Gian Domenico LICCIARDO | Pubblicazioni

203415A resource constrained neural network for the design of embedded human posture recognition systems

	2021
	Articolo in rivista
	A resource constrained neural network for the design of embedded human posture recognition systems APPLIED SCIENCES. Vol. 11. Pag.4752-4764 ISSN:2076-3417.
	Licciardo, G. D.; Russo, A.; Naddeo, A.; Cappetti, N.; Di Benedetto, L.; Rubino, A.; Liguori, R.
	Versione online
	Digital Object Identifier (DOI): 10.3390/app11114752 Codice identificativo ISI: WOS:000659568800001 Codice identificativo SCOPUS: 2-s2.0-85106983718
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211409Highly-accurate binary tiny neural network for low-power human activity recognition

	2021
	Articolo in rivista
	Highly-accurate binary tiny neural network for low-power human activity recognition MICROPROCESSORS AND MICROSYSTEMS. Vol. 87. Pag.1-8 ISSN:0141-9331.
	De Vita, A.; Pau, D.; Di Benedetto, L.; Rubino, A.; Petrot, F.; Licciardo, G. D.
	Versione online
	Digital Object Identifier (DOI): 10.1016/j.micpro.2021.104371 Codice identificativo ISI: WOS:000722314700005 Codice identificativo SCOPUS: 2-s2.0-85118834748
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183943On the design of the channel region in 4H-SiC JBS diode through an analytical model of the potential barrier

	2021
	Articolo in rivista
	On the design of the channel region in 4H-SiC JBS diode through an analytical model of the potential barrier MATHEMATICS AND COMPUTERS IN SIMULATION. Vol. 183. Pag.1-198 ISSN:0378-4754.
	Di Benedetto, L.; Licciardo, G. D.; Rubino, A.
	Versione online
	Digital Object Identifier (DOI): 10.1016/j.matcom.2020.07.008 Codice identificativo ISI: WOS:000608589000015 Codice identificativo SCOPUS: 2-s2.0-85087797684
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216073A Model of the V-T Characteristics for an OTFT Temperature Sensor

	2021
	Contributo in Atti di convegno
	A Model of the V-T Characteristics for an OTFT Temperature Sensor. In: Proceedings of the International Semiconductor Conference, CAS Institute of Electrical and Electronics Engineers Inc. Vol.2021-, Pag.93-96 ISBN:978-1-6654-3571-0
	44th International Semiconductor Conference, CAS 2021 Romania 2021
	Liguori, R.; Di Benedetto, L.; Licciardo, G. D.
	Digital Object Identifier (DOI): 10.1109/CAS52836.2021.9604137 Codice identificativo ISI: WOS:000853482700017 Codice identificativo SCOPUS: 2-s2.0-85122981331
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204202Comparing Industry Frameworks with Deeply Quantized Neural Networks on Microcontrollers

	2021
	Contributo in Atti di convegno
	Comparing Industry Frameworks with Deeply Quantized Neural Networks on Microcontrollers. In: Digest of Technical Papers - IEEE International Conference on Consumer Electronics Institute of Electrical and Electronics Engineers Inc. Vol.2021-, Pag.1-6 ISBN:978-1-7281-9766-1
	2021 IEEE International Conference on Consumer Electronics, ICCE 2021 usa 2021
	Pau, Danilo; Lattuada, Marco; Loro, Francesco; DE VITA, Antonio; Licciardo, Gian Domenico
	Digital Object Identifier (DOI): 10.1109/ICCE50685.2021.9427638 Codice identificativo ISI: WOS:000675600200057 Codice identificativo SCOPUS: 2-s2.0-85106004023
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216071Linear organic transistor based temperature sensor between 230 and 330 K

	2021
	Contributo in Atti di convegno
	Linear organic transistor based temperature sensor between 230 and 330 K. In: Proceedings of IEEE Sensors Institute of Electrical and Electronics Engineers Inc. Vol.2021-, Pag.1-4 ISBN:978-1-7281-9501-8
	20th IEEE Sensors, SENSORS 2021 Sydney, Australia 2021
	Liguori, R.; Di Benedetto, L.; Licciardo, G. D.
	Digital Object Identifier (DOI): 10.1109/SENSORS47087.2021.9639628 Codice identificativo ISI: WOS:000755468300137 Codice identificativo SCOPUS: 2-s2.0-85123632927
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206738Quantized Fully Convolution Neural Network for HW Implementation of Human Posture Recognition

	2021
	Contributo in Atti di convegno
	Quantized Fully Convolution Neural Network for HW Implementation of Human Posture Recognition. In: 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021 Institute of Electrical and Electronics Engineers Inc. Pag.1-4 ISBN:978-1-6654-1913-0
	3rd IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021 usa 2021
	Russo, A.; Licciardo, G. D.; Di Benedetto, L.; Rubino, A.; Liguori, R.; Naddeo, A.; Cappetti, N.
	Versione online
	Digital Object Identifier (DOI): 10.1109/AICAS51828.2021.9458487 Codice identificativo ISI: WOS:000722241000040 Codice identificativo SCOPUS: 2-s2.0-85113290415
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216072Organic transistor parameter estimation and accurate modeling for process optimization

	2021
	Contributo in Atti di convegno
	Organic transistor parameter estimation and accurate modeling for process optimization. In: SMACD / PRIME 2021 - International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design and 16th Conference on PhD Research in Microelectronics and Electronics VDE VERLAG GMBH Pag.228-231 ISBN:978-3-8007-5588-2
	2021 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2021 and 16th Conference on PhD Research in Microelectronics and Electronics, PRIME 2021 Germany 2021
	Liguori, R.; Licciardo, G. D.; Di Benedetto, L.
	Codice identificativo SCOPUS: 2-s2.0-85117396786
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249115Design of digital controller for SVPWM algorithm with real-time control of the output amplitude and switching frequency

	2021
	Contributo in volume (Capitolo o Saggio)
	Design of digital controller for SVPWM algorithm with real-time control of the output amplitude and switching frequency. In Proceedings - IEEE International Symposium on Circuits and Systems - ISCAS 2021 Pag.1-5 345 E 47TH ST, NEW YORK, NY 10017 USA Institute of Electrical and Electronics Engineers Inc.. ISBN:978-1-7281-9201-7
	Donisi, A.; Di Benedetto, L.; Licciardo, G. D.; Rubino, A.; Piccirilli, E.; Lanzotti, E.
	Digital Object Identifier (DOI): 10.1109/ISCAS51556.2021.9401527 Codice identificativo ISI: WOS:000706507900050 Codice identificativo SCOPUS: 2-s2.0-85109028600
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249113Low-Power Anomaly Detection and Classification System based on a Partially Binarized Autoencoder for In-Sensor Computing

	2021
	Contributo in volume (Capitolo o Saggio)
	Low-Power Anomaly Detection and Classification System based on a Partially Binarized Autoencoder for In-Sensor Computing. In 2021 28th IEEE International Conference on Electronics, Circuits, and Systems, ICECS 2021 - Proceedings Pag.1-5 345 E 47TH ST, NEW YORK, NY 10017 USA Institute of Electrical and Electronics Engineers Inc.. ISBN:978-1-7281-8281-0
	Vitolo, P.; Licciardo, G. D.; Di Benedetto, L.; Liguori, R.; Rubino, A.; Pau, D.
	Digital Object Identifier (DOI): 10.1109/ICECS53924.2021.9665479 Codice identificativo ISI: WOS:000784162400026 Codice identificativo SCOPUS: 2-s2.0-85124598678
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