Lesson-1

CSINTERP 1-D piecewise cubic spline interpolation of tabulated data and calculation of first and second derivatives.

转子碰摩非线性计算，可计算出p1,p2,倍周期分岔，混沌等特性(Rotor rubbing the bifurcation diagram matlab source code)

说明：  加热控制模型，主要是根据固体传热进行仿真建模，内置了Matlab PID控制器自动学习(Heating control model, mainly based on solid heat transfer simulation modeling, built-in MATLAB built-in controller)

基于数字形态学的字符识别 recognizes the characters (in integer form) present in the image X and displays it as output. INTREC uses morphological operations(like (Based on the number of morphological character recognition recognizes the characters (in integer form) present in the image X and displays it as output. INTREC uses morphological operations (like)

matlab下傅里叶变换的程序例程，是pdf的(under the Fourier transform matlab routine procedure is the pdf)

锁相环实验，PLL参数测试，锁相环PLL原理与应用，环路滤波器 (The experimental phase-locked loop, PLL parameter testing, and application of the principle of the PLL loop filter )

For all classifier needs feature classification object .Here the program for height and width feature taken hand image.This program suitable for any single object in the image. input gray_img : input hand image or any measure image like 2d with 0 to 255 intensity output height : pixel count of height width : pixel count of width out_img : segmented image with line for height and width hand_height_width function implements hand height and width of the image using the method outlined in: calculate the hand height and width value for gray image. In Proceedings of the Information Sciences 275 (2014) 45–56. Hand shape identification on multirange images. [ height width out_img ] = hand_height_width ( gray_img ) function is to find the hand height and width value of the image. The input is given to the gray scale image.

创建和修改对象等，很不错的例子，希望对大家有用。(creat and modify the object. it is a good sample for labview.)

matlab 4 connectivity prgram ....in image processing..

介绍一个稳健性分析工具箱。主要做稳健性主成分、主成分回归、分类。(Our toolbox currently contains implementations of robust methods for location and scale estimation, covariance estimation (FAST-MCD), regression (FAST-LTS, MCD-regression), principal component analysis (RAPCA, ROBPCA), principal component regression (RPCR), partial least squares (RSIMPLS) and classification (RDA). Only a few of these methods will be highlighted in this paper. The toolbox also provides many graphical tools to detect and classify the outliers. The use of these features will be explained and demonstrated through the analysis of some real data sets.)