endpoint

this will helpful for region merging algorithm

说明：  二维光子晶体禁带的遗传优化算法.在平面波展开法的基础上，使用遗传算法寻找具有最大光子禁带的二维光子晶体结构(Photonic Band Gap Genetic Algorithm)

Variable Lattice, Triangle Lattice Self Avoiding Walk

SANSAC算法实现椭圆拟合，获取三轴磁力计的数据，对其进行椭圆拟合，用于磁力计数据矫正(SANSAC ellipse fitting algorithm to obtain three-axis magnetometer data, its ellipse fitting for magnetometer data correction)

说明：  井中地震正演程序，matlab程序编写(Borehole seismic forward procedure, matlab programming)

利用粒子群算法对函数进行优化处理，最终得到收敛效果图(Particle swarm optimization algorithm )

MATLAB函数的mie散射和吸收 MATLAB Functions for Mie Scattering and Absorption(MATLAB Functions for Mie Scattering and Absorption。A set of Mie functions has been developed in MATLAB to compute the four Mie coefficients an, bn, cn and dn, efficiencies of extinction, scattering, backscattering and absorption, the asymmetry parameter, and the two angular scattering functions S1 and S2.)

说明：  信号经过滤波器，会发生相移，本程序设计了零相位滤波器，矫正相位(The signal passes through the filter, will occur the phase shift, this procedure designs the zero phase filter, corrects the phase)

这是科学运算语言MATLAB程序设计有关的函数代码(This is a scientific computing language of MATLAB programming code related to the function)

这是模式识别中最小风险Bayes分类器的设计方案。在参考例程的情况下，自行完善了在一定先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。 全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。 调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度函数。 调用最小风险贝叶斯分类器决策子函数时，根据先验概率，再根据自行给出的5*5的决策表，通过比较概率大小判断一个体重身高二维向量代表的人是男是女，放入决策数组中。 主函数第一步打开“MAIL.TXT”和“FEMALE.TXT”文件，并调用最大似然估计求取概率密度子函数，对分类器进行训练。第二步打开“test2.txt”，调用最小风险贝叶斯分类器决策子函数，然后再将数组中逐一与已知性别的数据比较，就可以得到在一定先验概率条件下，决策表中不同决策的错误率的统计。 (This is a pattern recognition classifier minimum risk Bayes design .In reference to the case of routine , self- improvement in a certain a priori probability conditions, male , female and total error rate error rate statistics , into which each array . All programs from the main function , maximum likelihood estimation subroutine strike probability density , the minimum error rate Bayesian classifier composed of decision-making three subfunctions . Strike called maximum likelihood estimate probability density subroutine , the first step to obtain the sample data , stored as a matrix the second step of the matrix, each row sum , and divided by the total number of samples N, be the average vector The third step is the application of the formula ( 3-43 ) using matrix and loop control statements , obtain the covariance matrix fourth step through the variance-covariance matrix and correlation coefficient obtained , resulting in the probability density function . Bayesian classifier )