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MIT-BIH心电数据库中包含有大量的心电信号，用户可以直接下载这些信号用于信号分析，算法验证等。但是其数据存储格式复杂，只能用其提供的工具包进行读取，很不方便。本源码可以直接读取MIT-BIH中的format212和format16格式的心电数据，免去了学习其繁琐工具包的过程。(MIT-BIH ECG database contains a large number of ECG signals, users can directly download these signals used in signal analysis, verification algorithm. However, the complexity of its data storage format, can only provide a toolkit to read, very inconvenient. The source can be directly read the MIT-BIH in format212 and format16 ECG data format, which eliminates the cumbersome tool kit to learn their process.)

Matlab 脑电信号转mat数据工具包，脑电研究的福音。(EEG data transfer mat Matlab toolkit gospel EEG studies.)

说明：  基于matlab的贝叶斯网络预测模型，能够预测事件发生的概率。(Matlab based on Bayesian network model to predict the probability of events.)

给定一组数据，利用特征值方法简单的计算其主成分，简单易懂，适合初学者学习(simple matlab code of PCA)

五组在matlab中应用的小程序：（1）carspls and carsplslda，竞争性自适应重加权算法（cars）筛选特征变量，并建立PLS定量或定性模型（2）mcuvepls 蒙特卡罗抽样无关信息消除后，建立PLS定量和定性模型（3）osc，正交信号校正的多变量数据处理方法（4）plot cars-lda-mcs,表征cars-lda-mcs的结果（5）demo 对以上carspls and carsplslda，mcuvepls 方法在matlab中实现举例演示(Five groups codes in matlabapplication: (1) carspls and carsplslda, competitive adaptive re-weighted algorithm (cars) for characteristic variables selection, and the establishment of PLS ​ ​ quantitative or qualitative model (2) mcuvepls， Monte Carlo sampling and the uninformative variables elimination （UVE）， and the establishment of quantitative and qualitative PLS model (3) Orthagnal signal correction（osc）, multivariate data processing method (4) plot cars-lda-mcs, characterization results of cars-lda-mcs (5) the demo about carspls and carsplslda, mcuvepls method implementation in matlab)

ACS蚁群算法的一种简单方案程序，比较宜懂(ACS Ant Algorithm A simple procedures of the program, compared to understand)

蜂窝系统中分布蜂窝用户,蜂窝小区500米，随机分布的用户。(UE distribute)

这个就是MU的原版客户端文件里的ITEM备份，可以用来修改(Clients original files, you can back up with it, ha ha ha ha ha ha ha ha)

基于matlab的三相整流实现单位功率因数(Based matlab achieve unity power factor rectifier)

以最小二乘法进行曲线拟合 得到概率密度函数的参数(With the least squares curve fitting to get the parameters of the probability density function)