Holographic multiple-input multiple-output(HMIMO)has become an emerging technology for achieving ultra-high frequency spectral efficiency and spatial resolution in future wireless systems.The increasing antenna apertu...Holographic multiple-input multiple-output(HMIMO)has become an emerging technology for achieving ultra-high frequency spectral efficiency and spatial resolution in future wireless systems.The increasing antenna aperture leads to a more significant characterization of the spherical wavefront in near-field communications in HMIMO scenarios.Beam training as a key technique for wireless communication is worth exploring in this near-field scenario.Compared with the widely researched far-field beam training,the increased dimensionality of the search space for near-field beam training poses a challenge to the complexity and accuracy of the proposed algorithm.In this paper,we introduce several typical near-field beam training methods:exhaustive beam training,hierarchical beam training,and multi-beam training that includes equal interval multi-beam training and hash multi-beam training.The performances of these methods are compared through simulation analysis,and their effectiveness is verified on the hardware testbed as well.Additionally,we provide application scenarios,research challenges,and potential future research directions for near-field beam training.展开更多
【目的】基于协方差估计的多因变量回归(multivariate regression with covariance estimation,MRCE)模型进行多性状QTL定位分析,为动植物数量性状基因定位提供理论参考。【方法】构建适用QTL定位的MRCE模型,设计3个模拟试验对模型进行...【目的】基于协方差估计的多因变量回归(multivariate regression with covariance estimation,MRCE)模型进行多性状QTL定位分析,为动植物数量性状基因定位提供理论参考。【方法】构建适用QTL定位的MRCE模型,设计3个模拟试验对模型进行检验,通过计算机生成基因型和2个相关性状的表型值,并用2组数据对模型进行实际应用,其中一组为水稻DH群体数据,选自qtlnetwork软件;另一组为水稻永久F群体数据,由珍汕97×明恢63,含有210个株系的重组自交系(RIL)群体随机交配生成,分析MRCE模型在以上2组数据多性状QTL定位中的应用效果。【结果】用MRCE模型进行QTL定位的模拟试验结果表明,遗传变异所占方差比越大,相关系数绝对值越大,遗传率越大,则功效越好,估计值越接近效应值。MRCE的QTL定位应用结果显示,从水稻DH群体中识别出8个QTL与ph6性状有关,6个QTL与ph8性状有关;从1998年水稻永久F群体数据中识别出3个QTL与穗粒数相关,10个QTL与粒质量相关;从1999年数据识别出3个QTL与穗粒数相关,6个QTL与粒质量相关。【结论】利用MRCE模型进行多性状QTL定位是可行的。展开更多
Based on the macrofauna data(2008-2011) in Xiaoqing River estuary and its adjacent sea, Laizhou Bay of Bohai Sea, China, the AZTI Marine Biotic Index(AMBI) and Multivariate AMBI(M-AMBI) were used for benthic habitat q...Based on the macrofauna data(2008-2011) in Xiaoqing River estuary and its adjacent sea, Laizhou Bay of Bohai Sea, China, the AZTI Marine Biotic Index(AMBI) and Multivariate AMBI(M-AMBI) were used for benthic habitat quality(BHQ) assessment. Results showed that BHQ presented an obvious trend of improvement along the direction of stream channel and river mouth, and in the coastal areas. AMBI and M-AMBI were significantly related to environmental pressure gradient data. Therefore, the two indices can well indicate BHQ in the studied area. However, there were significant differences between results of the two indices. In the cases of low taxa number and high abundance of single species, AMBI might overestimate BHQ. We thus adjusted its thresholds to solve this problem. And M-AMBI might overestimate BHQ when benthic assemblage was dominated by the opportunistic species. Then we could raise the weight of AMBI in the calculation of M-AMBI to handle the problem.展开更多
