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ARMing the Edge: Designing Edge Computing–Capable Machine Learning Algorithms to Target ARM Doppler Lidar Processing

dc.contributor.authorJackson, Robert C.
dc.contributor.authorRaut, Bhupendra A.
dc.contributor.authorDematties, Dario
dc.contributor.authorCollis, Scott M.
dc.contributor.authorFerrier, Nicola
dc.contributor.authorBeckman, Pete
dc.contributor.authorSankaran, Rajesh
dc.contributor.authorKim, Yongho
dc.contributor.authorPark, Seongha
dc.contributor.authorShahkarami, Sean
dc.contributor.authorNewsom, Rob
dc.date.accessioned2024-12-24T15:00:28Z
dc.date.available2024-12-24T15:00:28Z
dc.date.copyright01 Oct. 2023
dc.date.issued2023
dc.identifier.otheraies-AIES-D-22-0062.1.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4301671
dc.languageEnglish
dc.publisherAmerican Meteorological Society
dc.titleARMing the Edge: Designing Edge Computing–Capable Machine Learning Algorithms to Target ARM Doppler Lidar Processing
dc.typeJournal Paper
dc.journal.volume2
dc.journal.issue4
dc.journal.titleArtificial Intelligence for the Earth Systems
dc.identifier.doi10.1175/AIES-D-22-0062.1
dc.journal.lastpage220062
dc.treeArtificial Intelligence for the Earth Systems:;2023:;volume( 002 ):;issue: 004
dc.contenttypeFulltext


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