An-efficient-routing

这是一个学习matlab很好的教程，里面有很多课件。(this is an usefull materials，it is helpful for matlab learner！)

The DocPolynom to Character Converter The char method produces a character string that represents the polynomial displayed as powers of an independent variable, x. Therefore, after you have specified a value for x, the string returned is a syntactically correct MATLAB expression, which you can evaluate. The char method uses a cell array to collect the string components that make up the displayed polynomial. The disp method uses char to format the DocPolynom object for display. Class users are not likely to call the char or disp methods directly, but these methods enable the DocPolynom class to behave like other data classes in MATLAB. Here is the char method.

说明：  本书主要介绍MATLAB的图形绘制和用户图形界面实现技术。全书主要由三个部分组成：MATLAB语言介绍、MATLAB二维和三维图形绘制方法以及GUI的开发与应用。书中通过大量的实例深入浅出地介绍了MATLAB二维、三维曲线和曲面图形的绘制方法以及图形用户界面的设计和编程，可以帮助MATLAB设计人员完成各种特征数据的可视化并建立良好的图形界面与用户进行交互，使MATLAB强大的计算和设计功能得以充分体现。 本书适用于MATLAB的使用开发人员、大中院校师生以及广大的业余爱好者阅读，可作为相关专业的教材或参考资料。 (err)

主要利用MATLAB进行有关图像的最基本处理，如图像增强，图像融合，滤波。。。(Mainly use MATLAB for the image of the most basic processing, such as image enhancement, image fusion, filter...)

beamforming of smart antena

Multisensor image fusion has its effective utilization for surveillance. In this paper, we utilize a pulse coupled neural network method to merge images different sensors, in order to enhance visualization for surveillance. On the basis of standard mathematical model of pulse coupled neural network, a novel step function is adopted to generate pulses. Subjective and objective image fusion performance measures are introduced to assess the performance of image fusion schemes. Experimental results show that the image fusion method using pulse coupled neural network is effective to merge images different sensor

说明：  模拟退火算法在许多事例中的应用例程，希望对大多初学者有所帮助(mon itu ihu osu anf aza ilich engzhongd e yngy ong)

利用matlab计算图像的辐射精度（包含均值、方差、偏斜度和陡度）和信息量。(Calculation of the radiation image using matlab precision (including mean, variance, skewness and steepness), and the amount of information.)

自己编写的稀疏阵列的matlab波束形成程序，可画出9阵元稀疏阵列的阵列方向图，以及相应阵列的排列示意图和同等数目阵元的均匀阵列的对比效果图(sparse array beamforming)

Image Texture Classification Using Combined Grey Level Co-Occurrence Probabilities and Support Vector Machines Texture refers to properties that represent the surface or structure of an object and is defined as something consisting of mutually related elements. The main focus in this study is to do texture segmentation and classification for texture digital images. Grey level co-occurrence probabilities (GLCP) method is being used to extract features from texture image. Gaussian support vector machines (GSVM) have been proposed to do classification on the extracted features. A popular Brodatz texture album had been chosen to test out the result. In this study, a combined GLCP-GSVM shows an improvement over GLCP in terms of classification accuracy.