ASTAR235

说明：  我自己完成的MATLAB仿真雷达视频积累程序，提供给大家一起来探讨，大家也来卡那看看这么样 (I completed the MATLAB simulation radar video accumulation procedures, and providing an up to you to explore, we have to look at the card so it kind)

Matlab潮流程序非常好用的程式 欢迎大家来使用(The trend of the procedure is very easy to use Matlab programs welcome you to use)

模拟退火算法，常用的数值优化算法，适合初学者，非常方便(simulated annealing method, commonly used numerical optimization algorithm, for beginners, a very convenient)

MATLAB code to Import two images and perform Image Addition,subtraction,multiplication, division,blending

matlab实验课件。详细介绍了matlab最基本的操作和功能，是两学期的内容。适合自学。(Matlab experiment courseware. Introduces in detail the matlab the basic operation and function, the contents of two semesters. Suitable for self-study.)

本程序是用Q值法求得的关于席位的分配问题， 并对最结果进行了一定的考虑，是否公平，公平程度如何。(This program is to use Q value method about optimal seats, the allocation of And the results were certain consideration, whether fair, fair to what extent. )

雷达系统7------计算雷达系统中的CNR(Radar System 7------ calculate the CNR of the radar system)

陀螺仪数据采集程序 采用转换串口采集数据后直接传到MATLAB中，适用于单轴陀螺仪数据快速采集与标定(the programme to get the datas from gyros)

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))

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