MVAcltbern2

QGA的原代码，你可以很快的上手进行编写你自己所有的程序，如果有什么困难可以联系我，谢谢！(QGA of the original code, you can quickly get started to write all your own procedures, if any difficulties you can contact me, thank you!)

回声隐藏利用人耳听觉系统的时域掩蔽特性，在载体数据的环境特性中嵌入水印信息(Echo hiding ear hearing system of time domain masking properties, environmental characteristics of the data in the carrier embedded watermark )

viterbi硬判决的程序，包括绘制信能曲线图等。基于matlab实现(hard-decision viterbi procedures, including mapping, such as a letter to curves. Based on matlab realize)

matlab的经典教程，初学者必备，内容繁多，一般用于查阅。(matlab tutorial.)

基于时隙aloha的防碰撞改进算法,效率很高哦，希望对大家有用(ALOHA anti-conclusion alogrithm dddddd)

This is a SVM classifier algorithm in Matlab.

说明：  二维自适应维纳滤波，维纳滤波对图像噪声的影响。(Two-dimensional adaptive filtering, Wiener filtering of image noise.)

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))

一种智能优化算法-遗传算法的matlab源程序(genetic algorithm)

利用雷达方程，分析在相干积累和非相干积累脉冲数两种情况下SNR增益与积累脉冲数的关系，得出相应的结论图像。(Using the radar equation to analyze the relationship between the SNR gain in the case of coherent integration and non-coherent accumulated pulse number two with the accumulation of the number of pulses, the corresponding conclusions images.)