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本程序包括了信道编码的一些仿真程序以及一些关于信道编码的介绍，并对仿真结果进行分析(The program includes a number of channel coding simulation program, and some on the introduction of channel coding, and analysis of simulation results)

众多恒虚警检测（CFAR）算法都是基于单元平均（CA:Cell Avarage）的方法，该算法需要较大的运算量。本程序给出了基于有序统计（OS:Order Statistic）的CFAR检测算法，弥补了CA_CFAR检测算法的不足，同时还配有本人硕士毕业论文中的检测图像以及实验结果，具有一定的参考价值。(CA_CFAR algorithm often requires large computational complexity. OS_CFAR algorithm is presented, which can make up the disadvantages in CA_CFAR. In the meantime, some processed images are given, which has a high value of reference.)

this is a matlab code for webinar.

CDMA2000下行链路仿真模块的matlab实现代码(CDMA2000 downlink simulation module)

GNSS软件接收机，源自《软件定义的GPS和伽利略接收机》书中配套程序，做过修改，在原有程序框架下使用了2阶锁频环辅助3阶锁相环(The GNSS software receiver, derived from GPS and Galileo receiver of software defined supporting program in the book, modification, the original application framework use 2 order frequency locked loop aided third-order phase-locked loop )

pca matlab pca matlab

说明：  一种自适应滑模控制器的MATLAB设计程序(An adaptive sliding mode controller design of the MATLAB program)

2010 数学建模大赛 赛前matlab培训4(MATLAB programming topics lecture 4.zip)

基于matlab GUI界面设计,可以动态调节运行环境的参数，详细画出了时域和频域的相关图，一种流形学习算法（很好用），结合PCA的尺度不变特征变换(SIFT)算法，用于建立主成分分析模型，实现了对10个数字音的识别程。( Based on matlab GUI interface design, Can dynamically adjust the parameters of the operating environment, Correlation diagram shown in detail the time domain and frequency domain, A fluid manifold learning algorithm (good use), Combined with PCA scale invariant feature transform (SIFT) algorithm, Principal component analysis model for establishing, Realization of 10 digital audio recognition progra.)

he files in this package comprise the Matlab implementation of a foreground segmentation algorithm based upon graph cuts, described in: Better Foreground Segmentation Through Graph Cuts, N. Howe & A. Deschamps. Tech report, http://arxiv.org/abs/cs.CV/0401017. The file extractForeground.m contains a sample function that will perform a complete foreground segmentation for static camera video. It uses a number of parameters, which are described in the documentation. An attempt was made to choose sensible default values, but they may need to be adjusted for some videos. More generally, the implementation in extractForeground.m is only a suggestion of how the graph-based segmentation might be used. The same approach can be applied with other (perhaps time-adaptive) background models. The key step is at line 113 of the code, once the deviation array has been created. The graph-cut segmentation can be applied similarly to a deviation matrix computed in any other way.