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of 4
pro vyhledávání: '"Berthié Gouin-Ferland"'
Autor:
Mohammad Mehdi Rahimifar, Quentin Wingering, Berthié Gouin-Ferland, Ryan Coffee, Audrey C Therrien
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 4, p 045041 (2024)
New scientific experiments and instruments generate vast amounts of data that need to be transferred for storage or further processing, often overwhelming traditional systems. Edge machine learning (EdgeML) addresses this challenge by integrating mac
Externí odkaz:
https://doaj.org/article/5be9fb7b66cc4320af55635d3161e17a
Publikováno v:
Frontiers in Physics, Vol 10 (2022)
Raw data generation for several existing and planned large physics experiments now exceeds TB/s rates, generating untenable data sets in very little time. Those data often demonstrate high dimensionality while containing limited information. Meanwhil
Externí odkaz:
https://doaj.org/article/34b80e065ef04a2e88dac9add8a975e9
Autor:
Mohammad Mehdi Rahimifar, Quentin Wingering, Berthié Gouin-Ferland, Hamza Ezzaoui Rahali, Charles-Étienne Granger, Audrey C Therrien
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 4, p 045035 (2023)
Over the past decade, innovations in radiation and photonic detectors considerably improved their resolution, pixel density, sensitivity, and sampling rate, which all contribute to increased data generation rates. This huge data increases the amount
Externí odkaz:
https://doaj.org/article/77c10f759f1d4aa38cb5bf8432734ad4
Publikováno v:
Applied Optics. 61:1930
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 th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08d984c8ef3c001079ca87a39e922d87
https://doi.org/10.1364/ao.445798
https://doi.org/10.1364/ao.445798