Zobrazeno 1 - 10
of 374
pro vyhledávání: '"Carlo S. Regazzoni"'
Publikováno v:
International Journal of Distributed Sensor Networks, Vol 2, Iss 3, Pp 201-223 (2006)
Externí odkaz:
https://doaj.org/article/33620f9840794837b7de428043cc74c9
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2004, Iss 12, Pp 1778-1790 (2004)
The use of time-frequency distributions is proposed as a nonlinear signal processing technique that is combined with a pattern recognition approach to identify superimposed transmission modes in a reconfigurable wireless terminal based on software-de
Externí odkaz:
https://doaj.org/article/2c5bf21e421a422dba380aa8182a19d6
Publikováno v:
EURASIP Journal on Wireless Communications and Networking, Vol 2011 (2011)
Externí odkaz:
https://doaj.org/article/596388d22b0b41c8964e3ab50b5db42d
Autor:
Lucio Marcenaro, Damian Campo, Mahdyar Ravanbakhsh, Mohamad Baydoun, Carlo S. Regazzoni, Pablo Marin, David Martin
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 22:3372-3386
The technology for autonomous vehicles is close to replacing human drivers by artificial systems endowed with high-level decision-making capabilities. In this regard, systems must learn about the usual vehicle's behavior to predict imminent difficult
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a016c52fe9779b31b834a5a7183a4a73
https://doi.org/10.1109/tits.2020.2984735
https://doi.org/10.1109/tits.2020.2984735
Publikováno v:
Proceedings of the IEEE. 108:971-975
Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4df1e517b8aee1b72f6e1dc90ee41e89
https://doi.org/10.1109/jproc.2020.2990784
https://doi.org/10.1109/jproc.2020.2990784
Publikováno v:
IEEE Transactions on Cognitive Communications and Networking. 6:1-5
We are delighted to introduce this special section of the IEEE Transactions on Cognitive Communications and Networking (TCCN), which aims at addressing the evolution of cognitive radio (CR) to intelligence radio and networks by exploring recent advan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c039fd78fd13d35d2c52521f4c37324
https://doi.org/10.1109/tccn.2020.2975440
https://doi.org/10.1109/tccn.2020.2975440
Autor:
Yingxu Wang, Bernard Widrow, Witold Pedrycz, Robert C. Berwick, Paolo Soda, Sam Kwong, Okyay Kaynak, Carlo S. Regazzoni, Christine Chan, Marina Gavrilova, Guoyin Wang
Publikováno v:
2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e3736758df96e17849a0f6d293f2fbcf
https://doi.org/10.1109/iccicc53683.2021.9811324
https://doi.org/10.1109/iccicc53683.2021.9811324
Publikováno v:
2021 IEEE International Conference on Autonomous Systems (ICAS).
When performing anomaly detection on an autonomous vehicle’s sensory data, it is fundamental to infer the cause of the found anomalies. This paper proposes a method for learning prediction models and detecting anomalies by decomposing the evolution
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4cdd4927c2ba284afd4615273e3003ae
https://doi.org/10.1109/icas49788.2021.9551111
https://doi.org/10.1109/icas49788.2021.9551111
Autor:
Carlo S. Regazzoni
Publikováno v:
2021 IEEE International Conference on Autonomous Systems (ICAS).
Multisensor signal Data Fusion and Perception, including processing of signals are important cognitive functionalities that can be included in artificial systems to increase their level of autonomy. However, the techniques they rely on have been deve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::98372b41dc9fbd4c7602254a7d4f790f
https://doi.org/10.1109/icas49788.2021.9551132
https://doi.org/10.1109/icas49788.2021.9551132
Publikováno v:
2021 IEEE International Conference on Autonomous Systems (ICAS).
This paper proposes an adaptive method to enable imitation learning from expert demonstrations in a multi-agent context. The proposed system employs the inverse reinforcement learning method to a coupled Dynamic Bayesian Network to facilitate dynamic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::543c9f9919dc6d3fa719687f7e404d56
https://doi.org/10.1109/icas49788.2021.9551152
https://doi.org/10.1109/icas49788.2021.9551152