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比较实用的MATLAB 程序 需要的可以下载哦(The practical needs of the MATLAB program can be downloaded Oh)

matlab in power system

5/10/15/20阶的语音信号LPC 预测及其功率谱，并绘制误差函数图像(5/10/15/20 LPC prediction order of the voice signal and the power spectrum, and the mapping of the image error function)

这是基于matlab的一个用于线路分析计算的程序(This is based on a matlab for line analysis process)

本程序用来计算matlab中两点坐标之间的距离，坐标必须是经纬度，并表明所要计算的单位(This procedure used to calculate the matlab in the distance between two coordinates, latitude and longitude coordinates must be, and show that to calculate the unit)

KDE for segmentation such as how to locate the person from the envrionment(KDE for segmentation)

信号处理源码，包含常用的各种滤波器，功率谱计算，Z变换，线性卷积等，Matlab实现。(Signal processing source code that contains a variety of commonly used filters, power spectrum calculation, Z transforms, linear convolution and so on, Matlab implementation.)

sc-fdma系统的2X1mimo采用STBC编码方式高斯白噪声 (sc-fdma system with STBC encoding 2X1mimo white Gaussian noise)

EZW (Embedded Zerotrees of Wavelet Transforms) is a lossy image compression algorithm. At low bit rates (i.e. high compression ratios) most of the coefficients produced by a subband transform (such as the wavelet transform) will be zero, or very close to zero. This occurs because "real world" images tend to contain mostly low frequency information (highly correlated). However where high frequency information does occur (such as edges in the image) this is particularly important in terms of human perception of the image quality, and thus must be represented accurately in any high quality coding scheme.

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.