Showing papers by "University of Extremadura published in 2017"

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Abstract: Recent advances in airborne and spaceborne hyperspectral imaging technology have provided end users with rich spectral, spatial, and temporal information. They have made a plethora of applications feasible for the analysis of large areas of the Earth?s surface. However, a significant number of factors-such as the high dimensions and size of the hyperspectral data, the lack of training samples, mixed pixels, light-scattering mechanisms in the acquisition process, and different atmospheric and geometric distortions-make such data inherently nonlinear and complex, which poses major challenges for existing methodologies to effectively process and analyze the data sets. Hence, rigorous and innovative methodologies are required for hyperspectral image (HSI) and signal processing and have become a center of attention for researchers worldwide.

Advanced Spectral Classifiers for Hyperspectral Images: A review

Abstract: Hyperspectral image classification has been a vibrant area of research in recent years. Given a set of observations, i.e., pixel vectors in a hyperspectral image, classification approaches try to allocate a unique label to each pixel vector. However, the classification of hyperspectral images is a challenging task for a number of reasons, such as the presence of redundant features, the imbalance among the limited number of available training samples, and the high dimensionality of the data.

A new deep convolutional neural network for fast hyperspectral image classification

Abstract: Artificial neural networks (ANNs) have been widely used for the analysis of remotely sensed imagery. In particular, convolutional neural networks (CNNs) are gaining more and more attention in this field. CNNs have proved to be very effective in areas such as image recognition and classification, especially for the classification of large sets composed by two-dimensional images. However, their application to multispectral and hyperspectral images faces some challenges, especially related to the processing of the high-dimensional information contained in multidimensional data cubes. This results in a significant increase in computation time. In this paper, we present a new CNN architecture for the classification of hyperspectral images. The proposed CNN is a 3-D network that uses both spectral and spatial information. It also implements a border mirroring strategy to effectively process border areas in the image, and has been efficiently implemented using graphics processing units (GPUs). Our experimental results indicate that the proposed network performs accurately and efficiently, achieving a reduction of the computation time and increasing the accuracy in the classification of hyperspectral images when compared to other traditional ANN techniques.

Dietary protein oxidation: A silent threat to human health?

Abstract: Protein oxidation has become a topic of great scientific interest in the field of food science and nutrition. Food proteins are known to be preferential targets of radical species, and protein oxidation has relevant consequences on protein functionality and food quality. Current trends in this field call attention to the nutritional and health dimensions of oxidized foods. Both lipid and protein oxidation products are accumulated in food during processing and storage and also upon food intake during the subsequent digestion phases. The gastrointestinal tract and internal organs are exposed to the cytotoxic and mutagenic potential of these species. While the molecular basis of the pathogenesis of particular dietary lipid oxidation products is well known, the impact of dietary oxidized proteins on human health has been largely ignored. The well-established association between in vivo protein oxidation and aging and age-related diseases urges scientists to investigate the contribution of dietary protein oxidation to particular pathological conditions. Recent reports indicate the involvement of dietary protein oxidation species on particular health disorders, which emphasizes the link between dietary and in vivo protein oxidation.

Properties of interfacial transition zones (ITZs) in concrete containing recycled mixed aggregate

Abstract: Concretes containing mixed recycled aggregate (RA) have a larger number of coarse aggregate/paste interfacial transition zones (ITZs) than conventional concretes, due to the various component materials present in recycled aggregate. This study investigated the properties of various RA/paste ITZs in concrete using nanoindentation and scanning electron microscopy (SEM) and analysed the possible impact of the properties of the ITZs on the macro-mechanical performance of recycled concrete. It was found that the elastic modulus of the ITZ varies with the type of constituent materials present in recycled aggregate, with ITZs associated with organic components (e.g. wood, plastic and asphalt) exhibiting lower minimum elastic modulus values. The impact of ITZ properties on macro-mechanical properties of concrete depends on the relative content of different constituent materials present in the recycled aggregate and the micro-mechanical properties of the ITZs involved.

The origin of the animals and a 'Savannah' hypothesis for early bilaterian evolution.

