bp

fit_ML_normal - Maximum Likelihood fit of the normal distribution of i.i.d. samples!. Given the samples of a normal distribution, the PDF parameter is found fits data to the probability of the form: p(r) = sqrt(1/2/pi/sig^2)*exp(-((r-u)^2)/(2*sig^2)) with parameters: u,sig^2 format: result = fit_ML_normal( x,hAx ) input: x - vector, samples with normal distribution to be parameterized hAx - handle of an axis, on which the fitted distribution is plotted if h is given empty, a figure is created. output: result - structure with the fields sig^2,u - fitted parameters CRB_sig2,CRB_u - Cram?r-Rao Bound for the estimator value RMS - RMS error of the estimation type - ML

This MATLAB .m file takes a "true" image, and an "observed" image that is the true image that has been noised with uncorrelated Gaussian noise. The function then performs Tikhonov regularization to clean up the defects in the image, with the Tikhonov term used to preserve image edges as much as possible.

matlab code for genreration of QPSK and demodulation

三种信号进行EMD分解，IMF分量绘图 进行比较显示(Three signals for EMD decomposition, IMF component drawing comparison shows)

无线传感器网络 分簇网络拓扑 leach算法改进，能量非均衡节点 (Wireless sensor network clustering algorithm for network topology leach improved energy non-equilibrium node)

说明：  非线性滤波器信息泄漏的关键问题研究及其说明(Nonlinear filter information disclosure and description of the key issues)

求点d1到直线d2-d3的距离，并画线。范例： [d dl]=find_dl([2 1],[8 3],[4 7]) 则 dl=[6 5]。(Example: [d dl] = find_dl ([2 1], [8 3], [4 7]) if dl = [6 5].)

decisor using energy and zero cross

model maintain the level of water at the pump station

The p erceptro n algo rithm ceases to update the para meters only when all the training images ar e classified corr e ctly (no mistakes, no update s). So, if the training images are p ossible to clas sify co rrectly with a linear c la ss ifier, will the p erceptr on al gorithm find such a classifier? Yes , it does, and it w ill co nverge to such a classifier in a finite numb er of updates (mistakes ). We’ll sho w this in lectur e 2.