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Your search keyword '"Vagliasindi, G."' showing total 2 results
Start Over Author "Vagliasindi, G." Topic image processing
2 results on '"Vagliasindi, G."'

1. Cellular Neural Network for Real Time Image Processing.

Author

Vagliasindi, G., Arena, P., Fortuna, L., Mazzitelli, G., and Murari, A.

Subjects
*NUCLEAR fusion, *IMAGE processing, *ARTIFICIAL neural networks, *TOKAMAKS, *IMAGING systems
Abstract

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). [ABSTRACT FROM AUTHOR]

Published
2008
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2. Cellular nonlinear networks for strike-point localization at JET.

Author

Arena, P., Fortuna, L., Bruno, M., Vagliasindi, G., Murari, A., Andrew, P., and Mazzitelli, G.

Subjects
IMAGE processing, IMAGING systems, INFORMATION processing, DETECTORS, INTEGRATED circuits, SEMICONDUCTORS
Abstract

At JET, the potential of fast image processing for real-time purposes is thoroughly investigated. Particular attention is devoted to smart sensors based on system on chip technology. The data of the infrared cameras were processed with a chip implementing a cellular nonlinear network (CNN) structure so as to support and complement the magnetic diagnostics in the real-time localization of the strike-point position in the divertor. The circuit consists of two layers of complementary metal-oxide semiconductor components, the first being the sensor and the second implementing the actual CNN. This innovative hardware has made it possible to determine the position of the maximum thermal load with a time resolution of the order of 30 ms. Good congruency has been found with the measurement from the thermocouples in the divertor, proving the potential of the infrared data in locating the region of the maximum thermal load. The results are also confirmed by JET magnetic codes, both those used for the equilibrium reconstructions and those devoted to the identification of the plasma boundary. [ABSTRACT FROM AUTHOR]

Published
2005
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