Vitoria Pascual, Ignacio

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https://academica-e.unavarra.es/handle/2454/50177

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Vitoria Pascual

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Ignacio

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Ingeniería Eléctrica, Electrónica y de Comunicación

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ISC. Institute of Smart Cities

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0000-0003-2430-882X

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751416

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811433

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2020 - 20222
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Subject: Optical fiber ×

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Now showing 1 - 2 of 2
  • Thumbnail Image
    PublicationOpen Access
    Copper oxide coated D-shaped optical fibers for the development of LMR refractometers
    (IEEE, 2020) Ozcariz Celaya, Aritz; Vitoria Pascual, Ignacio; Arregui San Martín, Francisco Javier; Ruiz Zamarreño, Carlos; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Institute of Smart Cities - ISC; Ingeniería Eléctrica, Electrónica y de Comunicación; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA26
    Lossy mode resonance (LMR) based refractometers obtained by means of copper oxide thin-films fabricated onto side-polished (D-shaped) are presented in this work. The high refractive index of copper oxide combined with the propagation mode isolation capabilities of D-shaped fibers allows for the observation of narrow (30 nm) and high sensitive (10,336 nm per refractive index unit) LMRs, which could enable to improve the performance of LMR-based refractometers as well as provide an alternative label-free sensing platform for LMR-based sensors.
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    PublicationOpen Access
    Alfalfa quality detection by means of VIS-NIR optical fiber reflection spectroscopy
    (IEEE, 2022) Ruiz Zamarreño, Carlos; Gracia Moisés, Ander; Vitoria Pascual, Ignacio; Imas González, José Javier; Castaño de Egüés, Lorena Yveth; Avedillo de la Casa, Amaia; Matías Maestro, Ignacio; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute of Smart Cities - ISC; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
    A first approach study for the classification of alfalfa (medicago sativa) quality has been performed by means of VIS-NIR optical fiber reflection spectroscopy. Reflection spectral data has been obtained from alfalfa samples comprising six different qualities. Obtained data has been classified and organized to feed supervised self-learning algorithms. Neural networks have been used in order to differentiate the quality level of the samples. Obtained results permit to validate the proposed approach with 72% of the samples properly classified. In addition, proposed solution was implemented in a low cost automated detection prototype suitable to be used by non-qualified operators. Obtained equipment consist of a first step towards its utilization in quality monitoring and classification of many other products in the agri-food field.