【目的】中国传统园林空间复杂多变,传统的分析方法难以深入探索传统园林复杂的三维视觉空间特征。三维激光雷达(light detection and ranging,LiDAR)点云技术在传统园林空间研究方面具有精度高、信息全等优势,借助LiDAR点云技术深入探...【目的】中国传统园林空间复杂多变,传统的分析方法难以深入探索传统园林复杂的三维视觉空间特征。三维激光雷达(light detection and ranging,LiDAR)点云技术在传统园林空间研究方面具有精度高、信息全等优势,借助LiDAR点云技术深入探索中国传统园林视觉空间特征,可为现代人居环境空间营造提供借鉴与参考。【方法】基于LiDAR点云技术提出一套适用于中国传统园林视觉空间分析的方法,并以三维可视性、视野舒展度及景物视野占比3项指标量化描述视觉空间。【结果】以寄畅园为例,构建了寄畅园LiDAR点云体素模型,利用提出的方法对锦汇漪西侧滨水步道11个视点的视觉空间进行了量化分析,解析了寄畅园的视觉空间特征,并探究了该步道的空间感知序列。【结论】证实了利用LiDAR点云技术展开传统园林视觉空间研究的可行性与准确性,提出的方法可应用于其他传统园林视觉空间的分析研究中。展开更多
With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. ...With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.展开更多
文摘【目的】中国传统园林空间复杂多变,传统的分析方法难以深入探索传统园林复杂的三维视觉空间特征。三维激光雷达(light detection and ranging,LiDAR)点云技术在传统园林空间研究方面具有精度高、信息全等优势,借助LiDAR点云技术深入探索中国传统园林视觉空间特征,可为现代人居环境空间营造提供借鉴与参考。【方法】基于LiDAR点云技术提出一套适用于中国传统园林视觉空间分析的方法,并以三维可视性、视野舒展度及景物视野占比3项指标量化描述视觉空间。【结果】以寄畅园为例,构建了寄畅园LiDAR点云体素模型,利用提出的方法对锦汇漪西侧滨水步道11个视点的视觉空间进行了量化分析,解析了寄畅园的视觉空间特征,并探究了该步道的空间感知序列。【结论】证实了利用LiDAR点云技术展开传统园林视觉空间研究的可行性与准确性,提出的方法可应用于其他传统园林视觉空间的分析研究中。
基金supported in part by The Science and Technology Development Fund, Macao SAR, China (0108/2020/A3)in part by The Science and Technology Development Fund, Macao SAR, China (0005/2021/ITP)the Deanship of Scientific Research at Taif University for funding this work。
文摘With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.
