MATLAB-Programming-for-Engineers

machine vision run project1.m

matlab 的simulink快速入门，老师总结的，对初学者很有帮助( learning of simulink)

该压缩包包含了众多函数 可以供大家方便调用(The compressed packet contains many functions for your convenience call.)

一个OFDM符号128个有效符号，插入128个虚拟子载波 符号率（即有效数据率）fsymbol，而发送速率（需要包括虚拟子载波）2fsymbol 输入的数据需要2倍的发送数据率，以便进行内插，实现位定时跟踪，即4倍fsymbol采样率的调制信号 OFDM symbol Nfft=128 points 一个OFDM符号数据点 Nfft*2 = 256 ， Guard Interval点 ＝Ng*2=64 ( a OFDM symbol 128 effective symbols, insert 128 virtual sub-carrier symbol rate (i.e. valid data rate), and fsymbol sending rate (need to include virtual sub-carrier) 2fsymbol input data need 2 times the sending data rate for interpolation, realize a timing tracking, namely fsymbol sampling rates four times the modulation signal OFDM symbol Nfft = 128 points a OFDM symbol of data points Nfft* 2 = 256, Guard Interval point = Ng* 2 = 64 )

switching median filter for removing impulse noise

说明：  本程序实现多模态故障检测与故障诊断，用的方法是LPP_GMM_FJ，对象为TE,GMM是用的FJ算法，效果很好(This procedure realize multimodal, fault detection and fault diagnosis method is LPP_GMM_FJ, object for TE, GMM FJ algorithm is used, the effect is very good)

能够提取出故障的特征，并对故障的特征进行分类，为进一步识别故障做好基础。(It could give charasterics of different faults and divide them.As a result,it is based on recongnizing the faults.)

说明：  matlab的各种自适应仿真分析。。自适应信息处理的算法、方案繁多，究其实质可归纳为遵循最小均方误差(Least Mean Square，LMS)准则及最小二乘(Least Square，LS)准则两大类，其他算法大多是这两种算法的演进。(matlab simulation analysis of various adaptive. . Adaptive information processing algorithms, a variety of plans, their essence can be summarized as follows the minimum mean square error (Least Mean Square, LMS) criterion and least squares (Least Square, LS) criteria for two categories, most of the two other algorithms kind of algorithm evolution.)

rectification of stereo images using stereovision

matlab环境下目标函数为求最大值，且解非负整数解 ％bounds 边界约束 ％Myfun 为目标函数 ％num 初始种群数 ％N 最大迭代次数 ％CP 交叉概率 ％P 突变概率 ％f 目标最优解 ％x 最优解向量(matlab environment for the sake of the objective function value, and the solution of non-negative integer solutions％ bounds border％ Myfun bound for the objective function％ num species the initial number of％ N maximum number of iterations％ CP crossover probability％ P mutation probability％ f-objective optimal solution％ x the optimal solution vector)