Active-contour-models-snake

用matlab语言读取Renix格式文件的程序代码(Read Renix format file with the matlab language code)

基于LabView2011的自相关分析和互相关分析，测试两个信号的互相关函数和自相关函数。(Based on the autocorrelation and cross-correlation analysis LabView2011 analysis, test cross-correlation function of two signals and autocorrelation function.)

说明：  这个程序是EMD进行后对IMF的各个能量进行计算得出每个分量的能量分布，这个种方法也是一种常见方法,其中还包括这特征提取后的绘制可视程序。(This procedure is carried out after the IMF EMD various energies calculated energy distribution of each component, this method is also a common method.)

MATLAB实现中值滤波源代码,此文件为MATLAB做图像中值滤波的源代码，并且计算均方误差，归一化均方误差以及峰值信噪比(MATLAB median filter source code, the source code of this file for MATLAB image median filtering, and calculate the mean square error, normalized mean square error and peak signal-to-noise ratio)

说明：  matlab 图像处理程序例子 供初学者参考(matlab image processing procedure example of reference for beginners)

matlab中s-function的使用方法(s-function in matlab)

lab experiments of wireless communication laboratory

英国设菲尔德大学遗传算法工具箱。用matlab导入路径即可使用。(University of Sheffield genetic algorithm toolbox. Using matlab import path can be used.)

三种信道 瑞利 赖斯 高斯的误码率仿真，通过MATLAB实现(The three channel Ruililaisi Gaussian error rate simulation)

这是模式识别中最小错误率Bayes分类器设计方案。 自行完善了在不同先验概率的条件下，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。 调用最小错误率贝叶斯分类器决策子函数时，根据先验概率数组，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。 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”，调用最小错误率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，就可以得到不同先验概率条件下错误率的统计。 (This is the minimum error rate pattern recognition Bayes classifier design. Self- improvement prior probability in different 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 third step is to 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 . Call the minimum error rate decision Functions Bayesian)