PLS_toolbox

一种基于脑电信号脑机介面（BCI）从设计，实时运行在Windows的标准组件(Design of an EEG-based Brain-Computer Interface (BCI) from standard components running in real-time under Windows)

dds信号发生器，matlab7.0，dspbuilder7.0(dds signal generator, matlab7.0, dspbuilder7.0)

Filtering using Weiner filter

线性拟合程序,航天器运算结果可以进行拟合运算(Linear fitting procedure, spacecraft operations to fit the computing results can be)

这是一道很基本的程序，一方面希望它对初学遗传算法的兄弟们有用（包含了遗传算法实现的大部分步骤，而且有详细的注释），另一方面旨在抛砖引玉， 在下浅见： 1、遗传算法在进行大规模计算时，数据交换量大，速度确实是一个瓶颈，就如这道程序，在种群规模50，进化代数1000时运行需要40秒左右(cpu:duron 1G),当然，小弟编程能力弱，望各位大侠指教啊，或帮我修改一下这道程序 (This is a very basic procedure, While they want it beginner GA brothers useful (including the genetic algorithm to achieve the most steps and a detailed note), on the other hand aims to attract the next suggestions : 1. GA conducting large-scale calculations, the data exchange capacity, speed is a bottleneck, as this practice. 50 in population size, the evolution of algebra required for the operation of 1000 when about 40 seconds (cpu : duron 1G), Of course, the younger programming weak, and hopes to enlighten ah Shanhaiguan, or help me rephrase this practice)

emd去噪，不同于小波去噪，去噪效果比小波还要好，能够反映数据的最基本特点。(emd de-noising, unlike wavelet de-noising, even better than the wavelet denoising effect, to reflect the basic characteristics of the data.)

IMPROVING THE ALLOCATION STRATEGIES INCORPORATING ALLOCATION WITH AVAILABILITY AND RESOURCE ALLOCATION FOR MULTIHOP TRAFFICS IN CELLULAR ARCHITECTURE

LEACH的源代码，详细注释，适合初学WSN路由协议的同学学习。(LEACH source code, detailed notes, WSN routing protocols suitable for novice students to learn.)

求解线性规划的单纯形法，M文件，里面有注释，可以求标准的线性规划问题。(Solve the linear programming of the simplex method, there are comments )

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)