Abstract: The earliest evolution of the animals remains a taxing biological problem, as all extant clades are highly derived and the fossil record is not usually considered to be helpful. The rise of the bilaterian animals recorded in the fossil record, commonly known as the ‘Cambrian explosion’, is one of the most significant moments in evolutionary history, and was an event that transformed first marine and then terrestrial environments. We review the phylogeny of early animals and other opisthokonts, and the affinities of the earliest large complex fossils, the so-called ‘Ediacaran’ taxa. We conclude, based on a variety of lines of evidence, that their affinities most likely lie in various stem groups to large metazoan groupings; a new grouping, the Apoikozoa, is erected to encompass Metazoa and Choanoflagellata. The earliest reasonable fossil evidence for total-group bilaterians comes from undisputed complex trace fossils that are younger than about 560 Ma, and these diversify greatly as the Ediacaran–Cambrian boundary is crossed a few million years later. It is generally considered that as the bilaterians diversified after this time, their burrowing behaviour destroyed the cyanobacterial mat-dominated substrates that the enigmatic Ediacaran taxa were associated with, the so-called ‘Cambrian substrate revolution’, leading to the loss of almost all Ediacara-aspect diversity in the Cambrian. Why, though, did the energetically expensive and functionally complex burrowing mode of life so typical of later bilaterians arise? Here we propose a much more positive relationship between late-Ediacaran ecologies and the rise of the bilaterians, with the largely static Ediacaran taxa acting as points of concentration of organic matter both above and below the sediment surface. The breaking of the uniformity of organic carbon availability would have signalled a decisive shift away from the essentially static and monotonous earlier Ediacaran world into the dynamic and burrowing world of the Cambrian. The Ediacaran biota thus played an enabling role in bilaterian evolution similar to that proposed for the Savannah environment for human evolution and bipedality. Rather than being obliterated by the rise of the bilaterians, the subtle remnants of Ediacara-style taxa within the Cambrian suggest that they remained significant components of Phanerozoic communities, even though at some point their enabling role for bilaterian evolution was presumably taken over by bilaterians or other metazoans. Bilaterian evolution was thus an essentially benthic event that only later impacted the planktonic environment and the style of organic export to the sea floor.

Plant functional traits and canopy structure control the relationship between photosynthetic CO2 uptake and far-red sun-induced fluorescence in a Mediterranean grassland under different nutrient availability

Abstract: Summary Sun-induced fluorescence (SIF) in the far-red region provides a new noninvasive measurement approach that has the potential to quantify dynamic changes in light-use efficiency and gross primary production (GPP). However, the mechanistic link between GPP and SIF is not completely understood. We analyzed the structural and functional factors controlling the emission of SIF at 760 nm (F760) in a Mediterranean grassland manipulated with nutrient addition of nitrogen (N), phosphorous (P) or nitrogen–phosphorous (NP). Using the soil–canopy observation of photosynthesis and energy (SCOPE) model, we investigated how nutrient-induced changes in canopy structure (i.e. changes in plant forms abundance that influence leaf inclination distribution function, LIDF) and functional traits (e.g. N content in dry mass of leaves, N%, Chlorophyll a+b concentration (Cab) and maximum carboxylation capacity (Vcmax)) affected the observed linear relationship between F760 and GPP. We conclude that the addition of nutrients imposed a change in the abundance of different plant forms and biochemistry of the canopy that controls F760. Changes in canopy structure mainly control the GPP–F760 relationship, with a secondary effect of Cab and Vcmax. In order to exploit F760 data to model GPP at the global/regional scale, canopy structural variability, biodiversity and functional traits are important factors that have to be considered.

Sustainable improvement of competitiveness in rural tourism destinations: The quest for tourist loyalty in Spain

Abstract: Rural tourism in Spain has developed intensively in recent years and although this branch of tourism is now well established, it still faces some challenges that causal marketing research can address. Tourism destinations now must operate in extremely competitive markets, forcing destination managers to seek out innovative strategies and sustainable competitive advantage, including cultivating tourist loyalty. This paper focuses on developing a better understanding of how destination loyalty is generated in rural tourism by identifying factors that contribute to this loyalty. The paper first examines previous theoretical analyses of the importance of loyalty as a sustainable competitive advantage and the capacity of rural tourism destinations to generate tourist loyalty. It then presents a structural model that includes the main antecedents of destination loyalty that are applicable to Spanish rural tourism. Based on a sample of 464 rural tourists and data analysis using the partial least squares technique, the results indicate that image, quality and destination attribute satisfaction are the direct antecedents of rural tourism destination loyalty. The findings also show that these are indirect precursors of loyalty through overall satisfaction.

