In modern tokamaks, visible and infrared video cameras are becoming more and more important in monitoring plasma evolution during fusion experiments. Analyzing these images in real time can provide relevant information for controlling plasma and improving machine safety. The real-time image processing capability of the cellular nonlinear/neural network-based chips that are available nowadays has been applied to several tasks, both at Frascati Tokamak Upgrade (FTU) and at Joint European Torus (JET). The successful applications range from the identification of plasma instabilities, such as multifaceted asymmetric radiations from the edge (MARFEs), to the determination of the strike-point position in the divertor and to the detection of the so-called "hot spots." © 2009 IEEE.
|Pages (from-to)||2417 - 2425|
|Number of pages||9|
|Journal||IEEE Transactions on Instrumentation and Measurement|
|Publication status||Published - 2009|
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
Vagliasindi, G., Murari, A., Arena, P., Fortuna, L., & Mazzitelli, G. (2009). Cellular neural network algorithms for real-time image analysis in plasma fusion. IEEE Transactions on Instrumentation and Measurement, 58(8), 2417 - 2425. https://doi.org/10.1109/TIM.2009.2016383