convolution_code

machine similink model

MATLAB2010的教程，电子书籍，很详细(MATLAB2010 tutorials, e-books, very detailed)

此为模式识别中的Fisher线性判别方法求分类器。进行了男、女错误率和总错误率的统计。 全部程序流程如下： 1、读取FAMALE.TXT文件把身高或体重给数组，并求x1的样本均值m1和内离散度S1； 2、读取MALE.TXT文件把身高或体重给数组，并求x2的样本均值m2和内离散度S2； 3、求最佳变换向量和阀值点； 4、读取Test2.txt文件把对应的身高或体重给数组A并求A的样本数M； 5、把最佳变换向量和阀值点、x代入判别方程g（x）； 6、对本判别的错误率进行统计。(This is the pattern recognition method, to calculate the Fisher linear discriminant classifier .Were male , female and total error rate error rate statistics . All program flow is as follows : 1 , read FAMALE.TXT file to height or weight to the array, and find the sample mean m1 x1 and internal dispersion S1 2 , read MALE.TXT file to height or weight to the array, and find the sample mean m2 and x2 within the dispersion S2 3 , for the best transformation vector and the threshold point 4 , read Test2.txt file to the corresponding height or weight requirements for the array A and A is the number of samples M 5 , the best transformation vector and the threshold point , x substituted into the discriminant equation g (x) 6 , determine the error rate of the statistics . )

该文件包括六个程序,分别是在matlab环境下运行,绘制三维的TE模式下的Ex,Ey,Hz和TM模式下的Hx,Hy,Ez非常好.(this package included six software for TE(Ex,Ey,Hz) and TM(Hx,Hy,Ez))

基于matlab的粗糙集工具箱，与网上可查的“matlab的粗糙集工具箱的设计”论文的软件(Matlab toolbox based on rough sets, and online to check the " matlab toolbox rough set design" paper software)

Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999.[1] Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving. The algorithm is patented in the US the owner is the University of British Columbia.

matlab实现的D-S证据理论代码实现(the matlab implementation DS evidence theory code to achieve)

很好的一个打开电话薄的例子,欢迎大家下载(very good.windows mobile book)

by using optisystem filter design

Improving compact gravity inversion using new weighting functions