matlab-for-sensor-nodes

本文件利用matlab实现dijistra算法。主要方法是标记未走完的点，然后动态优化穷举法。(This document uses MATLAB to achieve dijistra algorithm. The main method is to mark the incomplete points, and then dynamically optimize the exhaustive method.)

应用地球物理，电法教学，球体，视电阻率 东华理工大学(Application of geophysics, electrical method of teaching, ball, depending on the resistivity of the Tung Wah Group of Hong Kong Polytechnic University)

说明：  这是在网上下的一个东东，很有用的，现在拿来和大家共享,是《参数估计和逆问题》的matlab程序(This is the Internet under a Dongdong, very useful, and we used to share, "parameter estimates and the inverse problem" Matlab procedures)

管理员您好，这是最新的期刊论文，学生初学软件，没有比较高深的程序源代码，希望您能满意(Administrator hello, this is the latest journal papers, students begin to learn the software, there is no more profound source code, I hope you enjoy)

能够实现蚁群算法寻优的程序，可以直接运行，运行后出来收敛图(Ant colony optimization algorithm can achieve a program that can be run directly after running out of the convergence plan)

说明：  视频帧序列实现基于GMM（高斯混合模型）的背景建模(Video frame sequence implementation based on GMM (Gauss mixture model) background modeling)

Newmark法是一种逐步积分的方法，避免了任何叠加的应用，能很好的适应非线性的反应分析。(The Newmark method is a method of gradual integration, which avoids any superimposed application and can be well adapted to the nonlinear reaction analysis.)

该源码是采用dspbuilder进行设计，目的是完成一个为随机m序列校验器，很有用看看就知道了(The source is dspbuilder design, the purpose is to complete a sequence of random m validator, useful look at the know)

本文使用三维FDTD算法实现文献《Application of the three_Dimensional Method to the analysis if Planar Microtrip Circuits》IEEE trans. On MTT 1990 38(7)的一个矩形微带贴片天线的S11参数的计算。采用MATLAB编程完成数值计算，并与文中的结果进行了比较。(This three-dimensional FDTD algorithm using the document " Application of the three_Dimensional Method to the analysis if Planar Microtrip Circuits" IEEE trans. On MTT 1990 38 (7) of a rectangular microstrip patch antenna of the S11 parameter of the calculation. Complete the numerical calculation using MATLAB programming, and with the text of the results were compared.)

传统的K-medoids聚类算法的聚类结果随初始中心点的 不同而波动，且计算复杂度较高不适宜处理大规模数据集； 快速K-medoids聚类算法通过选择合适的初始聚类中心改进 了传统K-medoids聚类算法，但是快速K-medoids聚类算法 的初始聚类中心有可能位于同一类簇。为了克服传统的K- medoids聚类算法和快速K-medoids聚类算法的缺陷，提出 一种基于粒计算的K-medoids聚类算法。(The traditional K-medoids clustering algorithm clustering results with different initial center points and volatility, and high computational complexity is not suitable for processing large data sets; K-medoids clustering algorithm by choosing proper initial cluster centers to improve the traditional K-medoids clustering algorithm, but the initial cluster center of K-medoids clustering algorithm can be located in the same cluster. In order to overcome the shortcomings of the traditional K- medoids clustering algorithm and the fast K-medoids clustering algorithm, a K-medoids clustering algorithm based on granular computing is proposed.)