lianxuxiaobo

matlab下的绘制等温线的源程序，有对等温线加密，值得借鉴(Under matlab draw isotherm source, there is encryption on isotherm, is worth learning)

说明：  这是蚂蚁算法求解TSP的源代码，Matlab中实现。(This is the ant algorithm for TSP source code, Matlab achieve.)

利用matlab实现kmeans聚类算法的简单实现代码(matlab kmens algrithom )

用MATLAB实现CCSDS_LDPC编码，并生成相应的校验矩阵H。压缩包内包含相应源程序以及CCSDS_LDPC编码原理的说明文档。(Using MATLAB CCSDS_LDPC coding, and generate the corresponding check matrix H. Compressed package that contains the corresponding documentation and CCSDS_LDPC source coding theory.)

说明：  噪声中的非平稳信号的频谱分析的matlab代码(Matlab code for spectrum analysis of non-stationary signals in noise)

均匀介质各向同性二维弹性波数值模拟，采用cerjan边界条件。有公式及参考文献。带有速度模型以及雷克子波函数，解压后直接运行就可以。(The homogeneous medium isotropic two-dimensional elastic wave numerical simulation, using cerjan boundary conditions. formulas and references Sample are added.With the velocity model and the the Ricker wave function, and can be run directly after decompression.)

authorware小例子 1、打台球 2、古诗欣赏 3、投篮 4、移动图形 5、制作一个钟表(authorware small example 1, billiards 2, 3 poetry appreciation, shooting four, five mobile graphics to produce a watch)

Distributed Space Time Block Code

一种新的改进的遗传编程，调用的子函数描述的很清晰，涵盖了经典遗传编程的所有步骤，方便大家的学习和参考(A new and improved genetic programming, call the subroutine described in very clear and covers all the steps of classical genetic programming, we facilitate learning and reference)

K最邻近密度估计技术是一种分类方法，不是聚类方法。 不是最优方法，实践中比较流行。 通俗但不一定易懂的规则是： 1.计算待分类数据和不同类中每一个数据的距离（欧氏或马氏）。 2.选出最小的前K数据个距离，这里用到选择排序法。 3.对比这前K个距离，找出K个数据中包含最多的是那个类的数据，即为待分类数据所在的类。(K nearest neighbor density estimation is a classification method, not a clustering method. It is not the best method, but it is popular in practice. Popular but not necessarily understandable rule is: 1. calculate the distance between the data to be classified and the data in each other (Euclidean or Markov). 2. select the minimum distance from the previous K data, where the choice sorting method is used. 3. compare the previous K distances to find out which K data contains the most data of that class, that is, the class to which the data to be classified is located.)