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75 results on '"Molinara, M."'

1. Using Genetic Programming to Learn Behavioral Models of Lithium Batteries

Author

Di Capua, G., Bourelly, C., De Stefano, C., Fontanella, F., Milano, F., Molinara, M., Oliva, N., Porpora, F., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Correia, João, editor, Smith, Stephen, editor, and Qaddoura, Raneem, editor

Published
2023
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3. Chemicals Detection in Water by SENSIPLUS Platform: Current State and Ongoing Progress

Author

Bourelly, Carmine, Ferdinandi, M., Molinara, M., Ferrigno, L., Simmarano, Roberto, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Di Francia, G., editor, Di Natale, C., editor, Alfano, B., editor, De Vito, S., editor, Esposito, E., editor, Fattoruso, G., editor, Formisano, F., editor, Massera, E., editor, Miglietta, M. L., editor, and Polichetti, T., editor

Published
2020
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4. A Fast Gas Sensing Layer Working at Room Temperature for IOT in Air Quality Scenario

Author

Massera, E., Alfano, B., Miglietta, M. L., Polichetti, T., De Vito, S., Formisano, F., Di Francia, G., Delli Veneri, P., Ferdinandi, M., Molinara, M., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Di Francia, G., editor, Di Natale, C., editor, Alfano, B., editor, De Vito, S., editor, Esposito, E., editor, Fattoruso, G., editor, Formisano, F., editor, Massera, E., editor, Miglietta, M. L., editor, and Polichetti, T., editor

Published
2020
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13. A False Positive Reduction System For Continuous Water Quality Monitoring

Author

Bria, A., primary, Ferrigno, L., additional, Gerevini, L., additional, Marrocco, C., additional, Molinara, M., additional, Bruschi, P., additional, Cicalini, M., additional, Manfredini, G., additional, Ria, A., additional, Cerro, G., additional, Simmarano, R., additional, Teolis, G., additional, and Vitelli, M., additional

Published
2021
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39. Measure for participatory valorization of Cultural Heritage

Author

Michela Cigola, Gallozzi, A., Senatore, L. J., Di Maccio, R., Molinara, M., Trovini, G., Marinelli, E., Montaquila, A., Polito, E., and Rosa, G.

Subjects
Cultural Heritage, Architectural/Archaeological Heritage, Gamification, Citizen science

46. An End to End Indoor Air Monitoring System Based on Machine Learning and SENSIPLUS Platform

Author

Mario Molinara, Ettore Massera, Luigi Ferrigno, Marco Ferdinandi, Gianni Cerro, Molinara, M., Ferdinandi, M., Cerro, G., Ferrigno, L., and Massera, E.

Subjects
IoT, General Computer Science, Computer science, air monitoring, 02 engineering and technology, Machine learning, computer.software_genre, Convolutional neural network, Sensor array, sensor networks, 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, Artificial neural network, Contaminant detection, deep learning, neural networks, business.industry, Deep learning, 020208 electrical & electronic engineering, General Engineering, 020206 networking & telecommunications, Microcontroller, Proof of concept, Multilayer perceptron, Artificial intelligence, lcsh:Electrical engineering. Electronics. Nuclear engineering, business, Wireless sensor network, computer, lcsh:TK1-9971
Abstract

In the framework of indoor air monitoring, this paper proposes an Internet of Things ready solution to detect and classify contaminants. It is based on a compact and low-power integrated system including both sensing and processing capabilities. The sensing is composed of a sensor array on which electrical impedance measurements are performed through a microchip, named SENSIPLUS, while the processing phase is mainly based on Machine Learning techniques, embedded in a low power and low resources micro controller unit, for classification purposes. An extensive experimental campaign on different contaminants has been carried out and raw sensor data have been processed through a lightweight Multi Layer Perceptron for embedded implementation. More complex and computationally costly Deep Learning techniques, as Convolutional Neural Network and Long Short Term Memory, have been adopted as a reference for the validation of Multi Layer Perceptron performance. Results prove good classification capabilities, obtaining an accuracy greater than 75% in average. The obtained results, jointly with the reduced computational costs of the solution, highlight that this proposal is a proof of concept for a pervasive IoT air monitoring system.

Published
2020

47. A Fast Gas Sensing Layer Working at Room Temperature for IOT in Air Quality Scenario

Author

S. De Vito, Ettore Massera, G. Di Francia, P. Delli Veneri, Tiziana Polichetti, Mario Molinara, Marco Ferdinandi, Brigida Alfano, M. L. Miglietta, F. Formisano, Massera, E., Alfano, B., Miglietta, M. L., Polichetti, T., De Vito, S., Formisano, F., Di Francia, G., Delli Veneri, P., Ferdinandi, M., and Molinara, M.

