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.

Fast and automatic processing of multi-level events in nanopore translocation experiments

Abstract: We have developed a method to analyze in detail, translocation events providing a novel and flexible tool for data analysis of nanopore experiments. Our program, called OpenNanopore, is based on the cumulative sums algorithm (CUSUM algorithm). This algorithm is an abrupt change detection algorithm that provides fitting of current blockages, allowing the user to easily identify the different levels in each event. Our method detects events using adaptive thresholds that adapt to low-frequency variations in the baseline. After event identification, our method uses the CUSUM algorithm to fit the levels inside every event and automatically extracts their time and amplitude information. This facilitates the statistical analysis of an event population with a given number of levels. The obtained information improves the interpretation of interactions between the molecule and nanopore. Since our program does not require any prior information about the analyzed molecules, novel molecule-nanopore interactions can be characterized. In addition our program is very fast and stable. With the progress in fabrication and control of the translocation speed, in the near future, our program could be useful in identification of the different bases of DNA.

Hyperspectral Super-Resolution of Locally Low Rank Images From Complementary Multisource Data

Abstract: Remote sensing hyperspectral images (HSIs) are quite often low rank, in the sense that the data belong to a low dimensional subspace/manifold. This has been recently exploited for the fusion of low spatial resolution HSI with high spatial resolution multispectral images in order to obtain super-resolution HSI. Most approaches adopt an unmixing or a matrix factorization perspective. The derived methods have led to state-of-the-art results when the spectral information lies in a low-dimensional subspace/manifold. However, if the subspace/manifold dimensionality spanned by the complete data set is large, i.e., larger than the number of multispectral bands, the performance of these methods mainly decreases because the underlying sparse regression problem is severely ill-posed. In this paper, we propose a local approach to cope with this difficulty. Fundamentally, we exploit the fact that real world HSIs are locally low rank, that is, pixels acquired from a given spatial neighborhood span a very low-dimensional subspace/manifold, i.e., lower or equal than the number of multispectral bands. Thus, we propose to partition the image into patches and solve the data fusion problem independently for each patch. This way, in each patch the subspace/manifold dimensionality is low enough, such that the problem is not ill-posed anymore. We propose two alternative approaches to define the hyperspectral super-resolution through local dictionary learning using endmember induction algorithms. We also explore two alternatives to define the local regions, using sliding windows and binary partition trees. The effectiveness of the proposed approaches is illustrated with synthetic and semi real data.

Physico-Chemical Characterization of Palm from Phoenix Dactylifera-L, Preparation of Cellulose Whiskers and Natural Rubber-Based Nanocomposites

Abstract: The microstructure and chemical composition of the leaflets and the rachis of the palm from phoenix dactylifera L were investigated. It was found that the ratio of slot in the rachis was higher compared to the leaflets. The cellulose content was higher in the rachis whereas the lignin content in the leaflets was twice the one in the rachis. The aspect ratio of the cellulose whiskers extracted from the rachis (around 43) was significantlyhigher compared to the aspect ratio of those from the leaflets which is around 30. The former were used to prepare nanocomposite films using natural rubber as the matrix. These films were obtained bycasting/evaporation. The thermal and mechanical properties of the ensuing nanocomposite films were investigated using differential scanning calorimetry, dynamic mechanical analysis and tensile tests. The stiffness of the natural rubber was significantly increased above the T g. Favorable interactions between the polymeric matrix and the cellulosic nanoparticles were also evidenced.

Comparison of the Device Lifetime in Wireless Networks for the Internet of Things

Abstract: This paper presents a comparison of the expected lifetime for Internet of Things (IoT) devices operating in several wireless networks: the IEEE 802.15.4/e, Bluetooth low energy (BLE), the IEEE 802.11 power saving mode, the IEEE 802.11ah, and in new emerging long-range technologies, such as LoRa and SIGFOX. To compare all technologies on an equal basis, we have developed an analyzer that computes the energy consumption for a given protocol based on the power required in a given state (Sleep, Idle, Tx, and Rx) and the duration of each state. We consider the case of an energy constrained node that uploads data to a sink, analyzing the physical (PHY) layer under medium access control (MAC) constraints, and assuming IPv6 traffic whenever possible. This paper considers the energy spent in retransmissions due to corrupted frames and collisions as well as the impact of imperfect clocks. The comparison shows that the BLE offers the best lifetime for all traffic intensities in its capacity range. LoRa achieves long lifetimes behind 802.15.4 and BLE for ultra low traffic intensity; SIGFOX only matches LoRa for very small data sizes. Moreover, considering the energy consumption due to retransmissions of lost data packets only decreases the lifetimes without changing their relative ranking. We believe that these comparisons will give all users of IoT technologies indications about the technology that best fits their needs from the energy consumption point of view. Our analyzer will also help IoT network designers to select the right MAC parameters to optimize the energy consumption for a given application.