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Jacks 频谱的相关性测试，可以验证得到的jaks频谱是否正确 (Jacks test the relevance of the spectrum can be obtained to verify the correctness of the spectrum jaks)

MATLAB作业一 分别用for和while循环结构编写程序，求出 。并考虑一种避免循环语句的程序设计算法，比较不同算法的运行时间。(MATLAB operating a program were used for and while loops structure determined. And consider a way to avoid the loop statement programming algorithm, the running time of the different algorithms.)

认知雷达方面的国外书籍 对学习知识辅助处理的科研人员有很大的借鉴价值！(Cognitive radar knowledge of foreign books on learning auxiliary processing researchers have great reference value!)

relic is matlab code for classification

这个程序实现了基于主成份的人脸可视化分析,这是一个完整程序代码.(This program implements the main components of the human face based on visual analysis, this is a complete code.)

Walsh wavelet transform used to compress and decompress the 2D images in matlab

精通matlab与C++混合程序设计。讲述matlab与C语言混合编程设计。(matlab and C mixed programming language design.)

FRAME地固系和惯性系坐标转换（极移、岁差、章动、参考历元）(FRAME orbit transfer)

此程序主要是提供了信号非线性分解的一种新方法，局部均值分解，程序运行完整，可以使用，可供初学者学习使用，模拟仿真。(This program is mainly provides a new kind of nonlinear signal decomposition method, the local mean decomposition, programs run is complete, you can use, for beginners learning to use, and simulation.)

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)