Projects

matlab课件，很好的，已经看过了，非常不错，大家想学的看看(matlab)

无线信道多径传播特性仿真实验，用MATLAB编程仿真，分析无线信道多径传播特性(Radio channel multipath propagation simulation, simulation using MATLAB programming, analyzing radio channel multipath propagation characteristics)

本代码实现了三种直方图相关的算法，分别为直方图均衡化，直方图规定化，对比受限自适应直方图均衡化。三种方法放在3个不同的文件夹下，直接运行test文件，就可以得到运行结果(This code implements three histogram algorithm, respectively histogram equalization, histogram provides contrast limited adaptive histogram equalization. Three methods on three different folder, run directly test file, you can get the results)

功能描述：用matlab语言实现LMS自适应算法 函数名：LMS_Algo 输入参数： (1)M:滤波器的阶数 (2)N:LMS算法迭代的次数 (3)mu:自适应参数 (4)xn:LMS算法的输入序列 输出参数： (1)系数矢量A (Function Description: The LMS adaptive algorithm matlab language function name: LMS_Algo Input parameters: (1) M: the order of filter (2) N: LMS algorithm the number of iterations (3 ) mu: adaptive parameters (4) xn: LMS algorithm for the input sequence Output parameters: (1) coefficient vector A )

Allow to read UFF file (all format) in matlab

Emd分解，通过峭度和相关系数选择IMF，进行信号重构，小波分解，小波包分解。(Emd decomposition, selecting IMF by kurtosis and correlation coefficient, signal reconstruction, wavelet decomposition, wavelet packet decomposition.)

code based on upsampling

BPSK载频估计，用它的平方谱和四次方谱进行估计，能够在0dB情况下完成有效估计(BPSK carrier frequency estimation, with its squared spectrum and quartic spectrum estimation can be completed in the 0dB case valid estimation)

蚁群算法用于TSP问题的matlab程序源代码(Ant colony algorithm for TSP problem of matlab source code)

网上能下载的本书的中文版均缺少89和97两页，又此书已绝版，本人只好从英文完整版中摘录缺少的内容，分别补全了这两页的内容。注：英文完整版也已经作为PDF的附件加了进去。 我也上传到了IASK，不能下载的朋友可以去搜索“(中文版)支持向量机导论.rar”(Internet can download the Chinese version of the book were missing two 89 and 97, and the book is out of print, I had to excerpts missing content from the English full version, respectively, to complement the content of the two pages. Note: The full version of English has also been added as an attachment inside a PDF. I also uploaded to the IASK, can not download friends can go search for "(Chinese version) Introduction to Support Vector Machines. Rar")