MATLAB

Long Term Evolution LTE block interleaver and deinterleaver

Symbol Timing Tracking Using Early-Late Techniques by matlab(Symbol Timing Tracking Using Early-Late T echniques by Matlab)

identification of pundulum

连续相位调制（CPM），维特比译码，整个调制解调系统(Continuous phase modulation (CPM), Viterbi decoding, the modem system)

各种随机信号的自相关函数和功率谱密度函数、画图(Autocorrelation function and power spectral density function of a variety of random signals, drawing)

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)

一个duffing模型，一个噪声模型，一篇duffing检测原理，。可检测不同频率的待测信号(a duffing model, a noise model, a duffing detection principle)

这个个程序是利用卡尔曼滤波实现人体关节点的跟踪问题，运用的是CV模型，噪声为高斯白噪声！有用的拿去看看！(This procedure is to use a Kalman filter to achieve a key point in human tracking problems, the use of the CV model, noise is Gaussian white noise! Take useful to see!)

论文题目：基于Matlab无刷直流电机控制系统建模与仿真 为验证控制算法和控制策略的合理性，在 分析无刷直流电机数学模型的基础上，用Matlab建 立了无刷直流电机控制系统的仿真模型，并详细介 绍了控制系统的各个子模块。通过对实例电机的仿 真，给出了仿真结果，证明了该模型的可行性。(To verify the control algorithm and control strategy is reasonable, in the Analysis of brushless DC motor based on the mathematical model, using Matlab to build Li of the brushless DC motor control system simulation model, referred in detail Shao-control system, the various sub-modules. Through the examples of motor imitation True, gives the simulation results prove the feasibility of the model. )

Elementary Signals and Input-Output-Signal-Relations this file contains very good,detailed and well run codes of different scenarios.it will help a lot.