MPC-5

说明：  实现了GMSK调制方式下OFDM系统的仿真(Achieved a GMSK modulation mode OFDM system simulation)

matlab filter elements

Script for computing the BER for PSK in OFDM modulation

彩色多普勒成像分析，通过多普勒频谱进行彩色成像(Color Doppler)

IMPLEMENTATION OF A ROBUST CONTROLLER

程序来自《现代通信系统－使用matlab》英文版 已经调通！并加上了注释。 希望对大家有帮助 GOOD　LUCK！(Program from the modern telecommunication system- using matlab "in English Have adjustable tong! Plus comments. We want to help m applying to! )

说明：  它主要研究的是多因变量对多自变量的回归建模，特别当各变量内部高度线性相关时，用偏最小二乘回归法更有效。另外，偏最小二乘回归较好地解决了样本个数少于变量个数等问题。 偏最小二乘法是集主成分分析、典型相关分析和多元线性回归分析3种分析方法的优点于一身。它与主成分分析法都试图提取出反映数据变异的最大信息，但主成分分析法只考虑一个自变量矩阵，而偏最小二乘法还有一个“响应”矩阵，因此具有预测功能。(It is mainly a result of the study is a multi-variable regression modeling and more independent variables, especially when the internal height of each variable linear correlation, partial least squares regression method is more effective. In addition, partial least squares regression solves the number of samples is less than the number of variables and other issues. Partial least squares method is a set of principal component analysis, canonical correlation analysis and multiple linear regression analysis of the advantages of the three methods in one. It is the principal component analysis are trying to extract the maximum information reflects data variability, but the principal component analysis considers only one independent variable matrix, and partial least squares and a &quot response&quot matrix, predictive capabilities.)

实验matlab实验matlab实验matlab实验matlab(experimental matlab experimental matlabexperimental matlab experimental matlab)

我自己编的串音误差计算函数，对公式进行了一定化简，比本网站其他同类计算函数运算速度要快。用于盲源分离的质量评价，另外还包含ICA中有关性能评价指标的文章。(My own series of crosstalk error calculation function, to the formula for a certain simplification, other than this site similar calculation function operation faster. Quality Evaluation for blind source separation, also contains articles on the ICA performance uation index.)

小波分析，对振动信号的分析，用于提取故障特征。(Wavelet analysis, the analysis of vibration signals for extracting fault feature.)