Potential of edge machine learning for instrumentation

Autor:	Audrey C. Therrien, Berthié Gouin-Ferland, Mohammad Mehdi Rahimifar
Rok vydání:	2022
Předmět:	Machine Learning Photons Electrical and Electronic Engineering Engineering (miscellaneous) Software Atomic and Molecular Physics and Optics
Zdroj:	Applied Optics. 61:1930
ISSN:	2155-3165 1559-128X
DOI:	10.1364/ao.445798
Popis:	New developments in radiation and photonic detectors improve resolution, sensitivity, size, and rate, all of which contribute to a gigantic increase in the data production rate. Moving data analysis and compression adjacent or even embedded within the detector hardware will reduce the data volumes generated, thereby reducing material cost, power, and data management requirements. Several solutions are already being developed both on the hardware and on the software side to facilitate the use of machine learning as a real-time data analysis solution.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08d984c8ef3c001079ca87a39e922d87 https://doi.org/10.1364/ao.445798 Zobrazit plný text záznamu