Discriminative Low-Rank Gabor Filtering for Spectral–Spatial Hyperspectral Image Classification

Abstract: Spectral–spatial classification of remotely sensed hyperspectral images has attracted a lot of attention in recent years. Although Gabor filtering has been used for feature extraction from hyperspectral images, its capacity to extract relevant information from both the spectral and the spatial domains of the image has not been fully explored yet. In this paper, we present a new discriminative low-rank Gabor filtering (DLRGF) method for spectral–spatial hyperspectral image classification. A main innovation of the proposed approach is that our implementation is accomplished by decomposing the standard 3-D spectral–spatial Gabor filter into eight subfilters, which correspond to different combinations of low-pass and bandpass single-rank filters. Then, we show that only one of the subfilters (i.e., the one that performs low-pass spatial filtering and bandpass spectral filtering) is actually appropriate to extract suitable features based on the characteristics of hyperspectral images. This allows us to perform spectral–spatial classification in a highly discriminative and computationally efficient way, by significantly decreasing the computational complexity (from cubic to linear order) compared with the 3-D spectral–spatial Gabor filter. In order to theoretically prove the discriminative ability of the selected subfilter, we derive an overall classification risk bound to evaluate the discriminating abilities of the features provided by the different subfilters. Our experimental results, conducted using different hyperspectral images, indicate that the proposed DLRGF method exhibits significant improvements in terms of classification accuracy and computational performance when compared with the 3-D spectral–spatial Gabor filter and other state-of-the-art spectral–spatial classification methods.

Optimization of Nanoparticle-Based SERS Substrates through Large-Scale Realistic Simulations

Abstract: Surface-enhanced Raman scattering (SERS) has become a widely used spectroscopic technique for chemical identification, providing unbeaten sensitivity down to the single-molecule level. The amplification of the optical near field produced by collective electron excitations —plasmons— in nanostructured metal surfaces gives rise to a dramatic increase by many orders of magnitude in the Raman scattering intensities from neighboring molecules. This effect strongly depends on the detailed geometry and composition of the plasmon-supporting metallic structures. However, the search for optimized SERS substrates has largely relied on empirical data, due in part to the complexity of the structures, whose simulation becomes prohibitively demanding. In this work, we use state-of-the-art electromagnetic computation techniques to produce predictive simulations for a wide range of nanoparticle-based SERS substrates, including realistic configurations consisting of random arrangements of hundreds of nanoparticles with var...

Destination image and loyalty development: the impact of tourists’ food experiences at gastronomic events

Abstract: This paper discusses an analysis of the impact of tourists’ food experiences at gastronomic events on destination image and these events’ contribution to both destination and event loyalty. Data were collected with a survey conducted during two renowned Spanish gastronomic festivals: the Cherry Blossom Festival, in the Jerte Valley, and the National Cheese Fair, in Trujillo. A total of 616 valid responses were obtained and analysed using the partial least squares path modelling technique. The results show that gastronomic experiences have a positive effect on destination image and loyalty. Furthermore, destinations’ structural elements have a significant relationship to destination brand but not to destination loyalty. Loyalty to gastronomic events is positively correlated with event brand and destination brand and image. Together, these elements have a positive relationship to destination loyalty. This study provides a novel approach to gastronomic experiences associated with place branding. The ...

