framing-code

a document ppt on how to perform fingure print recognition .....

eeg数据处理,此代码为美国ucsd脑科学实验室开发的著名实验程序。(eeg data processing)

说明：  掌握矩阵和数组的一般操作，包括创建、保存、修改和调用等。学习矩阵和数组的加减运算与乘法。掌握对数组中元素的寻访与赋值，会对数组进行一般的操作。 (Master matrix and an array of general operations, including create, save, modify, and recalls. Learning addition and subtraction and multiplication of matrices and arrays. Mastery of the search and the assignment of elements in the array, the array will carry out general operations.)

说明：  本程序用matlab编写，给出了特定参数下，光纤掺杂深度与阈值之间的关系(pumping threshold vs adoping density)

matlab 数字基带传输系统 全真模拟(Matlab digital base-band transmission system simulation of the whole truth)

蓄电池充放电simulink模型，基于三阶模型的铅酸蓄电池建模与仿真_纪伯公(Simulink model for charging and discharging of storage battery)

对基带数字通信的位同步过程进行了仿真，采用Gardner算法，没有用插值。符号用SRRC进行脉冲成形，环路滤波器没有用PI回路，是简单的alfa，1-alfa低通滤波，NCO调整也比较简单。(Baseband digital communications for the bit synchronization process of the simulation, using Gardner algorithm, did not use interpolation. Carried out with SRRC pulse shaping symbols, did not use PI loop filter circuit is a simple alfa ,1-alfa low-pass filter, NCO is also relatively simple to adjust.)

该压缩包为matlab语言的常用算法程序集(The archive is commonly used algorithm matlab assembly language)

再现全息图，读入图片再现全息程序，该路径后可运行，用再现光的方法(Reproducing hologram reproducing holographic read into the program, the path can be run with the method of reproducing light)

说明：  珍藏版，可实现，新K均值聚类算法，分为如下几个步骤： 一、初始化聚类中心 1、根据具体问题，凭经验从样本集中选出C个比较合适的样本作为初始聚类中心。 2、用前C个样本作为初始聚类中心。 3、将全部样本随机地分成C类，计算每类的样本均值，将样本均值作为初始聚类中心。 二、初始聚类 1、按就近原则将样本归入各聚类中心所代表的类中。 2、取一样本，将其归入与其最近的聚类中心的那一类中，重新计算样本均值，更新聚类中心。然后取下一样本，重复操作，直至所有样本归入相应类中。 三、判断聚类是否合理 采用误差平方和准则函数判断聚类是否合理，不合理则修改分类。循环进行判断、修改直至达到算法终止条件。(NewK-means clustering algorithm ，Divided into the following several steps: A, initialize clustering center 1, according to the specific problems, from samples with experience selected C a more appropriate focus the sample as the initial clustering center. 2, with former C a sample as the initial clustering center. 3, will all samples randomly divided into C, calculate the sample mean, each the sample mean as the initial clustering center. Second, initial clustering 1, according to the sample into the nearest principle clustering center represents the class. 2, as this, take the its recent as clustering center of that category, recount the sample mean, update clustering center. And then taking off, as this, repeated operation until all samples into the corresponding class. Three, judge clustering is reasonable Adopt error squares principles function cluster analysis.after clustering whether reasonable, no reasonable criterion revisio)