Search results for "WARE"
showing 10 items of 9184 documents
Run-time scalable NoC for FPGA based virtualized IPs
2017
The integration of virtualized FPGA-based hardware accelerators in a cloud computing is progressing from time to time. As the FPGA has limited resources, the dynamic partial reconfiguration capability of the FPGA is considered to share resources among different virtualized IPs during runtime. On the other hand, the NoC is a promising solution for communication among virtualized FPGA-based IPs. However, not all the virtualized regions of the FPGA will be active all the time. When there is no demand for virtualized IPs, the virtualized regions are loaded with blank bitstreams to save power. However, keeping active the idle components of the NoC connecting with the idle virtualized regions is …
Computer-aided analysis and design procedure for rotating induction machine magnetic circuits and windings
2018
The aim of this study is to present a new, accurate, and user-friendly software procedure for the analysis and rapid design of rotating induction machine windings, considering both the electric and the magnetic specifications of the machine itself. This procedure is a valid aid for quick first stage design without the necessity of using finite element method (FEM)-based design procedures. FEM can be used in a second design phase in order to refine the first stage results. The design procedure is hereafter outlined and some examples show its capability.
Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods
2020
We present an implementation of distance-based machine learning (ML) methods to create a realistic atomistic interaction potential to be used in Monte Carlo simulations of thermal dynamics of thiol...
The MIRS computer package for modeling the rovibrational spectra of polyatomic molecules
2003
International audience; The MIRS spectroscopic software for the modeling of ro-vibrational spectra of polyatomic molecules is presented. It is designed for the global treatment of complex band systems of molecules to take full account of symmetry properties. It includes e cient algorithms based on the irreducible tensor formalism. Predictions and simultaneous data fi tting (positions and intensities) are implemented as well as advanced options related to group theory algebra. Illustrative examples on CH3D, CH4, CH3Cl and PH3 are reported and the present status of data available is given. It is written in C++ for standard PC computer operating under Windows. The full package including on-lin…
Digital thermal monitoring of the Amazon forest: an intercomparison of satellite and reanalysis products
2015
Remote sensing and climate digital products have become increasingly available in recent years. Access to these products has favored a variety of Digital Earth studies, such as the analysis of the impact of global warming over different biomes. The study of the Amazon forest response to drought has recently received particular attention from the scientific community due to the occurrence of extreme droughts and anomalous warming over the last decade. This paper focuses on the differences observed between surface thermal anomalies obtained from remote sensing moderate resolution imaging spectroradiometer (MODIS) and climatic (ERA-Interim) monthly products over the Amazon forest. With a few e…
Spectroscopic tools for remote sensing of greenhouse gases CH4, CF4 and SF6
2003
International audience; Highly symmetrical molecules such as CH4, CF4 or SF6 are known to be atmospheric pollutants and greenhouse gases. High-resolution spectroscopy in the infrared is particularly suitable for the monitoring of gas concentration and radiative transfers in the earth's atmosphere. This technique requires extensive theoretical studies for the modeling of the spectra of such molecules (positions, intensities and shapes of absorption lines). Here, we have developed powerful tools for the analysis and the simulation of absorption spectra of highly symmetrical molecules. These tools have been implemented in the spherical top data system (STDS) and highly-spherical top data syste…
Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3
2012
Abstract ESA's upcoming satellites Sentinel-2 (S2) and Sentinel-3 (S3) aim to ensure continuity for Landsat 5/7, SPOT-5, SPOT-Vegetation and Envisat MERIS observations by providing superspectral images of high spatial and temporal resolution. S2 and S3 will deliver near real-time operational products with a high accuracy for land monitoring. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods. Machine learning regression algorithms may be powerful candidates for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. By using data from …
Recent Advances in Techniques for Hyperspectral Image Processing
2009
International audience; Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in less than thirty years from being a sparse research tool into a commodity product available to a broad user community. Currently, there is a need for standardized data processing techniques able to take into account the special properties of hyperspec- tral data. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral image processing. Our main focus is on the design of techniques able to deal with the high-dimensional nature of the data, and to integrate the spa- tial and spectral information. Performance of the discussed techniques is evaluated in …
Statistical retrieval of atmospheric profiles with deep convolutional neural networks
2019
Abstract Infrared atmospheric sounders, such as IASI, provide an unprecedented source of information for atmosphere monitoring and weather forecasting. Sensors provide rich spectral information that allows retrieval of temperature and moisture profiles. From a statistical point of view, the challenge is immense: on the one hand, “underdetermination” is common place as regression needs to work on high dimensional input and output spaces; on the other hand, redundancy is present in all dimensions (spatial, spectral and temporal). On top of this, several noise sources are encountered in the data. In this paper, we present for the first time the use of convolutional neural networks for the retr…
Cloud detection on the Google Earth engine platform
2017
The vast amount of data acquired by current high resolution Earth observation satellites implies some technical challenges to be faced. Google Earth Engine (GEE) platform provides a framework for the development of algorithms and products built over this data in an easy and scalable manner. In this paper, we take advantage of the GEE platform capabilities to exploit the wealth of information in the temporal dimension by processing a long time series of satellite images. A cloud detection algorithm for Landsat-8, which uses previous images of the same location to detect clouds, is implemented and tested on the GEE platform.