Programmingproblem

BPSK 调制在 AWGN 信道下的 性能仿真(BPSK modulation in AWGN channel performance simulation)

模糊函数绘制软件,可用于绘制多种信号的模糊函数。(ambiguity function)

水果图像识别，利用MATLAB进行图形的特征提取，边缘检测，阈值分割等(Fruit and image recognition, the use of MATLAB for graphics feature extraction, edge detection, thresholding, etc.)

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

FSK 信号的循环谱程序，验证了是正确的(The cycle of FSK signal spectrum procedure, verify the is correct)

本程序和文件主要用蒙特卡洛的思想来仿真骰子投掷事件发生的概率。用增加投掷次数的方法来逼近实际。(This procedure, and the paper s main ideas to use the Monte Carlo simulation of the probability of dice throwing incident. With the increase in the number of ways to throw close to the actual.)

在同温度和和照度下，n改变时，光生电流不变。(when the temperature and irradiation are the same the output photocurrent Iph of the same piece of solar cell remains constant although the diode quality factors and reverse saturation currents are different.)

仿真n 个城市的TSP问题的最优解返回一个新的城市构型(The simulation n city TSP problem optimal solution to return to a new city configurations )

article of pattern recognizer

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))