Search results for "Propagation"

showing 10 items of 676 documents

AIOC2: A deep Q-learning approach to autonomic I/O congestion control in Lustre

2021

Abstract In high performance computing systems, I/O congestion is a common problem in large-scale distributed file systems. However, the current implementation mainly requires administrator to manually design low-level implementation and optimization, we proposes an adaptive I/O congestion control framework, named AIOC 2 , which can not only adaptively tune the I/O congestion control parameters, but also exploit the deep Q-learning method to start the training parameters and optimize the tuning for different types of workloads from the server and the client at the same time. AIOC 2 combines the feedback-based dynamic I/O congestion control and deep Q-learning parameter tuning technology to …

ExploitComputer Networks and CommunicationsComputer sciencebusiness.industryQ-learningInterference (wave propagation)SupercomputerComputer Graphics and Computer-Aided DesignTheoretical Computer ScienceNetwork congestionArtificial IntelligenceHardware and ArchitectureEmbedded systemLustre (file system)Latency (engineering)businessThroughput (business)SoftwareParallel Computing
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Disentangling Derivatives, Uncertainty and Error in Gaussian Process Models

2020

Gaussian Processes (GPs) are a class of kernel methods that have shown to be very useful in geoscience applications. They are widely used because they are simple, flexible and provide very accurate estimates for nonlinear problems, especially in parameter retrieval. An addition to a predictive mean function, GPs come equipped with a useful property: the predictive variance function which provides confidence intervals for the predictions. The GP formulation usually assumes that there is no input noise in the training and testing points, only in the observations. However, this is often not the case in Earth observation problems where an accurate assessment of the instrument error is usually a…

FOS: Computer and information sciencesComputer Science - Machine Learning010504 meteorology & atmospheric sciencesComputer science0211 other engineering and technologiesMachine Learning (stat.ML)02 engineering and technology01 natural sciencesMachine Learning (cs.LG)symbols.namesakeStatistics - Machine LearningGaussian process021101 geological & geomatics engineering0105 earth and related environmental sciencesVariance functionPropagation of uncertaintyVariance (accounting)Function (mathematics)Confidence intervalNonlinear systemNoiseKernel method13. Climate actionKernel (statistics)symbolsAlgorithmIGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
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Accounting for Input Noise in Gaussian Process Parameter Retrieval

2020

Gaussian processes (GPs) are a class of Kernel methods that have shown to be very useful in geoscience and remote sensing applications for parameter retrieval, model inversion, and emulation. They are widely used because they are simple, flexible, and provide accurate estimates. GPs are based on a Bayesian statistical framework which provides a posterior probability function for each estimation. Therefore, besides the usual prediction (given in this case by the mean function), GPs come equipped with the possibility to obtain a predictive variance (i.e., error bars, confidence intervals) for each prediction. Unfortunately, the GP formulation usually assumes that there is no noise in the inpu…

FOS: Computer and information sciencesComputer Science - Machine LearningComputer sciencePosterior probability0211 other engineering and technologiesMachine Learning (stat.ML)02 engineering and technologyMachine Learning (cs.LG)symbols.namesakeStatistics - Machine LearningElectrical and Electronic EngineeringGaussian process021101 geological & geomatics engineeringPropagation of uncertaintyNoise measurementbusiness.industryFunction (mathematics)Geotechnical Engineering and Engineering GeologySea surface temperatureNoiseKernel methodsymbolsGlobal Positioning SystemErrors-in-variables modelsbusinessAlgorithmIEEE Geoscience and Remote Sensing Letters
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A perspective on Gaussian processes for Earth observation

2019

Earth observation (EO) by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet. In the last decade, machine learning and Gaussian processes (GPs) in particular has attained outstanding results in the estimation of bio-geo-physical variables from the acquired images at local and global scales in a time-resolved manner. GPs provide not only accurate estimates but also principled uncertainty estimates for the predictions, can easily accommodate multimodal data coming from different sensors and from multitemporal acquisitions, allow the introduction of physical knowledge, and a formal treatment of uncertainty quantification and error pr…

FOS: Computer and information sciencesComputer Science - Machine LearningEarth observationComputer scienceDatenmanagement und AnalyseMachine Learning (stat.ML)02 engineering and technology010402 general chemistrycomputer.software_genreStatistics - Applications01 natural sciencesMachine Learning (cs.LG)symbols.namesakeStatistics - Machine LearningApplications (stat.AP)Uncertainty quantificationGaussian processPhysical lawPropagation of uncertaintyMultidisciplinarybusiness.industryPerspective (graphical)gaussian processes021001 nanoscience & nanotechnology0104 chemical sciences13. Climate actionCausal inferenceComputer ScienceGlobal Positioning SystemsymbolsData mining0210 nano-technologybusinesscomputerPerspectivesNational Science Review
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Deep Non-Line-of-Sight Reconstruction

