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.

Multi-Modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009–2010 Data Fusion Contest

Abstract: The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society was focused on the detection of flooded areas using multi-temporal and multi-modal images. Both high spatial resolution optical and synthetic aperture radar data were provided. The goal was not only to identify the best algorithms (in terms of accuracy), but also to investigate the further improvement derived from decision fusion. This paper presents the four awarded algorithms and the conclusions of the contest, investigating both supervised and unsupervised methods and the use of multi-modal data for flood detection. Interestingly, a simple unsupervised change detection method provided similar accuracy as supervised approaches, and a digital elevation model-based predictive method yielded a comparable projected change detection map without using post-event data.

Luffa cylindrica as a lignocellulosic source of fiber, microfibrillated cellulose and cellulose nanocrystals

Abstract: In this work the annual plant called Luffa cylindrica (LC) has been characterized and used to prepare macroscopic lignocellulosic fibers and cellulosic nanoparticles, viz. microfibrillated cellulose (MFC) and whiskers, each of which can be used as a reinforcing phase in bionanocomposites. The morphological, chemical, and physical properties of LC fibers were first characterized. The contents of lignin, hemicellulose, and other constituents were determined, and scanning electron microscopy (SEM) observations were performed to investigate the surface morphology of the LC fibers. Sugars contents were determined by ionic chromatography, and it was shown that glucose was the main sugar present in the residue. MFC and whiskers were prepared after chemical treatments (NaOH and NaClO2), purifying cellulose by eliminating lignin and hemicellulose. Transmission electron microscopy (TEM) and SEM made it possible to determine the dimensions of LC whiskers and MFC. Tensile tests were carried out to investigate the mechanical properties of LF nanoparticles.

Ultrathin Nanogenerators as Self‐Powered/Active Skin Sensors for Tracking Eye Ball Motion

Abstract: Ultrathin piezoelectric nanogenerator (NG) with a total thickness of ≈16 μm is fabricated as an active or self-powered sensor for monitoring local deformation on a human skin. The NG was based on an anodic aluminum oxide (AAO) as an insulating layer grown on a thin Al foil by anodization, on which a thin film made of aligned ZnO nanowire compacted arrays is grown by solution chemistry. The performance of the NG is characterized with the assistance of the finite element method (FEM) simulation. The extremely thin NG is attached on the surface of an eyelid, and its output voltage/current characterizes the motion of the eye ball underneath. Since there is no external power needed for the operation of the NG, this self-powered or active sensor can be effective in monitoring sleeping behavior, brain activities, and spirit status of a person as well as any biological associated skin deformation.

A Markov Chain Monte Carlo technique to sample transport and source parameters of Galactic cosmic rays - II. Results for the diffusion model combining B/C and radioactive nuclei

Abstract: Context. Ongoing measurements of the cosmic radiation (nuclear, electronic, and γ-ray) are providing additional insight into cosmicray physics. A comprehensive picture of these data relies on an accurate determination of the transport and source parameters of propagation models. Aims. A Markov Chain Monte Carlo method is used to obtain these parameters in a diffusion model. By measuring the B/C ratio and radioactive cosmic-ray clocks, we calculate their probability density functions, placing special emphasis on the halo size L of the Galaxy and the local underdense bubble of size rh. We also derive the mean, best-fit model parameters and 68% confidence level for the various parameters, and the envelopes of other quantities. Methods. The analysis relies on the USINE code for propagation and on a Markov Chain Monte Carlo technique previously developed by ourselves for the parameter determination. Results. The B/C analysis leads to a most probable diffusion slope δ = 0.86 +0.04