When the hotter cools more quickly: mpemba effect in granular fluids

Abstract: • Ministerio de Economia, Industria y Competitividad. Beca FIS2013-42840-P (I+D+i), para Antonio Lasanta Becerra, Francisco Vega Reyes y Andres Santos Reyes • Ministerio de Economia, Industria y Competitividad. Beca FIS2016-76359-P (I+D+i), para Francisco Vega Reyes y Andres Santos Reyes • Ministerio de Economia, Industria y Competitividad. Beca MTM2014-56948-C2-2-P, para Antonio Lasanta Becerra • Junta de Extremadura y parcialmente Fondos Europeo de Desarrollo Regional. GR15104, para Antonio Lasanta Becerra, Francisco Vega Reyes y Andres Santos Reyes

Solving the multi-objective path planning problem in mobile robotics with a firefly-based approach

Abstract: Currently, autonomous robotics is one of the most interesting and researched areas of technology. At the beginning, robots only worked in the industrial sector but, gradually, they started to be introduced into other sectors such as medicine or social environments becoming part of society. In mobile robots, the path planning (PP) problem is one of the most researched topics. Taking into account that the PP problem is an NP-hard problem, multi-objective evolutionary algorithms (MOEAs) are good candidates to solve this problem. In this work, a new multi-objective approach based on the flashing behavior of fireflies in nature, the multi-objective firefly algorithm (MO-FA), is proposed to solve the PP problem. This proposed algorithm is a swarm intelligence algorithm. The proposed MO-FA handles three different objectives to obtain accurate and efficient solutions. These objectives are the following: the path safety, the path length, and the path smoothness (related to the energy consumption). Furthermore, and to test the proposed MOEA, we have used eight realistic scenarios for the path's calculation. On the other hand, we also compare our proposal with other approaches of the state of the art, showing the advantages of MO-FA. In particular, to evaluate the obtained results we applied specific quality metrics. Moreover, to demonstrate the statistical evidence of the obtained results, we also performed a statistical analysis. Finally, the study shows that the proposed MO-FA is a good alternative to solve the PP problem.

Co-occurrence of colistin-resistance genes mcr-1 and mcr-3 among multidrug-resistant Escherichia coli isolated from cattle, Spain, September 2015.

Abstract: Colistin resistance genes mcr-3 and mcr-1 have been detected in an Escherichia coli isolate from cattle faeces in a Spanish slaughterhouse in 2015. The sequences of both genes hybridised to same plasmid band of ca 250 kb, although colistin resistance was non-mobilisable. The isolate was producing extended-spectrum beta-lactamases and belonged to serotype O9:H10 and sequence type ST533. Here we report an mcr-3 gene detected in Europe following earlier reports from Asia and the United States.

TRPs in pain sensation

Abstract: According to the International Association for the Study of Pain (IASP) pain is characterized as an “unpleasant sensory and emotional experience associated with actual or potential tissue damage”. The TRP super-family, compressing up to 28 isoforms in mammals, mediates a myriad of physiological and pathophysiological processes, pain among them. TRP channel might be constituted by similar or different TRP subunits, which will result in the formation of homomeric or heteromeric channels with distinct properties and functions. In this review we will discuss about the function of TRPs in pain, focusing on TRP channles that participate in the transduction of noxious sensation, especially TRPV1 and TRPA1, their expression in nociceptors and their sensitivity to a large number of physical and chemical stimuli.

A global threats overview for Numeniini populations: synthesising expert knowledge for a group of declining migratory birds

Abstract: The Numeniini is a tribe of 13 wader species (Scolopacidae, Charadriiformes) of which seven are Near Threatened or globally threatened, including two Critically Endangered. To help inform conservation management and policy responses, we present the results of an expert assessment of the threats that members of this taxonomic group face across migratory flyways. Most threats are increasing in intensity, particularly in non-breeding areas, where habitat loss resulting from residential and commercial development, aquaculture, mining, transport, disturbance, problematic invasive species, pollution and climate change were regarded as having the greatest detrimental impact. Fewer threats (mining, disturbance, problematic native species and climate change) were identified as widely affecting breeding areas. Numeniini populations face the greatest number of non-breeding threats in the East Asian-Australasian Flyway, especially those associated with coastal reclamation; related threats were also identified across the Central and Atlantic Americas, and East Atlantic flyways. Threats on the breeding grounds were greatest in Central and Atlantic Americas, East Atlantic and West Asian flyways. Three priority actions were associated with monitoring and research: to monitor breeding population trends (which for species breeding in remote areas may best be achieved through surveys at key non-breeding sites), to deploy tracking technologies to identify migratory connectivity, and to monitor land-cover change across breeding and non-breeding areas. Two priority actions were focused on conservation and policy responses: to identify and effectively protect key non-breeding sites across all flyways (particularly in the East Asian- Australasian Flyway), and to implement successful conservation interventions at a sufficient scale across human-dominated landscapes for species’ recovery to be achieved. If implemented urgently, these measures in combination have the potential to alter the current population declines of many Numeniini species and provide a template for the conservation of other groups of threatened species.

