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

An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment

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; in revised form: 18 June 2012 / Accepted: 26 June 2012 / Published: 27 June 2012
(This article belongs to the Section Physical Sensors)
PDF Full-text Download PDF Full-Text [454 KB, uploaded 27 June 2012 11:09 CEST]
Abstract: 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

Article Statistics

Load and display the download statistics.

Citations to this Article

Cite This Article

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.

AMA Style

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.

Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert