Publication type: Conference paper
Type of review: Peer review (publication)
Title: Deep-learning for multi-parameter luminescence sensing : demonstration of dual sensor
Authors: Venturini, Francesca
Michelucci, Umberto
Baumgartner, Michael
et. al: No
Proceedings: Proceedings Frontiers in Optics / Laser Science
Conference details: OSA Frontiers in Optics / Laser Science, online, 14-17 September 2020
Issue Date: 2020
Publisher / Ed. Institution: OSA Publishing
ISBN: 978-1-943580-80-4
Language: English
Subjects: Oxygen sensor; Luminescence; Luminescence quenching; Temperature sensor; Artificial intelligence; Dual sensor
Subject (DDC): 006: Special computer methods
600: Technology
Abstract: The 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.
Further description: From the session : Machine Learning and Tomography (FTu2B), Paper FTu2B.5
URI: https://digitalcollection.zhaw.ch/handle/11475/21143
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Applied Mathematics and Physics (IAMP)
Appears in collections:Publikationen School of Engineering

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