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说明：  BCI2008竞赛的数据集说明，对下过该数据集的研究者帮助甚大，这也是国际最新的BCI竞赛数据(BCI2008 competition data set shows the data for the next set of researchers have contributed greatly, and this is the latest international BCI competition data)

可以用来求信号的均值，滑动系数可以进行更改，速度很快(Can be used to request the signal mean sliding coefficient can be changed fast)

使用5个隐含节点的ANN模型对污水生化处理中的沉速进行拟合，matlab调试通过。(The five hidden ANN fit the model of the settling velocity of the sewage biochemical treatment)

KSVDBox - Implementation of the K-SVD and K-SVD denoise algorithms.(K-SVD dictionary training. ksvd - K-SVD dictionary training. Signal denoising. ompdenoise - OMP denoising. ompdenoise1 - OMP denoising of 1-D signals. ompdenoise2 - OMP denoising of 2-D signals. ompdenoise3 - OMP denoising of 3-D signals. ksvddenoise - K-SVD denoising. Overcomplete DCT dictionary. odctdict - Overcomplete DCT dictionary. odct2dict - 2-D overcomplete DCT dictionary. odct3dict - 3-D overcomplete DCT dictionary. odctndict - Multi-dimensional overcomplete DCT dictionary. Demonstrations. ksvddemo - K-SVD training demonstration. ksvddenoisedemo- K-SVD denoising demonstration. Miscellaneous. showdict - Display a dictionary of image patches. ksvdver - K-SVD toolbox version information.)

一些源程序和一些电子课件，还有数字信号处理试验，很不错~！(Some source code and a number of electronic courseware, as well as digital signal processing test, very good ~!)

压缩包里是matlab三个著名工具箱的一些教程，包括gaot，BSheffield大学gatbs工具箱，matlab自带的gads，还有工具箱的安装方法。(Three well-known compression bag is matlab toolbox tutorials, including gaot, BSheffield University gatbs toolbox, matlab built-gads, there Toolbox installation.)

说明：  对振动加速度信号进行逆傅里叶变换，支持Excel和text读取，希望大家批评指正(The inverse Fourier transform of the vibration acceleration signal supports the reading of Excel and text. I hope you can criticize and correct it)

decode simple domain log map

自己写的遗传算法程序，对初学者来说很实用，可以运行！(very good！)

demonstrates the compressive sensing using a sparse signal in Time domain. The signal consists of a UWB (Ultra Wide Band) pulse in time domain. The signal is sparse in Time domain and therefore K random measurements are taken in Frequency domain.