ID 原文 译文
18675 最终输出既包含RGB低层信息又融合RGB-D高层多模态信息的显著图。 Finally, the information of lower layers is fused to generate the ultimate saliency maps.
18676 在3个公开数据集上的实验表明，该文所提网络因为使用了双流侧边监督模块和多模态特征融合模块，其性能优于目前主流的RGB-D显著性检测模型，具有较强的鲁棒性。 Experiments on three open data sets show that the proposed network has better performance and strongerrobustness than the current RGB-D saliency detection models.
18677 高清晰度的图像是信息获取和精确分析的前提，研究多帧图像的超分辨率重建能够有效解决因外部拍摄环境引起的图像细节丢失、边缘模糊等问题。 The high-resolution image is the prerequisite of information acquisition and precise analysis. Multi-frame super-resolution images reconstruction technologies are able to address many image degraded issues(caused by external shooting environment), such as detail information lost, blurred edges, and so forth.
18678 该文基于纳米级忆阻器，设计一种多通道忆阻脉冲耦合神经网络模型(MMPCNN)，能够有效模拟网络中连接系数的动态变化，解决神经网络中固有的参数估计问题。 According to the nanoscale memristor, a Multi-channel Memristive Pulse Coupled Neural Network (MMPCNN)model is proposed. This model is able to simulate the adaptive-variable linking coefficient in pulse coupledneural network.
18679 同时，将提出的网络应用于多帧图像超分辨率重建中，实现低分辨率配准图像的融合操作，并通过基于稀疏编码的单帧图像超分辨率重构算法对获得的初始高分辨率图像进行优化。 Meanwhile, the proposed network is applied to the multi-frame super resolution reconstructionfor fusing the registered low resolution images. Furthermore, the sparse coding based super resolution method is performed to improve the original high-resolution image.
18680 最终，一系列计算机仿真及分析(主观/客观分析)验证了该文提出方案的正确性和有效性。 Finally, a series of computer experiments and therelevant subjective/objective analysis jointly illustrate the validity and effectiveness of the entire scheme.
18681 针对多蜂窝多用户异构无线网络干扰管理和效率提升问题，该文研究了基于干扰效率最大的下行链路基站(BS)-用户匹配和功率分配问题。 To solve interference management and efficiency improvement of multi-cell multi-user heterogeneous wireless networks, the downlink Base Station (BS)-user matching and power allocation problem are studied to maximize the interference efficiency of femtocells.
18682 首先，考虑宏用户和微蜂窝用户的服务质量，将问题建模为多变量混合整数非线性规划问题。 Firstly, consideration of quality of service of macro cell usersand femtocell users, the problem is formulated as a multivariate mixed integer nonlinear programming problem.
18683 其次将原问题分解为基站选择和功率分配两个子问题。 Secondly, the problem is decomposed into two subproblems.
18684 针对基站选择问题，利用凸优化问题获得最优基站选择策略； The BS selection problem is solved by convex optimization technique.