dc.creator | de Cos Juez, Francisco J. | |
dc.creator | Sanchez Lasheras, Fernando | |
dc.creator | Roqueni, Nieves | |
dc.creator | Osborn, James | |
dc.date.accessioned | 2024-01-10T13:45:45Z | |
dc.date.available | 2024-01-10T13:45:45Z | |
dc.date.created | 2024-01-10T13:45:45Z | |
dc.date.issued | 2012 | |
dc.identifier | 10.3390/s120708895 | |
dc.identifier | 1424-8220 | |
dc.identifier | MEDLINE:23012524 | |
dc.identifier | https://doi.org/10.3390/s120708895 | |
dc.identifier | https://repositorio.uc.cl/handle/11534/79074 | |
dc.identifier | WOS:000306796500027 | |
dc.description.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). | |
dc.language | en | |
dc.publisher | MDPI | |
dc.rights | acceso abierto | |
dc.subject | MOAO | |
dc.subject | adaptive | |
dc.subject | optics | |
dc.subject | neural | |
dc.subject | networks | |
dc.subject | reconstructor | |
dc.subject | Zernike | |
dc.subject | OPTIMAL LINEAR-COMBINATIONS | |
dc.subject | ARTIFICIAL NEURAL-NETWORKS | |
dc.subject | ADAPTIVE-OPTICS | |
dc.subject | PERFORMANCE | |
dc.subject | HARDWARE | |
dc.subject | NONLINEARITIES | |
dc.subject | PRINCIPLES | |
dc.title | An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment | |
dc.type | artículo | |