RD_zhukang

This is a model of a QPSK modulation system for transmission over a bandpass channel with fc = 100 Hz and B = 30 Hz and AWGN at the receiver. SRRC fi lters with excess bandwidth α = 0.18 are employed. The receiver includes an NDA timing recovery loop followed by a DD phase recovery loop. (This is a model of a QPSK modulation system f or transmission over a bandpass channel with fc = 100 Hz and B = 30 Hz and AWGN at the receiver. SRRC)

一些关于数据车牌识别的资料，对你进行车牌的识别程序设计有用(Some information about the data license plate recognition, license plate recognition program is useful for the design)

本程序实现Lena图像的压缩传感，测量矩阵为哈达玛测量矩阵，重构算法采用OMP重构算法(This procedure Lena image compression sensing, measurement matrix is ​ ​ Hadamard measurement matrix, reconstruction algorithm using OMP reconstruction algorithm)

多种模式识别算法的编程实现，非常详细，包括PCA，MPCA，LLE等(Several pattern recognition algorithm is introduced, including PCA, MPCA, LLE etc.)

一个基于matlab的运动目标跟踪的资料，包括源代码和视频样本(A moving target tracking based on matlab, including source code and video samples )

里面的内容还是不错 能够好好参考 matlab功能强大哦 ^_^(The contents of which is good to make reference to matlab powerful ^ _ ^ Oh)

bpsk ser matlabcode share

This code performs likelihood ratio test (LRT) for VAR model to determine optimal lag length, constant term is automatically included. Test is performed on contiguous lags, thus DOF=neqn^2*1. Guido Travaglini, 06.15.2011

Matlab直流潮流程序，Matlab直流潮流程序(Matlab直流潮流程序Matlab直流潮流程序)

说明：  珍藏版，可实现，新K均值聚类算法，分为如下几个步骤： 一、初始化聚类中心 1、根据具体问题，凭经验从样本集中选出C个比较合适的样本作为初始聚类中心。 2、用前C个样本作为初始聚类中心。 3、将全部样本随机地分成C类，计算每类的样本均值，将样本均值作为初始聚类中心。 二、初始聚类 1、按就近原则将样本归入各聚类中心所代表的类中。 2、取一样本，将其归入与其最近的聚类中心的那一类中，重新计算样本均值，更新聚类中心。然后取下一样本，重复操作，直至所有样本归入相应类中。 三、判断聚类是否合理 采用误差平方和准则函数判断聚类是否合理，不合理则修改分类。循环进行判断、修改直至达到算法终止条件。(NewK-means clustering algorithm ，Divided into the following several steps: A, initialize clustering center 1, according to the specific problems, from samples with experience selected C a more appropriate focus the sample as the initial clustering center. 2, with former C a sample as the initial clustering center. 3, will all samples randomly divided into C, calculate the sample mean, each the sample mean as the initial clustering center. Second, initial clustering 1, according to the sample into the nearest principle clustering center represents the class. 2, as this, take the its recent as clustering center of that category, recount the sample mean, update clustering center. And then taking off, as this, repeated operation until all samples into the corresponding class. Three, judge clustering is reasonable Adopt error squares principles function cluster analysis.after clustering whether reasonable, no reasonable criterion revisio)