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dc.contributor.authorVenturini, Francesca-
dc.contributor.authorMichelucci, Umberto-
dc.contributor.authorBaumgartner, Michael-
dc.date.accessioned2020-12-30T16:12:13Z-
dc.date.available2020-12-30T16:12:13Z-
dc.date.issued2020-
dc.identifier.isbn978-1-943580-80-4de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/21143-
dc.descriptionFrom the session : Machine Learning and Tomography (FTu2B), Paper FTu2B.5de_CH
dc.description.abstractThe determination of multiple parameters via luminescence sensing is of great interest for many applications in different fields, like biosensing and biological imaging, medicine, and diagnostics. The typical approach consists in measuring multiple quantities and in applying complex approximated mathematical models to characterize the sensor response from the relevant parameters. Here a new approach for luminescence sensors is proposed, which allows the determination of multiple physical parameters simultaneously from a single measurement. The new approach is demonstrated by a dual oxygen concentration and temperature sensor. These results are achieved using multi-task deep-learning neural networks.de_CH
dc.language.isoende_CH
dc.publisherOSA Publishingde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectOxygen sensorde_CH
dc.subjectLuminescencede_CH
dc.subjectLuminescence quenchingde_CH
dc.subjectTemperature sensorde_CH
dc.subjectArtificial intelligencede_CH
dc.subjectDual sensorde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc600: Technikde_CH
dc.titleDeep-learning for multi-parameter luminescence sensing : demonstration of dual sensorde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Mathematik und Physik (IAMP)de_CH
zhaw.conference.detailsOSA Frontiers in Optics / Laser Science, online, 14-17 September 2020de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings Frontiers in Optics / Laser Sciencede_CH
zhaw.webfeedPhotonicsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Venturini, F., Michelucci, U., & Baumgartner, M. (2020). Deep-learning for multi-parameter luminescence sensing : demonstration of dual sensor. Proceedings Frontiers in Optics / Laser Science.
Venturini, F., Michelucci, U. and Baumgartner, M. (2020) ‘Deep-learning for multi-parameter luminescence sensing : demonstration of dual sensor’, in Proceedings Frontiers in Optics / Laser Science. OSA Publishing.
F. Venturini, U. Michelucci, and M. Baumgartner, “Deep-learning for multi-parameter luminescence sensing : demonstration of dual sensor,” in Proceedings Frontiers in Optics / Laser Science, 2020.
VENTURINI, Francesca, Umberto MICHELUCCI und Michael BAUMGARTNER, 2020. Deep-learning for multi-parameter luminescence sensing : demonstration of dual sensor. In: Proceedings Frontiers in Optics / Laser Science. Conference paper. OSA Publishing. 2020. ISBN 978-1-943580-80-4
Venturini, Francesca, Umberto Michelucci, and Michael Baumgartner. 2020. “Deep-Learning for Multi-Parameter Luminescence Sensing : Demonstration of Dual Sensor.” Conference paper. In Proceedings Frontiers in Optics / Laser Science. OSA Publishing.
Venturini, Francesca, et al. “Deep-Learning for Multi-Parameter Luminescence Sensing : Demonstration of Dual Sensor.” Proceedings Frontiers in Optics / Laser Science, OSA Publishing, 2020.


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