Gauss

code for calculating zernike moments

gold code generation function

this work provides overview about control system using matlab-simulink tools

MATLAB快速入门学习资料，讲解通俗易懂，易于初学者学习(Study on MATLAB)

是学习鲁棒自适应控制的很好的例子,对进一步掌握matlab很有帮助.(learning robust adaptive control of the good examples for further grasp Matlab helpful.)

CHAPTER 4 (C4 Folder) TRANSFORMERS Project 1: Short-circuit and RL Load Terminations SIMULINK file s1a.mdl (linear transformer) SIMULINK file s1b.mdl (piece-wise linear saturation) SIMULINK file s1c.mdl (lookup table saturation) MATLAB M-file m1.m MATLAB M-file fftplot.m (fftplot.m should be placed in a directory on MATLAB s search path) Project 2: Open-Circuit Termination, In-rush Current, and DC Bias Core Saturation SIMULINK file s1c.mdl (lookup table saturation) MATLAB M-file m1.m Use the given FFTPLOT.m to obtain the discrete Fourier transform

边缘是指图像中像素灰度有阶跃变化或屋顶状变化的那些像素的集合。学会并掌握使用matlab进行图像的边缘检测，设计用户界面实现上述功能。 实际问题： 1、采用matlab Guide设计图像界面，可以读入并显示图像，设计实现以下功能的菜单，点击相应的菜单（如radio button）在图像界面上可以显示相应的功能。 2 使用sobel检测算子； 3 使用prewitt检测算子； 4 使用canny检测算子； (Edge is refers to the image pixel has a step change in the collection of those pixels or roof shape change. Learn and master using matlab for image edge detection, design the user interface to realize the function. The real problem: 1, Guide design based on matlab image interface, can be read and displayed image, design and implement the following function menu, click on the corresponding menu (such as a radio button) in the image on the interface can display the corresponding function. 2 use the sobel detection operator 3 use prewitt detection operator 4 use the canny detection operator )

matlab_7.0应用指南，比较完整的开发教程，节省很多时间(matlab_7.0 Application Guide, more complete development tutorial, save a lot of time)

fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!. Given the samples of a laplace distribution, the PDF parameter is found fits data to the probability of the form: p(x) = 1/(2*b)*exp(-abs(x-u)/b) with parameters: u,b format: result = fit_ML_laplace( x,hAx ) input: x - vector, samples with laplace 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 u,b - fitted parameters CRB_b - Cram?r-Rao Bound for the estimator value RMS - RMS error of the estimation type - ML

说明：  一个LDPC的例程使用matlab编写，可直接运行，信道编码程序(a LDPC the use of Matlab routines prepared to be directly run channel coding procedures)