Edgelinking

MIT的一个工具箱，可以看看，觉得还可以(a toolbox can see, I felt that I could)

对二阶系统进行辨识。。。采用曲线拟合法对二阶系统进行辨识。。。采用曲线拟合法对二阶系统进行辨识。。。采用曲线拟合法对二阶系统进行辨识。。。采用曲线拟合法(Second-order system identification. . . Using curve fittingSecond-order system identification. . . Using curve fittingSecond-order system identification. . . Using curve fittingSecond-order system identification. . . Using curve fitting)

MA 在迁移的过程中，动态决定节点是否需要进行数据融合 运算，以进一步降低MA 的能量消耗。(MA in the course of migration, determine the need for data fusion node operators to further reduce the energy consumption of MA.)

运动图像的模糊复源程序，可以将因为运动造成的图像模糊进行很好的复原。(This is a source program of image restoration.It can recover the fuzzy image which caused by movement . )

matlab 教程。。。对于没用过matlab的 可以作为入门学习(matlab tutorial. . . For matlab can not used as a learning portal)

matlaB通信工程书籍配套演示源代码，第五版()

few more adaptive lab programs

本书主要介绍如何运用Matlah与C.C + +进行混合程序设计。共分8章。主要包括Matlab程序设计初步、Matlab编译器、Matlab与C语言的接口、生成可独立运行的iatlah程序、VC++调用Matlah程序、Matlab DotNet Builder与VC++,Matcom与C/C++以及VC++调用Matlab C++数学库。另外，附录中介绍有关动态链接库的基础知识。各章包含大量 的实例程序，可供寻求将Matlab程序脱离Matlab环境的Matlab程序设计人员、寻求在Matlab中调用C/C++程序的程序设计人员、寻求在C,C++中调用Matlab程序的程序设计人员学习和参考。(This book describes how to use Matlah mixed with the CC++ programming. Divided into 8 chapters. Including Matlab programming initially, Matlab compiler, Matlab and C language interface to generate can be run independently of the iatlah program, VC++ calls Matlah program, Matlab DotNet Builder and VC++, Matcom and C/C++ and VC++ calling Matlab C++ math library. In addition, the appendix describes the dynamic link library on the basis of knowledge. Each chapter contains a large number of example programs are available for the Matlab program from the Matlab environment, Matlab programmer, seeking to Matlab to call C/C++ program programmers seek to C, C++ calling Matlab program programmers study and reference.)

一个关于压缩感知的正交匹配追踪算法的Matlab程序，很好用的。(Compressvie sampling achieve in matlab by OMP algorithm.)

聚类算法，不是分类算法。分类算法是给一个数据，然后判断这个数据属于已分好的类中的具体哪一类。聚类算法是给一大堆原始数据，然后通过算法将其中具有相似特征的数据聚为一类。这里的k-means聚类，是事先给出原始数据所含的类数，然后将含有相似特征的数据聚为一个类中。所有资料中还是Andrew Ng介绍的明白。首先给出原始数据{x1,x2,...,xn}，这些数据没有被标记的。初始化k个随机数据u1,u2,...,uk。这些xn和uk都是向量。根据下面两个公式迭代就能求出最终所有的u,这些u就是最终所有类的中心位置。(Clustering algorithm, not a classification algorithm. Classification algorithm is to give a figure, and then determine the data belonging to a specific class of good which category. Clustering algorithm is to give a lot of raw data, and then through the algorithm which has similar characteristics data together as a class. Here k-means clustering, is given in advance the number of classes contained in the raw data, then the data contain similar characteristics together as a class. All information presented in or Andrew Ng understand. Firstly, raw data {x1, x2, ..., xn}, the data is not labeled. K random initialization data u1, u2, ..., uk. These are the vectors xn and uk. According to the following two formulas can be obtained final iteration all u, u is the ultimate all these classes the center position.)