Del-Valle-Soto, Carolina

Main Affiliation

Preferred name

Official Name

del Valle Soto, Carolina

ORCID

0000-0002-0272-3275

Researcher ID

T-5779-2017

Scopus Author ID

56257456700

Now showing 1 - 10 of 110

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Tecnología Funcional de Nariz Electrónica para el Monitoreo de Gases en el Aire
(Escuela Politecnica Nacional, 2025-11-30)
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Claudia L. Garzón-Castro
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Annamaría Filomena-Ambrosio
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Roberto De Fazio
En los últimos años, las narices electrónicas se han consolidado como herramientas innovadoras para el monitoreo ambiental, particularmente en la detección de contaminantes en el aire. En este trabajo, se presenta el diseño e implementación de una tecnología funcional, portátil y de bajo costo de nariz electrónica, capaz de identificar gases como el monóxido de carbono, el metano y varios compuestos volátiles. Esta tecnología integra un arreglo de sensores y un módulo de adquisición de datos junto con algoritmos avanzados de procesamiento de señales. Se propone la aplicación del Método de Filtrado y Diagonalización (FDM) para la extracción de características espectrales, combinado con Bosques Aleatorios (RF) para la clasificación de gases. Los resultados experimentales demuestran una precisión del 96.4 % en la identificación de compuestos gaseosos, validando la efectividad de la combinación FDM-RF. Este estudio contribuye al avance de tecnologías accesibles para el monitoreo de la calidad del aire y así como de nuevos métodos de detección y clasificación de gases ambientales.
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Sizing and Characterization of Load Curves of Distribution Transformers Using Clustering and Predictive Machine Learning Models
(MDPI AG, 2025-04-04)
Pedro Torres-Bermeo
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Kevin López-Eugenio
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Guillermo Palacios-Navarro
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José Varela-Aldás
The efficient sizing and characterization of the load curves of distribution transformers are crucial challenges for electric utilities, especially given the increasing variability of demand, driven by emerging loads such as electric vehicles. This study applies clustering techniques and predictive models to analyze and predict the behavior of transformer demand, optimize utilization factors, and improve infrastructure planning. Three clustering algorithms were evaluated, K-shape, DBSCAN, and DTW with K-means, to determine which one best characterizes the load curves of transformers. The results show that DTW with K-means provides the best segmentation, with a cross-correlation similarity of 0.9552 and a temporal consistency index of 0.9642. For predictive modeling, supervised algorithms were tested, where Random Forest achieved the highest accuracy in predicting the corresponding load curve type for each transformer (0.78), and the SVR model provided the best performance in predicting the maximum load, explaining 90% of the load variability (R2 = 0.90). The models were applied to 16,696 transformers in the Ecuadorian electrical sector, validating the load prediction with an accuracy of 98.55%. Additionally, the optimized assignment of the transformers’ nominal power reduced installed capacity by 39.27%, increasing the transformers’ utilization factor from 31.79% to 52.35%. These findings highlight the value of data-driven approaches for optimizing electrical distribution systems.
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Mechanical, Thermal, and Environmental Energy Harvesting Solutions in Fully Electric and Hybrid Vehicles: Innovative Approaches and Commercial Systems
(MDPI AG, 2025-04-11)
Giuseppe Rausa
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Maurizio Calabrese
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Roberto De Fazio
Energy harvesting in the automotive sector is a rapidly growing field aimed at improving vehicle efficiency and sustainability by recovering wasted energy. Various technologies have been developed to convert mechanical, thermal, and environmental energy into electrical power, reducing dependency on traditional energy sources. This manuscript provides a comprehensive review of energy harvesting applications/methodologies, aiming to trace the research lines and future developments. This work identifies the main categories of harvesting solutions, namely mechanical, thermal, and hybrid/environmental solar–wind systems; each section includes a detailed review of the technical and scientific state of the art and a comparative analysis with detailed tables, allowing the state of the art to be mapped for identification of the strengths of each solution, as well as the challenges and future developments needed to enhance the technological level. These improvements focus on energy conversion efficiency, material innovation, vehicle