Zerroumda-Tp_FD_NR_GS

Snake Methods for Image 2D

Rayleigh fading simulation

对最小相位系统的性能研究以及DMC的仿真实例。(The simulation study of the performance of the system and the minimum phase of DMC.)

apclustering完整程序代码，供大家参考学习(apclustering complete program code, for your reference learning)

说明：  核极限学习机是一种单隐层前馈神经网络(the single-hidden layer feedforward neural networks,SLFNs),其只需要设置隐藏层节点数,然后采用最小二乘法计算出权值即可。因此,核极限学习机在学习速度和泛化能力上具有很大优势。(Kernel limit learning machine is a single hidden layer feedforward neural networks (slfns). It only needs to set the number of hidden layer nodes, and then use the least square method to calculate the weight. Therefore, the kernel limited learning machine has a great advantage in learning speed and generalization ability.)

％％％ Demos for PUMA algorithms ％％％ We present four matlab demos for PUMA. demo1, demo2, demo3, and demo4 illustrate PUMA working with different parameters and with four different images. All you need to do is to run each of the demos. Please be sure that all the files are put on an accessible path for matlab. Notice that this code is intended for research purposes only. For further reference see "Phase Unwrapping via Graph Cuts, IEEE Transactions on Image Processing, 2007( Demos for PUMA algorithms We present four matlab demos for PUMA. Demo1, demo2, demo3, and demo4 illustrate PUMA working with different parameters and with four different images.All you need to do is to run each of the demos . Please be sure thatall the files are put on an accessible path for matlab.Notice that this code is intended for research purposes only.For further reference see )

MATLAB编程时用到的函数参考介绍及功能描述。(Function used in the MATLAB Programming Reference introduction and functional description.)

将基于最大峰值和ＪＡＤＥ盲分离算法相结合的一种盲分离算法．(Will be based on the maximum peak and the JADE algorithm for blind separation of a combination of blind source separation algorithm.)

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))

本程序计算HSV空间的非均匀量化直方图，空间维数为36柄(HSV space, the program calculates the histogram of non-uniform quantization, space dimension of 36 handle)