贪心算法解决部分背包问题

资源描述基于9轴传感器融合，串口输出姿态欧拉角。

资源描述在本资源中提供了基数排序的实现源码，并附有相应的注释，在vs2012中可以成功编译运行。

dtw是语音识别中的一种重要算法，该算法在语音识别中得到广泛的应用，是一种很实用的算法。

资源描述摘要：在分析无线传感器网络国内外研究现状及技术成熟度的基础上，从技术层面阐述了无线传感器网络与物联 网之间的相互关系，总结了无线传感器网络系统执行所需要的信息采集系统设计、网络服务支持和网络通信协议设 计等关键技术，说明了无线传感器网络未来发展所面临的挑战，并提出了面向物联网的无线传感器网络发展新思路。 关键词：无线传感器网络；物联网；信息采集系统；网络服务支持；网络通信协议

SPHysics is a platform of Smoothed Particle Hydrodynamics (SPH) codes inspired by the formulation of Monaghan (1992) developed jointly by researchers at the Johns Hopkins University (U.S.A.), the University of Vigo (Spain), the University of Manchester (U.K.) and the University of Rome La Sapienza (Italy). Developed over a number of years primarily to study free-surface flow phenomena where Eulerian methods can be difficult to apply, such as waves, impact of dam-breaks on off-shore structures.

这是改进的欧拉算法源码-This is an improved Euler algorithm source code

经典关联分析算法apriori java版。可解决关联分析问题，比如分析超市购物关联关系，产品交叉销售可能性，客户价值提升等

自动谱分析：可用于丢失/采样/子束光谱分析；矢量自动迭代，可用于建模，故障诊断；-The applications of this additional toolbox are: - Automatic spectral analysis for Irregular sampling/Missing data, analysis of spectral subbands, - Vector Autoregressive modeling and Detection [uses ARMASA] - Reduced statistics ARMAsel: A compact yet accurate ARMA model is obtained based on a given power spectrum. Can be used for generation of colored noise with a prescribed spectrum. - ARfil algorithm: The analysis of missing data/irregularly sampled signals - Subband analysis: Accurate analysis of a part of the power spectrum - Detection: Generally applicable test statistic to determine whether two signals have been generated by the same process or not. Based on the Kullback-Leibler index or Likelihood Ratio. - Analysis of segments of data, possibly of unequal length.

经典循环赛日程表算法-classic round robin schedule algorithm

在模式识别中，尤其需要对一些样本进行分类，聚类分析是常用的方法，本程序基于最小最大距离的聚类原则实现对样本的聚类-At pattern recognition, in particular a number of samples required for classification, clustering analysis is a commonly used method, the procedure based on the smallest maximum distance of the cluster principle implementation on sample clustering