Author

Sergio Sánchez, Prashanth R. Marpu, Antonio Plaza, Abel Paz-Gallardo

JCR Journal
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, November 2015
vol. 8, no. 11, pp. 1939-5387, ISSN: 1939-1404. DOI: 10.1109/JSTARS.2015.2471083
(Impact factor = 2.145 in 2015)

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Autores

L. Ramírez, R. Bojo, J. Valero, S. Wilbert, L.F. Zarzalejo, A. Paz, G. García, W. Reinalter, R.X. Valenzuela, G. Díaz-Herrero, A. Campos, T. Wolfertstetter

1^st Workshop on Complex Problems over High Performance Computing Architectures (CPHPCA’15), 

ERA SOLAR - Fototérmica & Fotovoltaica. Edición 187, julio / agosto 2015, Año XXXIII.
Revista técnica fundada en el año 1983

Resumen

El objetivo del Proyecto ARES (Acceso a Red de Estaciones Solares) es facilitar el acceso a datos registrados en estaciones de medida de la radiación solar, a través de la definición y puesta a punto de una plataforma para la incorporación y el acceso estandarizado a la información. Así, entre los objetivos, se incluyen la homogeneización de los procedimientos de: adquisición de datos, control de calidad, almacenamiento y tratamiento. Aunque se pretende desarrollar una herramienta que pueda extenderse a estaciones semejantes por configuración o variables medidas, el lanzamiento de la iniciativa se enmarca en el contexto de la colaboración entre distintas Divisiones del CIEMAT con el Departamento de Qualification del “Institute of Solar Research” del DLR (Deutsches Zentrum für Luft- und Raumfahrt). En esta primera etapa, se unificarán criterios en el contexto de las estaciones ubicadas en la PSA.

Authors

José M. Franco-Valiente; César Suárez-Ortega

International Journal of Image Mining (IJIM), Vol. 1, No. 2/3, 2015

DOI: http://dx.doi.org/10.1504/IJIM.2015.073022

Abstract

This article presents an overview of the ALOE platform. ALOE provides a service-oriented architecture aimed at the research in the early detection of breast cancer diagnosis. The development of the ALOE platform is carried out by collaboration among CETA-CIEMAT, INEGI, FMUP-HSJ and UA. ALOE supports two research lines in breast cancer diagnosis: the development of well performing computer aided diagnosis (CAD) systems and the development of new tools-based on e-learning techniques to improve radiologists training. All ALOE modules are designed to work as a whole system but can be used individually in other systems and expose RESTful interfaces to be exploited by third party systems. ALOE components make use of e-Infrastructure resources to accomplish their tasks. The final objective of this work is to provide a reference platform for researchers, specialists, and students in breast cancer diagnosis.

Authors

Frederico Valente; Augusto Silva; Carlos Manuel Azevedo Costa; José Miguel Franco Valiente; César Suárez-Ortega

International Journal of Image Mining (IJIM), Vol. 1, No. 2/3, 2015

DOI: http://dx.doi.org/10.1504/IJIM.2015.073027

Abstract

Machine learning and imaging analytics are major algorithmic components of the software used by medical practitioners in the diagnosis and treatment of diseases. Whether employed by computer aided diagnosis (CADx) or content-based image retrieval (CBIR) tools, the accuracy and relevance of the results to the practitioner are paramount to the success of any such application. In order to improve on the existing results researchers often find themselves in the need to explore various approaches and methodologies, often using very large datasets and multiple sources of information. Each of these trials can, by itself, be a very time-consuming operation. One tried and true strategy to speed up operations is the use of a distributed computing platform (delivering the computational load to a number of machines). This raises a set of problems which are often orthogonal to a researcher's interest such as which algorithmic implementations scale or how to distribute data and tasks on the grid. In this article, we present a framework that empowers researchers to quickly design sets of tests, schedule their execution and have them automatically allocated to a grid environment for execution. We describe the design and implementation of the solution, and present as an example an experiment concerning the classification of mammography segmentations.