TDE_Targer_Positioning_a_Tracking

这个软件包实现了一个快速算法，用来解决一些线性逆问题。(This set of MATLAB files contain an implementation of the algorithms described in the following papers: "Fast Algorithm for the Constrained Formulation of Compressive Image Reconstruction and Other Linear Inverse Problems")

这里讲述的是matlab的迭代问题，含有多个程序可提供参考(About matlab difference, containing more than one program can provide a reference)

场景文字检测的一种matlab实现，效果比CCV差，但可提供给研究人员另外一种思路(text detection image, not very good performance)

是机器学习的例程，构成不同频率的调制信号，有详细的注释，在MATLAB中求图像纹理特征，包括调制，解调，信噪比计算，利用自然梯度算法，可实现对二维数据的聚类，有PMUSIC 校正前和校正后的比较。 ( Machine learning routines, Constituting the modulated signals of different frequencies, There are detailed notes, In the MATLAB image texture feature, Includes the modulation, demodulation, signal to noise ratio calculation, Use of natural gradient algorithm, Can realize the two-dimensional data clustering, A relatively before correction and after correction PMUSIC. )

自适应局部二值模式完整代码,对于需要局部二值模式的学者很有用。(Adaptive LBP code)

to generate number gumbel random (hidrology)

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 )

大学matlab课程大作业包裹一些图形的基本画法(University matlab courses job wrapped graphics painting)

estimate drougth frequency for 100 years using SPI index

一维等离子体FDTD的Matlab源代码(One-dimensional plasma FDTD source code in Matlab)