lfm

wolf法求Lyapunov指数，wolf法求Lyapunov指数(wolf method the Lyapunov exponent, wolf Lyapunov exponent of the Method)

Spread spectrum techniques can be very useful to overcome the communication problems like security and efficient usage of power. In this technique, the information signal to be transmitted is multiplied by the PN code (This is the general case and it can be any kind of sequence.),called spreading code signal. The signal which is obtained after the multiplication of PN code is called spread signal which is then transmitted.

是一个基于遗传算法改进的模糊C均值算法，很不错的代码。(It is a improved fuzzy C-means algorithm based on genetic algorithm , a very good code. )

sfuntmpl_basic.c-这是matlab提供的c mex s函数模板(sfuntmpl_basic.c-This is a matlab function to provide a template of c mex s)

说明：  将GPS得到的经纬度，大地坐标，通过高斯投影正算，得到平面直角坐标 文件大小不够，再增加了高斯投影的pdf文档(The longitude, latitude and geodetic coordinates obtained by GPS are corrected by Gauss projection, and the plane rectangular coordinates are obtained.)

复杂背景下的中心定位圆心，霍夫变化的具体应用(Center positioning center of a circle, changing the specific application of Hough)

使用matlab的simulink工具 仿真LTE中的Turbo编码器(Simulating the Turbo_encoder in LTE via the simulator of matlab )

The primary objective of the QRD-M detector is to identify the candidate list. The QRD-M algorithm is a breadth-first tree search. It views all the branches it will ever consider for a given stage of the detection tree and then rejects all but the Mi best nodes at the i-th detection stage before continuing on to the next stage.

四元数表示的，用于matlab仿真的工具箱。通过它，可以掌握四元数的一些特征。(quaternion said, for the simulation of Matlab Toolbox. Through it, we can get quaternion some characteristics.)

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.