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An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment
AbstractIn astronomy, the light emitted by an object travels through the vacuum of space and then the turbulent atmosphere before arriving at a ground based telescope. By passing through the atmosphere a series of turbulent layers modify the light’s wave-front in such a way that Adaptive Optics reconstruction techniques are needed to improve the image quality. A novel reconstruction technique based in Artificial Neural Networks (ANN) is proposed. The network is designed to use the local tilts of the wave-front measured by a Shack Hartmann Wave-front Sensor (SHWFS) as inputs and estimate the turbulence in terms of Zernike coefficients. The ANN used is a Multi-Layer Perceptron (MLP) trained with simulated data with one turbulent layer changing in altitude. The reconstructor was tested using three different atmospheric profiles and compared with two existing reconstruction techniques: Least Squares type Matrix Vector Multiplication (LS) and Learn and Apply (L + A).
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Juez, F.J.C.; Lasheras, F.S.; Roqueñí, N.; Osborn, J. An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment. Sensors 2012, 12, 8895-8911.View more citation formats
Juez FJC, Lasheras FS, Roqueñí N, Osborn J. An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment. Sensors. 2012; 12(7):8895-8911.Chicago/Turabian Style
Juez, Francisco J. de Cos; Lasheras, Fernando Sánchez; Roqueñí, Nieves; Osborn, James. 2012. "An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment." Sensors 12, no. 7: 8895-8911.
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