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xinjianwenjian
雷诺方程的数值解法，采用列出矩阵的形式求解(Numerical Solution of Reynolds equation, using the matrix set out in the form of solving)
- 2008-12-23 22:42:20下载
- 积分：1
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CPP-Examples
THIS EXAMPLES CODE FOR C++
- 2014-10-22 22:32:55下载
- 积分：1
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bos
matlab的波束成型的程序,好不容易找到的,大家下载啊.(matlab beamforming of the procedure, easy to find and download U.S. ah.)
- 2007-09-09 21:28:20下载
- 积分：1
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llinux
数字水印在linux下面运行的代码，内有详细的注释说名(a watermarking program which can run under Linux. Attached with clear instructions.)
- 2005-04-25 19:11:14下载
- 积分：1
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COMTest2005_v0.8
在vc.net中调用Matlab生成的COM组建实现曲线拟合。(in vc.net Calling Matlab generated COM form and curve fitting.)
- 2007-04-26 04:28:35下载
- 积分：1
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work
基于DCT系数的自适应视频水印算法与实现与应用(DCT coefficients based Adaptive Video Watermarking Algorithm and Implementation)
- 2011-06-07 04:24:43下载
- 积分：1
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kl
说明： （1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象；
（2）将所有子图象按列相接变成一个81维的行向量；
（3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量；
（4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。
（5）求出所有子块的特征向量。
((1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image (2) all sub-images according to out-phase into a 81-dimensional row vector (3) all 200 lines for KL transform vector, derived its corresponding covariance matrix of eigenvectors and eigenvalues, in descending order by eigenvalue and the corresponding eigenvector (4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the PCA, the original image block to the 40 feature vectors on the projection, the projection coefficients obtained by this sub-block eigenvector. (5) calculated for all sub-block eigenvector.)
- 2007-08-07 18:04:13下载
- 积分：1
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draw
伪彩色图、等高线图、三维曲面图matlab例程。数据来自2011年全国大学生数模竞赛本科组A题。
(Pseudo-color map, contour, 3D surface chart matlab routine. Data the 2011 National Undergraduate Mathematical Contest in Modeling undergraduate group A title.)
- 2015-03-27 00:44:57下载
- 积分：1
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MATLAB-CPP
用实例演示如何在matlab中调用c++，适用于初学者，很好的例子(With examples demonstrate how to call c++ in matlab for beginners, a good example)
- 2014-01-07 14:04:04下载
- 积分：1
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EM_GM
％ EM algorithm for k multidimensional Gaussian mixture estimation
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％ Inputs:
％ X(n,d) - input data, n=number of observations, d=dimension of variable
％ k - maximum number of Gaussian components allowed
％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none)
％ maxiter - maximum number of iteration allowed ([] for none)
％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none)
％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none)
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％ Ouputs:
％ W(1,k) - estimated weights of GM
％ M(d,k) - estimated mean vectors of GM
％ V(d,d,k) - estimated covariance matrices of GM
％ L - log likelihood of estimates
％( EM algorithm for k multidimensional Gaussian mixture estimation Inputs: X (n, d)- input data, n = number of observations, d = dimension of variable k- maximum number of Gaussian components allowed ltol- percentage of the log likelihood difference between 2 iterations ([] for none) maxiter- maximum number of iteration allowed ([] for none) pflag- 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) Init- structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) Ouputs: W (1, k)- estimated weights of GM M (d, k)- estimated mean vectors of GM V (d, d, k)- estimated covariance matrices of GM L- log likelihood of estimates)
- 2008-04-27 15:51:27下载
- 积分：1
