2DPCA(1)

旋转不变技术估计信号参数(ESPRIT)算法是子空间估计的两个经典方法,它们都能够有效地估计特征子空间。程序包里面实现了用LS估计和TLS估计的ESPRIT算法。(ESPRIT estimates signal parameters (ESPRIT) algorithm is estimated that sub-space of two classical methods, they will be able to estimate the characteristics of effective subspace. Inside a package with LS estimates and estimates of the ESPRIT algorithm TLS.)

基于双正交小波包的图像压缩研究，用于于2的图形开发的研究(Biorthogonal wavelet packet based image compression research)

介绍关于IEEE34-with-wind及其仿真模型(About IEEE34-with-wind and its simulation model)

Beamforming _M _QAM

通过此代码我们可以准确的了解粒子滤波的准确性和高效性(Through this code we can accurately understand particle filter the accuracy and efficiency)

说明：  matlab的一些经典算法 (Some of the classic algorithm matlab)

胡广书 数字信号处理 理论算法与实现 第二版(HU-book theory of digital signal processing algorithms with the second version)

图像的拉普拉斯变换及其matlab代码实现及代码测试(Images of the Laplace transform and its matlab code implementation and testing )

K最邻近密度估计技术是一种分类方法，不是聚类方法。 不是最优方法，实践中比较流行。 通俗但不一定易懂的规则是： 1.计算待分类数据和不同类中每一个数据的距离（欧氏或马氏）。 2.选出最小的前K数据个距离，这里用到选择排序法。 3.对比这前K个距离，找出K个数据中包含最多的是那个类的数据，即为待分类数据所在的类。(K nearest neighbor density estimation is a classification method, not a clustering method. It is not the best method, but it is popular in practice. Popular but not necessarily understandable rule is: 1. calculate the distance between the data to be classified and the data in each other (Euclidean or Markov). 2. select the minimum distance from the previous K data, where the choice sorting method is used. 3. compare the previous K distances to find out which K data contains the most data of that class, that is, the class to which the data to be classified is located.)

说明：  matlab pwm整流器网侧变流器仿真(Simulation of grid-side converter)