chapter2-vectorization

说明：  上传是以.tex形式的matlab100个各种仿真例程源码！非常有用的哦！(From on. Tex form of a variety of simulation routines matlab100 source! Oh, very useful!)

MATLAB GUI教学视频8：选择参数绘制曲线(MATLAB GUI instruction video 8: Select the parameters to draw the curve)

针对无线射频识别(radio frequency identification，RFID)系统的多标签碰撞问题，在分析RFID标签编码规则的基础上，提出根据标签编码规则进行标签分组和标签预淘汰的方法，在读写器识别的初始阶段根据特定的规则淘汰部分标签，从而减少系统工量，提高系统工作效率.实例证明：在不增加标签结构复杂度的基础上，充分利用已有的有效信息能够有效减少阅读器的查询次数和系统的总通信量．(For radio frequency identification (radio frequency identification, RFID) system is a multi-tag collision problem, based on the analysis of the RFID tag encoding rules based on the proposed label grouping and labeling pre-elimination method according to the label encoding rules identified in the initial reader phase-out of the label according to specific rules, thus reducing the amount of system engineering, to improve system efficiency examples demonstrate: the label without increasing the complexity of the structure on the basis of full and effective use of existing information can be effective in reducing the number of queries and reader total traffic of the system.)

说明：  卡尔曼的二维程序代码，可以让大家了解卡尔曼的二维性质。(kalman)

惯性导航轨迹生成器，用于生成惯性导航轨迹(INS trajectory generator, used to generate the inertial navigation path)

该pdf详细描述了RLS自适应滤波器的原理以及其算法实现(Pdf detailed description of the RLS adaptive filter theory and its algorithm)

非结构网格剖分，进行平面网格的剖分，可以插值复杂地形(Unstructured mesh generation, the plane of the grid mesh, can be interpolated complex terrain)

雷达机动目标跟踪源程序，对学习卡尔曼滤波有指导性意义。(The source program of maneuvering target tracking, radar kalman filter has a guiding significance to the study.)

子空间matlab建模程序实力，是期中的MOESP方法！(Subspace matlab modeling program strength is period of MOESP way!)

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.