dilation

自编中值滤波 matlab matlab matlab(Self median filter matlab matlab matlab)

window下 出口同步通信工具，提供c＋＋源码，说明详细(window exporting synchronous communication tools, c++ source code, specify details)

GPRMAX2.0中读取geo、out文件的命令。(file command to GPRMAX2.0 in using to read geo&out file.)

基于matlab编制的地震波转反应谱程序，动力分析用的线性加速度法。(Based on matlab programming turn seismic response spectrum and dynamic analysis with the linear acceleration method.)

本程序用于提取视频中的某种颜色，颜色可以自行设置。通俗易懂，且结果准确。(This program is used to extract a color video, you can set your own color. Easy to understand, and the results are accurate.)

turbo码的C程序，许多行前面都有些序号 请自己更正以后运行(turbo code of C procedures, many firms are some of the serial number in front of your own running after correction)

MIMO雷达成像，资料比较全的，是一个压缩包(MIMO radarimaging)

在matlab上将图像从空间域变换到频率域，主要包括Radon变换和反变换、傅里叶变换和反变换、离散余弦变换和反变换等(In matlab image will be transformed the spatial domain to the frequency domain, including the Radon transform and inverse transform, Fourier transform and inverse transform, the discrete cosine transform and inverse transform)

利用多种方法对图像进行分割的matlab程序(Using a variety of methods for image segmentation matlab program)

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 )