Samuel Domínguez-Cid

Los laboratorios de Electrónica de la Escuela Politécnica Superior son de los más usados de este centro en cuanto a número de alumnos, ya que es donde se imparte la asignatura de Electrónica Industrial, común a 4 titulaciones de Grado y 3 de Doble Grado. Esta es la primera asignatura de electrónica de los planes de estudios en este centro, por lo que es en ella donde los alumnos tienen su primera toma de contacto con la electrónica y el instrumental típicamente usado en sus laboratorios. En la asignatura de Electrónica Industrial se suelen realizar 6 prácticas en las que los alumnos realizan diversos montajes electrónicos y medidas utilizando el instrumental del laboratorio. Además, hay otras asignaturas de electrónica dentro del Grado en Ingeniería Electrónica Industrial que también utilizan el mismo instrumental. Por ello, este recurso sirve como método de consulta y repaso del manejo del instrumental del laboratorio de electrónica. Además de que los alumnos puedan usar este recurso durante el desarrollo de las prácticas, también podrán utilizarlo en caso de que necesiten consultar el manejo del instrumental mientras se encuentran fuera del laboratorio durante su tiempo de estudio.

Heating, Ventilation, and Air Conditioning (HVAC) systems are crucial in the hotel sector, accounting for a significant share of energy consumption, and their unexpected failures are costly. Predictive maintenance offers an effective strategy to reduce these costs. In particular, HVAC chillers contribute substantially to overall energy consumption in hotels, and their sudden failures can result in significant expenses. This study presents a hybrid framework comprising a Fuzzy-based layer and an Artificial Neural Network-based layer, providing a practical, flexible, and interpretable solution for the predictive maintenance of hotel HVAC chillers. The framework addresses challenges related to sensor limitations, data scarcity, and system heterogeneity. The fuzzy logic (FL) layer incorporates expert knowledge through rule-based reasoning, generating meaningful outputs even without historical failure data, which is critical given the infrequent occurrence of HVAC failures. However, an FL-only system cannot adapt to the specific context of each hotel. The ANN-based layer enables continual adaptation through retraining guided by technician feedback. Each output r j ∈ [ 0 , 1 ] represents the estimated risk of an upcoming failure in a chiller component and triggers an alert when r j exceeds its threshold T j . The primary contribution of this work is the development of a flexible hybrid framework that integrates expert-driven fuzzy reasoning with data-driven ANN adaptation to estimate failure risk in hotel HVAC chillers, even under sensor limitations and scarce fault data. The system demonstrated strong performance with synthetic data and was validated in a five-star hotel in Seville, Spain.

During the growing season, olives progress through nine different phenological stages, starting with bud development and ending with senescence. During their lifespan, olives undergo changes in their external color and chemical properties. To tackle these properties, we used hyperspectral imaging during the growing season of the olives. The objective of this study was to develop a lightweight model capable of identifying olives in the hyperspectral images using their spectral information. To achieve this goal, we utilized the hyperspectral imaging of olives while they were still on the tree and conducted this process throughout the entire growing season directly in the field without artificial light sources. The images were taken on-site every week from 9:00 to 11:00 a.m. UTC to avoid light saturation and glitters. The data were analyzed using training and testing classifiers, including Decision Tree, Logistic Regression, Random Forest, and Support Vector Machine on labeled datasets. The Logistic Regression model showed the best balance between classification success rate, size, and inference time, achieving a 98% F1-score with less than 1 KB in parameters. A reduction in size was achieved by analyzing the wavelengths that were critical in the decision making, reducing the dimensionality of the hypercube. So, with this novel model, olives in a hyperspectral image can be identified during the season, providing data to enhance a farmer’s decision-making process through further automatic applications.

The proposal of this paper is to introduce a low-level blockchain marketplace, which is a blockchain where participants could share its power generation and demand. To achieve this implementation in a secure way for each actor in the network, we proposed to deploy it over efficient and generic low-performance devices. Thus, they are installed as IoT devices, registering measurements each fifteen minutes, and also acting as blockchain nodes for the marketplace. Nevertheless, it is necessary that blockchain is lightweight, so it is implemented as a specific consensus protocol that allows each node to have enough time and computer requirements to act both as an IoT device and a blockchain node. This marketplace will be ruled by Smart Contracts deployed inside the blockchain. With them, it is possible to make registers for power generation and demand. This low-level marketplace could be connected to other services to execute matching algorithms from the data stored in the blockchain. Finally, a real test-bed implementation of the marketplace was tested, to confirm that it is technically feasible.