ravesh_1

All of these matlab files deal with SFBC/STBC. i,e transmit diversity. Additionally, 2x2 ZF channel estimation is demonstrated. Also MMSE channel estimation in Rayleigh fading environment is demonstrated.

Bayesian classification experimentandA Bayesian formulation for large data pattern recognition

一个用于到达角估计的MUSIC程序，用MATLAB写的，希望对大家有用(an estimate for the arrival of MUSIC procedures, using MATLAB written in the hope that it may be useful to)

关于最优化方法的Matlab仿真程序，线搜索技术。(Matlab simulation program optimization method, line search technique.)

道路检测中的SVM方法，供研习者学习与参考。(Road detection SVM method for learning to learn and reference.)

nodes movement in ad hoc networks

matlab下傅里叶变换的程序例程，是pdf的(under the Fourier transform matlab routine procedure is the pdf)

计算正弦信号加噪声的相关函数。系数b 决定余弦相关函数在噪声指数型相关函数中的“比重”。T为正弦信号周期。正弦信号加相关噪声的相关函数图像为一个指数函数和一个余弦函数叠加的波形。(Calculated sinusoidal signal plus noise correlation function. Coefficient b determine the cosine correlation function in the noise correlation function of exponential type in " proportion." T for the sinusoidal signal cycle. Sinusoidal signal plus noise correlation function related to the image as an index function and a cosine function superimposed waveform.)

双闭环直流脉宽调速系统仿真，采用外环为转速环，内环为电流环(Double Loop DC PWM System Simulation)

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.