FSK

MATLAB环境下V-BLAST算法的仿真程序(MATLAB environment, V-BLAST algorithm simulation program)

说明：  if we wanted complex baseband model or to impose some filtering function

viterbi硬判决代码，用于卷积码译码(hard decision viterbi code for decoding convolutional codes)

粒子滤波几个历程 里面有英文标注 步骤明确 用于学习使用(Particle filter which has a history of several steps clearly marked in English for learning to use)

moteur asynchrone simulation mli

PCM/FM信号生成函数，包含基于FM调制以及CPM调制方式，前者是利用matlab中的fm模拟频率调制方式，是对PCM/FM信号比较直观的生成方法，后者是利用CPM信号的生成方法，从相位调制的角度生成。(Here is PCM/FM signal generating function, including on CPM modulation and FM modulation, the former is the use matlab simulation frequency fm modulation method is PCM/FM signal generation method more intuitive, which is generated using the method of CPM signals, generated from the phase angle modulation.)

实现聚类K均值算法： K均值算法：给定类的个数K，将n个对象分到K个类中去，使得类内对象之间的相似性最大，而类之间的相似性最小。 缺点：产生类的大小相差不会很大，对于脏数据很敏感。 改进的算法：k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用，这样的一个medoid就标识了这个类。步骤： 1，任意选取K个对象作为medoids（O1,O2,…Oi…Ok）。 以下是循环的： 2，将余下的对象分到各个类中去（根据与medoid最相近的原则）； 3，对于每个类（Oi）中，顺序选取一个Or，计算用Or代替Oi后的消耗—E（Or）。选择E最小的那个Or来代替Oi。这样K个medoids就改变了，下面就再转到2。 4，这样循环直到K个medoids固定下来。 这种算法对于脏数据和异常数据不敏感，但计算量显然要比K均值要大，一般只适合小数据量。(achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n will be assigned to target K to 000 category, making target category of the similarity between the largest category of the similarity between the smallest. Disadvantages : class size have no great difference for dirty data is very sensitive. Improved algorithms : k-medoids methods. Here a selection of objects called mediod to replace the center of the above, the logo on a medoid this category. Steps : 1, arbitrary selection of objects as K medoids (O1, O2, Ok ... ... Oi). Following is a cycle : 2, the remaining targets assigned to each category (in accordance with the closest medoid principle); 3, for each category (Oi), the order of selection of a Or, calculated Oi Or replace the consumption-E (Or))

extension of CS的可运行代码，很好的实现了论文中的实验，代码可运行，很详细(extension of CS can run code to achieve a good thesis experiment, the code can be run in great detail)

实现各阶本原多项式的小m序列的长生，通过改变本原多项式的生成矩阵可以产生想要的m序列的(Order to achieve the small-m primitive polynomial sequence longevity, by changing the primitive polynomial generator matrix m can produce the desired sequence)

自适应有限元求解第一类和第二类边界条件的拉普拉斯方程的相关子程序(Adaptive finite element solution of the first and second boundary conditions related to the Laplace equation subroutine)