Engnering-FEM-_ZP

说明：  线性调频信号测距的程序以及实现操作，主要是matlab进行操作(LFM signal ranging procedures and implementation of the operation, mainly matlab operation)

基于kalman滤波的目标跟踪代码，用matlab实现了对目标的跟踪。(Kalman filter based target tracking code, matlab achieve the target tracking.)

Dijkstra通用算法，最近一段时间一直在搞图论的相关算法，发现Dijkstra是一个很好的单元最短路径算法，此程序很短，非常适合学习(Generic Dijkstra algorithm, the most recent period has been engaged in graph theory algorithms, Dijkstra is a good unit the shortest path algorithm, this procedure is very short, very suitable for learning)

说明：  Solves steam reformer problem in matlab

说明：  插值和拟合算法实验源代码，希望给大家带来帮助。(Interpolation and the fitting method experimental source code, hope to bring you help. )

Employee management system project for students.

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 )

简单的BPSK误码率仿真曲线，用MATLAB软件，画图很精美(Simple BPSK BER simulation curves, using MATLAB software, drawing a very fine)

说明：  基于MATLAB的ＯＦＤＭ的系统性能分析(MATLAB-based performance analysis of OFDM system)

估计信号源数不同时的MUSIC算法性能比较(Not the same source estimates the number of performance comparison of MUSIC algorithm)