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tv模型代码,用到的偏微分方程技巧和各项异性扩散(tv model code, techniques used in partial differential equations and anisotropic diffusion)

这是一个关于完成QAM调制的Matlab示例程序(This is a complete QAM modulation on the sample of Matlab procedures)

这是一个在MATLAB环境下，gardner位恢复源码，很有参考价值(This is a MATLAB environment, gardner-bit source to resume, a good reference)

混沌的一些具体行为，混沌的分岔等等理论，以及混沌的一些特征问题等等。(Some of the specific acts of Chaos, chaos theory of bifurcation, etc., as well as problems, and so some of the characteristics of chaos.)

白噪声背景下，均匀圆阵列的波束形成，并用三维形式显示出来(White noise background, the uniform circular array beamforming, and displayed three-dimensional form)

This matlab code is about the Time history of Keppler Orbit.

说明：  用于单变量的偏最小二乘回归，直接给出回归系数。 X0是自变量矩阵，Y0是因变量向量，PRESS是去除其中某一个样本而回归出的方程对 缺失样本的误差估计，SS则是使用全部样本回归出的方程对同一个样本的误差估计。 在h个成分时，计算PRESS_h与SS_h-1，若1-PRESS/SS大于0.0975时，应增加成份。 [row,col]=size(pz) (For single-variable partial least squares regression, regression coefficients are given directly. X0 is the independent variable matrix, Y0 is the dependent variable vector, PRESS is to remove one of them out of the sample and the regression equation for missing samples error estimates, SS is the use of all of the sample regression equation out of a sample of the same error estimates. In h constituent, the calculation PRESS_h and SS_h-1, if the 1-PRESS/SS greater than 0.0975, to increase the ingredients. [Row, col] = size (pz) )

kriging（克里金）插值方法，参照2011年国赛建模A题前两问。(kriging (kriging) interpolation method, with reference to 2011 model A country race before the two asked questions.)

说明：  信号SG平滑滤波Matlab程序仿真，简单实用，对信号的前期处理很有用(SG signal smoothing filter Matlab simulation program, simple and practical, pre-treatment of the useful signal)

在源信号和传输信道未知情况下，只利用接收天线的观测数据抽取源信号，称为盲信号分离.盲信号分离不仅是信号处理界、而且也是神经网络界的研究热点课题，在无线数据通信、雷达、图像、语音、医学以及地震信号处理等领域都具有广阔的应用前景.采用自然梯度法和分阶段学习法。(The source signal and transmission channel is unknown circumstances, using only observational data extraction receiving antenna source signal, known as blind signal separation. Blind signal separation is not only the signal processing community, but also a neural network hot topic in research circles, in the wireless data communications , radar images, audio, medical and seismic signal processing and other fields have broad application prospects. and the phases using the natural gradient learning method.)