Multiple reflections and improvement of edge scattering in GRECO RCS prediction code

GRECO code for monostatic RCS prediction in real time has been extended by considering multiple reflections between surfaces and improving the edge diffraction coefficients. Multiple reflections are analysed through a very efficient ray-tracing algorithm based on the graphical processing technique. Method of equivalent currents for edge scattering has been improved by Mitzner's and Michaeli's incremental length diffraction coefficients (ILDC). This communication presents the general features of GRECO code, in particular the advantages of the new graphical processing technique. Emphasis will be placed in the new features of GRECO still unpublished: the ray-tracing algorithm and the implementation of incremental length diffraction coefficients. Multiple reflections and improvement of edge scattering in GRECO RCS prediction code. Available from: https://www.researchgate.net/publication/238507602_Multiple_reflections_and_improvement_of_edge_scattering_in_GRECO_RCS_prediction_code [accessed May 31, 2017].Peer ReviewedPostprint (published version

Actitud clínica ante la dislipemia en pacientes con elevado riesgo cardiovascular en España. Estudio ALMA

Ojetivo: Evaluar la actitud de los médicos de atención primaria (MAP) y de atención especia- lizada (MAE) ante el tratamiento de pacientes con dislipemia, especialmente en aquellos con factores de riesgo cardiovascular (RCV). Disen ̃o: Estudio observacional, descriptivo, multicéntrico, basado en una encuesta. Emplazamiento: Diferentes áreas sanitarias de Espan ̃a. Participantes: 1.402 MAP y 596 MAE. Mediciones principales: Perfil de los médicos, hábitos de tratamiento en pacientes con dislipe- mia. Resultados: El 84,3% consideraban el RCV para establecer el tratamiento. El objetivo de con- centración de cLDL en pacientes sin factores de RCV fue < 130 mg/dl y < 160 mg/dl para el 51,9 y el 29,0%, respectivamente. En pacientes con hipertensión, tabaquismo o diabetes el objetivo de cLDL fue < 100 mg/dl para el 49-55%, mientras que en pacientes con complicación cardiovas- cular, cardiopatía isquémica o ictus fue < 70 mg/dl para el 71-88%. El fármaco de elección en pacientes sin factores de RCV fue atorvastatina (66%), mientras que en pacientes con diabetes, enfermedad renal o síndrome metabólico fue pitavastatina (80-89%). Los MAE mostraron una mayor tendencia que los MAP a considerar un objetivo de cLDL <70mg/dl en pacientes con antecedentes de ictus (77,5% vs 66,8%) o enfermedad coronaria (92,1% vs 80,6%) (p < 0,0001), y una mayor preferencia por el tratamiento combinado al no alcanzar el objetivo de cLDL (58,1% vs 50,2%; p = 0,0013). Conclusiones: Aunque el cálculo del RCV se acepta de forma general, existe disparidad en los objetivos del cLDL. Los MAE consideran unos objetivos más ambiciosos y la asociación de fármacos hipolipemiantes con más frecuencia que los MAP

Combined analysis of primary metabolites and phenolic compounds to authenticate commercial monovarietal peach purees and pear juices

Here we authenticated single-varietal peach purees and pear juices on the basis of primary metabolite and phenolic compound analysis by Proton Nuclear Magnetic Resonance (1H-NMR) and Ultra Performance Liquid Chromatography coupled to Photodiode Array and Tandem Mass Spectrometry (UPLC-PDA-MS/MS), respectively. After suitable preprocessing, the 1H-NMR and chromatographic data were evaluated by principal component analysis (PCA). The PCA combining data from primary metabolites and phenolic compounds allowed the separation of the clusters in all cases, allowing discrimination of processed and unprocessed peach purees, both separately and pooled. The PCA of primary metabolites allowed the cluster separation of purees of distinct peach varieties but not between processed and non-processed purees. The PCA of phenolic compounds allowed better cluster separation than of primary metabolites. For pear juices, both PCA approaches allowed satisfactory discrimination of Alejandrina, Conference, and Blanquilla cultivars. These approaches may help to better control cultivar authenticity in fruit products. It could therefore contribute to the development of a process to achieve products characterized by a quality characteristic of a given cultivar.This research was funded by the Catalan Government, grant number [2017 SGR 828]

Autoencoders for Semi-Supervised Water Level Modeling in Sewer Pipes with Sparse Labeled Data

More frequent and thorough inspection of sewer pipes has the potential to save billions in utilities. However, the amount and quality of inspection are impeded by an imprecise and highly subjective manual process. It involves technicians judging stretches of sewer based on video from remote-controlled robots. Determining the state of sewer pipes based on these videos entails a great deal of ambiguity. Furthermore, the frequency with which the different defects occur differs a lot, leading to highly imbalanced datasets. Such datasets represent a poor basis for automating the labeling process using supervised learning. With this paper we explore the potential of self-supervision as a method for reducing the need for large numbers of well-balanced labels. First, our models learn to represent the data distribution using more than a million unlabeled images, then a small number of labeled examples are used to learn a mapping from the learned representations to a relevant target variable, in this case, water level. We choose a convolutional Autoencoder, a Variational Autoencoder and a Vector-Quantised Variational Autoencoder as the basis for our experiments. The best representations are shown to be learned by the classic Autoencoder with the Multi-Layer Perceptron achieving a Mean Absolute Error of 9.93. This is an improvement of 9.62 over the fully supervised baseline.Peer ReviewedPostprint (published version

Fast algorithms for radar cross section computation of complex objects

Radar cross section (RCS) of complex targets can be obtained in real time using the hardware capabilities of a high performance graphic workstation. Target geometry is modelled by a computer-aided design package. First order contribution to RCS is computed under physical optics high-frequency approximation. Real time computation is achieved through graphical processing of an image obtained with local illumination modeling of the target. Multiple scattering contribution can be obtained using radiosity algorithm, a recently developed global illumination method.Peer ReviewedPostprint (published version

Sección recta de blancos radar complejos en tiempo real: aproximación de óptica física

Peer ReviewedPostprint (published version

High-Frequency RCS of Perfectly Conducting or Coated Complex Objects in Real Time

This paper present a new and original approach to compute high-freqency radar cross section (RCS) of complex radar targets in real time using a 3-D graphic workstation. The aircraft is modelled with I-DEAS solid modeling software using a parametric surface approach. High-frequency RCS is obtained through Physical Optics (PO), Method of Equivalent Currents (MEC), Physical Theory of Diffraction (PTD) and Impedance Boundary Condition (IBC). Multiple scattering between target surfaces is also considered.Peer ReviewedPostprint (published version

Pla director de l’espai del Circ Romà de Tarraco

Primer premiAward-winningPostprint (published version