Open Access
Micro sized interdigital capacitor for humidity detection based on agarose coating
(2021) Vitoria Pascual, Ignacio; Armas, Dayron; Coronel Camones, Carlos Manuel; Ozcariz Celaya, Aritz; Ruiz Zamarreño, Carlos; Matías Maestro, Ignacio; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute of Smart Cities - ISC; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
A micro sized interdigital capacitor has been proposed for the detection of relative humidity. The photolithography technique enables the fabrication of fingers with a size of 10x500 um. A thin film of agarose functionalizes the sensor for humidity sensing, which improves its performance by 155 times, obtaining a sensitivity of 32.98 pF/%RH.
Open Access
Contribución al desarrollo de sensores de gases basados en resonancias ópticas
(2022) Vitoria Pascual, Ignacio; Matías Maestro, Ignacio; Ruiz Zamarreño, Carlos; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Los sensores basados en resonancias ópticas han incrementado su popularidad estos últimos años. Cobran especial relevancia en aplicaciones como la detección de gases gracias a su alta sensibilidad y robustez en ambientes agresivos. Esta tesis ha contribuido a la mejora de sensores de gases basados en resonancias ópticas empleando dos enfoques; la búsqueda de materiales sensibles a gases y el desarrollo de nuevas técnicas de interrogación. Los materiales investigados están formados por una matriz polimérica con materiales nanoestructurados: nanopartículas de óxido tungsteno, nanodiamantes y nanosheets de óxido de grafeno. Las propiedades de estos materiales son testeadas ante diferentes gases e índices de refracción externo (surrouding refractive index SRI). En la búsqueda de sensores ultrasensibles al SRI, se desarrollan dos líneas de investigación. Una estudia el efecto LMR y su sensibilidad en la región de infrarrojo medio (MIR) empleando fibras ópticas fluoradas y TiO2 como material. La otra, se centra en resonancias acuñadas por primera como surface exciton polariton resonance (SEPR) basándose en el efecto long range surface exciton polariton (LRSEP). Para ello se desarrolla un sensor en configuración Kretschmann-Raether con dos películas de Cr y MgF2. También se estudia los papeles de los distintos parámetros del sensor, así como experimentalmente la sensibilidad al ángulo de incidencia y SRI.
Embargo
Optimization of optical spectroscopy classification algorithms for limited data scenarios in the food industry: tomato sauce samples case
(Elsevier, 2025-01-01) Gracia Moisés, Ander; Vitoria Pascual, Ignacio; Avedillo de la Casa, Amaia; Moreno Pérez, María; Imas González, José Javier; Ruiz Zamarreño, Carlos; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC; Gobierno de Navarra / Nafarroako Gobernua; Universidad Pública de Navarra / Nafarroako Unibertistate Publikoa
This study addresses the problem of training deep learning models with limited datasets, a significant challenge in sectors like medical imaging and food quality analysis. To tackle this issue, generative adversarial networks (GANs) will be employed to augment the available data and improve model performance. An innovative approach is introduced here, integrating semi-supervised learning and generative modeling to maximize the use of small datasets in developing robust models. The method involves reversing the conventional distribution of training and testing data to focus on model evaluation and generalization from limited samples. Wasserstein GANs (WGANs) and Semi-Supervised GANs (SGANs), are utilized to supplement datasets with synthetic but realistic examples, enhancing the training process in scenarios of data scarcity. These techniques are applied in the context of visible reflectance spectroscopy to analyze tomato sauces, demonstrating the method's effectiveness in non-invasively assessing key quality parameters such as oil content, °Brix, and pH. The results show significant improvements in model performance metrics: for %Oil content, overall accuracy increased from 0.47 to 0.66; for °Bx, it rose from 0.65 to 0.71; and for pH measurement, accuracy improved from 0.43 to 0.62. These outcomes highlight the model's improved capability to generalize and maintain accuracy with limited data.
