Modified-response-spectrum

说明：  自适应滤波的MATLAB实现（包括LMS，LRS、NLMS等算法(Adaptive Filtering MATLAB implementation (including the LMS, LRS, NLMS algorithm, etc.)

说明：  通信系统仿真与分析中经常用到的高斯白噪声的产生方法(Communication System Simulation and Analysis in the frequently used Gaussian white noise, the method of)

交流BUCKBOOST拓扑的MATLAB仿真 (AC_BUCK_BOOST )

ti关于rf天线的设计、调试方法，13.56M精讲( ti on rf antenna design, debugging method, 13.56M succinctly)

说明：  hslogic算法仿真，基于matlab的详细的CDMA系统的仿真与测试(dBS2UE_LargeScale = struct( 'PathLoss',zeros(nNumofSector,nTotalNumofUser),... 'ShadowFading',zeros(nNumofSector,nTotalNumofUser),... 'AntennaGain',zeros(nNumofSector,nTotalNumofUser),... 'TotalPathLossIndB',zeros(nNumofSector,nTotalNumofUser),... 'TotalPathLoss',zeros(nNumofSector,nTotalNumofUser),... 'Distance',zeros(nNumofSector,nTotalNumofUser)... );)

用于演化博弈中，列出复制动态方程后，求解复制动态方程的均衡点(Solving the equilibrium point of the replication dynamic equation)

利用偏移量插值实现图像的透视变换（运用了双线性变换原理）(Offset interpolation using the image perspective transformation (using the bilinear transformation principle))

基于Matlab环境下的OQPSK QPSK系统仿真(Of OQPSK QPSK system simulation based on Matlab environment)

一个老外开发的地质统计学matlab工具箱，具有较全的统计算法如克里金、SVM(development of a foreigner geostatistics Matlab toolbox, compared with all the statistics as Kriging algorithm, SVM, etc.)

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)