ID 原文 译文
22855 针对软扩频信号因采用了编码技术使得伪码序列难以估计的问题，该文提出一种基于 K-means 聚类改进的软扩频信号伪码序列盲估计方法。 For the problem of the soft spread spectrum signal Pseudo-Noise (PN) sequence is difficult to estimate by using the coding technology, a blind estimation PN sequence method of soft spread spectrum signal is proposed based on improved K-means algorithm.
22856 该方法首先以单倍伪码周期的窗长对接收信号进行数据分段以构造观测数据矩阵，其次利用相似测度的理论从观测数据中寻找出 K-means 算法最优的初始聚类中心点，然后通过搜索平均轮廓系数(Silhouette  Coefficient,  SC)最大的绝对值以完成伪码集合规模数的估计，最后找到估计的伪码集合规模数所对应的聚类中心点集合，进一步完成对软扩频信号伪码序列的盲估计。 Firstly, the received signal is divided into continuous non-overlapping temporal vectors according to one period of PN sequence to construct observation data matrix. Secondly, the similarity measure theory is applied to find out the optimal initial clustering center point of K-means algorithm from the observed matrix. Then the number of scale of PN sequence can be estimated by searching for the maximum absolute value of the average Silhouette Coefficient (SC). Finally, the estimated clustering center point corresponding to the number of scale of PN sequence is found, and the blind estimation PN sequence of the soft spread spectrum signal is further completed.
22857 通过仿真实验表明，在伪码序列估计错误概率低于 0.1 的情况下，该文方法比未改进方法提高信噪比约 4  d B； The simulation results show that the proposed method improves the Signal-to-Noise Ratio (SNR) about 4 dB compared to the traditional method under the condition of the estimation error probability of PN sequence is less than 0.1.
22858 而且在同一条件下，该文方法对信号的盲解扩性能优于未改进的方法。 Moreover, the blind dispreading performance is also better than unmodified method under the same condition.
22859 为了进一步降低芯片内无损压缩的运算复杂度和编码时间，该文在保持高压缩率的基础上，提出一种基于方向预测和混合熵编码的快速无损压缩算法。 A fast-lossless compression using texture prediction and mixed golomb coding is proposed to reduce the computational complexity while keeping high compression ratio.
22860 该算法首先采用自适应方法进行纹理方向的预测，以获得当前像素的参考像素，并计算预测残差； First, the reference pixel of the current pixel is gotten by texture direction prediction, meanwhile, the pixel difference is calculated.
22861 然后对预测残差进行混合哥伦布编码，最终大幅度地提高了无损压缩的压缩性能。 Then, the pixel difference is entropy coded through mixed Golomb. Thus, the compression performance is improved greatly.
22862 实验结果显示，与基于梯度预测和变长编码的无损压缩算法相比，该算法在平均压缩率略有提升的前提下，平均编码时间减少了 36.86%。 Simulation results show that compared with lossless frame memory compression using pixel gain prediction and dynamic order entropy coding, the proposed algorithm reduce the average coding time by 36.86%. Moreover, the average compression ratio is increased slightly in the proposed algorithm.
22863 使用拍卖方式来进行资源分配可以使得资源提供商获得更大的收益，是云计算领域近年来研究的重点。 Auction-based resource allocation is a major challenging problem for cloud computing.
22864 但现有研究多是基于非可信、单资源、单需求的前提。 However, the existing research is based on untruthful, single resource, single requirement for the premise.