Open Access
Air bubble detection in water flow by means of ai-assisted infrared reflection system
(IEEE, 2024-06-26) Gracia Moisés, Ander; Vitoria Pascual, Ignacio; Imas González, José Javier; Ruiz Zamarreño, Carlos; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC
This letter introduces an innovative, cost-effective solution for detecting air bubbles in water flow systems using an AI-assisted infrared reflection system. In industries, such as chemical, mechanical, oil, and nuclear, the presence of air bubbles in fluids can compromise both product quality and process efficiency. Our research develops a system that combines infrared optical sensors with machine learning algorithms to detect and quantify bubble presence effectively. The system’s design utilizes infrared emitters and photodetectors arranged around a pipe to capture detailed data on bubble characteristics, which is then analyzed using a support vector machine (SVM) model to predict bubble concentrations. Experimental results demonstrate the system’s ability to accurately identify different levels of bubble presence, offering significant improvements over existing methods. Key performance metrics include a mean squared error of 0.0694, a root mean squared error of 0.2634, and a coefficient of determination of 0.9765, indicating high accuracy and reliability. This approach not only enhances operational reliability and safety but also provides a scalable solution adaptable to various industrial settings.
Open Access
Surface exciton polariton resonances (SEPR)-based sensors
(Elsevier, 2023) Vitoria Pascual, Ignacio; Ruiz Zamarreño, Carlos; Ozcariz Celaya, Aritz; Imas González, José Javier; Del Villar, Ignacio; Matías Maestro, Ignacio; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Institute of Smart Cities - ISC; Ingeniería Eléctrica, Electrónica y de Comunicación
A new type of resonance in the development of sensors using long-range surface exciton polariton (LRSEP) phenomena has been coined: surface exciton plasmon resonance (SEPR). The resonance was obtained in the reflected spectrum of a Kretschmann-Raether setup with a two-coupled-interface structure composed of 412 nm magnesium fluoride and 50 nm chromium thin films. The roles of different parameters such as thicknesses of the films and the incidence angles have been simulated. Some preliminary experimental results show a promising performance with a shift of the resonance central wavelength with changes in the incidence angle of -136.52 nm/° and a sensitivity of 23,221 nm/refractive index unit.
Open Access
Micro sized interdigital capacitor for gases detection based on graphene oxide coating
(Springer, 2023) Vitoria Pascual, Ignacio; Armas, Dayron; Coronel Camones, Carlos Manuel; Algarra González, Manuel; Ruiz Zamarreño, Carlos; Matías Maestro, Ignacio; Mukhopadhyay, Subhas C.; Institute for Advanced Materials and Mathematics - INAMAT2; Institute of Smart Cities - ISC
A micro sized interdigital capacitor sensible to CO2 and NO is studied in this work. The photolithography technique enables to obtain fingers with dimensions of 10 × 500 µm and separated 7 µm between them. The deposition of a film composed of graphene oxide particles as the dielectrics of the capacitor allows to measure the gas concentration of CO2 and NO mixed with N2. The sensors were characterized in a gas chamber with a constant flow, obtaining promising results in changes of capacitance at 100 Hz. The sensors have a good linearity and sensitivity with a R2 = 0.996 and 5.026·10-1 pF/ % v/v for CO2 and R2=0.972 and 1.433·10-1 pF/ppb for NO.
Open Access
Beyond near-infrared lossy mode resonances with fluoride glass optical fiber
(Optica, 2021) Vitoria Pascual, Ignacio; Ruiz Zamarreño, Carlos; Ozcariz Celaya, Aritz; Imas González, José Javier; Matías Maestro, Ignacio; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Institute of Smart Cities - ISC; Ingeniería Eléctrica, Electrónica y de Comunicación; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
The objective of this Letter consists of the exploration of the lossy mode resonance (LMR) phenomenon beyond the nearinfrared region and specifically in the short wave infrared region (SWIR) and medium wave infrared region (MWIR). The experimental and theoretical results show for the first time, to the best of our knowledge, not only LMRs in these regions, but also the utilization of fluoride glass optical fiber associated with this phenomenon. The fabricated devices consist of a nanometric thin-film of titanium dioxide used as LMR generating material, which probed extraordinary sensitivities to external refractive index (RI) variations. RI sensitivity was studied in the SWIR and MWIR under different conditions, such as the LMR wavelength range or the order of resonance, showing a tremendous potential for the detection of minute concentrations of gaseous or biological compounds in different media.
