GLCT

通过巴特沃斯滤波器的通带和阻带截止频率以及通带和阻带衰减做出相关的图形。(Through the Butterworth filter passband and stopband cut-off frequency and passband and stopband attenuation to make the relevant graphics.)

This paper will concentrate on investigating the effects on capacity associated with the ACI scenario .

robocup 2d 仿真足球base代码(the robocup 2d simulation Soccer base code)

its an rectifier circuit with PWM PI control technique for high accuracy and better output with very low THD.

基本的boost PFC simulink仿真，带有功率因数测量。控制采用滞环控制，功率因数可达99 以上。(The basic boost PFC simulink simulation, with a power factor measurement. Control using hysteresis control, power factor up to 99 .)

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))

基于PEGASIS的无线传感器路由改进算法(Wireless sensor network protocol based on improved algorithm PEGASIS)

FFT sample matlab code

电机双闭环电流环速度环PID控制，无刷直流电机(double close loop control C program)

Gaussian Elimination with partial pivoting (LU factorization)