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Direct3DCameraSample
- 一个实现了Direct3D的动态摄影机的例子 在这个例子中我先创建了一个Mesh模型 然后通过动态的给view矩阵赋值实现了像CS中的摄影机移动的效果 用户可以使用WASD平移摄影机 使用鼠标调整摄影机的焦点-An implementation of the Direct3D example of the dynamic camera in this example I first created a Mesh model and then to the view through the dyn
CSlearning
- CS理论里观测矩阵的优化算法以及训练稀疏字典的算法-optimization of measurement matrix and dictionary learning for sparse approximations
A-REMARK-ON-COMPRESSED-SENSING
- 一篇关于压缩感知的经典文章，压缩感知（Compressed sensing，简称CS，也称为Compressive sampling)理论异于近代奈奎斯特采样定理，它指出：利用随机观测矩阵可以把一个稀疏或可压缩的高维信号投影到低维空间上，然后再利用这些少量的投影通过解一个优化问题就可以以高概率重构原始稀疏信号，并且证明了这样的随机投影包含了原始稀疏信号的足够信息。-A classic article on compressed sensing, compressive sensing (Comp
25-8x8-LED
- 基于CS-51单片机实现的LED点阵实例小程序，实现简单数字显示-Based on CS-51 Microcontroller LED dot matrix applet instance, simple figures
kmeans
- function [L,C] = kmeans(X,k) KMEANS Cluster multivariate data using the k-means++ algorithm. [L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class label per column in X and a size(X,1)-by-k matrix C containing the centers
CS_OMP
- 使用OMP的CS重构算法，包含有lena图像。重构生成的图像质量由随机生成的重构矩阵决定-The use of OMP CS reconstruction algorithm, contains Lena image. Reconstruction image quality by the random generation of reconstruction matrix decision
CSlunwen
- 关于压缩感知理论的测量矩阵和重构算法分析！-Measurement matrix and reconstruction algorithm about CS
Wavelet_IRLS
- 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为ILRS算法，对256*256的lena图处理，比较原图和IRLS算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间-Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix
Wavelet_OMP
- 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为OMP算法，对256*256的lena图处理，比较原图和OMP算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间 -Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix
Wavelet_SP
- 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为SP算法，对256*256的lena图处理，比较原图和SP算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间-Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and
Wavelet_ROMP
- 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为ROMP算法，对256*256的lena图处理，比较原图和ROMP算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间 -Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matr
Wavelet_SL0
- 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为SL0算法，对256*256的lena图处理，比较原图和SL0算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间 -Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix
files
- 压缩感知的很简单的入门小例子，基矩阵为正弦基，能很好地重构出稀疏信号-A simple example for the introduction of CS theory, the basis matrix is sinosoidal matrix, which can fully reconstruct the sparse signal.
Short-duration-power_CS
- 根据压缩传感(Compressed Sensing，cs)N论，首次提出了短时电能质量扰动信号的压缩采样方法，该方法突破了奈奎斯特采样频率的限制，实现了低于奈奎斯特采样频率的低速率采样。文中对比分析了CS理论与传统采样理论，研究了cS短时电能质量信号压缩采样的实现方法，包括：测量矩阵的构建、稀疏基的选取和电能质量信号快速贝叶斯匹配追踪重构算法(FBMP)-Compressed sensing ( Compressed Sensing , cs ) N theory , first propose
LGME
- input: param: parameters of the LMGE algorithm param.mu, param.alpha, param.beta are regularization parameters. param.p: dimension of shared subspace param.k: number of nearest neighbors for Laplacian matrix X: input data Y: ground
App2
- 我是一名在读大学生，非CS专业的学生，只是因为自己感兴趣，所以踏上了学习java的路，现在还是处于初步阶段，这个代码是一段工业工程仿真模拟中的随机关系矩阵的产生 。-I was a university student in reading, non-CS majors, just because they are interested, so embarked on the road to learn java, now or at a preliminary stage, this code
ExtractBackground
- he files in this package comprise the Matlab implementation of a foreground segmentation algorithm based upon graph cuts, described in: Better Foreground Segmentation Through Graph Cuts, N. Howe & A. Deschamps. Tech report, http://arxiv.org/
Compression-perception-theory-
- 压缩感知理论及其研究进展,文综述了cs理论框架及关键技术问题，并着重介绍了信号稀疏变换、观测矩阵设计和重构算法三个方面的最新进展，是一篇综述。-Compression perception theory and research progress, cs paper reviews the theoretical framework and key technical issues and focuses on the latest developments signal sparse tran
TQ043TSCM_VO.1_40P
- TQ043TSCM is a transmissive type col or active matrix li quid crystal display (LCD) whi ch uses amorphous thin film transistor (TFT) as switching devi ces. Thi s product is composed of TFT LCD panel, driver I Cs, FPC and a backlight uni t. The
P3
- 本程序可以绘制p3曲线，直接输入cv、cs、EX,也通过降雨或径流数据计算绘制 [cs,cv,ma] = p3plot(a,kk,b,bb,y1,cs_cv,ma,cs,cv,x0) a=20 横向网格条数 kk=200 纵坐标标注间隔（间隔要是最大值和最小值差的整数倍） b=2000 纵坐标最大值 bb=0 纵坐标最小值 y1=[] 降雨量，横向矩阵输入,如果没有请输入[] cs_cv=[2] 没有，请输入[] ma=[] 年平均降雨量，没有输