随着物联网(Internet of Things,Io T)技术的飞速发展,物联网设备在智慧城市、工业自动化、智能家居等领域都得到了非常广泛的应用,为人们的生活和工作带来了前所未有的便利。然而,物联网设备的大规模部署也带来了诸多挑战,比如设备类...随着物联网(Internet of Things,Io T)技术的飞速发展,物联网设备在智慧城市、工业自动化、智能家居等领域都得到了非常广泛的应用,为人们的生活和工作带来了前所未有的便利。然而,物联网设备的大规模部署也带来了诸多挑战,比如设备类型多样、网络环境复杂,导致人们对设备进行统一监控管理困难;设备故障难以及时发现,严重影响业务连续性;不同品牌和型号的设备之间,缺乏统一的管理平台,管理运维效率低下。因此,构建一套简单、高效、可靠、稳定的物联网设备进行监控系统,解决物联网设备状态监测、故障预警以及性能优化等重要问题,为物联网系统的可靠运行提供有力支持,具有非常重要的现实意义。展开更多
Advancements in mode-division multiplexing(MDM)techniques,aimed at surpassing the Shannon limit and augmenting transmission capacity,have garnered significant attention in optical fiber communica-tion,propelling the d...Advancements in mode-division multiplexing(MDM)techniques,aimed at surpassing the Shannon limit and augmenting transmission capacity,have garnered significant attention in optical fiber communica-tion,propelling the demand for high-quality multiplexers and demultiplexers.However,the criteria for ideal-mode multiplexers/demultiplexers,such as performance,scalability,compatibility,and ultra-compactness,have only partially been achieved using conventional bulky devices(e.g.,waveguides,grat-ings,and free space optics)—an issue that will substantially restrict the application of MDM techniques.Here,we present a neuro-meta-router(NMR)optimized through deep learning that achieves spatial multi-mode division and supports multi-channel communication,potentially offering scalability,com-patibility,and ultra-compactness.An MDM communication system based on an NMR is theoretically designed and experimentally demonstrated to enable simultaneous and independent multi-dataset transmission,showcasing a capacity of up to 100 gigabits per second(Gbps)and a symbol error rate down to the order of 104,all achieved without any compensation technologies or correlation devices.Our work presents a paradigm that merges metasurfaces,fiber communications,and deep learning,with potential applications in intelligent metasurface-aided optical interconnection,as well as all-optical pat-tern recognition and classification.展开更多
文摘随着物联网(Internet of Things,Io T)技术的飞速发展,物联网设备在智慧城市、工业自动化、智能家居等领域都得到了非常广泛的应用,为人们的生活和工作带来了前所未有的便利。然而,物联网设备的大规模部署也带来了诸多挑战,比如设备类型多样、网络环境复杂,导致人们对设备进行统一监控管理困难;设备故障难以及时发现,严重影响业务连续性;不同品牌和型号的设备之间,缺乏统一的管理平台,管理运维效率低下。因此,构建一套简单、高效、可靠、稳定的物联网设备进行监控系统,解决物联网设备状态监测、故障预警以及性能优化等重要问题,为物联网系统的可靠运行提供有力支持,具有非常重要的现实意义。
基金supported by the National Key Research and Development Program of China(2023YFB2804704)the National Natural Science Foundation of China(12174292,12374278,and 62105250).
文摘Advancements in mode-division multiplexing(MDM)techniques,aimed at surpassing the Shannon limit and augmenting transmission capacity,have garnered significant attention in optical fiber communica-tion,propelling the demand for high-quality multiplexers and demultiplexers.However,the criteria for ideal-mode multiplexers/demultiplexers,such as performance,scalability,compatibility,and ultra-compactness,have only partially been achieved using conventional bulky devices(e.g.,waveguides,grat-ings,and free space optics)—an issue that will substantially restrict the application of MDM techniques.Here,we present a neuro-meta-router(NMR)optimized through deep learning that achieves spatial multi-mode division and supports multi-channel communication,potentially offering scalability,com-patibility,and ultra-compactness.An MDM communication system based on an NMR is theoretically designed and experimentally demonstrated to enable simultaneous and independent multi-dataset transmission,showcasing a capacity of up to 100 gigabits per second(Gbps)and a symbol error rate down to the order of 104,all achieved without any compensation technologies or correlation devices.Our work presents a paradigm that merges metasurfaces,fiber communications,and deep learning,with potential applications in intelligent metasurface-aided optical interconnection,as well as all-optical pat-tern recognition and classification.
基金~~Supported by the National Natural Science Foundation of China under Grant No.90104002 the National High Technology Development 863 Program of China under Grant No.863-306-ZD-07-01