Fusion of Hyperspectral and LiDAR Data Using Sparse and Low-Rank Component Analysis

Abstract: The availability of diverse data captured over the same region makes it possible to develop multisensor data fusion techniques to further improve the discrimination ability of classifiers. In this paper, a new sparse and low-rank technique is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR)-derived features. The proposed fusion technique consists of two main steps. First, extinction profiles are used to extract spatial and elevation information from hyperspectral and LiDAR data, respectively. Then, the sparse and low-rank technique is utilized to estimate the low-rank fused features from the extracted ones that are eventually used to produce a final classification map. The proposed approach is evaluated over an urban data set captured over Houston, USA, and a rural one captured over Trento, Italy. Experimental results confirm that the proposed fusion technique outperforms the other techniques used in the experiments based on the classification accuracies obtained by random forest and support vector machine classifiers. Moreover, the proposed approach can effectively classify joint LiDAR and hyperspectral data in an ill-posed situation when only a limited number of training samples are available.

Hyperspectral Unmixing Using Double Reweighted Sparse Regression and Total Variation

Abstract: Spectral unmixing is an important technique in hyperspectral image applications. Recently, sparse regression has been widely used in hyperspectral unmixing, but its performance is limited by the high mutual coherence of spectral libraries. To address this issue, a new sparse unmixing algorithm, called double reweighted sparse unmixing and total variation (TV), is proposed in this letter. Specifically, the proposed algorithm enhances the sparsity of fractional abundances in both spectral and spatial domains through the use of double weights, where one is used to enhance the sparsity of endmembers in spectral library, and the other is introduced to improve the sparsity of fractional abundances. Moreover, a TV-based regularization is further adopted to explore the spatial–contextual information. As such, the simultaneous utilization of both double reweighted $l_{1}$ minimization and TV regularizer can significantly improve the sparse unmixing performance. Experimental results on both synthetic and real hyperspectral data sets demonstrate the effectiveness of the proposed algorithm both visually and quantitatively.

A New Sparse Subspace Clustering Algorithm for Hyperspectral Remote Sensing Imagery

Abstract: Robust techniques such as sparse subspace clustering (SSC) have been recently developed for hyperspectral images (HSIs) based on the assumption that pixels belonging to the same land-cover class approximately lie in the same subspace. In order to account for the spatial information contained in HSIs, SSC models incorporating spatial information have become very popular. However, such models are often based on a local averaging constraint, which does not allow for a detailed exploration of the spatial information, thus limiting their discriminative capability and preventing the spatial homogeneity of the clustering results. To address these relevant issues, in this letter, we develop a new and effective $\ell _{2} $ -norm regularized SSC algorithm which adds a four-neighborhood $\ell _{2} $ -norm regularizer into the classical SSC model, thus taking full advantage of the spatial-spectral information contained in HSIs. The experimental results confirm the potential of including the spatial information (through the newly added $\ell _{2} $ -norm regularization term) in the SSC framework, which leads to a significant improvement in the clustering accuracy of SSC when applied to HSIs.

Antioxidant protection of cooked meatballs during frozen storage by whey protein edible films with phytochemicals from Laurus nobilis L. and Salvia officinalis

Abstract: The aim of this study was to assess the effectiveness of whey protein isolate based edible films (WPFs) incorporated with natural antioxidant extracts (NAE) from laurel (LA, Laurus nobilis L.) or sage (SA, Salvia officinalis.) on the oxidative stability of cooked meatballs during frozen storage at −18 °C for 60 days. TBA values of the meatballs with NAE-WPF were lower than the TBA values of the meatballs with WPF and the control (C) group of samples. Higher antioxidant activity by LA-WPF than SA-WPF was determined with a 2,2′-diphenyl-2-picrylhydrazyl radical-scavenging assay. Incorporation of NAE from LA or SA into the edible films (EFs) resulted in reduced para -Anisidine value (p-Av) (P