802.11a model

基于小波变换的数字水印提取Matlab源代码(Digital Watermarking Based on Wavelet Transform Matlab source code extraction)

【谷速软件】灰度图像各向异性扩散 在学习过程中作为参考使用([Valley] speed software grayscale image anisotropic diffusion in the learning process as a reference)

精通Matlab与C_C++混合程序设计.rar 是精通Matlab与C_C++混合程序设计 这本书的配套源码(Proficient in Matlab and C_C++ Mixed program design. Rar is proficient in Matlab and C_C++ Mixed programming matching this book source)

一维时域有限差分算法，能够进行波的模拟研究(One-dimensional FDTD algorithm for wave simulation can)

说明：  The program can be used. ABC algorithm can solve the VRP Problem for reference.

说明：  基于matlab和小波的图像压缩实践文档，每一步操作都有完整代码和必要说明，并有运行效果图，便于初学者快速学习小波图像压缩(Matlab and wavelet based image compression practice document, every step of the operation code and the necessary instructions are complete and have run effect picture, easy for beginners to learn fast wavelet image compression)

直接序列扩频，信道采用重复编码，2psk调制，针对白噪声，多径环境下的误码率仿真(Direct sequence spread spectrum, channel coding using repeated, 2psk modulation, for white noise, BER simulation of multipath environments)

it is used to design filters using rectangular window......

OFDM的仿真程序，经过瑞利多径的仿真。其中子载波数为4.(OFDM simulation procedure, after the simulation of multipath Rayleigh. Subcarriers of which the number of 4.)

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))