qpsk-matlab

摘　要:针对茄子图像的灰度和颜色特点,利用MATLAB中丰富的图像处理函数,分别进行了色差分割和色调分割。在 色调分割中,采用了自动选取阈值的Otsu法。在去除残留噪音的处理中,采用标注的方法对二值图像的各连通区域进行面积 统计。保留最大面积的区域,从而使分割效果大大改善。利用多参数来衡量分割效果,使评价做到最大程度的客观、合理。(Based on the gray- level and color characteristics of eggp lant image, hue and color- difference segmentation were conducted by using the rich image p rocessing functions embedded inMATLAB. The auto threshold- adap tivemethod ofOtsu operation was specially used for hue segmentation. And upon the elimination of the residue noise, labelingmethod for statistical calculation was introduced for the connected regions of the binary image. Themaximum areaswere p reserved to imp rove segmentation effects. In addi2 tion, multi- indiceswere app lied to assess the effect of segmentation, in order to get impersonal and reasonable assessment)

this is about solving physic problem whit matlab

matlab code for that pdf

在相空间重构中，用互信息法求最小关联嵌入维(In phase space reconstruction, the use of mutual information method associated minimum embedding dimension)

说明：  一维光子晶体传输矩阵法，matlab显示带隙图像。(One-dimensional photonic crystal transfer matrix method, matlab display images bandgap.)

说明：  快速单词拼写检错器，能实现一般的文本操作，另外还能检查单词拼写的对错，字典可以自定义。(rapid spelling error device, able to operate normally text, and checking the wrong spelling, dictionary can define.)

基于Taubin论文方法的最小二乘圆拟合法 (Circle fit by Taubin G. Taubin, "Estimation Of Planar Curves, Surfaces And Nonplanar Space Curves Defined By Implicit Equations, With Applications To Edge And Range Image Segmentation",IEEE Trans. PAMI, Vol. 13, pages 1115-1138, (1991))

You all might have used CTime or CTimeSpan to manipulate the system timer. Here is an article to show you how your system timer works. I will give you an idea of port communications behind the system clock manipulations. Those who are a little biased on hardware and software interfacing practices would find this article of great help.

余子式、行列式、逆矩阵、对数组进行全排列(Bulbil style, determinant, inverse matrix, wide array of arrays)

Edge detection result should be enhanced using linear method like Median filter to remove the garbage around the pupil to gain clear pupil to determine perfect centre. Get the centre of the pupil by counting the number of black pixels (zero value) of each column and row. Then get each row and column that has the maximum number of these black pixels. Then determine the center by simple calculation according to the image coordinate to set it correct on the image, consequently we can determine the radius of the pupil. Thus we can find the pupillary boundary (inner). A similar procedure is extended by using a coarse scale to locate the outer boundary (limbus) which can be apparent by using the mid-point algorithms of circle and ellipse.