picPCA

matlab编写结构计算振型分解法。。。。。。。。。。。。。。。(the preparation of structural calculations matlab vibration mode。。。。。。。。。。。。。。。。。。。。)

用周期循环检测算法检测： 首先运行a.m程序，通过此程序运算得出循环频率a值； 再运行zhouqi.m程序，对a值进行确定，通过周期循环算法对信号进行检测； (With cycle detection algorithm to detect: First run am program, through this program computing a value obtained cyclic frequency zhouqi.m then run the program, for a value determined by the cycle algorithm of signal detection )

该例子介绍了zemax在序列模式下融入非序列，光线从每个被定义的场点(Field Point)射向系统孔径，并且穿越非序列性表面(NSC Surface)前的所有序列性表面(In NSC with Port Design, the system uses the system as defined in aperture (System Aperture) and field (Field) in the sequence mode. The light from each field defined in point (Field Point) fired at the system aperture, and through all sequences of non-sequential surface (NSC Surface) before surface)

说明：  画海洋中一些海图，对于学海洋学生来说很重要(It is important for ocean students to draw charts in the ocean)

模式识别matlab工具箱,包括SVM,ICA,PCA,NN等等模式识别算法,很有参考价值(pattern recognition Matlab toolbox, including SVM, ICA, PCA, NN pattern recognition algorithms, and so on, of great reference value)

特征选择matlab程序, 该程序由多种特征选择算法组成，可根据需要进行选择。(Feature selection matlab program, which composed by a variety of feature selection algorithm can be selected according to need.)

这是用身高体重数据进行性别分类的实验。 用最小错误率贝叶斯分类器决策时，首先通过比较概率大小判断一个体重身高二维向量代表的人是男是女，然后再逐一与已知性别的数据比较，就可以得到错误率的统计。然后改变先验概率，重复上面的过程，观察数据结果的变化。 用最小风险贝叶斯分类器决策时，首先求出用最小错误率贝叶斯分类器得到的条件概率；然后根据人为给定的决策表，根据公式算出条件风险；然后逐一比较条件风险，找出使条件风险最小的决策（也就是分类）。最后用分类得到的结果逐一比较已经知道的原始数据，统计处错误率。 (This is the height and weight data for gender classification experiment. With the minimum error rate Bayesian classifier decisions , first by comparing the probability of the size and weight to height to determine a person represented by two-dimensional vector is male or female , and then one by one with known gender data comparison, the statistical error rate can be . Then change the prior probability , repeat the above process , the results of the changes observed data . Bayesian classifier with the minimum risk decision-making , first find the minimum error rate using Bayesian classifier to get the conditional probability then artificially given decision table , according to the formula to calculate conditional risk and then one by one more conditional risk , to find ambassador to the conditions of minimum risk decision making (ie classification) . Finally, the results obtained with the classification by-side comparison of the raw data have been aware of SD error rate . )

A MATEMATICA DOS CODIGOS DE BARRAS

Emulates a Turing Machine

A new multi hop routing scheme for athlete monitoring using wban