(Springer)-Algorithm-Collections-for-Digital-Sign

Power Systems Oscillations C3 examples

WSN LOCALAZATION SIMULATION

思科认证CCNA study guide 640-802 英文版教材(ccna study guide 640-802)

说明：  用MC方法来求pi 正方形与圆形的面积比 最简单的方法(MC method with the easiest way to find pi)

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))

简单的Dijkstra求最短路的MATLAB程序，很好用，对于两点之间最短距离。(Dijkstra' s shortest seek simple MATLAB program, useful for the shortest distance between two points.)

在matlab的环境下实现拉格朗日乘子法 (matlab achieve Lagrange multipliers)

FMCW调频连续波雷达的测距测角，信号处理中的旋转不变子空间法，研究生时的现代信号处理的作业。( FMCW frequency modulated continuous wave radar range and angular measurements, Signal Processing ESPRIT method, Modern signal processing jobs when the graduate.)

Double side band suppressed carrier modulation (DSB-SC)

A multi-target tracking toolbox based on the MTT Library of the InstantVision ISE with expanded functionality and tools for off-line design, analysis and testing. The toolbox contains the implementation of distance calculation methods (e.g. city-block based), data assignment and association strategies (e.g. ENN and JVC), state prediction filters (e.g. IMM) with video marking and debugging tools in order to support a complex multi-target tracking system design.