MIMO_UWB

MATLAB6.0数学手册，一本很好的工具书(MATLAB6.0 Mathematics Handbook)

上一个的继续,连续有九个压缩文件,大家可以打开解压后自己慢慢享用,这个都是本人调试通过了的,希望共同进步!(A continuation of a row has nine compressed file, everyone can open their own after extracting slowly to enjoy, this is my debug adopted, and I hope common progress!)

说明：  用matlab绘制bezier曲线，逼近一个n形状。(Matlab draw with Bezier curve, approaching a n shape.)

particle swarm optimization

通过matlab读取文件中的视频，通过代码读取后并保存至盘中(Video read and saved to disk)

说明：  用matlab实现信号系统及系统响应，采取界面编程，功能有时域采样分析，卷积验证，启动相应(Using matlab signal system and the system response, to take programming interface, the functional domain sampling and analysis in some cases, convolution verify, start the corresponding)

MATLAB R2007基础教程 对于matlab的基础介绍(Essentials of MATLAB R2007)

基于rosenstein法的混沌时间序列最大利亚普诺夫指数算法(Method based on chaotic time series rosenstein largest Liapunov exponent algorithm)

基于matlab的OFDM仿真程序； 我再论坛找了很多OFDM的代码和文章,但总有遗患,为什么呢原因是大部分只有简单的映射,IDFTDFT变换等等,但主要的同步和信道估计去没有人提出来,甚至做出来.. 现在我终于找到了一份,它以FUNCTION的形式,再现了OFDM映射,IDFTDFT,同步(时间频率同步)和信道估计,以及频率偏移的估计等一系列代码.(Based on the OFDM matlab simulation program I' ll find a lot of forums and articles OFDM code, but there are left suffering, why because most only simple mapping, IDFTDFT transformation, etc., but the main synchronization and channel estimation to no one proposed, and even do it .. and now I finally found a copy of it in the form FUNCTION, Reproduction of OFDM mapping, IDFTDFT, synchronous (time and frequency synchronization) and channel estimation, and the estimated frequency offset a series of code.)

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.