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Sensors (Basel). 2012;12(7):8895-911. Epub 2012 Jun 27.

An ANN-based smart tomographic reconstructor in a dynamic environment.

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  • 1Project Engineering Area, Department of Exploitation and Exploration of Mines, University of Oviedo, c/ Independencia No 13, Oviedo 33004, Spain.


In 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).


MOAO; Zernike; adaptive; networks; neural; optics; reconstructor

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