name-of-4000-series

In this simulink model , BPSK modulation and demodulation is simulated .A square root raised cosine pulse shaped signal is modulated and given as input to the demodulator.The received signal is demodulated and given to the detector where it is matched filtered and down-sampled .The effects of symbol timing offset can be seen by varying the phase of down-sampling operation.The signal transmitted is recovered at the detector output.

VLFEAT,一个强大的SIFT库函数,适合MATLAB和VC应用(VLFEAT, a powerful SIFT library functions for MATLAB and VC application)

zero forcing implementation in matlab for a certain channel respon in a noisy channel

模拟实验室中产生的清音和浊音，通过仿真产生清浊音的频谱图，非常有效的对比，可以看出它们之间的差异。(Voiceless and voiced simulation laboratory generated by simulation generated Voicing the spectrum, very effective contrast, you can see the difference between them. )

chapter 3 of the fdtd method for electromagnetics with matlab simulations

直线一级顺摆LQR 控制MATLAB 计算程序,可以对倒立摆实现稳定控制(Shun linear pendulum LQR control level MATLAB program, you can realize stability of the inverted pendulum control)

说明：  里面包含了插值，函数逼近的各个算法。很知道大家去学习(Which contains the interpolation, function approximation of the various algorithms. We are aware that to learn)

说明：  这是一本有关matlab的书，对matlab学习者有用(This is a book about matlab and is useful for matlab learners )

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)

capon波束形成算法，经典的波束形成算法，能有效形成波束(Capon beamforming algorithm, the classic beam forming algorithm, can effectively form the beam)