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About correlation. In signal processing, cross-correlation is a measure of similarity of two waveforms as a function of a time-lag applied to one of them. This is also known as a sliding dot product or inner-product. It is commonly used to search a long duration signal for a shorter, known feature. It also has applications in pattern recognition, single particle analysis, electron tomographic averaging, cryptanalysis, and neurophysiology.

通过给出的两组图片以及对应的光照位置，计算图片的albedo以及图像的表面法向量和位置并显示，最后自动生成任意角度照射得到物体图像，并以视频的方式显示(Photometric stereo Albedo and Normal)

semiconductor fault limiter ( SFCL) in power system

NSC program calculates BER of NSC

It is the Simulated Annealing method coded by matlab and has a good results.

维纳滤波器的MATLAB源程序，处理高斯白噪声，对一维数据进行滤波。(Wiener filter MATLAB source, dealing with gaussian white noise, the one dimensional data filtering. )

matlab code for digital signal processing

也是利用最小二乘法对椭圆进行数据拟合，不过考虑到图像大小问题，这里只选取100个边缘点，点的个数也可以自行设置。(Ellipse is the least square method of data fitting, but taking into account the size of the image, here select only edge points 100, the number of points you can set your own.)

使用google gears离线技术来实现的一个小web程序(Use google gears offline techniques to achieve a small web application)

EM算法在神经网络中的应用，可以用来进行视频数据分类。(In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar-xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.)