IHT

掌握和精通matlab之gui设计 共75页 配有相关的程序代码(Grasp and mastery of program code matlab gui design with a total of 75 related)

ainsi permettre la simplification du calcul ultérieur des courants et tensions dans une partie de celui-ci. La méthode de Norton abouti invariablement au circuit suivant soit une source de courant, une résistance parallèle (correspond à la Ri de la source) et une résistance de charge. La résistance de charge est une des

这是基于小波变换的数字水印代码，在嵌入的水印加上一个pn尾随机序列(This is based on the wavelet transform digital watermark code, the embedded watermark with a random sequence late pn)

sboyer收集的matlab工具箱下载地址。(sboyer collection of Matlab Toolbox download locations.)

matlab code log MLE extreme values

显示进程列表，PID等相关信息，是初识windows进程编程的好材料(list shows the process, PID, and other relevant information, recognize the windows of the programming process good materials)

μ律（美国标准，μ=255）pcm，包括编码、解码过程(μ-law (American Standard, μ = 255) pcm, including encoding, decoding)

该演示中包含的代码演示如何氡相似的功能，可以用来提高（以及部分）在Connectome电磁图像单元格边界。 请举出下列文件如果您发现此代码有用： Ritwik库马尔，阿梅里奥五雷纳和Hanspeter Pfister说“氡样的特点及其应用Connectomics”，接受，IEEE计算机学会研讨会在生物医学图像分析（MMBIA）2010年数学方法 http://seas.harvard.edu/〜 rkkumar radonLikeFeaturesDemo.m：演示边缘增强用氡相似的功能， getEdgeFeatures.m：助手为高斯函数的二阶导数滤波器 makeBarFilters.m：助手为高斯函数的二阶导数滤波器 sample.png：示例图像在此演示使用(The demo included in the code demonstrates how Radon-Like Features can be used to enhance (and segment) cell boundaries in Connectome EM images. Please cite the following paper if you find this code useful: Ritwik Kumar, Amelio V. Reina & Hanspeter Pfister, “Radon-Like Features and their Application to Connectomics”, accepted, IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA) 2010 http://seas.harvard.edu/~rkkumar radonLikeFeaturesDemo.m : Demonstrates edge enhancement using Radon-Like Features getEdgeFeatures.m : Helper function for Gaussian Second Derivative Filters makeBarFilters.m : Helper function for Gaussian Second Derivative Filters sample.png : Sample image for use in this demo )

Motion Tracking =============== This tarball contains all code required to run the tracking algorithm on a sequence of images. Run the file run_tracker.m in Matlab and follow the instructions. You will need to have a directory of sequentially numbered images available. After entering the path and file types the tracker will begin processing. Once the data window appears the algorithm begins building a background model and attempts to track objects. By clicking on any of the four subwindows you can investigate the background representation (a Mixture of Gaussians) of any pixel. The two windows that then appear display the mixture once as a two-dimensional scatter plot (ignoring the blue colour component), and once as a one-dimensional evolution of the red colour component only. These plots make the internal processing visible and should help determining suitable parameters to be set in mixture_parameters.m.

matlab的pso工具箱 很好用 直接不用编程序了 只需要修改一下函数就可以(The good toolbox matlab pso not directly programmed with the only change it can function)