GPSmatlab

gaussain pulse propagation in simple medium- Generates movies

代码已经整合到文档，直接copy到matlab命令行，运行即可。用阈值分割、形态学处理等解决了很多很难分割的图像。(The code has been integrated into the document, direct copy to the Matlab command line can be run. Thresholding and morphological processing to solve many difficult to split the image.)

线性调频信号的分数阶傅里叶变换，完整可用。分数阶傅里叶变换采用Pei采样算法，在傅里叶域呈现宽带特性的线性调频信号，在分数阶域呈现窄带特征。(The fractional Fourier transform of linear frequency modulation signal, available. fractional Fourier transform using Pei s sampling algorithm, in Fourier domain present broadband characteristics of linear frequency modulation signal, the fractional domain present narrowband feature. )

二自由度四分之一车模型，matlab/simulink(Two degrees of freedom quarter car model, matlab/simulink)

MATLAB通信仿真pdg教程MATLAB Tutorial Communication Simulation pdg(MATLAB Simulation of Communication pdg Tutorial MATLAB Tutorial Communication Simulation pdg)

不用交织的turbo码 很好用 自己试过的 应该会有所帮助的(this is a matlab programme zbout turbo code!you can use it comfortably!)

光伏电池的最大功率点跟踪simulink模型 (PV maximum power point tracking simulink model)

M=512 原图像长度 N=64 K=8 I=zeros(M,M) J=zeros(N,N) BLOCK=zeros(K,K) 显示原图像 I=imread( f:ceshiyuanshitu.bmp ) figure imshow(I) xlabel( 原始图像 ) 显示水印图像 J=imread( f:ceshilogistic_nuaa64.bmp ) figure imshow(J) xlabel( 水印图像 ) 嵌入水印 tem=1 for p=1:N for q=1:N x=(p-1)*K+1 y=(q-1)*K+1 BLOCK=I(x:x+K-1,y:y+K-1) BLOCK=dct2(BLOCK) if J(p,q)==0 a=-1 else a=1 end BLOCK(2,1)=BLOCK(2,1)*(1+a*0.01) BLOCK=idct2(BLOCK) I(x:x+K-1,y:y+K-1)=BLOCK end end 显示嵌入水印后的图像 figure imshow(I) xlabel( 嵌入水印后的图像 ) imwrite(I, f:ceshiembedded.bmp )

这是一个经典的形变模型实施,在一个单一的文件用简单的可以理解的代码。 功能包括两部分一个简单的弱分类器和一个促进部分: 弱分类器试图找到最佳阈值的数据维数对数据进行分离成两个阶级1和1 要求的进一步提高分类器部分迭代,每一步是变化分类权重miss-classified例子。这造成了一连串的“弱分类器”,表现得像一个“强大分类器” (This a classic AdaBoost implementation, in one single file with easy understandable code. The function consist of two parts a simple weak classifier and a boosting part: The weak classifier tries to find the best threshold in one of the data dimensions to separate the data into two classes-1 and 1 The boosting part calls the classifier iteratively, after every classification step it changes the weights of miss-classified examples. This creates a cascade of "weak classifiers" which behaves like a "strong classifier" . )

关于boost库的开发，使用的知道教程。里面的内容很详细，说明都有，适合新手使用。(Know about the boost library development, the use of the tutorial.The inside of the content is very detailed description, all have, suitable for beginners to use.)