Cellular Neural Network for Real Time Image Processing
- Dipartimento di Ingegneria Elettrica Elettronica e dei Sistemi - Universita degli Studi di Catania, I-95125 Catania (Italy)
- ENEA-Gestione Grandi Impianti Sperimentali, via E. Fermi 45, I-00044 Frascati, Rome (Italy)
- Consorzio RFX-Associazione EURATOM ENEA per la Fusione, I-35127 Padova (Italy)
Since their introduction in 1988, Cellular Nonlinear Networks (CNNs) have found a key role as image processing instruments. Thanks to their structure they are able of processing individual pixels in a parallel way providing fast image processing capabilities that has been applied to a wide range of field among which nuclear fusion. In the last years, indeed, visible and infrared video cameras have become more and more important in tokamak fusion experiments for the twofold aim of understanding the physics and monitoring the safety of the operation. Examining the output of these cameras in real-time can provide significant information for plasma control and safety of the machines. The potentiality of CNNs can be exploited to this aim. To demonstrate the feasibility of the approach, CNN image processing has been applied to several tasks both at the Frascati Tokamak Upgrade (FTU) and the Joint European Torus (JET)
- OSTI ID:
- 21136821
- Journal Information:
- AIP Conference Proceedings, Vol. 988, Issue 1; Conference: International conference on burning plasma diagnostics, Varenna (Italy), 24-28 Sep 2007; Other Information: DOI: 10.1063/1.2905120; (c) 2008 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA). JET EFDA Contributors; ISSN 0094-243X
- Country of Publication:
- United States
- Language:
- English
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