-
Quantity_SNR_Compare
均匀量化和非均匀量化在性能上存在着显著的差异，这种差异可以用信号通过量化器后的量躁比来反映出来。本程序用曲线表示了理论和实际两个方面下两种量化的量躁比。从而可观察他们的性能上的差别。(There are significant differences with the function between Uniform quantization and non-uniform quantization ,which can be quantified by SNR from the quantizer.This program use the curves to denote the SNR of theory and practical signal.Then we can observe the performance of the differences.)
- 2009-11-10 15:55:41下载
- 积分：1
-
2DBVP
说明： 二维梁单元有限元分析，包中有详细的WORD文档说明(Two-dimensional finite element analysis of beam elements, including the documentation detailed WORD)
- 2011-03-18 09:43:28下载
- 积分：1
-
PLL(pdf)
锁相环的设计方法介绍（PLL），可作为设计的参考。(Design method for PLL (PLL), can be used as a reference design.)
- 2010-05-09 11:01:41下载
- 积分：1
-
chang-wei-fen-fangcheng
微分方程求解，利用matlab软件，解决微分方程领域各种问题(Solving differential equations using matlab software, field problems to solve differential equations)
- 2013-09-14 11:46:06下载
- 积分：1
-
ricker
创建雷克子波，可用于地震资料分析与反演等，Matlab环境。(Create ricker wavelet)
- 2009-07-08 20:52:15下载
- 积分：1
-
portfolio-code
markowitz 均值方差模型
Black-litterman 模型
resample模型
robust模型(markowitz mean-variance model
Black-litterman model
resample model
robust model)
- 2012-06-26 10:00:32下载
- 积分：1
-
chap2
先进PIDmatlab仿真数字pidmatlab仿真历程(PIDmatlab advanced digital simulation pidmatlab simulation process)
- 2009-04-29 17:37:44下载
- 积分：1
-
chap10
关于用遗传算法改进BP神经网络的matlab实现(转引)
ga优化神经网权值&阈值程序。
优化的基本原理和过程很多论文可以查到，在此不必赘述我就把用gaot5的小程序贴在下面吧，也是y=1/x(为看的方便，比较繁杂的也有) (About matlab improved BP neural network using genetic algorithm implementation (quoted) ga optimizing neural network weights & Threshold Program. Optimization of the basic principles and procedures can be found in many papers, this does not have to go into details, I' ll stick with gaot5 small program below it, is y = 1/x (for watching convenience, there are also more complicated))
- 2014-11-19 17:51:53下载
- 积分：1
-
LogitMul
基于逻辑回归的多分类问题，可以实现至少三类数据的分类(Classification based on multiple logistic regression, classification can be achieved at least three types of data)
- 2016-04-22 11:22:06下载
- 积分：1
-
dct
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 )
- 2010-06-13 18:43:39下载
- 积分：1
