Zobrazeno 1 - 10
of 422
pro vyhledávání: '"S. Regazzoni"'
Publikováno v:
International Journal of Distributed Sensor Networks, Vol 13 (2017)
Cognitive radio is a promising technology for frequency allocation to improve the spectrum utilization efficiency of licensed bands. However, in recent years, the attention of the researchers is focused on security issues that have to be faced by cog
Externí odkaz:
https://doaj.org/article/92e4d5977a614dac80ce54e5dd11911a
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
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
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
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).
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:
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
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
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
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