integration, energy savings, and environmental sustainability. The mechanical harvesting section focuses on energy recovery from vehicle vibrations, with emphasis on regenerative suspensions and piezoelectric-based solutions. Specifically, solutions applied to suspensions with electric generators can achieve power outputs of around 1 kW, while piezoelectric-based suspension systems can generate up to tens of watts. The thermal harvesting section, instead, explores methods for converting waste heat from an internal combustion engine (ICE) into electrical power, including thermoelectric generators (TEGs) and organic Rankine cycle systems (ORC). Notably, ICEs with TEGs can recover above 1 kW of power, while ICE-based ORC systems can generate tens of watts. On the other hand, TEGs integrated into braking systems can harvest a few watts of power. Then, hybrid solutions are discussed, focusing on integrated mechanical and thermal energy recovery systems, as well as solar and wind energy harvesting. Hybrid solutions can achieve power outputs above 1 kW, with the main contribution from TEGs (≈1 kW), compared to piezoelectric systems (hundreds of W). Lastly, a section on commercial solutions highlights how current scientific research meets the automotive sector’s needs, providing significant insights for future development. For these reasons, the research results aim to be guidelines for a better understanding of where future studies should focus to improve the technological level and efficiency of energy harvesting solutions in the automotive sector.
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Real-Time Object Finding for the Visually Impaired Using an Image-to-Speech Wearable Device
(Springer Nature Switzerland, 2025-11-18)
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Edwige Pissaloux
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Claudia L. Garzón-Castro
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Roberto de Fazio
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Projection of Photovoltaic System Adoption and Its Impact on a Distributed Power Grid Using Fuzzy Logic
(MDPI AG, 2025-06-06)
Kevin López-Eugenio
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Pedro Torres-Bermeo
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José Varela-Aldás
The increasing adoption of photovoltaic systems presents new challenges for energy planning and grid stability. This study proposes a fuzzy logic-based methodology to identify potential PV adopters by integrating variables such as energy consumption, electricity tariff, solar radiation, and socioeconomic level. The approach was applied to a real distribution grid and compared against a previously presented method that selects users based solely on high energy consumption. The fuzzy logic model demonstrated superior performance by identifying 77.03 [%] of real adopters, outperforming the previous selection strategy. Additionally, the study evaluates the technical impact of PV integration on the distribution grid through power flow simulations, analysing energy losses, voltage stability, and asset loadability. Findings highlight that while PV systems reduce energy losses, they can also introduce voltage regulation challenges under high penetration. The proposed methodology serves as a decision-support tool for utilities and regulators, enhancing the accuracy of adoption projections and informing infrastructure planning. Its flexibility and rule-based nature make it adaptable to different regulatory and technical environments, allowing it to be replicated globally for sustainable energy transition initiatives.
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A Resilient Energy-Efficient Framework for Jamming Mitigation in Cluster-Based Wireless Sensor Networks
(MDPI AG, 2025-09-29)
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Aimé Lay-Ekuakille
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Paolo Visconti
This paper presents a resilient and energy-efficient framework for jamming mitigation in cluster-based wireless sensor networks (WSNs), addressing a critical vulnerability in hostile or interference-prone environments. The proposed approa ch integrates dynamic cluster reorganization, adaptive MAC-layer behavior, and multipath routing strategies to restore communication capabilities and sustain network functionality under jamming conditions. The framework is evaluated across heterogeneous topologies using Zigbee and Bluetooth Low Energy (BLE); both stacks were validated in a physical testbed with matched jammer and traffic conditions, while simulation was used solely to tune parameters and support sensitivity analyses. Results demonstrate significant improvements in Packet Delivery Ratio, end-to-end delay, energy consumption, and retransmission rate, with BLE showing particularly high resilience when combined with the mitigation mechanism. Furthermore, a comparative analysis of routing protocols including AODV, GAF, and LEACH reveals that hierarchical protocols achieve superior performance when integrated with the proposed method. This framework has broader applicability in mission-critical IoT domains, including environmental monitoring, industrial automation, and healthcare systems. The findings confirm that the framework offers a scalable and protocol-agnostic defense mechanism, with potential applicability in mission-critical and interference-sensitive IoT deployments.
