reed_solomon_code

通过M atlab 模拟了随机产生的一定概率分布的信号序列, 叠加高斯噪声后利用最大后验概率(MAP)检 测到的误码率, 做出BER~ SNR曲线, 并与理论计算得到的曲线进行比较, 两者吻合.(M atlab simulated by a certain probability distribution of random signal sequence, use of superposition of Gaussian noise is the maximum a posteriori (MAP) detection to the error rate, making BER ~ SNR curves obtained with the theoretical curve of the combined fit.)

利用模式理论，对给定几何尺寸以及折射率分布的光纤进行模式求解与绘图，该程序给出LP02模的求解与绘图。(the LP02 mode of the fiber)

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.

电力系统潮流计算matlab牛拉法潮流计算(Niu Lafa Power Flow Calculation)

downquad function for wavelet processing

说明：  是调制解调的VC远程序,对于要学习这方面知识的初学者很有帮助,快来下载吧(Is the modulation and demodulation of the VC process is far, for to learn this knowledge useful for beginners, Come download it)

Function uses Standard Hough Transform to detect circles in a binary image. According to the Hough Transform for circles, each pixel in image space corresponds to a circle in Hough space and vise versa. upper left corner of image is the origin of coordinate system.

一篇有关利用轮廓在自然图像中检测和定位交点的英文文献，该方法是在64位操作系统中进行的(One on the use of contours in natural images to detect and locate the intersection of English literature, the method is a 64-bit operating system for the)

说明：  主要用于探测PSINSAR过程中的形变信息(used for PSINSAR detect deformation)

SURF_DETECTOR SURF (Speeded Up Robust Feature) is a robust image detector and descriptor, first presented by Herbert Bay et al. in 2006, that can be used in computer vision tasks like object recognition or 3D reconstruction. It is partly inspired by the SIFT descriptor.