toolfordeterminignoofclusters

在DCT域，利用8*8分块DM方法，嵌入隐藏信息。可设置量化步长、选择各种经典的攻击方式和控制攻击强弱。返回误码率和峰值信噪比。利用Matlab编写。(In the DCT domain, the use of 8* 8 block DM methods, embedding hidden information. Quantization step size can be set to choose a variety of classic attacks and control the strength of the attack. Return to the bit error rate and peak signal to noise ratio. Prepared using Matlab.)

建立EDFA的放大模块模型，不考虑损耗，仅考虑功率放大功能( Build EDFA amplification module model, without considering the loss, only consider the power zoom function)

非常全面的matlab参考资料，方便新手入门以及查阅，可以用作示范教材。(Matlab very comprehensive reference material, easy for beginners as well as access to, can be used as demonstration material.)

使用matlab编写的一个m文件，主要是计算图像的信息熵(Matlab use m the preparation of a document, the main image is the calculation of information entropy)

说明：  牛顿法的一个有用介绍,对学习用牛顿法求解非线性方程的有用(a usful description of newton method )

convolution of a signal

MATLAB PROGRAMS MATLAB PROGRAMS MATLAB PROGRAMS

本文介绍了分治法的基本思想和基本步骤，通过实例讨论了利用分治策略设计算法的途径。 目录 简介 分治法的基本思想 分治法的适用条件 分治法的基本步骤 分治法的复杂性分析 分治法的几种变形 分治法的实例分析 (This article describes the basic idea of sub-rule method and the basic steps, through an example design of algorithms using divide and conquer approach. Contents Introduction The basic idea of divide and conquer divide and conquer divide and conquer the conditions of application of the basic steps of divide and conquer the complexity analysis of divide and conquer divide and conquer several examples of deformation analysis)

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.)

Game institute - C Plus Plus Programming For Game Developers - Module 2