Imas González, José Javier

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Imas González

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José Javier

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Ingeniería Eléctrica, Electrónica y de Comunicación

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Now showing 1 - 2 of 2
  • PublicationOpen Access
    Optical fiber thermo-refractometer
    (Optica, 2022) Imas González, José Javier; Ruiz Zamarreño, Carlos; Del Villar, Ignacio; Cardozo da Silva, Jean Carlos; Oliveira, V.; Matías Maestro, Ignacio; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
    This work presents the implementation of a thermo-refractometer, which integrates the measurement of both refractive index and temperature in a single optical fiber structure. To this purpose, a lossy mode resonance (LMR)-based refractometer is obtained by means of the deposition of a titanium dioxide (TiO2) thin film onto a side-polished (D-shaped) single mode fiber. Measurement and subsequent temperature compensation are achieved by means of a fiber Bragg grating (FBG) inscribed in the core of the D-shaped region. The LMR wavelength shift is monitored in transmission while the FBG (FBG peak at 1533 nm) displacement is observed in reflection. The LMR is sensitive to both the surrounding refractive index (SRI), with a sensitivity of 3725.2 nm/RIU in the 1.3324-1.3479 range, and the temperature (- 0.186 nm/°C); while the FBG is only affected by the temperature (32.6 pm/°C in the 25°C - 45°C range). With these values, it is possible to recover the SRI and temperature variations from the wavelength shifts of the LMR and the FBG, constituting a thermo-refractometer, where it is suppressed the effect of the temperature over the refractometer operation, which could cause errors in the fourth or even third decimal of the measured SRI value.
  • 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.