ID 原文 译文
57648 根据重新定义的路由问题，提出一种满足时延软约束的低能耗路由算法. Also，an energy-efficient routing algorithm is developed，which could meets the soft-constraint of the transmission delay.
57649 仿真结果表明，该算法可以实现能量消耗与传输时延的权衡 Simulations validate that the proposed algorithm achieves the tradeoff between en- ergy consumption and transmission delay.
57650 互联网的发展导致各种信息传输量的激增，对于大数据的处理，奈奎斯特采样定律已经不能满足许多实际工程应用的需要，而压缩感知的出现解决了这个问题. The rapid development of Internet induces huge requirement on transmission，which calls for much more efficient techniques to compress information. Compressive sensing method can sample sparse signals with much less samples than Nyquist’s sampling law with payload of recovery quality and compu- tation complexity.
57651 压缩感知理论指出稀疏信号可以用远小于奈奎斯特采样定律的采样数进行采样，并将原始数据恢复出来. Compressive sensing can not only compress data，but also encrypt them，and thus can be applied to encrypt the information and network.
57652 压缩感知不仅可以将数据进行压缩，而且还具有加密功能，被广泛应用到信息和网络的安全理论中.为了实现更好的加密效果，对压缩感知与其他多种技术相结合( 如混沌系统、置乱和扩散等) 的安全方案进行汇总，并对现有的多种方案的对比结果进行了对比分析. The article reviews the compressive sensing-based en- cryption methods，which combines other technologies ( such as chaotic system，scrambling and diffu- sion) .
57653 结果表明，现有的这些压缩感知安全方案都可以达到良好的加密效果，可以抵抗暴力攻击、统计攻击、已知明文攻击等多种攻击，具有良好的安全性. After analyzing the performance comparison of existing methods，the compressive sensing-based schemes is verified to achieve a great security capability to resist brute-force attack，statistical attack， known plaintext attack and other attacks.
57654 为满足海量数据处理要求，提出了一种基于网格的 K-means 快速聚类算法( SPGK) . To meet the requirements of massive data processing，a grid-based K-means fast clustering al- gorithm ( SPGK) is proposed.
57655 设计基于网格质心的聚类簇个数选取算法，对数据进行网格划分得到每个网格的质心，将质心作为 K-means 聚类的样本点，从而减少 K- means 的欧氏距离计算次数. Selection for optimal clustering initial point and the number of clusters al- gorithm is presented. The grids of different clusters are meshed to obtain the centroid of each grid. These centroid points are used as sample points for K-means clustering，thereby reducing the number of Euclidean distance calculations of K-means.
57656 该算法基于 Spark 平台实现并行计算，进一步地提高了算法的运行效率. SPGK realizes parallel computation based on Spark platform，which fur- ther improves the running efficiency of the algorithm.
57657 SPGK 不但能够获得良好的聚类效果，而且缩减了欧氏距离计算次数，适用于海量数据的快速聚类.在千万级数据集上的实验结果表明，SPGK 的性能明显优于现有的 K-means + + 和基于 K 均值聚类的递归划分方法. SPGK not only obtains good clustering effect but also greatly reduces the number of Euclidean distance calculations，which is suitable for fast clustering of mass data. With 10 millions of data，the experiments show that SPGK is superior to the existing K-means + + and recursive partition based K-means clustering algorithms obviously.