importfile

MATLAB对于网络定位的仿真，是针对dv-hop的算法的仿真(MATLAB simulation for network, which is based on the orientation of the dv-hop algorithm simulation)

this som method in NeuralNetwork

基于Fisher准则线性分类器设计 进一步了解分类器的设计概念，能够根据自己的设计对线性分类器有更深刻地认识，理解Fisher准则方法确定最佳线性分界面方法的原理 (Fisher linear classification criteria for classification designed to further understanding of the design concept According to the design of linear classification for a more profound understanding, Fisher understood the criteria to determine the best method of linear interface of the theory)

通过矩不变量法，判断字母是否与原字母相同(letter s distinction)

中频通信信号信噪比的快速盲估计 中频通信信号信噪比的快速盲估计 (Fast Blind SNR Estimation of IF Communication Signals )

The aim of this application is to implement scalable mpeg type compression by varying DCT coefficients and finding minimum number of DCT coefficients that can give minimum acceptable quality.. This application only works for gray scale.

fundamentals in physics, molecules and quantum physic s

病态线性方程组的计算题，涉及Gauss消元法、雅可比迭代法、高斯-赛德尔迭代法、最速下降法和共轭梯度法。每一个方法，都编写一个m文件，封装成函数的形式。然后通过总的HilbLineEquSet.m文件来调用执行，画出误差曲线图，得到运行结果。总的Matlab程序流程，如下所示： 病态方程组的计算包括：HilbLineEquSet.m、gauss.m、jacobi.m、gauss_seidel.m、fastest_descend.m和conjugated_grad.m六个文件。 程序执行结果包括：求解结果、迭代次数、迭代误差数据、误差曲线图等。 (Morbid Linear Equations calculation problems involving Gauss elimination method, Jacobi' s iterative method, Gauss- Seidel iterative method, steepest descent method and conjugate gradient method. Each method, all the preparation of an m file, packaged in the form of a function. Then the total HilbLineEquSet.m file to invoke the implementation of draw error curve, to be running results. General Matlab program flow, as follows: the calculation of morbid equations include: HilbLineEquSet.m, gauss.m, jacobi.m, gauss_seidel.m, fastest_descend.m and conjugated_grad.m six documents. Procedures for implementation of the findings include: solving a result, the number of iterations, iterative error data, error curve and so on.)

image retrieval using L2 distance:euclidien distance between 2 histograms

几种分类器，包括adaboost和随机森林(Several classifiers including adaboost and random forest)