doadec105

2个不同调制方式的MATLAB仿真程序，是QPSK和QAM的，都非常好(Two different modulation of the MATLAB simulation program is QPSK and QAM, are very good)

牛顿法使用MATLAB编写的,请大家分享,我愿和大家一起努力!(Newton prepared using MATLAB, please share, I would like to work with everyone!)

QpSk Enhanced version on Matlab

matlab cod efor 2013 ieee paper a two level fh cdma

这是一个（2.1.3）卷积码编码、BSC信道传输、Viterbi译码组成的程序合集，运行main.m即可得到仿真结果(This is a (2.1.3) convolutional code, Viterbi decoding, BSC channel transmission is composed of collection procedures, running main.m can be obtained by the simulation results)

EOF时空分解源代码，详细细节和注释，一看就懂，基于matlab(EOF temporal decomposition source code, further details and comments, one can understand, based on matlab)

Autoencoders for dimensionality reduction using stacked restricted boltzmann machines

基于labvieW的光纤光栅采集程序，使用了MOI公司的sm125解调仪。(FBG based labvieW collection procedures, the use of MOI' s sm125 demodulator.)

Adaptive Sparse Channel Estimation for Time-Variant MIMO-OFDM Systems

高速铁路路基沉降是一个复杂的系统过程,涉及不同时期、不同工况,选用单一的预测模型不足以反映路基整体的变形规律.Logistic模型及Verhulst模型的曲线均为S形,可以反映全过程的地基沉降与时间的关系.针对路基填筑期和降水期两个时段利用MATLAB软件编制程序计算模型参数,得到Logistic模型和灰色Verhulst模型,进而求得两者的最优加权组合模型.对比结果表明:在路基填筑期由两者的最优加权组合模型预测得到的结果与实测结果较吻合,拟合精度高于单一模型 而在降水期Verhulst模型的预测值更接近实测值,平均相对误差约为1.75％,拟合精度较高.(High-speed railway subgrade settlement process is a complex system involving different times and in different conditions, the choice of a single forecasting model does not adequately reflect the overall deformation of roadbed. Curve Logistic model and Verhulst models are S-shaped, the whole process can be reflected foundation settlement versus time. against roadbed construction period of two hours and precipitation using MATLAB software programming model parameters, the Logistic model and gray Verhulst model, and then get the optimal model of a weighted combination of the two. comparative results indicate : results and experimental results in the roadbed construction period by the weighted combination of the best of both models predicted than the anastomosis fitting accuracy than a single model precipitation predicted value of Verhulst model closer to the actual value of the average relative error approximately 1.75 fitting accuracy.)