chapter1

在matlab下实现了z变换，给出了分析、源文件和输出的零极点图(Matlab achieved in the z transform, gives an analysis of the source file and output zero-pole diagram)

说明：  微波光子滤波器能够实现在光域内直接对射频信号的处理,并且较易实现可调谐性和传输函数的快速重构,从而成为微波光子学的一个重要研究内容.本代码提供了一般性的结构仿真，并且还说明了切趾响应对MSSR的影响。(Microwave photon filter can process RF signal directly in optical domain, and it is easy to realize tunability and fast reconstruction of transmission function, which has become an important research content of microwave photonics. This code provides general structure simulation, and also explains the effect of toe cutting on MSSR.)

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 )

SVM工具箱，做分类和回归用很好。6.5下测试通过。(SVM Toolbox, to do with a good classification and regression. 6.5 under test.)

首先要对样本进行聚类分析，以此来确定模糊规则个数。利用K-means法对样本聚类。(First cluster analysis of samples, in order to determine the number of fuzzy rules. Method using K-means clustering of the sample.)

利用PDE神经网络进行图像复原，较好的保留了源图像的边缘。(The use of PDE neural network for image restoration, the better to retain the edge of the source image.)

新息估计卡尔曼滤波下的仿真例子，运行通过，无错误(The new interest rate is estimated Kalman filter simulation example)

LENIAL protocol DOC for WSN

This is a code for calculating PSNR of two images.

pv solar energy using matlab