Open Access
A comprehensive study of optical resonances in metals, dielectrics, and excitonic materials in double interface structures
(Elsevier, 2025-02-01) Imas González, José Javier; Matías Maestro, Ignacio; Del Villar, Ignacio; Ozcariz Celaya, Aritz; Vitoria Pascual, Ignacio; Ruiz Zamarreño, Carlos; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC
From an optical perspective, depending on the relationship between the real (n) and imaginary (k) parts of its refractive index, three broad categories of materials can be distinguished: metals (k ¿ n), dielectrics (n ¿ k), and materials in which n ¿ k (termed here excitonic materials). The modes and optical resonances that appear in a thin film bounded by two dielectrics with similar refractive index, what we call here a double interface structure, have been widely studied in the case of metals, but not for dielectrics, or materials with n ¿ k. In this work, we propose a new approach, based on employing the phase matching condition to correlate the resonances that appear in the wavelength versus incident angle color maps of the reflected power with the modal analysis of the cross section of the structure. This analysis is performed, using an attenuated total reflection (ATR) setup, for thin film materials that belong to each of the mentioned categories: a metal (gold, Au), a dielectric (titanium dioxide, TiO2), and a material with n ¿ k (chromium, Cr). The theoretical analysis is supported with experimental results. It is demonstrated that this method enables to identify any resonance at any wavelength or incident angle, being valid for all three types of materials. Therefore, it is considered the suggested approach will help the research in these materials and in the double interface structure in the optics and photonics field.
Open Access
A technology review and field testing of a soil water quality monitoring system
(Springer, 2023) Afridi, Waqas A. K.; Akhter, Fowzia; Vitoria Pascual, Ignacio; Mukhopadhyay, Subhas C.; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
Soil water quality is one of the most influential factors in ensuring the productivity of agricultural farms. Soil water quality and soil quality are hugely dependent on each other. Hence, it is essential to have a clear understanding of the essential soil quality parameters and the existing technologies to detect those parameters. This paper briefly discusses the vital soil quality parameters for their significance towards fostering sustainable agriculture. Moreover, a technology review of recent studies has been critically analyzed, and their strengths and weaknesses have been addressed. Moreover, an Internet of Things (IoT)- enabled low-cost, low-power soil monitoring system has been proposed to overcome the drawbacks of the existing technologies. The initially developed system has been deployed in a residential garden for preliminary testing and results. However, the findings of the proposed system satisfy the expected outcome as the testing soil parameters, such as soil moisture content and temperature, vary accordingly with the increase in depth underneath the surface. Also, environmental parameters such as ambient temperature, carbon dioxide and humidity vary expectedly over day and night. Data obtained from this system will be beneficial to derive realistic water-balance estimations and sustainable agriculture decision-making.
Open Access
Data augmentation techniques for machine learning applied to optical spectroscopy datasets in agrifood applications: a comprehensive review
(MDPI, 2023) Gracia Moisés, Ander; Vitoria Pascual, Ignacio; Imas González, José Javier; Ruiz Zamarreño, Carlos; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Institute of Smart Cities - ISC
Machine learning (ML) and deep learning (DL) have achieved great success in different tasks. These include computer vision, image segmentation, natural language processing, predicting classification, evaluating time series, and predicting values based on a series of variables. As artificial intelligence progresses, new techniques are being applied to areas like optical spectroscopy and its uses in specific fields, such as the agrifood industry. The performance of ML and DL techniques generally improves with the amount of data available. However, it is not always possible to obtain all the necessary data for creating a robust dataset. In the particular case of agrifood applications, dataset collection is generally constrained to specific periods. Weather conditions can also reduce the possibility to cover the entire range of classifications with the consequent generation of imbalanced datasets. To address this issue, data augmentation (DA) techniques are employed to expand the dataset by adding slightly modified copies of existing data. This leads to a dataset that includes values from laboratory tests, as well as a collection of synthetic data based on the real data. This review work will present the application of DA techniques to optical spectroscopy datasets obtained from real agrifood industry applications. The reviewed methods will describe the use of simple DA techniques, such as duplicating samples with slight changes, as well as the utilization of more complex algorithms based on deep learning generative adversarial networks (GANs), and semi-supervised generative adversarial networks (SGANs).