BPSK_baseband_simulink

本程序仿真了正交频分复用的调制与发射，其中包括循环前缀，FFT变换，白噪声信道模拟等部分，最后得出不同信噪比先的系统误码率。(This program simulates the orthogonal frequency division multiplexing modulation with the launch, including the cyclic prefix, FFT, white noise channel analog part of the conclusion that a different signal to noise ratio before the BER.)

图像颜色空间转换，包括RGB、YUV、YCbCr等色彩空间分量之间的变换(Image color space conversion, including conversion between RGB, YUV, YCbCr color space component)

针对条纹图中的随机散斑噪声，编制旋滤波程序，能够去除散斑噪声的影响，同时不损害条纹信息。(Fringe pattern for the random speckle noise, the preparation of spin filtering procedures to remove the speckle noise, while not jeopardizing the fringe information.)

3D Radon and its inverse

说明：  matlab语言的人工蜂群算法，包括最初版本和改进版两版(Matlab language artificial bee colony algorithm, including the initial version and the improved version)

INTRODUCCIÓ N AL USO DE MATLAB SCIENTIFIC WORK PLACE, MATHEMATICS Y SCILAB

图像平滑算法及比较分析 (Image smoothing algorithm and a comparative analysis of)

在3d不光滑曲面上的射线跟踪算法，效果比较好，希望可以帮助到大家(3d surface is not smooth in the ray-tracing algorithm, the effect is good, hope you can help to)

k-d树的matlab程序，包含了KDTREE, KDTREEIDX and KDRANGEQUERY functions。提供了两个Demo scripts(kdtree_demo.m & kdrange_demo.m),很实用( This distribution contains the KDTREE, KDTREEIDX and KDRANGEQUERY functions. Included are the source code files in kdtree/src, as well as precompiled mex files for the following systems: - Mac OS X (Matlab 6.5) - Redhat Linux (Matlab 6.5) - Windows (Matlab 6.1) Two demo scripts are provided (kdtree_demo.m & kdrange_demo.m). The new features added June 2004: + You can now use N dimensional data points, where N>3. + Use KDTREEIND to return the index value to the closest point, instead of the closest point values. + KDRANGEQUERY can now return these index values as well. Written by/send comments or suggestions to : Guy Shechter guy at jhu dot edu June 2004 )

Canny edge detector algorithm matlab codes This part gives the algorithm of Canny edge detector. The outputs are six subfigures shown in the same figure: • Subfigure 1: The initial "lena" • Subfigure 2: Edge detection along X-axis direction • Subfigure 3: Edge detection along Y-axis direction • Subfigure 4: The Norm of the image gradient • Subfigure 5: The Norm of the gradient after thresholding • Subfigure 6: The edges detected by thinning