OQPSK

这是一个关于matlab的课件，里面有几个小例子，可供参考！(This is a study on matlab courseware, which has several small example is available for reference!)

数据记录的是在意大利某一地区同一区域上三种不同品种的酒的化学成分分析，这里利用svm进行分类(Data records is a region in Italy on the same area of three different varieties of wine chemical analysis, where the use of svm classification)

说明：  基于博弈论的信道选择与功率控制仿真，在20次迭代后达到纳什均衡(Channel selection and power control simulation based on game theory, after 20 iterations to reach the Nash equilibrium)

利用SVM做回归线性测试。对于大盘指数的有效预测可以从整体上观测股市的变化提供强有力的信息，所以对上证指数的预测很有意义。通过对上证指数从1990.12.20-2009.08.19每日的开盘数进行回归分析，最终拟合的结果是：均方误差MSE=2.35705 e-005，平方相关系数 R=99.9195 。SVM的拟合结果还是比较理想的。(Make use of SVM linear regression testing. For the market index can predict the observed changes in the stock market as a whole to provide a strong information, so it makes sense to predict the Shanghai index. Through the Shanghai Composite Index opened daily from 1990.12.20-2009.08.19 number of regression analysis, the results of the final fit are: mean square error MSE = 2.35705 e-005, the square of the correlation coefficient R = 99.9195 . SVM fitting result is quite satisfactory.)

实现Kalman滤波功能，帮助理解Kalman滤波算法的实现过程。(The realization of Kalman filtering, to help understanding the realization of Kalman filtering algorithm.)

remove noise from one noisy copy of image

波前曲扭和幅度变化对阵列测向中相干源分辨的影响(Twisted and curved wavefront amplitude variation to distinguish the impact of coherent source direction finding in an array)

DEOS Matlab InSAR tolbox version 1.1， 包含针对Doris产品的仿真、可视化脚本，以及去线性趋势、多视处理等。(It contains scripts for simulation, visualization of the products of Doris, and for additional processing (trend removal, multilooking, etc.).)

说明：  本程序用于配电网的网络重构，能有效、快速的对配电网做出重构，方便实用。(This program is used for the network reconstruction of the distribution network, which can effectively and quickly reconstruct the distribution network, which is convenient and practical.)

说明：  线性判别分析（LDA）：将高维的样本投影到最佳鉴别矢量空间，来达到抽取分类信息和压缩特种空间维数的效果，投影后保证样本在新的子空间有最大的类间距离和最小的类内距离。(Linear discriminant analysis (LDA): projects high-dimensional samples into the best discriminant vector space to extract classification information and compress the dimension of special space. After projection, it ensures that the samples have the maximum inter class distance and the minimum intra class distance in the new subspace.)