Institution
University of Extremadura
Education•Badajoz, Spain•
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.
Papers published on a yearly basis
Papers
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TL;DR: Surfactant-assisted seeded growth of metal nanoparticles (NPs) can be engineered to produce anisotropic gold nanocrystals with high chiroptical activity through the templating effect of chiral micelles formed in the presence of dissymmetric cosurfactants.
Abstract: Surfactant-assisted seeded growth of metal nanoparticles (NPs) can be engineered to produce anisotropic gold nanocrystals with high chiroptical activity through the templating effect of chiral micelles formed in the presence of dissymmetric cosurfactants Mixed micelles adsorb on gold nanorods, forming quasihelical patterns that direct seeded growth into NPs with pronounced morphological and optical handedness Sharp chiral wrinkles lead to chiral plasmon modes with high dissymmetry factors (~020) Through variation of the dimensions of chiral wrinkles, the chiroptical properties can be tuned within the visible and near-infrared electromagnetic spectrum The micelle-directed mechanism allows extension to other systems, such as the seeded growth of chiral platinum shells on gold nanorods This approach provides a reproducible, simple, and scalable method toward the fabrication of NPs with high chiral optical activity
158 citations
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TL;DR: A scale-free CNN (SF-CNN) is introduced for remote sensing scene classification that not only allows the input images to be of arbitrary sizes but also retain the ability to extract discriminative features using a traditional sliding-window-based strategy.
Abstract: Fine-tuning of pretrained convolutional neural networks (CNNs) has been proven to be an effective strategy for remote sensing image scene classification, particularly when a limited number of labeled data sets are available for training purposes. However, such a fine-tuning process often needs that the input images are resized into a fixed size to generate input vectors of the size required by fully connected layers (FCLs) in the pretrained CNN model. Such a resizing process often discards key information in the scenes and thus deteriorates the classification performance. To address this issue, in this paper, we introduce a scale-free CNN (SF-CNN) for remote sensing scene classification. Specifically, the FCLs in the CNN model are first converted into convolutional layers, which not only allow the input images to be of arbitrary sizes but also retain the ability to extract discriminative features using a traditional sliding-window-based strategy. Then, a global average pooling (GAP) layer is added after the final convolutional layer so that input images of arbitrary size can be mapped to feature maps of uniform size. Finally, we utilize the resulting feature maps to create a new FCL that is fed to a softmax layer for final classification. Our experimental results conducted using several real data sets demonstrate the superiority of the proposed SF-CNN method over several well-known classification methods, including pretrained CNN-based ones.
158 citations
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01 Jan 2006158 citations
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TL;DR: A rigorous kinetic model is applied to calculate the different kinetic rate constants for the oxidation process of p-hydroxybenzoic acid for another 10 phenolic compounds present in agroindustrial and pulp paper wastewaters.
158 citations
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TL;DR: In this paper, the authors analyzed 66 holm oak cross sections and found a close correlation between tree rings and diameters, so that diameter seems to be a reliable indicator of tree age.
Abstract: Dehesas, rangelands occupied by scattered oak trees and characterized by silvopastoral uses, cover about 3.1 million ha in south-western Spain. There is considerable debate about the long-term persistence of holm oak (Quercus ilex) populations in dehesas, since most stands are overaged and seedlings and saplings are sparse. The forest cycle has been disrupted in most dehesas. Regeneration has been inhibited since stands were opened for agriculture and grazing. Oak diameters from three land-use groups (young dehesa [YD], middle-aged dehesa [MD], and old dehesa [OD]) in Caceres Province, Spain, were compared. These groups differed in the age of the land-use system, i.e. time since the original Mediterranean forest was cleared. The dehesa systems were established about 80 (YD), 150 (MD) and 500 (OD) years ago. An analysis of 66 holm oak cross sections revealed a close correlation (r2 = 91.2%) between tree rings and diameters, so that diameter seems to be a reliable indicator of tree age. Nested analysis of variance showed significant variation in diameters between the land-use groups. There is generally a positive relationship between tree age and the age of agrosilvopastoral use of the dehesas. Sparse holm oaks in the dehesas are primarily remnants from the first forest cycle. Local differences in growth conditions (for example soil quality and tree density) contribute further significant diameter variation on a between-plot level. Diameter structure of abandoned dehesas showed two peaks and a high proportion of trees in the smallest size class. This indicates that the forest degradation process is reversible. An effective regeneration policy should promote a rotating 20- to 30-year set-aside of dehesa parcels.
157 citations
Authors
Showing all 8001 results
Name | H-index | Papers | Citations |
---|---|---|---|
Russel J. Reiter | 169 | 1646 | 121010 |
Donald G. Truhlar | 165 | 1518 | 157965 |
Manel Esteller | 146 | 713 | 96429 |
David J. Williams | 107 | 2060 | 62440 |
Keijo Häkkinen | 99 | 421 | 31355 |
Robert H. Anderson | 97 | 1237 | 41250 |
Leif Bertilsson | 87 | 321 | 23933 |
Mario F. Fraga | 84 | 267 | 32957 |
YangQuan Chen | 84 | 1048 | 36543 |
Antonio Plaza | 79 | 631 | 29775 |
Robert D. Gibbons | 75 | 349 | 26330 |
Jocelyn Chanussot | 73 | 614 | 27949 |
Naresh Magan | 72 | 400 | 17511 |
Luis Puelles | 71 | 269 | 19858 |
Jun Li | 70 | 799 | 19510 |