Digital_modulation_FCN

ascii to binary converter

基础线性分类器的检测用于分离超平面是单向垂直的。( TESTING THRESHOLD CLASSIFIER)

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))

光通信中基本器件微环的基本分析，这个分析是在理想情况下分析的，没有考虑损耗(Basic analysis of the basic device in the optical communication microring, this analysis is in the ideal case the analysis, without considering loss)

十四单元的matlab程序，有少许改动。(14 unit matlab procedures, there are little changes.)

WVD变换的matlab工具包，非常有用的信号时频分析工具(WVD transform matlab toolkit, a very useful time-frequency analysis tool)

THE ZIP FILE CONTAINS ALL THE PROGRAMS OF A BOOK NEURAL NETWROK FOUNDATIONS BY SATISH KUMAR

SqueezeBox software version

实现三次样条插值信号包络线，本人在EMD算法时用到，效果已验证(Realize the cubic spline interpolation signal envelope, the effect is very good.)

Robot arm length and path