ID	原文	译文
5444	仿真结果表明，满足设计准则的空时分组编码方案能够获得满分集增益。	Simulation result shows that the code gain is foundrelated to the minimum singular value.
5445	随着网络中业务量的急剧增长以及宽带业务的普及，传统的波分复用光网络由于灵活性差、频谱资源浪费严重而面临严峻挑战。	With the rapid growth of the network traffic, the elastic optical network (EON) has been proposed as a prom-ising solution due to its high spectrum efficiency and flexible bandwidth provision.
5446	弹性光网络以灵活利用频谱为特征，可以根据用户需要和业务量大小动态分配适量的频谱资源并配置相应的调制格式，有效克服了波分复用光网络的缺陷。同时，弹性光网络中的多播路由和频谱分配以及网络的生存性问题也变得更加复杂。	Meanwhile, multicast routing andspectrum allocation, and the survivability of the network become more challenging than that in the conventional opticalnetwork. The routing for multicast traffic and its protection algorithm in EON was investigated.
5447	针对弹性光网络中多播路由和保护算法进行了研究，首先引入整数线性规划模型（ILP, integer linear programming），最大限度地利用网络中的频谱资源。	An integer linear pro-gramming (ILP) formulation with the objective to minimize total spectrum consumption was presented.
5448	在此基础上，提出了启发式算法——基于多播子树的分段路由频谱分配保护算法（MSPA, multicast sub-tree protection algorithm），为多播业务请求提供保护的同时最小化频谱资源的使用。	In addition, aheuristic algorithm called multicast sub-tree protection algorithm (MSPA) to achieve sufficient protection and satisfy re-sources savings was designed.
5449	仿真结果表明，与传统的多播路由算法及多播保护算法相比，所提算法通过改变信号调制格式，灵活运用链路上的频谱碎片，可以降低网络的阻塞率，提高网络的频谱利用率。	The simulation results demonstrate that comparing with the traditional multicast routingand protection algorithm, MSPA performs well in improving the blocking probability and the spectrum utilization of thenetwork.
5450	针对目前智能手机识别人体运动状态种类少、准确率低的问题，提出一种利用加速度传感器和重力传感器分层识别人体运动状态的方案。	To solve problems of low accuracy and fewer types of human motion state recognized by current smart phones,a method to do hierarchical recognition by using acceleration sensors and gravity sensors was proposed.
5451	首先，利用加速度和重力加速度的关系计算出与手机方向无关的惯性坐标系下的线性加速度；	Firstly, linearacceleration in inertial coordinate system and independent of phone direction was calculated by using the relation be-tween acceleration and gravity acceleration.
5452	其次，根据人体运动频率的变化范围和线性加速度矢量来确定脚步的波峰和波谷位置；	Secondly, according to the span of human motion frequency and linear ac-celeration vector, positions of peak and trough of footsteps were determined.
5453	最后，提取线性加速度在时域上的特征向量，使用层次支持向量机方法分层识别人体运动状态。	Finally, feature vector of linear accelerationin time domain was extracted and human motion states were recognized hierarchically by using hierarchical support vec-tor machine (H-SVM).