SAR-Sign-alPProcessinMatlab

这里是初学者在学习使用MATLAB过程中会经常遇到的问题。(Here are a beginner in learning to use MATLAB process will often encounter problems.)

说明：  duda模式分类第二版的分类器工具箱，源代码(duda pattern classification the second edition of the classifier toolbox, source code)

协作通信中，运用放大转发的误码率分析。运用最大比合并(Collaborative communication, the use of forward error rate analysis of amplification. The use of maximum ratio combining)

关于Vega Prime的12篇期刊论文，涉及VP多场景切换、坐标系、多通道、飞行视景仿真等问题，有相当的参考价值。(About Vega Prime' s 12 journal articles, and more scenes involving VP switching coordinates, multi-channel, flight visual simulation and other issues, there is considerable reference value.)

灰度共生矩阵提取纹理特征可用于图像纹理特征提取(GLCM texture)

These pages introduce a MATLAB Graphical User Interface (GUI) for control of several Blind Source Separation (BSS) algorithms. The GUI enables to work with input and output data, multiple signals plot and saving output variables to the base workspace of Matlab or to a file. The GUI also provides the Monte-Carlo Analysis to validate features of the algorithms ivolved.

说明：  本程序用matlab实现了求不规则图形的最小外接矩形和最大内接矩形，实验用的图形也附在了文件中，可直接运行(This matlab program graphical the irregular circum-rectangle minimum and maximum inscribed with rectangular, experiment graphics attached the documents and can be directly run)

Source Codes for the Matlab help files

不错的pso源程序，非常适合初学者学习。(Pso good source, very suitable for beginners to learn.)

Smooths a matrix (with/without NaN s) via recursive moving average method and eliminates data gaps. (MOVING_AVERAGE(X,F) smooths the vector data X with a boxcar window of size 2F+1, i.e., by means of averaging each element with the F elements at his right and the F elements at his left. The extreme elements are also averaged but with less data, obviously. Leaving the edges intact. The method is really fast. MOVING_AVERAGE2(X,M,N) smooths the matrix X with a boxcar window of size (2M+1)x(2N+1), i.e., by means of averaging each element with its surrounding elements that fits in the mentioned box centered on it. This one is also really fast. The elements at the edges are averaged too, but the corners are left intact. NANMOVING_AVERAGE(X,F) or NANMOVING_AVERAGE(X,F,1) accept NaN s elements in the vector X the latter interpolates also those NaN s elements surrounded by numeric elements. NANMOVING_AVERAGE2(X,M,N) or NANMOVING_AVERAGE2(X,M,N,1) accept elements NaN s in the matrix X the latter interpolates also those NaN elements surrounded by numeric elements.)