

Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production, for recovering compressive sensed speech. Since the linear transform is signal dependent and unknown, unlike the standard CS formulation, a codebook of transfer functions is proposed in a matching pursuit (MP) framework for CS recovery. It is found that MP is efficient and effective to recover CS encoded speech as well as jointly estimate the linear model. Moderate number of CS measurements and low order sparsity estimate will result in MP converge to the same linear transform as direct VQ of the LP vector derived the original signal. There is also high positive correlation between signal domain approximation and CS measurement domain approximation for a large variety of speech spectra.

斜磁化无限延伸程序使用很方便，matlab直接导入，直接出结果，图很漂亮(Unlimited extension of deep oblique magnetized magnetic plate body Abnormal Program)