Vocabulary plays an irreplaceable role in a language.As an important carrier to record foreign cultures in vocabulary,loanwords are widely used in all aspects of society.Modern Chinese Dictionary includes loanwords fr...Vocabulary plays an irreplaceable role in a language.As an important carrier to record foreign cultures in vocabulary,loanwords are widely used in all aspects of society.Modern Chinese Dictionary includes loanwords from a variety of languages from the perspective of universal use.This paper takes the loanwords in Modern Chinese Dictionary(7th Edition)as the closed corpus,and analyzes the etymology,structure,word length,and other aspects while making blanket statistics on the loanwords.The representative words are investigated by word frequency and described from multiple perspectives.展开更多
目的在点云场景中,语义分割对场景理解来说是至关重要的视觉任务。由于图像是结构化的,而点云是非结构化的,点云上的卷积通常比图像上的卷积更加困难,会消耗更多的计算和内存资源。在这种情况下,大尺度场景的分割往往需要分块进行,导致...目的在点云场景中,语义分割对场景理解来说是至关重要的视觉任务。由于图像是结构化的,而点云是非结构化的,点云上的卷积通常比图像上的卷积更加困难,会消耗更多的计算和内存资源。在这种情况下,大尺度场景的分割往往需要分块进行,导致效率不足并且无法捕捉足够的场景信息。为了解决这个问题,本文设计了一种计算高效且内存高效的网络结构,可以用于端到端的大尺度场景语义分割。方法结合空间深度卷积和残差结构设计空间深度残差(spatial depthwise residual,SDR)块,其具有高效的计算效率和内存效率,并且可以有效地从点云中学习到几何特征。另外,设计一种扩张特征整合(dilated feature aggregation,DFA)模块,可以有效地增加感受野而仅增加少量的计算量。结合SDR块和DFA模块,本文构建SDRNet(spatial depthwise residual network),这是一种encoder-decoder深度网络结构,可以用于大尺度点云场景语义分割。同时,针对空间卷积核输入数据的分布不利于训练问题,提出层级标准化来减小参数学习的难度。特别地,针对稀疏雷达点云的旋转不变性,提出一种特殊的SDR块,可以消除雷达数据绕Z轴旋转的影响,显著提高网络处理激光雷达点云时的性能。结果在S3DIS(stanford large-scale 3D indoor space)和Semantic KITTI(Karlsruhe Institute of Technology and Toyota Technological Institute)数据集上对提出的方法进行测试,并分析点数与帧率的关系。本文方法在S3DIS数据集上的平均交并比(mean intersection over union,mIoU)为71.7%,在Semantic KITTI上的m Io U在线单次扫描评估中达到59.1%。结论实验结果表明,本文提出的SDRNet能够直接在大尺度场景下进行语义分割。在S3DIS和Semantic KITTI数据集上的实验结果证明本文方法在精度上有较好表现。通过分析点数量与帧率之间的关系,得到的数据表明本文提出的SDRNet能保持较高精度和较快的推理速率。展开更多
文摘Holographic multiple-input multiple-output(HMIMO)has become an emerging technology for achieving ultra-high frequency spectral efficiency and spatial resolution in future wireless systems.The increasing antenna aperture leads to a more significant characterization of the spherical wavefront in near-field communications in HMIMO scenarios.Beam training as a key technique for wireless communication is worth exploring in this near-field scenario.Compared with the widely researched far-field beam training,the increased dimensionality of the search space for near-field beam training poses a challenge to the complexity and accuracy of the proposed algorithm.In this paper,we introduce several typical near-field beam training methods:exhaustive beam training,hierarchical beam training,and multi-beam training that includes equal interval multi-beam training and hash multi-beam training.The performances of these methods are compared through simulation analysis,and their effectiveness is verified on the hardware testbed as well.Additionally,we provide application scenarios,research challenges,and potential future research directions for near-field beam training.
