Quadratic-programming-matlab-code

经典无网格教程的无网格算法的例程，属于第六章例程(Classical meshless tutorial meshless algorithm routine, part of Chapter VI of the routine)

分别使用感知机，最小二乘，SVM进行样本分类的基础算法。(Perceptron，least squares,SVM based method were used for sample classification.)

用matlab程序编写的一个三维雷达跟踪粒子滤波器，超强(using Matlab procedures prepared by the three 3D radar tracking a particle filter, super)

CDMA2000下行链路仿真模块的matlab实现代码(CDMA2000 downlink simulation module)

gvf snake 邊緣檢測範例(包含範例圖)(1. Introduction and Installation Instructions ---------------------------------------------- Welcome to the Gradient Vector Flow (GVF) Active Contour Toolbox. The Matlab*.m files in this software distribution implement our GVF snake, the original (Kass, Witkin, and Terzopoulos) snake, the balloon snake, and the distance snake. These programs were designed, written and tested by Chenyang Xu and Jerry Prince. To install this software, simply unzip the file into an empty directory, making sure to ask your unzipper program to restore the directory structure of the zipped files. In this documentation, we will assume that you have unzipped the files into C:GVF. 2. Documentation ---------------- We have included two research papers that describe GVF. You can find these in C:GVFpapers. Brief descriptions of the software modules can be found in this file below. For more detail, you should run Matlab, add the path C:GVFsnake, and type "help function-name". F)

空间功率谱估计之music算法的源代码 代码不长 很常见 很方便(Spatial power spectrum estimation of the music algorithm source code is not long, convenient common)

image mosaicing functions

说明：  这是一个VC++和MATLAB联合环境下的编程,很有用的,MATLAB的数据显示很强大的,希望对你们有用.(This is a example used combation of vc++ and matlab ,because the vistual of data is very strong ,I hope it will benefit to your.)

基于三相异步感应式风力发电机的风力发电机的风速仿真模型(Based on three-phase asynchronous induction wind generator wind turbine wind simulation model)

A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional)