Atmospheric Tomography Using Convolutional Neural Networks

González Gutiérrez, Carlos; Beltramo Martin, O.; Osborn, J.; Calvo Rolle, José Luis; Cos Juez, Francisco Javier de

doi:10.1007/978-3-030-62365-4_54

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Atmospheric Tomography Using Convolutional Neural Networks

dc.contributor.author	González Gutiérrez, Carlos
dc.contributor.author	Beltramo Martin, O.
dc.contributor.author	Osborn, J.
dc.contributor.author	Calvo Rolle, José Luis
dc.contributor.author	Cos Juez, Francisco Javier de
dc.date.accessioned	2021-06-01T09:41:13Z
dc.date.available	2021-06-01T09:41:13Z
dc.date.issued	2020
dc.identifier.isbn	9783030623647
dc.identifier.issn	0302-9743
dc.identifier.uri	http://hdl.handle.net/10651/58654
dc.description	International Conference Intelligent Data Engineering and Automated Learning – IDEAL (21st. 2020. Guimaraes, Portugal)
dc.description.sponsorship	The authors acknowledge Spanish ministry projects MINECO AYA2017-89121- P, and support from the European Union’s Horizon 2020 research and innovation program under the H2020-INFRAIA-2018-2020 grant agreement No 210489629. This work has been partially funded by the French National Research Agency (ANR) program APPLY - ANR-19-CE31-0011. This work also benefited from the support of the WOLF project ANR-18-CE31-0018 of the and the OPTICON H2020 (2017–2020) Work Package 1. James Osborn acknowledges support from the UKRI Future Leaders Fellowship (UK) (MR/S035338/1).
dc.format.extent	p. 561-569
dc.language.iso	eng
dc.relation.ispartof	Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part II
dc.relation.ispartofseries	Lecture Notes in Computer Science; 12490
dc.rights	©,
dc.source	Scopus
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097146111&doi=10.1007%2f978-3-030-62365-4_54&partnerID=40&md5=ca8122304222dc98b2c0a2116117a727
dc.title	Atmospheric Tomography Using Convolutional Neural Networks
dc.type	conference output	spa
dc.identifier.doi	10.1007/978-3-030-62365-4_54
dc.relation.projectID	MINECO/AYA2017-89121- P
dc.relation.projectID	info:eu‐repo/grantAgreement/EC/H2020/210489629
dc.relation.publisherversion	http://dx.doi.org/10.1007/978-3-030-62365-4_54

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