vfractal_matle

卡尔曼滤波算法和扩展卡尔曼滤波算法在目标跟踪到的应用(Kalman filter and extended Kalman filter algorithm to the application in target tracking)

说明：  北京邮电大学通信原理升余弦滚降系统的源程序(Beijing University of Posts and Communication Theory Cosine system source code)

meshgrid，gradient和quiver的基本用法，meshgrid，gradient和quiver的基本用法(meshgrid, gradient and the quiver of the basic usage, meshgrid, gradient, and basic use of quiver)

matlab开发的一个消消看游戏 规则很简单 鼠标点击不同形状的对象 则界面内相同形状的对象会消去 上方的对象相应往下掉 并随机增添新的对象 根据消去的对象数量得到相应的分类 总共操作次数是10次 比比看，谁的最终得分高！！(A game programme by matlab.)

open cv software implementation is done

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 )

ICA介绍课件。There has been a wide discussion about the application of Independence Component Analysis (ICA) in Signal Processing, Neural Computation and Finance, first introduced as a novel tool to separate blind sources in a mixed signal. The Basic idea of ICA is to reconstruct from observation sequences the hypothesized independent original sequences(ICA Introduction courseware. There has been a wide discussion about the application of Independence Component Analysis (ICA) in Signal Processing, Neural Computation and Finance, first introduced as a novel tool to separate blind sources in a mixed signal. The Basic idea of ICA is to reconstruct from observation sequences the hypothesized independent original sequences)

扩展卡尔曼滤波算法简单例子以及对应的matlab程序和仿真结果(Examples of extended Kalman filter algorithm is simple and the corresponding matlab procedures and simulation results)

语音增强的多带谱减算法,函数可直接调用，简单易操作(Speech enhancement of the multi band spectral subtraction algorithm, the function can be called directly)

canny边缘算法的实现，代码比较简单，能实现。(canny edge algorithm, the code is relatively simple to achieve.)