Publication :
Machine learning implementation for unambiguous refractive index measurement using a self-referenced fiber refractometer

Notice brève
bul.description.provenanceelcou28 sbpar chlac
bul.rights.raisonEmbargoInfiniPour que le document soit diffusé en libre accès, en accord avec le délai prescrit par l’éditeur, il faudrait déposer la version acceptée pour publication, incluant toutes les modifications demandées, mais sans la mise en page de la revue. Votre version est une version éditeur avec la mention "© 2022 IEEE. Personal use of this material is permitted" Pour ce faire, effectuez une demande de modification à l’aide de la liste des dépôts diffusés à partir du tableau de suivi.
dc.contributor.authorMartínez-Manuel, Rodolfo
dc.contributor.authorValentín-Coronado, Luis M.
dc.contributor.authorEsquivel-Hernández, Jonathan
dc.contributor.authorMonga, Kaboko Jean-Jacques
dc.contributor.authorLaRochelle, Sophie
dc.date.accessioned2023-01-20T17:18:28Z
dc.date.available2023-01-20T17:18:28Z
dc.date.issued2022-06-21
dc.description.abstractThe implementation of a machine learning algorithm for measuring refractive index of liquid samples using Fresnel reflection at the tip of a fiber is proposed in order to overcome the measurement ambiguity between samples having refractive index values below and above the effective refractive index of the fiber fundamental mode. This is the first time that a machine learning algorithm is implemented in a fiber refractometer. The algorithm, used for pattern classification, is the Support Vector Machine (SVM). The sensing head is formed by two-cascaded cavities that generate an interference pattern that changes each time the fiber is immersed in a different sample. The changes in the interference pattern are classified by the proposed algorithm, which extends the sensing range and eliminates any ambiguity in the obtained RI values. The proposed system is also self-referenced, and therefore it is unaffected by any intensity change of the optical source. A theoretical model and experimental results are presented in detail to demonstrate the effectiveness of the proposed system.en
dc.identifier.doi10.1109/JSEN.2022.3183475
dc.identifier.issn1530-437X
dc.identifier.urihttp://hdl.handle.net/20.500.11794/108883
dc.languageeng
dc.publisherIEEE Sensors Council
dc.rightshttp://purl.org/coar/access_right/c_16ec
dc.subjectMachine learningen
dc.subjectFiber-optic refractometeren
dc.subjectFresnel reflectionen
dc.subjectOptical fiber sensorsen
dc.subject.rvmAlgorithmes d'apprentissage
dc.subject.rvmRéfractométrie
dc.subject.rvmRéfractomètres
dc.subject.rvmFibres optiques
dc.titleMachine learning implementation for unambiguous refractive index measurement using a self-referenced fiber refractometer
dc.typearticle de recherche
dcterms.bibliographicCitationIEEE Sensors Journal, Vol. 22 (14), 14134 - 14141 (2022)
dcterms.dateAccepted2022-06-21
dspace.accessstatus.time2023-09-13 18:00:59
dspace.entity.typePublication
relation.isAuthorOfPublication23cd7c00-c28c-48ad-b487-c4c3f3e4809a
relation.isAuthorOfPublicatione8df24b1-dcf2-4696-bf01-cbcb1878641e
relation.isAuthorOfPublicationb669d36d-66f6-4320-a346-b1a7ab352ebc
relation.isAuthorOfPublication.latestForDiscoveryb669d36d-66f6-4320-a346-b1a7ab352ebc
relation.isResourceTypeOfPublication4c433ef5-3937-4530-8252-cca17d715747
relation.isResourceTypeOfPublication.latestForDiscovery4c433ef5-3937-4530-8252-cca17d715747
rioxxterms.project.funder-nameNatural Sciences and Engineering Research Council of Canada
rioxxterms.project.funder-nameConsejo Nacional de Ciencia y Tecnología (CONACYT), México
rioxxterms.version-of-recordhttps://doi.org/10.1109/JSEN.2022.3183475
Fichiers
Bundle original
Voici les éléments 1 - 1 sur 1
Pas de vignette d'image disponible
Nom :
Martinez-Manuel_Machine-doi.org_10.1109_JSEN.2022.3183475.pdf
Taille :
1.86 MB
Format :
Adobe Portable Document Format
Télécharger
Bundle de license
Voici les éléments 1 - 1 sur 1
Pas de vignette d'image disponible
Nom :
license.txt
Taille :
922 B
Format :
Item-specific license agreed upon to submission
Description :
Télécharger
Collections
Articles publiés dans des revues avec comité de lecture