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
116 results
Now showing 1 - 10 of 116
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Item type:Publication, Integrated Dynamic Power Management Strategy with a Field Programmable Gate Array-Based Cryptoprocessor System for Secured Internet-of-Medical Things Networks(MDPI AG, 2025-02-04) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Electrodermal Response Patterns and Emotional Engagement Under Continuous Algorithmic Video Stimulation: A Multimodal Biometric Analysis(MDPI AG, 2026-01-18); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Literature Review on Real-Time Crime Detection Using Deep Learning and Edge Computing(IEEE, 2025-10-21); Varela-Aldás, José - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Audio’s Impact on Deep Learning Models: A Comparative Study of EEG-Based Concentration Detection in VR Games(MDPI AG, 2025-10-29); ; ; ; Francisco R. Castillo-SoriaThis 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Forehead and In-Ear EEG Acquisition and Processing: Biomarker Analysis and Memory-Efficient Deep Learning Algorithm for Sleep Staging with Optimized Feature Dimensionality(MDPI AG, 2025-10-01); Massimo De VittorioAdvancements in electroencephalography (EEG) technology and feature extraction methods have paved the way for wearable, non-invasive systems that enable continuous sleep monitoring outside clinical environments. This study presents the development and evaluation of an EEG-based acquisition system for sleep staging, which can be adapted for wearable applications. The system utilizes a custom experimental setup with the ADS1299EEG-FE-PDK evaluation board to acquire EEG signals from the forehead and in-ear regions under various conditions, including visual and auditory stimuli. Afterward, the acquired signals were processed to extract a wide range of features in time, frequency, and non-linear domains, selected based on their physiological relevance to sleep stages and disorders. The feature set was reduced using the Minimum Redundancy Maximum Relevance (mRMR) algorithm and Principal Component Analysis (PCA), resulting in a compact and informative subset of principal components. Experiments were conducted on the Bitbrain Open Access Sleep (BOAS) dataset to validate the selected features and assess their robustness across subjects. The feature set extracted from a single EEG frontal derivation (F4-F3) was then used to train and test a two-step deep learning model that combines Long Short-Term Memory (LSTM) and dense layers for 5-class sleep stage classification, utilizing attention and augmentation mechanisms to mitigate the natural imbalance of the feature set. The results—overall accuracies of 93.5% and 94.7% using the reduced feature sets (94% and 98% cumulative explained variance, respectively) and 97.9% using the complete feature set—demonstrate the feasibility of obtaining a reliable classification using a single EEG derivation, mainly for unobtrusive, home-based sleep monitoring systems. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, IoT-Based Smart Gas Meter With LTE Connectivity and Cloud Analytics for Stationary Tanks(Institute of Electrical and Electronics Engineers (IEEE), 2026); ; Millions of homes in developing countries rely on stationary LPG tanks, yet the methods for monitoring fuel levels remain manual, unsafe, and highly inefficient. This paper addresses this issue by presenting the design, development, and implementation of an IoT-based smart gas meter that uses a noninvasive Hall-effect sensor to digitally read existing level gauges. Data is transmitted via LTE, eliminating the need for Wi-Fi and optimizing connectivity. The system is designed for low power consumption, achieving a battery life of more than eight years. Additionally, a cloud architecture is implemented in AWS to process the collected data, allowing real-time analysis, predictive maintenance, and logistics optimization. A field test was also conducted with 15 prototypes, demonstrating accurate gas level monitoring, reliable refill detection, and gas theft prevention. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Projection of Photovoltaic System Adoption and Its Impact on a Distributed Power Grid Using Fuzzy Logic(MDPI AG, 2025-06-06); José Varela-AldásThe 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks(MDPI AG, 2025-03-20); ; ; José Varela-AldásWireless sensor networks (WSNs) are fundamental for modern IoT applications, yet they remain highly vulnerable to jamming attacks, which significantly degrade communication reliability and energy efficiency. This paper proposes a novel adaptive cluster-based jamming mitigation algorithm designed for heterogeneous WSNs that integrate LoRa and Bluetooth Low Energy (BLE) technologies. The proposed strategy dynamically switches between communication protocols, optimizes energy consumption, and reduces retransmissions under interference conditions by leveraging real-time network topology adjustments and adaptive transmission power control. Through extensive experimental validation, we demonstrate that our mitigation mechanism reduces energy consumption by up to 38% and lowers packet retransmission rates by 47% compared to single-protocol networks under jamming conditions. Additionally, our results indicate that the hybrid LoRa-BLE approach outperforms standalone LoRa and BLE configurations in terms of network resilience, adaptability, and sustained data transmission under attack scenarios. This work advances the state-of-the-art by introducing a multi-protocol interference-resilient communication strategy, paving the way for more robust, energy-efficient, and secure WSN deployments in smart cities, industrial IoT, and critical infrastructure monitoring. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Resilient Energy-Efficient Framework for Jamming Mitigation in Cluster-Based Wireless Sensor Networks(MDPI AG, 2025-09-29); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Efficient Deep Learning-Based M-PSK Detection for OFDM V2V Systems Using MobileNetV3(MDPI AG, 2026-03-11);