Matlab04

说明：  matlab环境下图象图形处理的基本函数的介绍以及简单示例(matlab environment images deal with the basic function of graphical presentation, as well as simple examples)

zooming pgm using matlab...without using in built functions....

高等數位訊號處理第九章作業 可以參考看看 裡面還有其他章作業(Advanced Discrete-Time Signal Processing )

自抗扰控制器，ADRC由m函数和simulink组成(ADRC, ADRC functions and simulink components of m)

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)

眼睛图像虹膜区域提取，内附简单说明。采用粗精双重定位。(iris recognize)

三自由度强迫振动MATLAB/simulink模型(Three DOF forced vibration model in MATLAB/simulink)

随机线性分组编码，信道编码，纠错编码，检错编码(Random linear block coding, channel coding)

a trial version of PCA and Knn to face recognition. This file is not a full version of a code knn, however it is a good approach to start one.

ReBEL is a Matlab® toolkit of functions and scripts, designed to facilitate sequential Bayesian inference (estimation) in general state space models. This software consolidates research on new methods for recursive Bayesian estimation and Kalman filtering by Rudolph van der Merwe and Eric A. Wan at the OGI School of Science & Engineering at OHSU (Oregon Health & Science University