Hamming

这个程序主要是用来设计实现一个加解密的系统 采用matlab设计 用了二进制循环 异或的知识(与刚才对应的解密程序)(This program is mainly used to design and implement a system of encryption and decryption using matlab design cycle using the binary XOR of the knowledge (and have the corresponding decryption process))

说明：  互信息法求混沌序列的最优时延 文件说明： 1、Mutual_Information_main.m 主文件，直接运行此程序即可 2、LorenzData.dll 产生Lorenz离散序列 3、Mutual_Information.dll 互信息函数 4、buffer.dll 缓存文件(Mutual information method for optimal time-delay chaotic sequence document describes: 1, Mutual_Information_main.m master file, run this program directly to 2, LorenzData.dll generated Lorenz discrete sequence 3, Mutual_Information.dll mutual information function 4, buffer.dll cache file)

基于muscl格式一维喷管流动计算，格式采用参数η取为0 的Van Leer限量因子。(muscl)

matlab中比较经典的微分方程求解.其中涉及到一维微分方程与2维平面PDE模型.希望有兴趣的可以看看.(matlab more classic solving differential equations which relate to the two-dimensional plane PDE model one-dimensional differential equations. that are interested can look at.)

this is the leach protocol simulation in matlab of wireless sensor network

5自由度机器人，运用牛顿欧拉定理，最终求得力矩 注解为西班牙语，google翻译一下比较好懂(5 DOF robot, using Newton Euler' s theorem, the final moment annotations obtained Spanish, google translate better understand)

说明：  matlab仿真程序，自己编的很好用。可以稍加修改应用到其他系统中去。(matlab simulation program that they made good use. Applications can be slightly modified to other systems.)

Ergodic Capacity of Doubly Selective Rayleigh Fading MIMO Channels

说明：  在原始的fcm算法基础上，对算法中的聚类数c和加权指数m给出优选方法，进而而出了fcm参数优选自适应算法，通过人造数据与具有实际背景的数据验证可以看出该算法是有效的，该算法不但可以自适应的给出最佳的聚类数，而且可以验证聚类的有效性，达到最佳聚类的目的。(In the original fcm algorithm based on the number of clusters on the algorithm and the weighted index m given c preferred method, and then out of the fcm parameter optimization adaptive algorithm, artificial data and data validation with real background can be seen that algorithm is effective, the adaptive algorithm can give the best clustering number, and can verify the validity of clustering, to achieve the best clustering purposes.)

The first scheme is based on the spatial locality of feature vectors corresponding to similar images. Learning is effected by modifying the query vector to incorporate the positive examples. The second scheme is based on “distorting” our view of the feature space. An new similarity distance between an image and the query is learned from the relevance feedback.