| ID | 原文 | 译文 |
| 19385 | 考虑到位于特定区域的多用户可能对于相同内容存在内容请求，该文引入成簇思想，提出一种成簇及内容部署机制，通过为各簇头推送热点内容，而簇成员基于D2D通信模式关联簇头获取所需内容，可实现高效内容获取。 | Assuming that multiple users located in a specific area may have content requests for the same content, a clustering and content deployment mechanism is presented in order to achieve efficient content acquisition. |
| 19386 | 综合考虑成簇数量、用户关联簇头、簇头缓存容量及传输速率等限制条件，建立基于用户总业务时延最小化的联合成簇及内容部署优化模型。该文运用拉格朗日部分松弛法，并基于迭代算法及Kuhn-Munkres算法，从而得到联合成簇及内容部署优化策略。 | A joint clustering and content deployment optimization model is formulated to minimize total user service delay, which can be solved by Lagrange partial relaxation, iterative algorithm and Kuhn-Munkres algorithm, and the joint clustering and content deployment optimization strategies can be obtained. |
| 19387 | 最后通过MATLAB仿真验证所提算法的有效性。 | Finally, the effectiveness of the proposed algorithm is verified by MATLABsimulation. |
| 19388 | 针对2维主成分分析(2DPCA)算法无法实现在线特征提取及无法体现完整数据结构信息等问题，该文提出一种基于图像协方差无关的增量式2DPCA(I2DPCA)算法。该算法无需对图像协方差矩阵进行特征值分解奇异值分解，复杂度将大为降低，提高了特征提取速度。 | To solve the problems that Two-Dimensional Principal Component Analysis (2DPCA) can not implement the on-line feature extraction and can not represent the complete structure information, an Incremental 2DPCA (I2DPCA) without estimating covariance matrices is presented by an iterative estimation method, not to deal with the image covariance matrices by the eigenvalue decomposition or the singular value decomposition. The complexity will be greatly reduced and the on-line feature extraction speed can be improved. |
| 19389 | 针对I2DPCA仅提取了横向特征的问题，又提出一种增量式行列顺序2DPCA(IRC2DPCA)算法，该算法对I2DPCA的特征矩阵再次进行纵向特征提取，保留了图像的横向与纵向结构信息，实现了行列两个方向上的特征提取与数据降维。 | The proposed I2DPCA can only extract the horizontal features, and thus another Incremental Row-Column 2DPCA (IRC2DPCA) is proposed to incrementally extract the longitudinal ones from the feature matrices of the I2DPCA. The IRC2DPCA can preserve the horizontal and longitudinal features and implement the dimensionality reduction in both row and column directions. |
| 19390 | 最后，以自建的物块数据集、通用的ORL和Yale人脸数据集分别进行对比实验， | Finally, a series of experiments are carried outwith the self-built block dataset, ORL and Yale face datasets, respectively. |
| 19391 | 结果表明，该文算法在收敛率、分类率及复杂度等性能方面均得到了显著提高，其收敛率达到99%以上，分类率可达97.6%，平均处理速度为29 帧/s，能够满足增量特征提取的实时处理需求。 | The results show that the proposedalgorithms have significantly improved the performances of the convergence rate, the classification rate and thecomplexity. The convergence rate is over 99%, the classification rate can reach 97.6% and the averageprocessing speed is about 29 frames per second, and it can meet the on-line feature extraction requirements forincremental learning. |
| 19392 | 为了提高无色无向无冲突灵活的可重构光分插复用器(CDC-F ROADM)节点的弹性光网络IP组播频谱-能耗效率，该文提出一种全光组播能效调度算法(AMEESA)。 | In order to improve multicast’s spectrum energy-efficient of elastic optical network configured withColorless, Directionless and Contentionless-Flexible Reconfigurable Optical Add/Drop Multiplexer (CDC-FROADM) nodes, an All-optical Multicast Energy Efficiency Scheduling Algorithm (AMEESA) is proposed. |
| 19393 | 在算法路由阶段，考虑能耗和链路频谱资源使用情况设计链路代价函数，构建最小代价光树算法组播光树。 | Inthe routing phase, considering both energy consumption and link spectrum resource utilization, the link costfunction is designed to establish the multicast tree with the least cost. |
| 19394 | 在频谱分配阶段，设计基于高效光谱分辨率(HSR)光树中间节点频谱转换方法，选择节能频谱转换方案为组播光树分配频谱块资源。 | In the spectrum allocation phase, aspectrum conversion method based on High Spectral Resolution (HSR) is designed by changing the spectrumslot index of adjacent links according to links availability of spectrum blocks. And an energy-saving spectrumconversion scheme is selected to allocate spectrum block resources for the multicast tree. |