文摘【目的】基于协方差估计的多因变量回归(multivariate regression with covariance estimation,MRCE)模型进行多性状QTL定位分析,为动植物数量性状基因定位提供理论参考。【方法】构建适用QTL定位的MRCE模型,设计3个模拟试验对模型进行检验,通过计算机生成基因型和2个相关性状的表型值,并用2组数据对模型进行实际应用,其中一组为水稻DH群体数据,选自qtlnetwork软件;另一组为水稻永久F群体数据,由珍汕97×明恢63,含有210个株系的重组自交系(RIL)群体随机交配生成,分析MRCE模型在以上2组数据多性状QTL定位中的应用效果。【结果】用MRCE模型进行QTL定位的模拟试验结果表明,遗传变异所占方差比越大,相关系数绝对值越大,遗传率越大,则功效越好,估计值越接近效应值。MRCE的QTL定位应用结果显示,从水稻DH群体中识别出8个QTL与ph6性状有关,6个QTL与ph8性状有关;从1998年水稻永久F群体数据中识别出3个QTL与穗粒数相关,10个QTL与粒质量相关;从1999年数据识别出3个QTL与穗粒数相关,6个QTL与粒质量相关。【结论】利用MRCE模型进行多性状QTL定位是可行的。
基金supported by the NSFC-Shandong Joint Fund for Marine Science Research Centers (Grant No. U1406404)the National Marine Public Welfare Research Project of China (Grant No. 201405007)
文摘Based on the macrofauna data(2008-2011) in Xiaoqing River estuary and its adjacent sea, Laizhou Bay of Bohai Sea, China, the AZTI Marine Biotic Index(AMBI) and Multivariate AMBI(M-AMBI) were used for benthic habitat quality(BHQ) assessment. Results showed that BHQ presented an obvious trend of improvement along the direction of stream channel and river mouth, and in the coastal areas. AMBI and M-AMBI were significantly related to environmental pressure gradient data. Therefore, the two indices can well indicate BHQ in the studied area. However, there were significant differences between results of the two indices. In the cases of low taxa number and high abundance of single species, AMBI might overestimate BHQ. We thus adjusted its thresholds to solve this problem. And M-AMBI might overestimate BHQ when benthic assemblage was dominated by the opportunistic species. Then we could raise the weight of AMBI in the calculation of M-AMBI to handle the problem.
文摘Vocabulary plays an irreplaceable role in a language.As an important carrier to record foreign cultures in vocabulary,loanwords are widely used in all aspects of society.Modern Chinese Dictionary includes loanwords from a variety of languages from the perspective of universal use.This paper takes the loanwords in Modern Chinese Dictionary(7th Edition)as the closed corpus,and analyzes the etymology,structure,word length,and other aspects while making blanket statistics on the loanwords.The representative words are investigated by word frequency and described from multiple perspectives.
文摘目的在点云场景中,语义分割对场景理解来说是至关重要的视觉任务。由于图像是结构化的,而点云是非结构化的,点云上的卷积通常比图像上的卷积更加困难,会消耗更多的计算和内存资源。在这种情况下,大尺度场景的分割往往需要分块进行,导致效率不足并且无法捕捉足够的场景信息。为了解决这个问题,本文设计了一种计算高效且内存高效的网络结构,可以用于端到端的大尺度场景语义分割。方法结合空间深度卷积和残差结构设计空间深度残差(spatial depthwise residual,SDR)块,其具有高效的计算效率和内存效率,并且可以有效地从点云中学习到几何特征。另外,设计一种扩张特征整合(dilated feature aggregation,DFA)模块,可以有效地增加感受野而仅增加少量的计算量。结合SDR块和DFA模块,本文构建SDRNet(spatial depthwise residual network),这是一种encoder-decoder深度网络结构,可以用于大尺度点云场景语义分割。同时,针对空间卷积核输入数据的分布不利于训练问题,提出层级标准化来减小参数学习的难度。特别地,针对稀疏雷达点云的旋转不变性,提出一种特殊的SDR块,可以消除雷达数据绕Z轴旋转的影响,显著提高网络处理激光雷达点云时的性能。结果在S3DIS(stanford large-scale 3D indoor space)和Semantic KITTI(Karlsruhe Institute of Technology and Toyota Technological Institute)数据集上对提出的方法进行测试,并分析点数与帧率的关系。本文方法在S3DIS数据集上的平均交并比(mean intersection over union,mIoU)为71.7%,在Semantic KITTI上的m Io U在线单次扫描评估中达到59.1%。结论实验结果表明,本文提出的SDRNet能够直接在大尺度场景下进行语义分割。在S3DIS和Semantic KITTI数据集上的实验结果证明本文方法在精度上有较好表现。通过分析点数量与帧率之间的关系,得到的数据表明本文提出的SDRNet能保持较高精度和较快的推理速率。
