整理好的压缩感知的信号处理应用MATLAB环境源码

这是matlab编写的Logistic Discrimination 和 KNN分类器代码。这两个算法的实现参考了《Introduction to Machine Learning》。 除此在代码中还包含了调用matlab自带的libsvm的例程。rumLogisticDiscrimination， runKnn， runSvm分别对这3个算法在数据集liver_train_data上的分类准确度进行测试。测试结果在code report.doc 中有简要描述。(This code implements the Logistic Discrimination and KNN classifier. The implementation is according to the text book <<Introduction to Machine Learning>>. We have also compared the classification accuracy among Logistic Discrimination, KNN and SVM on data set liver_train_data. A brief test report is included as well. Enjoy :).)

mexConnect函数详细介绍，Matlab与STK连接函数库(mexConnect function in detail, Matlab and STK connection library)

在MATLAB水印随基序列生成嵌如到图象中 不错的源码 很好(In MATLAB watermark generated with the base sequence such as embedded to the image of good source is very good)

角点检测程序源码，使用的是HARRIS角点提取方法，效果很好。(Corner detection program source code, using extraction methods HARRIS corner, very good.)

MATLAB code with the creation of analog communication modulation techniques

信号处理中的旋转不变子空间法，包含了阵列信号处理的常见算法，与理论分析结果相比，包含收发两个客户端的链路级通信程序,最小均方误差（MMSE）的算法，MIMO OFDM matlab仿真。

说明：  绘制GPS导航系统中卫星轨道的仿真程序，采用MATLAB开发工具编写(Mapping GPS Navigation System satellite orbit simulation program using MATLAB development tools to prepare)

图像特征提取 特征提取 matlab 特征提取 图像特征 (Image feature extraction feature extraction feature extraction matlab image features)

说明：  通过分析交通流量时间序列的特点，引入BP神经网络进行短时交通流预测。首先，分析了短时交通流量预测的意义及研究背景；然后，介绍了BP神经网络的结构模型、学习规则以及BP算法的改进算法；最后，通过BP神经网络对短时交通流进行预测，并分析了在各种不同条件下的预测情况。(through the analysis of the characteristics of traffic flow time series, introduces BP neural network for short-term traffic flow prediction. First of all, it analyzes the significance of short-term traffic flow prediction and research background. And then, it has discussed the model and structure, the study rule and the improved algorithm of BP neural network. Finally, by BP neural network to forecast the short-term traffic flow, it analyzes under different conditions of the neural network prediction.)

Nonlinear Total Variation based noise removal algorithms. Based on: L. Rudin, S. Osher, E. Fatemi