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Las Palmas de Gran Canaria, Spain

Guzman D.,Durham University | Guzman D.,Pontifical Catholic University of Chile | De Juez F.J.C.,University of Oviedo | Lasheras F.S.,Tecniproject SL | And 2 more authors.
Optics Express | Year: 2010

Open-loop adaptive optics is a technique in which the turbulent wavefront is measured before it hits the deformable mirror for correction. We present a technique to model a deformable mirror working in open-loop based on multivariate adaptive regression splines (MARS), a nonparametric regression technique. The model's input is the wavefront correction to apply to the mirror and its output is the set of voltages to shape the mirror. We performed experiments with an electrostrictive deformable mirror, achieving positioning errors of the order of 1.2% RMS of the peakto-peak wavefront excursion. The technique does not depend on the physical parameters of the device; therefore it may be included in the control scheme of any type of deformable mirror. © 2010 Optical Society of America.

De Cos Juez F.J.,University of Oviedo | Suarez-Suarez M.A.,University of Oviedo | Sanchez Lasheras F.,Tecniproject SL | Murcia-Mazon A.,University of Oviedo
Mathematical and Computer Modelling | Year: 2011

Osteoporosis is characterized by low bone mineral density (BMD). This illness has a high-cost impact in all developed countries. The aim of this article is the development of a mathematical method able to predict the BMD of post-menopausal women, taking into account only certain nutritional variables. This research applies neural networks for the study of the influence of diet and lifestyle on the value of bone mineral density in post-menopausal women.A questionnaire on nutritional habits and lifestyle was drawn up. The variables obtained from this, together with the BMD of the patients calculated by densitometry, were processed using genetic algorithms in order to reduce the number of input variables. Finally, a neural network model using only those variables considered important was applied.It has been proved to be possible to build a neural network model able to forecast the BMD of post-menopausal women according to their responses to the questionnaire. This model can be used to determine which women should take a densitometry in order to verify their bone quality and thus prevent some risks associated with osteoporosis. © 2010 Elsevier Ltd.

Sanchez Lasheras F.,Tecniproject SL | Vilan Vilan J.A.,University of Vigo | Garcia Nieto P.J.,University of Oviedo | del Coz Diaz J.J.,University of Oviedo
Mathematical and Computer Modelling | Year: 2010

The hard chromium plating process aims at creating a coating of hard and wear-resistant chromium with a thickness of some micrometres directly on the metal part without the insertion of copper or nickel layers. Chromium plating features high levels of hardness and resistance to wear and it is due to these properties that they can be applied in a huge range of sectors. Resistance to corrosion of a hard chromium plate depends on the thickness of its coating, and its adherence and micro-fissures. This micro-fissured structure is what provides the optimal hardness of the layers. The hard chromium plating process is one of the most effective ways of protecting the base material against a hostile environment or improving the surface properties of the base material. However, in the electroplating industry, electroplaters are faced with many problems and undesirable results with chromium plated materials. Common problems faced in the electroplating industry include matt deposition, milky white chromium deposition, rough or sandy chromium deposition and insufficient thickness and hardness. This article presents an artificial neural network (ANN) model to predict the thickness of the layer in a hard chromium plating process. The optimization of the ANN was performed by means of the design of experiments theory (DOE). In the present work the purpose of using DOE is twofold: to define the optimal experiments which maximize the ratio of the model accuracy, and to minimize the number of necessary experiments (ANN models trained and validated). © 2010 Elsevier Ltd.

Alvarez Menendez L.,Hospital Materno Infantil Teresa Herrera. CH. La Coruna | de Cos Juez F.J.,University of Oviedo | Sanchez Lasheras F.,Tecniproject SL
Mathematical and Computer Modelling | Year: 2010

Breast screening is a method of detecting breast cancer at a very early stage. The first step involves taking an X-ray, called a mammogram, of each breast. The mammogram can detect small changes in breast tissue which may indicate cancers which are too small to be felt either by the woman herself or by a doctor.The World Health Organisation's International Agency for Research on Cancer (IARC) concluded that mammography screening for breast cancer reduces mortality. This means that out of every 500 women screened, one life will be saved.The present research uses the information obtained from the breast screening programme carried out in the public health area of Aviles (Principality of Asturias, Spain) from 1999 to 2007. The public health area of Aviles is formed by nine municipalities with a total of 160,000 inhabitants. The selection of the public health area was based on the following criteria: ̇This is the first screening programme performed in the area.̇Almost 100% of the population in the area benefit from the public health system.̇The Aviles public health area is a well-defined area of the region that does not send patients to other public health areas, which makes the study easier and more accurate. This paper describes a neural network based approach to breast cancer diagnosis; the model developed is able to determine which women are more likely to suffer from a particular kind of tumour before they undergo a mammography. © 2010 Elsevier Ltd.

Guzman D.,University of Santiago de Chile | Guzman D.,Durham University | De Juez F.J.C.,University of Oviedo | Myers R.,Durham University | And 3 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2010

Open-loop adaptive optics is a technique in which the turbulent wavefront is measured before it hits the deformable mirror for correction; therefore the correct control of the mirror in open-loop is key in achieving the expected level of correction. In this paper, we present non-parametric estimation techniques to model deformable mirrors working in open-loop. We have results with mirrors characterized by non-linear behavior: a Xinetics electrostrictive mirror and a Boston Micromachines MEMS mirror. The inputs for these models are the wavefront corrections to apply to the mirror and the outputs are the set of voltages to shape the mirror. We have performed experiments on both mirrors, achieving Go-To errors relative to peak-to-peak wavefront excursion in the order of 1 % RMS for the Xinetics mirror and 3 % RMS for the Boston mirror . These techniques are trained with interferometric data from the mirror under control; therefore they do not depend on the physical parameters of the device. © 2010 SPIE.

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