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New Autonomous Intelligent Sensor Design Approach for Multiple Parameter Inference

1
TOELT LLC, Birchlenstr. 25, 8600 Dübendorf, Switzerland
2
School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK
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Institute of Applied Mathematics and Physics, Zurich University of Applied Sciences, Technikumstrasse 9, 8401 Winterthur, Switzerland
*
Author to whom correspondence should be addressed.
Presented at the 7th Electronic Conference on Sensors and Applications, 15–30 November 2020; Available online: \url{https://ecsa-7.sciforum.net/}.
Eng. Proc. 2020, 2(1), 96; https://doi.org/10.3390/engproc2020002096
Published: 7 February 2021
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 and frequently just approximated mathematical models to characterize the sensor response. The use of machine learning to extract information from measurements in sensors have been tried in several forms before. But one of the problems with the approaches so far, is the difficulty in getting a training dataset that is representative of the measurements done by the sensor. Additionally, extracting multiple parameters from a single measurement has been so far an impossible problem to solve efficiently in luminescence. In this work a new approach is described for building an autonomous intelligent sensor, which is able to produce the training dataset self-sufficiently, use it for training a neural network, and then use the trained model to do inference on measurements done on the same hardware. For the first time the use of machine learning additionally allows to extract two parameters from one single measurement using multitask learning neural network architectures. This is demonstrated here by a dual oxygen concentration and temperature sensor.
Keywords: neural networks; machine learning; optical sensors; oxygen sensing; dual sensor neural networks; machine learning; optical sensors; oxygen sensing; dual sensor
MDPI and ACS Style

Michelucci, U.; Venturini, F. New Autonomous Intelligent Sensor Design Approach for Multiple Parameter Inference. Eng. Proc. 2020, 2, 96. https://doi.org/10.3390/engproc2020002096

AMA Style

Michelucci U, Venturini F. New Autonomous Intelligent Sensor Design Approach for Multiple Parameter Inference. Engineering Proceedings. 2020; 2(1):96. https://doi.org/10.3390/engproc2020002096

Chicago/Turabian Style

Michelucci, Umberto; Venturini, Francesca. 2020. "New Autonomous Intelligent Sensor Design Approach for Multiple Parameter Inference" Eng. Proc. 2, no. 1: 96. https://doi.org/10.3390/engproc2020002096

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