MADAMODAL

GUI for Multivariate Image Analysis of 4-dimensional data (Matlab Code)(GUI for Multivariate Image Analysis of 4-d imensional data (Matlab Code))

灰度共生矩阵和灰度梯度共生矩阵的提取方式，是比较重要的纹理特征提取方法，用matlab实现的(co-occurrence matrix and GGCM extraction, is the more important Texture feature extraction methods, achieved using Matlab)

matalab一些基本运算，对于初学者来说很有用！(matalab some of the basic operations for the newcomer useful!)

基于金字塔完善的KL算法描述，是matlab代码，值得学习(Pyramidal implementation of LK Feature Track)

matlab多目标优化问题实现，可以参考一下啊(matlab multi-objective optimization, you can refer to ah)

说明：  一个matlab mex 文件，可以用来在matlab程序中捕捉用户按下的特殊功能健：F1, ..., F12. 使用时，在你的应用程序的 KeyPressFcn的函数体里，是用wgfcnkey，根据其返回值，判断是否有功能健被按下。(a Matlab mex documents, in Matlab can be used to capture user proceedings of the special features? Kin : F1, ..., F12. When used in your application's function KeyPressFcn the body, is wgfcnkey, return value, determine whether there is any function Kin is pressed.)

一本关于控制系统仿真的书的程序 1、文件夹1中的程序是书中的一些仿真。 2、文件2中的是最近做的预测控制、PID控制及逻辑控制的对比综合研究。 3、文件夹3中是与maltab通信的数据采集板的设计文件。包括串口设计、USB1.1及USB20设计。这是最新的设计资料，很有参考价值。 (About a control system simulation procedure one book, folders, one in the book are a number of procedural simulation. 2, document 2 are recent to do predictive control, PID control and logic control study Comparative General. 3, folder 3 are maltab communication with data acquisition board design document. Including the serial design, USB1.1 and USB20 Designed. This is the latest design information, useful reference value.)

kelly capital allocation algorithm in matlab. risk management

In this paper, we propose and investigate a lowcomplexity human motion tracking system which is based on ultra-wideband (UWB) radio nodes.

实验一：三次样条插值（P56，例6） 一、实验目的： 1) 掌握三次样条插值的运用 2) 了解拉格朗日插值在高次上的误差 二、实验环境：Matlab6.5 三、实验内容： 1) 给定函数f(x)=1/(1+x2)，-5<=x<=5，节点xk=-5+k，(k=0,1,2…10)，用三次样条插值求S10(x)，S10’(-5)=f’(-5)，S10’(5)=f’(5)。 2）作原函数f(x)，拉格朗日插值函数Ln(x)，三次样条差值函数Sn(x)。画出三个函数的图像，比较它们的区别。 (Experiment I: cubic spline interpolation (P56, cases of 6) First, the experiment was: 1) cubic spline interpolation to master the use of 2) understanding of Lagrange interpolation in high-error second, experimental environment: Matlab6 .5 c, experimental elements: 1) a given function f (x) = 1/(1+ x2),-5 <= x <= 5, node xk =- 5+ k, (k = 0,1,2 ... 10), using cubic spline interpolation for S10 (x), S10 (-5) = f (-5), S10 (5) = f (5). 2) for the original function f (x), the Lagrange interpolation function Ln (x), cubic spline difference function Sn (x). Draw the three functions of images, compare the difference between them.)