Gauss

使用中文分词包ansj进行分词停词的代码使用中文分词包ansj进行分词停词的代码使用中文分词包ansj进行分词停词的代码使用中文分词包ansj进行分词停词的代码使用中文分词包ansj进行分词停词的代码使用中文分词包ansj进行分词停词的代码

在最近一份文件 （"多尺度表示和匹配的曲线密码子"，Paul 松香、 CVGIP： 图形化的模型和图像处理，Vol 55页 286 310，1993年） 描述了如何分割并由曲线其中包括密码由霍夫曼和理查兹加上定义的一组标签更多的处理开放曲线、 直线节等。为了克服即在其"自然"的尺度-平滑曲线噪声问题那些描述曲线的一些定性的独特结构。在不同尺度的密码子相连，形成一个层次结构 （"子树"）。图则的曲线然后匹配子模型，通过搜索密码子树。自便利匹配的密码子标签都附有各种形状的措施（如压实度，斜）。

资源描述mpu6050的内部文件，提供源代码的四元数算法，通过加速度和加速度计算出欧拉角

求解非线性方程组的一组源代码,FORTRAN90.用于解决N个未知数,N-1个方程.-Solving nonlinear equations of a set of source code, FORTRAN90. For settlement of N unknown, N-1 equations.

FPgrowth is the one of the algorithm in frequent item set mining. It is used to find the frequent item set in a database. It will give the output in tree structure format. It is more efficient than apriori algorithm because there is no candidate generation. 

开发环境：Visual C++ .net2003 功能：利用禁忌搜索思想求解TSP问题。-Development Environment: Visual C++. Net2003 functions: thinking of the use of tabu search for solving TSP problems.

应用背景On the Design of ZF and MMSE Tomlinson-Harashima Precoding in Multiuser MIMO Amplify-and-Forward Relay System,it is main described the mimo system in the multi-user  scenario.the main precoding algrithm is combined the zf precoding and the MMSE precoding.关键技术we design a zero-forcing (ZF) Tomlinson-Harashima precoding (THP) and a minimum mean square error (MMSE) THP for multiuser MIMO relay system with direct link. Based on these precoding structures, we propose the optimal combining weights of the ZF THP and the MMSE THP in half duplex relay system, and the sub-optimal weights of MMSE THP to reduce feedback channel state information(CSI). Through the Monte Carlo simulation, we verify that the optimal weights combining method outperforms the constant weights combining method. In addition, we confirm that the performance of the sub-optimal weights combining method is&

河内塔的数学解析。用VisualC++编写

指派问题的匈牙利解法，从文件中读入待指派的数据。-Assignment problem of the Hungarian method, read from a file to be assigned to data.

adaptive dimension reduction for clustering high dimensional data