daixiaoboshiyichengxu

通过本书可对pso有个全面深入地了解，为工程应用作准备(Through this book on PSO has a comprehensive and in-depth understanding for the engineering applications to prepare)

讲述了各种matlab在滤波器设计时可应用的函数，可供学习MATLAB的人使用。(About a variety of matlab filter design can be applied in a function, for people learning to use MATLAB.)

通信系统中基于MATLAB的四相移相键控调制的仿真(MATLAB-based communication system of the four-phase phase shift keying modulation simulation)

fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!. Given the samples of a laplace distribution, the PDF parameter is found fits data to the probability of the form: p(x) = 1/(2*b)*exp(-abs(x-u)/b) with parameters: u,b format: result = fit_ML_laplace( x,hAx ) input: x - vector, samples with laplace distribution to be parameterized hAx - handle of an axis, on which the fitted distribution is plotted if h is given empty, a figure is created. output: result - structure with the fields u,b - fitted parameters CRB_b - Cram?r-Rao Bound for the estimator value RMS - RMS error of the estimation type - ML

说明：  matlab图像程序.zip，含各个章节的实例，挺好的学习(matlab image process. zip, containing examples of the various chapters, very good learning)

自适应波束形成（智能天线）的MATLAB代码仿真程序(Adaptive beamforming (smart antennas) code of the MATLAB simulation program)

Matlab, picture, example of color mix

产生、播放DTMF音频，基于Goertzel算法估计DTMF音频，完成双音多频(DTMF)信号的检测(Generating, playing DTMF tones, DTMF tones based on the estimated Goertzel algorithm to detect the completion of dual tone multi-frequency (DTMF) signal)

说明：  数值分析中利用matlab实现牛顿法计算样例(Matlab Newton Methods)

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))