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Multi-vortex laser enabling spatial and temporal encoding 被引量:8
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作者 Zhen Qiao Zhenyu Wan +3 位作者 Guoqiang Xie Jian Wang Liejia Qian Dianyuan Fan 《PhotoniX》 SCIE EI 2020年第1期137-150,共14页
Optical vortex is a promising candidate for capacity scaling in next-generation optical communications.The generation of multi-vortex beams is of great importance for vortex-based optical communications.Traditional ap... Optical vortex is a promising candidate for capacity scaling in next-generation optical communications.The generation of multi-vortex beams is of great importance for vortex-based optical communications.Traditional approaches for generating multivortex beams are passive,unscalable and cumbersome.Here,we propose and demonstrate a multi-vortex laser,an active approach for creating multi-vortex beams directly at the source.By printing a specially-designed concentric-rings pattern on the cavity mirror,multi-vortex beams are generated directly from the laser.Spatially,the generated multi-vortex beams are decomposable and coaxial.Temporally,the multi-vortex beams can be simultaneously self-mode-locked,and each vortex component carries pulses with GHz-level repetition rate.Utilizing these distinct spatial-temporal characteristics,we demonstrate that the multi-vortex laser can be spatially and temporally encoded for data transmission,showing the potential of the developed multi-vortex laser in optical communications.The demonstrations may open up new perspectives for diverse applications enabled by the multi-vortex laser. 展开更多
关键词 Multi-vortex laser Spatial encoding temporal encoding
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Trajectory prediction based on grouped spatial-temporal encoder
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作者 Di ZHOU Ying GAO +2 位作者 Hui LI Xiaoya LIU Qinghua LIN 《Frontiers of Computer Science》 2025年第11期173-175,共3页
1 Introduction Due to the complexity of traffic scenarios,the motion of agents is influenced not only by road geometry and traffic rules but also by surrounding agents,making trajectory prediction for autonomous vehic... 1 Introduction Due to the complexity of traffic scenarios,the motion of agents is influenced not only by road geometry and traffic rules but also by surrounding agents,making trajectory prediction for autonomous vehicles exceptionally challenging.The movement pattern of a single vehicle is typically influenced by nearby vehicles and its surrounding environmental information.Social psychologists have pointed out that individuals often imitate or follow other members of a group[1],using them as a reference for their behavior,which leads to the frequent occurrence of the herd effect in vehicle movement patterns[2]. 展开更多
关键词 trajectory prediction autonomous vehicles traffic scenarios movement pattern imitate follow other members group using motion agents grouped spatial temporal encoder
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