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Sensors 2012, 12(7), 8895-8911; doi:10.3390/s120708895

An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment

1,* , 2
1 Project Engineering Area, Department of Exploitation and Exploration of Mines, University of Oviedo, c/ Independencia No 13, Oviedo 33004, Spain 2 Department of Construction and Manufacturing Engineering, University of Oviedo, Campus de Viesques, Gijón 33204, Spain 3 Department of Electrical Engineering, Centre for Astro-Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Santiago, Chile
* Author to whom correspondence should be addressed.
Received: 7 June 2012 / Revised: 18 June 2012 / Accepted: 26 June 2012 / Published: 27 June 2012
(This article belongs to the Section Physical Sensors)
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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).
Keywords: MOAO; adaptive; optics; neural; networks; reconstructor; Zernike MOAO; adaptive; optics; neural; networks; reconstructor; Zernike
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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.

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