steffenson

关于matlab的一篇很有用的文章，数学建模学习的必备啊！(on Matlab a very useful article, the study mathematical modeling required ah!)

统计信号处理不相干信号的估计，使用MUSIC算法(Estimate statistical signal processing irrelevant signals using MUSIC algorithm)

这是一个卷积码的编码的源程序，适用于实现卷积码编码的MATLAB仿真(This is a convolution code coding the source program, suitable to achieve convolution code coding of MATLAB simulation.)

netcdf插件安装和.nc格式数据的读取,主要操作mexcdf53.dll和netcdf.dll文件,适于从事海洋和大气专业的的数据格式.作者在实际中用matlab处理了一些此类数据，简单说来就是安装一个插件即可.现将我们的方法具体地写出，以供参考(NetCDF installation and plug-ins. nc format to read data, the main operating mexcdf53.dll and netcdf.dll document, suitable for marine and atmospheric data format professional. Author matlab used in the actual treatment a number of such data, simple to say that you can install a plug-in. The way we write specifically for reference)

说明：  用MATLAB实现的灰色理论代码，采用word的格式。欢迎下载。(Implemented using MATLAB code for gray theory, using word format. Welcome to download.)

这两个是通信中基本仿真程序，一个是单边带调幅和双边带调幅的对比，另一是使用希尔伯特滤波器进行的单边带振幅调制解调．(these are two basic communication simulation program, is a single-sideband AM band and bilateral AM contrast, Another is the use of Hilbert's single sideband filter amplitude modulation and demodulation.)

主成分分析代码，用matlab实现的，请大家参阅。(Principal component analysis code, matlab implementation, please refer to the.)

一个水下无人自主航行器（AUV）的MATLAB/simulink仿真程序，写得很详细，有附带的s函数和m文件供参考学习。看资源里水下无人航行器的仿真程序比较少，特上传来与大家分享。(An underwater autonomous Vehicle (AUV) in the MATLAB/simulink simulation program, written very detailed, with the s functions and m files for reference study. To see resources in unmanned underwater vehicle simulation program relatively small, special upload to share with you.)

to find the extremas of any 1-d signal

变步长自适应滤波算法，对VS—LMS算法进行了改进，建立了步长因子μ与误差信号e（n）之间另一种新的非线性函数关系．理论分析和计算机仿真结果表明，该关系不仅具有原有算法收敛速度快的优点，而且在低信噪比环境下比原有算法具有更好的抗噪声性能．(The variable step size adaptive filtering algorithm is discussed, and the VS-LMS algorithm is improved. A new nonlinear function relation between the step size factor μ and the error signal e (n) is established. Theoretical analysis and computer simulation results show that the proposed algorithm not only has the advantages of high convergence speed of the original algorithm, but also has better anti- noise performance in the low SNR environment than the original algorithm.)