Grenoble Institute of Technology

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.

Multiple Kernel Learning for Hyperspectral Image Classification: A Review

Abstract: With the rapid development of spectral imaging techniques, classification of hyperspectral images (HSIs) has attracted great attention in various applications such as land survey and resource monitoring in the field of remote sensing. A key challenge in HSI classification is how to explore effective approaches to fully use the spatial–spectral information provided by the data cube. Multiple kernel learning (MKL) has been successfully applied to HSI classification due to its capacity to handle heterogeneous fusion of both spectral and spatial features. This approach can generate an adaptive kernel as an optimally weighted sum of a few fixed kernels to model a nonlinear data structure. In this way, the difficulty of kernel selection and the limitation of a fixed kernel can be alleviated. Various MKL algorithms have been developed in recent years, such as the general MKL, the subspace MKL, the nonlinear MKL, the sparse MKL, and the ensemble MKL. The goal of this paper is to provide a systematic review of MKL methods, which have been applied to HSI classification. We also analyze and evaluate different MKL algorithms and their respective characteristics in different cases of HSI classification cases. Finally, we discuss the future direction and trends of research in this area.

Physico-Mechanical properties of biodegradable starch nanocomposites

Abstract: Nanocomposites of cassava starch reinforced with waxy starch nanocrystals were prepared. They showed a 380% increase of the rubbery storage modulus (at 50 °C) and a 40% decrease in the water vapor permeability. X-ray spectra show that the composite was more amorphous than the neat matrix, which was attributed to higher equilibrium water content in the composites. TGA confirmed this result and its thermal derivative suggested the formation of hydrogen bonding between glycerol and the nanocrystals. The reinforcing effect of starch nanocrystals was attributed to strong filler/matrix interactions due to the hydrogen bonding. The decrease of the permeability suggests that the nanocrystals were well dispersed, with few filler/filler interactions.

Measurement of charm production at central rapidity in proton-proton collisions at root s=7 TeV

Abstract: The p(t)-differential inclusive production cross sections of the prompt charmed mesons D-0, D+, and D*(+) in the rapidity range vertical bar y vertical bar K-pi(+), D+ -> K-pi(+)pi(+), D*(+) -> D-0 pi(+), and their charge conjugates, about 8,400 D-0, 2,900 D+, and 2,600 D*(+) mesons with 1 < p(t) < 24 GeV/c were counted, after selection cuts, in a data sample of 3.14 x 10(8) events collected with a minimum-bias trigger (integrated luminosity L-int = 5 nb(-1)). The results are described within uncertainties by predictions based on perturbative QCD.