NEURO-FUZZY-BASED-CONTROLLER-FOR-A-SHUNT-ACTIVE-P

基于3G标准wcdma的上下行通信链路，基于3GPP协议，采用matlab simulink的仿真(Up and down based on 3G standards wcdma line communication link, based on 3GPP protocol, using matlab simulink simulation)

matlab数值分析详解，新手入门必备，常用matlab技术。(matlab numerical analysis Xiangjie, the Getting Started essential, commonly used matlab technology.)

说明：  （1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象； （2）将所有子图象按列相接变成一个81维的行向量； （3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量； （4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。 （5）求出所有子块的特征向量。 ((1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image (2) all sub-images according to out-phase into a 81-dimensional row vector (3) all 200 lines for KL transform vector, derived its corresponding covariance matrix of eigenvectors and eigenvalues, in descending order by eigenvalue and the corresponding eigenvector (4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the PCA, the original image block to the 40 feature vectors on the projection, the projection coefficients obtained by this sub-block eigenvector. (5) calculated for all sub-block eigenvector.)

二进制删除信道下的喷泉码（LT码）的译码程序（含注释）。(under binary erasure channel，decoding process of one kind of the fountain Code (LT code)，including the Notes.)

matlab code for space time coding

pca做特征降维，然后进行特征空间随机分割构造多个svm分类器，并行处理，对样本进行分类，基于特征空间的svm多分类器(using pca reduce feature dimension,split feature space and then randomly divided over svm classifier construction, parallel processing, the samples were classified, based on multi-feature space classification svm )

说明：  托马斯算法求肋片传热问题，给定初始条件，从高到低(thomas algorithm)

说明：  sparse coprime array direction of arrival

cohen类信号的双线性信号时频分析，page分布，该函数用于计算离散时间信号X的page分布，或良信号的互page表示。(cohen category signals bilinear time-frequency analysis, page distribution, the function used to calculate the discrete-time signal X s page distribution, or good signal that each page.)

我们采用交警平台！对数据进行处理！我们会努力的加油(We use traffic platform! For data processing!)