University of Extremadura

About: University of Extremadura is a education organization based out in Badajoz, Spain. It is known for research contribution in the topics: Population & Hyperspectral imaging. The organization has 7856 authors who have published 18299 publications receiving 396126 citations. The organization is also known as: Universidad de Extremadura.

Reaction front in an A+B-->C reaction-subdiffusion process.

Abstract: We study the reaction front for the process A+B-->C in which the reagents move subdiffusively. Our theoretical description is based on a fractional reaction-subdiffusion equation in which both the motion and the reaction terms are affected by the subdiffusive character of the process. We design numerical simulations to check our theoretical results, describing the simulations in some detail because the rules necessarily differ in important respects from those used in diffusive processes. Comparisons between theory and simulations are on the whole favorable, with the most difficult quantities to capture being those that involve very small numbers of particles. In particular, we analyze the total number of product particles, the width of the depletion zone, the production profile of product and its width, as well as the reactant concentrations at the center of the reaction zone, all as a function of time. We also analyze the shape of the product profile as a function of time, in particular, its unusual behavior at the center of the reaction zone.

Mechanical properties of calcium phosphate scaffolds fabricated by robocasting

Abstract: The mechanical behavior under compressive stresses of β-tricalcium phosphate (β-TCP) and hydroxyapatite (HA) scaffolds fabricated by direct-write assembly (robocasting) technique is analyzed. Concentrated colloidal inks prepared from β-TCP and HA commercial powders were used to fabricate porous structures consisting of a 3-D tetragonal mesh of interpenetrating ceramic rods. The compressive strength and elastic modulus of these model scaffolds were determined by uniaxial testing to compare the relative performance of the selected materials. The effect of a 3-week immersion in simulated body fluid (SBF) on the strength of the scaffolds was also analyzed. The results are compared with those reported in the literature for calcium phosphate scaffolds and human bone. The robocast calcium phosphate scaffolds were found to exhibit excellent mechanical performances in terms of strength, especially the HA structures after SBF immersion, indicating a great potential of this type of scaffolds for use in load-bearing bone tissue engineering applications. © 2007 Wiley Periodicals, Inc. J Biomed Mater Res 2008

Capsule Networks for Hyperspectral Image Classification

Abstract: Convolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNN-based network architecture still finds obstacles when effectively exploiting the relationships between hyperspectral imaging (HSI) features in the spectral–spatial domain, which is a key factor to deal with the high level of complexity present in remotely sensed HSI data. Despite the fact that deeper architectures try to mitigate these limitations, they also find challenges with the convergence of the network parameters, which eventually limit the classification performance under highly demanding scenarios. In this paper, we propose a new CNN architecture based on spectral–spatial capsule networks in order to achieve a highly accurate classification of HSIs while significantly reducing the network design complexity. Specifically, based on Hinton’s capsule networks, we develop a CNN model extension that redefines the concept of capsule units to become spectral–spatial units specialized in classifying remotely sensed HSI data. The proposed model is composed by several building blocks, called spectral–spatial capsules, which are able to learn HSI spectral–spatial features considering their corresponding spatial positions in the scene, their associated spectral signatures, and also their possible transformations. Our experiments, conducted using five well-known HSI data sets and several state-of-the-art classification methods, reveal that our HSI classification approach based on spectral–spatial capsules is able to provide competitive advantages in terms of both classification accuracy and computational time.

EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges.

Abstract: Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.