heliuchaojihe

基于matlab的类似于iphone上tom猫(Based on the MATLAB is similar to iPhone tom cat )

说明：  MATLAB帮助,很好的初学者教程，很有用(MATLAb help，it is very usefull)

这是一个用来进行小波分析的工具包，他是美国斯坦福大学的一些人编的，对于做图像的人非常有用。(This is a tool used for wavelet analysis package, he is a Stanford University compiled a number of people, for people who make images very useful.)

calculates positional/rotational sensitivity of an

采用matlab调用HFSS来作图的程序(Using matlab to plot program called HFSS)

matlab环境下的张正友法标定，可以获得相机的内参外参(Zhang Zhengyou calibration method under matlab environment, you can get the camera' s internal reference external parameters)

这个是一个很好的程序，里面的参数可以修改一下(This is a very good program, which you can modify the parameters)

matlab入门手册，能在短时间内学会基本指令的使用。使用方便，适合初学者(matlab classic instruction set, easy to use query, use namely the investigation, can be started quickly.)

实现了 SVM 在医学图像分类中的应用，通过医学图像的实验结果验证了该方法的有效性。 对 SVM 算法进行了深入研究，结合量子行为的粒子群算法的优点，提出了一种量子行为的粒子群算法和 SVM 结合的混合分类模型，应用于医学图像的分类中。 深入研究了 K-means 算法，结合了无监督聚类和监督分类方法的优点，提出了一种 K-means 和 SVM 结合的混合分类模型，并应用于医学图像的分类中。 利用粒子群和量子行为的粒子群算法对 K-means 算法进行了改进，并将改进的K-means 算法与 SVM 算法结合形成全自动分类模型，并且把它应用于医学图像的分类中。 (SVM in the classification of medical image applications, medical images through the experimental results verify the effectiveness of the method. SVM algorithm conducted in-depth research, combined with the quantum behavior of the advantages of particle swarm optimization, PSO and SVM combined behavior of a quantum mixed classification model, applied to medical image classification. In-depth study of the K-means algorithm combines the advantages of unsupervised clustering and supervised classification method, propose a hybrid combination of a K-means and SVM classification model, and applied to the classification of medical images. Improved particle swarm algorithm using particle swarm and quantum behavior of the K-means algorithm, and improved K-means algorithm and SVM algorithm combined with the formation of a fully automatic classification model, and apply it to medical image classification.)

BALLBEAM demonsrates Proportional-Derivative (PD) control as applied to a ball and beam simulation experiment. Execute by calling with no arguments. P and PD controllers are implemented which manipulates the beam angle in response to the position of the ball on the beam. The setpoint is indicated by the red marker and adjusted using the slider at the bottom of the figure. The control gain, Kc, and the Derivative Time constant, Td, are adjusted by a sliders located in the right hand control panel. Simulation data is stored in ballbeam.mat for subsequent analysis and parameter estimation. This demo was originally developed with Matlab 5.3 and Matlab 6.0. Some of the user interface code was shamelessly ripped out of the standard Matlab demos. The demo is now updated for Matlab 2010b with additional documentation and suggested exercises for classroom use.