median

ofdm code sample codes including mimo

与潘左击鼠标拖动，绘制一个右键拖动zoombox，并检视双击所有数据。这也保留任何现有的WindowButtonXFcn这个数字已，以及任何轴UIContextMenus。 我写了实时数据采集这一点，因为linkaxes套XLimMode至“手动“ -这意味着将不会自动调整轴的新数据。 panzoom（figureHandle，axesHandles） 添加到这个数字，只能到指定的坐标轴功能的处理。 panzoom（figureHandle，axesHandles，XLink的） panzoom（figureHandle，axesHandles，XLink的，雁联） 假设你有四个轴处理：1，2，3，4。你可以连接所有的X轴一起：XLink的= [1，2，3，4]，或者你可以对链接在一个单元格数组中：XLink的={[1，2][3，4]}。这同样适用于雁联。 XLink的或雁联也可以为空。(Pan with left click mouse drag, draw a zoombox with right click drag, and view all data with double-click. This also retains any existing WindowButtonXFcn the figure has, as well as any axes UIContextMenus. I wrote this for live data capture, as linkaxes sets XLimMode to "manual"- which means the axes wouldn t automatically resize for new data. panzoom(figureHandle, axesHandles) Adds the functionality to that figure, ONLY to the axes handles specified. panzoom(figureHandle, axesHandles, xLink) panzoom(figureHandle, axesHandles, xLink, yLink) Assuming you have four axes handles: 1, 2, 3, 4. You could link all of the x axes together: xLink = [1, 2, 3, 4], or you could link them in pairs in a cell array: xLink = {[1, 2], [3, 4]}. The same applies for yLink. xLink or yLink can also be empty.)

PFUKFEKF三种滤波算法的对比比较，有图(The PF UKF is the EKF comparison of the three filtering algorithm, Fig.)

卡尔曼滤波程序实现，非线性卡尔曼滤波，matlab程序(Nonlinear Kalman filtering, matlab program)

this is face for matlab implementation yale data sets

说明：  matlab写的扫雷小游戏,有兴趣的可以试玩一下。(matlab write mine game, are interested in you can try.)

Gillespie Stochastic Simulation Algorithm

Compressive sensing SuOrEM code

设计一维纳滤波器 (1)产生三组观测数据：首先根据s(n)=a s(n-1)+w(n)产生信号s(n)，将其加噪（信噪比分别为20dB,10 dB，6 dB），得到观测数据x1(n), x2(n), x31(n). (2)估计xi(n)，i=1,2,3的AR模型参数。假设信号长度为L，AR模型阶数为N，分析实验结果，并讨论改变L,N对实验结果的影响。 (A Wiener filter design (1) to produce three sets of observations: first generating a signal s (n) according to s (n) = as (n-1)+w (n), to add noise (20dB SNR, respectively , 10 dB, 6 dB), to obtain observational data x1 (n), x2 (n), x31 (n). (2) estimate xi (n), i = AR model parameters 1,2,3. Assuming the signal length L, AR model of order N, analysis of experimental results and discuss the impact changing L, N of the experimental results.)

这是一个基于马尔科夫链的蒙特卡罗历程，可以帮助初学者理解mcmc(based on a Markov chain Monte Carlo process, can help beginners understand MCMC )