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Dynamic Jamming Mitigation in Wireless Sensor Networks: A Comprehensive Algorithm for Resilience and Efficiency
(Springer Nature Switzerland, 2025-11-18)
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Yehoshua Aguilar-Molina
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A smart glove to evaluate Parkinson's disease by flexible piezoelectric and inertial sensors
(Elsevier BV, 2025)
R. De Fazio
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V.M. Mastronardi
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M. De Vittorio
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P. Visconti
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Electrodermal Response Patterns and Emotional Engagement Under Continuous Algorithmic Video Stimulation: A Multimodal Biometric Analysis
(MDPI AG, 2026-01-18)
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David Contreras-Tiscareno
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Diego Sebastian Montoya-Rodriguez
Excessive use of short-form video platforms such as TikTok has raised growing concerns about digital addiction and its impact on young users’ emotional well-being. This study examines the relationship between continuous TikTok exposure and emotional engagement in young adults aged 20–23 through a multimodal experimental design. The purpose of this research is to determine whether emotional engagement increases, remains stable, or declines during prolonged exposure and to assess the degree of correspondence between facially inferred engagement and physiological arousal. To achieve this, multimodal biometric data were collected using the iMotions platform, integrating galvanic skin response (GSR) sensors and facial expression analysis via Affectiva’s AFFDEX SDK 5.1. Engagement levels were binarized using a logistic transformation, and a binomial test was conducted. GSR analysis, merged with a 50 ms tolerance, revealed no significant differences in skin conductance between engaged and non-engaged states. Findings indicate that although TikTok elicits strong initial emotional engagement, engagement levels significantly decline over time, suggesting habituation and emotional fatigue. The results refine our understanding of how algorithm-driven, short-form content affects users’ affective responses and highlight the limitations of facial metrics as sole indicators of physiological arousal. Implications for theory include advancing multimodal models of emotional engagement that account for divergences between expressivity and autonomic activation. Implications for practice emphasize the need for ethical platform design and improved digital well-being interventions. The originality and value of this study lie in its controlled experimental approach that synchronizes facial and physiological signals, offering objective evidence of the temporal decay of emotional engagement during continuous TikTok use and underscoring the complexity of measuring affect in highly stimulating digital environments.
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Audio’s Impact on Deep Learning Models: A Comparative Study of EEG-Based Concentration Detection in VR Games
(MDPI AG, 2025-10-29)
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Francisco R. Castillo-Soria
This study investigates the impact of audio feedback on cognitive performance during VR puzzle games using EEG analysis. Thirty participants played three different VR puzzle games under two conditions (with and without audio) while their brain activity was recorded. To analyze concentration levels and neural engagement patterns, we employed spectral analysis combined with a preprocessing algorithm and an optimized Deep Neural Network (DNN) model. The proposed processing stage integrates feature normalization, automatic labeling based on Principal Component Analysis (PCA), and Gamma band feature extraction, transforming concentration detection into a supervised classification problem. Experimental validation was conducted under the two gaming conditions in order to evaluate the impact of multisensory stimulation on model performance. The results show that the proposed approach significantly outperforms traditional machine learning classifiers (SVM, LR) and baseline deep learning models (DNN, DGCNN), achieving a 97% accuracy in the audio scenario and 83% without audio. These findings confirm that auditory stimulation reinforces neural coherence and improves the discriminability of EEG patterns, while the proposed method maintains a robust performance under less stimulating conditions.

Del-Valle-Soto, Carolina

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