Subjects
010302 applied physics, Chemiresistor, IOT, business.industry, Environment controlled, 02 engineering and technology, Gas sensors, 021001 nanoscience & nanotechnology, 01 natural sciences, Automotive engineering, Calibration, 0103 physical sciences, Environmental science, Graphene, Embedded system, 0210 nano-technology, Internet of Things, business, Air quality index, Layer (electronics), Open air
Abstract

The work deals with a technique adopted to calibrate in laboratory chemiresistor gas sensing film based on graphene that work at room temperature installed on a micro sensor board for applications in open air and IOT scenario. From the study in controlled environment the beginning of poisoning due to chemisorption can be estimated for the sensing layer and is possible to avoid harmful exposure to the analite during the calibration.

Published
2020

48. Design of a smart EIS measurement system

Author

Fabrizio Clemente, Giuliana Faiella, Paolo Bifulco, Mario Cesarelli, Mario Molinara, Maria Romano, Clemente, Fabrizio, Romano, M., Bifulco, Paolo, Faiella, Giuliana, Molinara, M., and Cesarelli, Mario

Subjects
Engineering, Health (social science), Biomedical Engineering, Health Informatics, law.invention, Software, law, Android, EIS measurement, bioimpedance, Electronic engineering, Android (operating system), Electrical impedance, Wearable technology, EIS, business.industry, System of measurement, App, Electrical engineering, Information technology, Computer Science Applications1707 Computer Vision and Pattern Recognition, EIS, smart sensors, Chip, Computer Networks and Communication, smart sensors, Resistor, EIS measurements, business
Abstract

The Electrical Impedance Spectroscopy (EIS) measurements, in addition to being used for research purposes, are increasingly spreading in clinical setting. Thanks to the improvement of electronic and information technology, the necessary equipment is becoming increasingly simple and small, suitable for example for home care applications. In this work, the characteristics of a smartphone based system for impedance measurements are described. Besides, it is tested its reliability by measuring resistors of known values which fall in the range of bioimpedance. The proof demonstrator, here assessed, consists of a prototypal analogic, low-power chip capable of performing impedance measurements at different frequencies, a hardware support for the connection with a standard smartphone and an application properly developed. After its final validation, the proof demonstrator could be useful to design wearable devices for sport and consumer electronic.

Published
2016

49. A Deep Learning Approach to Organic Pollutants Classification Using Voltammetry.

Author

Molinara M, Cancelliere R, Di Tinno A, Ferrigno L, Shuba M, Kuzhir P, Maffucci A, and Micheli L

Subjects
Hydroquinones analysis, Water, Benzoquinones, Nanotubes, Carbon, Deep Learning, Environmental Pollutants analysis
Abstract

This paper proposes a deep leaning technique for accurate detection and reliable classification of organic pollutants in water. The pollutants are detected by means of cyclic voltammetry characterizations made by using low-cost disposable screen-printed electrodes. The paper demonstrates the possibility of strongly improving the detection of such platforms by modifying them with nanomaterials. The classification is addressed by using a deep learning approach with convolutional neural networks. To this end, the results of the voltammetry analysis are transformed into equivalent RGB images by means of Gramian angular field transformations. The proposed technique is applied to the detection and classification of hydroquinone and benzoquinone, which are particularly challenging since these two pollutants have a similar electroactivity and thus the voltammetry curves exhibit overlapping peaks. The modification of electrodes by carbon nanotubes improves the sensitivity of a factor of about ×25, whereas the convolution neural network after Gramian transformation correctly classifies 100% of the experiments.

Published
2022
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50. From Online Handwriting to Synthetic Images for Alzheimer's Disease Detection Using a Deep Transfer Learning Approach.

Author

Cilia ND, D'Alessandro T, De Stefano C, Fontanella F, and Molinara M

Subjects
Handwriting, Humans, Machine Learning, Magnetic Resonance Imaging, Neural Networks, Computer, Alzheimer Disease diagnostic imaging
Abstract

Early diagnosis of neurodegenerative disorders, such as Alzheimer's Disease (AD), is very important to reduce their effects and to improve both quality and life expectancy of patients. In this context, it is generally agreed that handwriting is one of the first skills altered by the onset of AD. For this reason, the analysis of handwriting and the study of its alterations has become of great interest in order to formulate the diagnosis as soon as possible. A fundamental aspect for the use of these techniques is the definition of effective features, which allows the system to distinguish the natural alterations of handwriting due to age, from those caused by neurodegenerative disorders. Starting from these considerations, the aim of our study is to verify whether the combined use of both shape and dynamic features allows a decision support system to improve performance for AD diagnosis. To this purpose, starting from a database of on-line handwriting samples, we generated for each of them an off-line synthetic color image, where the color of each elementary trait encodes, in the three RGB channels, the dynamic information associated with that trait. To verify the role played by dynamic information, we also generated simple binary images, containing only shape information. Finally, we exploited the ability of Convolutional Neural Network (CNN) to automatically extract features on both color and binary images. The experimental results have confirmed that dynamic information allows a performance improvement with respect to the binary images.

Published
2021
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