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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, 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, Tecnología Funcional de Nariz Electrónica para el Monitoreo de Gases en el Aire(Escuela Politecnica Nacional, 2025-11-30); ; Roberto De FazioEn 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. - 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, Real-Time Object Finding for the Visually Impaired Using an Image-to-Speech Wearable Device(Springer Nature Switzerland, 2025-11-18); ; - 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); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Genre-Sensitive Prediction of Emotional Arousal in Virtual Reality: A Neural Modeling Approach Using Skin Conductance Peaks(Institute of Electrical and Electronics Engineers (IEEE), 2025-12); - 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, 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, A smart glove to evaluate Parkinson's disease by flexible piezoelectric and inertial sensors(Elsevier BV, 2025);