intensitytransformation

说明：  ofdm的bpsk实现，可以在修改后运用与mimo或者vmimo等(realize the BPSK OFDM, can be modified to use with vmimo such as MIMO or)

基于Matlab的object tracking的一个小程序(A small program about object tracking based on the matlab )

circle fitting to find damping

利用两幅图像对物体进行重建，基于计算机视觉的(two view reconstruction)

说明：  自己写的isodata聚类程序，已经在matlab下调试验证通过。(Wrote it myself ISODATA clustering procedure has been adopted to verify matlab debugging.)

matlab 教程。。。对于没用过matlab的 可以作为入门学习(matlab tutorial. . . For matlab can not used as a learning portal)

说明：  大家知道求解连通图的最小生成树算法很多，下面是利用MATLAB编写的常用的prim 算法，带例题哦!(We all know that solving the minimum spanning tree algorithm connected graph a lot, the following is written using common prim MATLAB algorithm, with examples Oh!)

说明：  100kW三相光伏并网详细模型 MPPT使用增量电导法(Detailed model of 100 kW three-phase photovoltaic grid-connected MPPT uses incremental conductivity method)

说明：  这是关于自适应的一个m文件，希望对大家有用(This is an m on the adaptive document, we hope to be useful)

把辨识分成两步进行：第一步：利用相关分析法获得对象的非参数模型（脉冲响应或相关函数）；第二步：利用最小二乘法、辅助变量法或增广最小二乘法等，进一步求的对象的参数模型。如果模型噪声与输入无关，则Cor-ls相关最小二乘法（二步法）可以得到较好的辨识结果。Cor-ls相关最小二乘法（二步法）实质上是先对数据进行一次相关分析，滤除了有色噪声的影响，再利用最小二乘法必然就会改善辨识结果。能适应较宽广的噪声范围，计算量不大，初始值对辨识结果影响较小。但要求输入信号与噪声不相关(The identification is divided into two steps: the first step: using the correlation analysis method for object nonparametric model (impulse response or related function) The second step: using the least squares method, auxiliary variable method or augmented least squares method and so on, further for the parameters of the object model. If the model input noise and independent, the Cor- ls related least squares (two footwork) can get good recognition results. Cor- ls related least squares (two footwork) is essentially the data to a correlation analysis, filter in addition to the influence of colored noise, using least squares inevitable will improve the identification result. To adapt to a wide range of noise and calculation is not big, the initial value to identification results less effect. But for the input signal and noise are not related)