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

An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment

Project Engineering Area, Department of Exploitation and Exploration of Mines, University of Oviedo, c/ Independencia No 13, Oviedo 33004, Spain
Department of Construction and Manufacturing Engineering, University of Oviedo, Campus de Viesques, Gijón 33204, Spain
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 3.0).

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MDPI and ACS Style

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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