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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, 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, 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. - 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); 7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Neural Network-based Wearable Devices for Limb Rehabilitation by Inertial Signal Classification(2024); 7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Innovative Driver Monitoring Systems and On-Board-Vehicle Devices in a Smart-Road Scenario Based on the Internet of Vehicle Paradigm: A Literature and Commercial Solutions Overview(MDPI, 2025-01-19); ; Donato CafagnaIn recent years, the growing number of vehicles on the road have exacerbated issues related to safety and traffic congestion. However, the advent of the Internet of Vehicles (IoV) holds the potential to transform mobility, enhance traffic management and safety, and create smarter, more interconnected road networks. This paper addresses key road safety concerns, focusing on driver condition detection, vehicle monitoring, and traffic and road management. Specifically, various models proposed in the literature for monitoring the driver’s health and detecting anomalies, drowsiness, and impairment due to alcohol consumption are illustrated. The paper describes vehicle condition monitoring architectures, including diagnostic solutions for identifying anomalies, malfunctions, and instability while driving on slippery or wet roads. It also covers systems for classifying driving style, as well as tire and emissions monitoring. Moreover, the paper provides a detailed overview of the proposed traffic monitoring and management solutions, along with systems for monitoring road and environmental conditions, including the sensors used and the Machine Learning (ML) algorithms implemented. Finally, this review also presents an overview of innovative commercial solutions, illustrating advanced devices for driver monitoring, vehicle condition assessment, and traffic and road management.29 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Machine Learning and IoT-Based Solutions in Industrial Applications for Smart Manufacturing: A Critical Review(2024); ; Donato Cafagna<jats:p>The Internet of Things (IoT) has radically changed the industrial world, enabling the integration of numerous systems and devices into the industrial ecosystem. There are many areas of the manufacturing industry in which IoT has contributed, including plants’ remote monitoring and control, energy efficiency, more efficient resources management, and cost reduction, paving the way for smart manufacturing in the framework of Industry 4.0. This review article provides an up-to-date overview of IoT systems and machine learning (ML) algorithms applied to smart manufacturing (SM), analyzing four main application fields: security, predictive maintenance, process control, and additive manufacturing. In addition, the paper presents a descriptive and comparative overview of ML algorithms mainly used in smart manufacturing. Furthermore, for each discussed topic, a deep comparative analysis of the recent IoT solutions reported in the scientific literature is introduced, dwelling on the architectural aspects, sensing solutions, implemented data analysis strategies, communication tools, performance, and other characteristic parameters. This comparison highlights the strengths and weaknesses of each discussed solution. Finally, the presented work outlines the features and functionalities of future IoT-based systems for smart industry applications.</jats:p>8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Available Technologies and Commercial Devices to Harvest Energy by Human Trampling in Smart Flooring Systems: A Review(2022); Scopus© Citations 12 37 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Development and Testing of Piezoresistive and Inertial-Based Chest Bands for Breathing Monitoring(2022)Scopus© Citations 4 45 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Reliable e-nose for air toxicity monitoring by filter diagonalization method(2022); ; Scopus© Citations 3 7 1