Del-Valle-Soto, Carolina
Main Affiliation
Preferred name
Del-Valle-Soto, Carolina
Official Name
del Valle Soto, Carolina
ORCID
0000-0002-0272-3275
Researcher ID
T-5779-2017
Scopus Author ID
56257456700
125 results
Now showing 1 - 10 of 125
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Item type:Publication, Multimodal Biometric Framework for Evaluating Emotional Impact of Chromatic Manipulation in Cinematic Content(MDPI AG, 2026-05-25); ; 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Does the use of dedicated mobile devices in magnetism classes improve student learning?(Frontiers Media SA, 2026-04-21); Palacios-Navarro, Guillermo8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Recent Advances in Multi-Camera Computer Vision for Industry 4.0 and Smart Cities: A Systematic Review(MDPI AG, 2026-03-25); 9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Innovative Solution with Wearable and Aboard-the-Vehicle Sensors Integrated with Machine Learning Algorithms for Monitoring the Driver's Psycho-Physical Condition for Safety Purposes(2024); 20 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Sizing and Characterization of Load Curves of Distribution Transformers Using Clustering and Predictive Machine Learning Models(MDPI AG, 2025-04-04); 27 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data Science(2024); Ramon A. Briseño<jats:p>Wireless networks play a pivotal role in various domains, including industrial automation, autonomous vehicles, robotics, and mobile sensor networks. This research investigates the critical issue of packet loss in modern wireless networks and aims to identify the conditions within a network’s environment that lead to such losses. We propose a packet status prediction model for data packets that travel through a wireless network based on the IEEE 802.15.4 standard and are exposed to five different types of interference in a controlled experimentation environment. The proposed model focuses on the packetization process and its impact on network robustness. This study explores the challenges posed by packet loss, particularly in the context of interference, and puts forth the hypothesis that specific environmental conditions are linked to packet loss occurrences. The contribution of this work lies in advancing our understanding of the conditions leading to packet loss in wireless networks. Data are retrieved with a single CC2531 USB Dongle Packet Sniffer, whose pieces of information on packets become the features of each packet from which the classifier model will gather the training data with the aim of predicting whether a packet will unsuccessfully arrive at its destination. We found that interference causes more packet loss than that caused by various devices using a WiFi communication protocol simultaneously. In addition, we found that the most important predictors are network strength and packet size; low network strength tends to lead to more packet loss, especially for larger packets. This study contributes to the ongoing efforts to predict and mitigate packet loss, emphasizing the need for adaptive models in dynamic wireless environments.</jats:p>11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Stability-Aware Security–Performance Trade-Off Analysis in Resource-Constrained IoT Systems: A Time-Series and Bootstrap-Based Evaluation of TLS and Hybrid ECC–AES Mechanisms(MDPI AG, 2026-05-02); ; 31 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Mechanical, Thermal, and Environmental Energy Harvesting Solutions in Fully Electric and Hybrid Vehicles: Innovative Approaches and Commercial Systems(MDPI AG, 2025-04-11); ; Roberto De FazioEnergy 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.19 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Dynamic Jamming Mitigation in Wireless Sensor Networks: A Comprehensive Algorithm for Resilience and Efficiency(Springer Nature Switzerland, 2025-11-18); ; 24