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On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing

An intelligent sensor for light wavelength readout, suitable for visible range optical applications, has been developed. Using buried triple photo-junction as basic pixel sensing element in combination with artificial neural network (ANN), the wavelength readout with a full-scale error of less than...

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Detalles Bibliográficos
Autores principales: Hafiane, Mohamed Lamine, Dibi, Zohir, Manck, Otto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3348813/
https://www.ncbi.nlm.nih.gov/pubmed/22574051
http://dx.doi.org/10.3390/s90402884
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author Hafiane, Mohamed Lamine
Dibi, Zohir
Manck, Otto
author_facet Hafiane, Mohamed Lamine
Dibi, Zohir
Manck, Otto
author_sort Hafiane, Mohamed Lamine
collection PubMed
description An intelligent sensor for light wavelength readout, suitable for visible range optical applications, has been developed. Using buried triple photo-junction as basic pixel sensing element in combination with artificial neural network (ANN), the wavelength readout with a full-scale error of less than 1.5% over the range of 400 to 780 nm can be achieved. Through this work, the applicability of the ANN approach in optical sensing is investigated and compared with conventional methods, and a good compromise between accuracy and the possibility for on-chip implementation was thus found. Indeed, this technique can serve different purposes and may replace conventional methods.
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spelling pubmed-33488132012-05-09 On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing Hafiane, Mohamed Lamine Dibi, Zohir Manck, Otto Sensors (Basel) Article An intelligent sensor for light wavelength readout, suitable for visible range optical applications, has been developed. Using buried triple photo-junction as basic pixel sensing element in combination with artificial neural network (ANN), the wavelength readout with a full-scale error of less than 1.5% over the range of 400 to 780 nm can be achieved. Through this work, the applicability of the ANN approach in optical sensing is investigated and compared with conventional methods, and a good compromise between accuracy and the possibility for on-chip implementation was thus found. Indeed, this technique can serve different purposes and may replace conventional methods. Molecular Diversity Preservation International (MDPI) 2009-04-21 /pmc/articles/PMC3348813/ /pubmed/22574051 http://dx.doi.org/10.3390/s90402884 Text en © 2009 by the authors; licensee MDPI, Basel, Switzerland This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Hafiane, Mohamed Lamine
Dibi, Zohir
Manck, Otto
On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing
title On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing
title_full On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing
title_fullStr On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing
title_full_unstemmed On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing
title_short On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing
title_sort on the capability of artificial neural networks to compensate nonlinearities in wavelength sensing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3348813/
https://www.ncbi.nlm.nih.gov/pubmed/22574051
http://dx.doi.org/10.3390/s90402884
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