SNR_esprit

通过MONTE GARLO仿真，说明一个使用二进制FSK并受到最坏部分频带干扰破坏的FH数字通信系统的性能。(MONTE GARLO through the simulation shows that the use of a binary FSK and are subject to the worst part of band interference and sabotage by FH digital communication system performance.)

Wiley, Introduction To Digital Signal Processing And Filter Design (2005) Ling Lotb [use of Matlab]

QPSK的基本结构原理与噪声下的SNR,这是最基本的方式(qpsk，basic construction and noise)

基于负熵的盲源分离源程序,对盲源分离领域属于最基本的程序(BSS based on negentropy)

matlab code for Plot data with error bars on both x and y axes

MATLAB实现的KNN 压缩文件包含m文件和wine.txt(机器学习著名的红酒数据集，下载自UCI大学机器学习数据集： http://archive.ics.uci.edu/ml/datasets/Wine） (knn wrote in MATLAB)

说明：  基于MIMO雷达的MUSIC算法，寻找峰值对应目标角度(MUSIC algorithm based on MIMO radar to find the target angle corresponding to the peak value)

系统辨识和自适应控制中极点配置直接自矫正控制(Direct from the poles corrective control in System identification and adaptive control)

calculates BER of BPSK on OFDM

汽车高斯曲面拟合 --- 2程序，以适应到表面二维高斯： 子= A *的进出口（ -（（西为X0）^2/2/sigmax^2 +（艺Y0的）^2/2/sigmay^ 2）。。）+ b的 这些例程是自动在某种意义上说，他们并不需要出发对模型参数的猜测规范。 autoGaussianSurfML（十一，彝，子）适合通过对模型参数的最大似然（最小二乘）。它首先计算了该模型在许多可能的参数值，然后选择最佳质量设置和细化与lsqcurvefit它。 autoGaussianSurfGS（十一，彝，紫）的估计，通过指定数据的贝叶斯生成模型，然后采取通过从模型吉布斯抽样样本后ofthis模型参数。这种(Auto Gaussian Surface fit --- 2 routines to fit a 2D Gaussian to a surface: zi = a*exp(-((xi-x0).^2/2/sigmax^2+ (yi-y0).^2/2/sigmay^2))+ b The routines are automatic in the sense that they do not require the specification of starting guesses for the model parameters. autoGaussianSurfML(xi,yi,zi) fits the model parameters through maximum likelihood(least-squares). It first evaluates the quality of the model at many possible values of the parameters then chooses the best set and refines it with lsqcurvefit. autoGaussianSurfGS(xi,yi,zi) estimates the model parameters by specifying a Bayesian generative model for the data, then taking samples from the posterior ofthis model through Gibbs sampling. This method is insensitive to local minimain posterior and gives meaningful error bars (Bayesian confidence intervals))