Institution
Grenoble Institute of Technology
Education•Grenoble, France•
About: Grenoble Institute of Technology is a education organization based out in Grenoble, France. It is known for research contribution in the topics: Hyperspectral imaging & Geology. The organization has 3427 authors who have published 5345 publications receiving 137158 citations. The organization is also known as: Grenoble INP.
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors investigate consumers' willingnessness to pay a price premium for two environmental attributes of a non-food agricultural product, i.e., eco-label and carbon footprint.
Abstract: This paper investigates consumers'willingness to pay a price premium for two environmental attributes of a non-food agricultural product. We study individual preferences for roses associated with an eco-label and a carbon footprint, using an economic experiment combining discrete choice questions and real economic incentives involving real purchases of roses against cash. The data are analysed with a mixed logit model and reveal significant premiums for both environmental attributes of the product.
123 citations
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TL;DR: More properties and new functionalities, such as sensing capabilities and tag-totag communication, are continually being developed, leading to the new paradigm of Internet of Things.
Abstract: Entering "RFID" on your Web browser will return you more than 50 million links. This huge number of references is a result of the impact of radio-frequency identification (RFID) technology worldwide [1]. Indeed, RFID technology is exploited in numerous domains, with thousands of applications, including more and more seen in everyday life. The RFID market is worth several billion dollars today, and its growth is more than 10% per year [2], [3]. There are two main classes of RFID devices. Figure 1 illustrates the main features of each class. The most known and broadly used class is the one based on the use of an integrated circuit (IC) chip in which the information is stored and that is connected to an antenna; the two together form the "tag." Such a technology exhibits several advantages, including flexibility and versatility in terms of the application. Nevertheless, it has some drawbacks mostly in terms of cost, robustness, reliability, data security, and poor recyclability of tags. More properties and new functionalities, such as sensing capabilities and tag-totag communication [4], are continually being developed, leading to the new paradigm of Internet of Things [5].
123 citations
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TL;DR: In this paper, the authors report the first in situ observations of the deformation behavior of an Al-Cu alloy in the semisolid state by using ultrafast, high-resolution X-ray microtomography.
122 citations
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TL;DR: A novel iterative technique based on genetic algorithms (GAs) is proposed to automatically optimize the selection of the optimal features from the profiles to classify three hyperspectral data sets achieving significantly high classification accuracy values.
Abstract: Morphological and attribute profiles have been proven to be effective tools to fuse spectral and spatial information for classification of remote sensing data. A wide range of filters (i.e., number of levels in the profiles) is usually necessary in order to properly model the spatial information in a remote sensing scene. A dense sampling of the values of the parameters of the filters generates profiles that have both a very large dimensionality (leading to the Hughes phenomenon in classification) and a high redundancy. In this paper, a novel iterative technique based on genetic algorithms (GAs) is proposed to automatically optimize the selection of the optimal features from the profiles. The selection of the filtered images that compose the profile is performed by dividing them into three classes corresponding to high, medium, and low importance. We propose to measure the importance (modeled in terms of discriminative power in the classification task) using a random forest classifier, which provides a rank for each feature with its model. Only the set of images associated with the highest importance is selected, i.e., preserved for classification. The proposed technique is applied to the features labeled with medium importance, whereas the images with the lowest importance are removed from the profile. This method is employed to classify three hyperspectral data sets achieving significantly high classification accuracy values. A parallel computing implementation has been developed in order to significantly reduce the time required for the run of the GAs.
122 citations
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TL;DR: In this article, the equation of state of KCl and KBr, compressed in a helium pressure medium in a diamond-anvil cell, has been measured by x-ray diffraction in the B1 and B2 phases up to 165 GPa at 298 K.
Abstract: The equation of state of KCl and KBr, compressed in a helium pressure medium in a diamond-anvil cell, has been measured by x-ray diffraction in the B1 and B2 phases up to 165 GPa at 298 K. The P-V- ...
122 citations
Authors
Showing all 3527 results
Name | H-index | Papers | Citations |
---|---|---|---|
J. F. Macías-Pérez | 134 | 486 | 94715 |
J-Y. Hostachy | 119 | 716 | 65686 |
Alain Dufresne | 111 | 358 | 45904 |
David Brown | 105 | 1257 | 46827 |
Raphael Noel Tieulent | 89 | 417 | 24926 |
Antonio Plaza | 79 | 631 | 29775 |
G. Conesa Balbastre | 76 | 208 | 18800 |
Jocelyn Chanussot | 73 | 614 | 27949 |
Ekhard K. H. Salje | 70 | 581 | 19938 |
Richard Wilson | 70 | 809 | 21477 |
Jerome Bouvier | 70 | 278 | 13724 |
David Maurin | 68 | 215 | 17295 |
Alessandro Gandini | 67 | 348 | 19813 |
Matthieu Tristram | 67 | 143 | 17188 |
D. Santos | 65 | 113 | 15648 |