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Xport Transport Stream Demuxer Updated 09/06/2007 - current version is 0.98(Xport Transport Stream Demuxer Updated 09/06/2007- current version is 0.98)

selforganising map for the matching algorithm for the biometric appilcations enroll ment

TCSC controller for everyone, i try my best, but maybe it s worst

Matlab环境下基频提取的代码，比较准确(Code to extract pitch in Matlab，which is comparatively correct)

some examples to learn basic c++

潮流准调和分析 Dd.m [DO1 dO1 DK1 dK1 DM2 dM2 DS2 dS2 DM4 dM4 DMS4 dMS4]=Dd(y,D,Y,tm,A1) 计算6个准调和分潮的振幅系数和初相角公式 aM.m [N n aa liu_u liu_v]=aM() 基础系数矩阵，目前D和d值是取用的各段数据的中间时间值，t是以中间为0左右对称取； 视差潮龄A1需要预先得知 同时本程序负责读取原始测流数据，格式为 站位名称 观测数据段数N（即大 中 小潮） 第一段起始时间YYYY MM DD HH mm ss 第一段个数n1 speed direction speed direction ... 第二段起始时间 YYYY MM DD HH mm ss 第二段个数n2 speed direction speed direction ... 第三段起始时间 YYYY MM DD HH mm ss 第三段个数n3 speed direction speed direction ... UVg.m [U V Ug Vg]=UVg() 求调和常数U V和迟角Ug Vg Tuoyuan.m 计算各准调和分潮的椭圆要素W theta K k (The quasi harmonic analysis of tidal current Dd.m [DO1 dO1 DK1 dK1 DM2 dM2 DS2 dS2 DM4 dM4 DMS4 dMS4]=Dd (y, D, Y, TM, A1) calculation of 6 quasi harmonic division of amplitude and initial phase angle formula of the coefficient of tide AM.m [N n AA liu_u liu_v]=aM () foundation coefficient matrix, the current D and D values are the middle time of each section of data taken the value of T is in the middle for symmetric about 0 Age of parallax A1 need to know in advance At the same time, this program is responsible for reading the original flow measurement data, format for Site name The observation data segment number N (that is, in the low tide) The first section of the YYYY MM DD HH mm the starting time of SS The first section number N1 Speed direction Speed direction ... The second section starting time YYYY MM DD HH mm SS The second section number N2 Speed direction Speed direction ... The third section starting time YYYY MM DD HH mm SS The third section number )

最小二乘拟合指数函数,自编程序，非常有用(Least squares fitting index function, the program is compiled, very useful)

适当选择辅助变量，使之满足相应条件，参数估计值就可以是无偏一致。估计辅助变量法的计算量与最小二乘法相当，但辨识效果却比最小二乘法好的多。尤其当噪声是有色的，而噪声的模型结构又不好确定时，增广最小二乘法和广义最小二乘法一般都不好直接应用，因为他们需要选用特定的模型结构，而辅助变量法不需要确定噪声的模型结构，因此辅助变量法就显得更为灵活，但辅助变量法不能同时获得噪声模型的参数估计。(Choose appropriate secondary variables, meet the relevant conditions and parameter estimate can be unbiased consistent. Estimated auxiliary variable method calculation and least square method is quite, but the identification effect is much better than the least square method. Especially when the noise is colored, and noise model structure and not sure, augmented the least squares and the generalized least squares method is generally not used directly, because they need to choose specific model structure, and auxiliary variable method does not need to make sure that noise model structure, so the auxiliary variable method is more agile, but not at the same time auxiliary variable method for noise model parameter estimation.)

matlab 入门教程 适合刚入门的学生学习(matlab Tutorial)

matlab灰色模型，基本的代码，写的比较详细 (Matlab grey model of the basic application )