2020

The recent years have seen a surge of interest in methods for imaging beyond the direct line of sight. The most prominent techniques rely on time-resolved optical impulse responses, obtained by illuminating a diffuse wall with an ultrashort light pulse and observing multi-bounce indirect reflections with an ultrafast time-resolved imager. Reconstruction of geometry from such data, however, is a complex non-linear inverse problem that comes with substantial computational demands. In this paper, we employ convolutional feed-forward networks for solving the reconstruction problem efficiently while maintaining good reconstruction quality. Specifically, we devise a tailored autoencoder architect…

FOS: Computer and information sciencesComputer Science - Machine Learningbusiness.industryComputer scienceComputer Vision and Pattern Recognition (cs.CV)Image and Video Processing (eess.IV)Computer Science - Computer Vision and Pattern RecognitionNonlinear optics020207 software engineering02 engineering and technologyIterative reconstructionInverse problemElectrical Engineering and Systems Science - Image and Video ProcessingAutoencoderRendering (computer graphics)Machine Learning (cs.LG)Non-line-of-sight propagation0202 electrical engineering electronic engineering information engineeringFOS: Electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer visionArtificial intelligencebusiness
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Application of Thin ZnO ALD Layers in Fiber-Optic Fabry-Pérot Sensing Interferometers

2016

International audience; In this paper we investigated the response of a fiber-optic Fabry-Pérot sensing interferometer with thin ZnO layers deposited on the end faces of the optical fibers forming the cavity. Standard telecommunication single-mode optical fiber (SMF-28) segments were used with the thin ZnO layers deposited by Atomic Layer Deposition (ALD). Measurements were performed with the interferometer illuminated by two broadband sources operating at 1300 nm and 1550 nm. Reflected interference signal was acquired by an optical spectrum analyzer while the length of the air cavity was varied. Thickness of the ZnO layers used in the experiments was 50 nm, 100 nm, and 200 nm. Uncoated SMF…

Fabry-Pérot interferometerOptical fiberMaterials scienceinterferenceZnO layer02 engineering and technologylcsh:Chemical technologyInterference (wave propagation)01 natural sciencesBiochemistryArticleAnalytical Chemistrylaw.inventionAtomic layer depositionOpticslawAstronomical interferometerlcsh:TP1-1185Fiber[PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]Electrical and Electronic EngineeringInstrumentation[PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics]business.industry010401 analytical chemistry021001 nanoscience & nanotechnologyAtomic and Molecular Physics and Optics0104 chemical sciencesInterferometryAtomic Layer DepositionFabry-Pérot interferometer; Atomic Layer Deposition; ZnO layer; interference0210 nano-technologybusinessRefractive indexFabry–Pérot interferometer
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Finite propagation speed for solutions of the wave equation on metric graphs

2012

We provide a class of self-adjoint Laplace operators on metric graphs with the property that the solutions of the associated wave equation satisfy the finite propagation speed property. The proof uses energy methods, which are adaptions of corresponding methods for smooth manifolds.

Finite propagation speedClass (set theory)Property (philosophy)Laplace transformMathematical analysisFOS: Physical sciencesMathematical Physics (math-ph)Wave equation34B45 35L05 35L20530Laplace operatorsMetric (mathematics)Energy methodWave equationMetric graphsMathematical PhysicsAnalysisMathematics
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Wave Energy Assessment around the Aegadian Islands (Sicily)

2019

This paper presents the estimation of the wave energy potential around the Aegadian islands (Italy), carried out on the basis of high resolution wave hindcast. This reanalysis was developed employing Weather Research and Forecast (WRF) and WAVEWATCH III ® models for the modelling of the atmosphere and the waves, respectively. Wave climate has been determined using the above-mentioned 32-year dataset covering the years from 1979 to 2010. To improve the information about wave characteristics regarding spatial details, i.e., increasing wave model resolution, especially in the nearshore region around the islands, a SWAN (Simulating WAves Nearshore) wave propagation model was used. Results obtai…

Flexible mesh model; Renewable energy; Resource assessment; SWAN; Wave energy; WaveWatch III; Renewable Energy Sustainability and the Environment; Energy Engineering and Power Technology; Energy (miscellaneous); Control and Optimization; Electrical and Electronic EngineeringRenewable energyControl and OptimizationWave propagation020209 energyWave energyEnergy fluxEnergy Engineering and Power Technology02 engineering and technology010501 environmental sciencesAtmospheric sciences01 natural scienceslcsh:TechnologyResource assessmentWaveWatch IIIWave modelHotspot (geology)0202 electrical engineering electronic engineering information engineeringHindcastElectrical and Electronic EngineeringEngineering (miscellaneous)Physics::Atmospheric and Oceanic Physics0105 earth and related environmental sciencesFlexible mesh modelSWANSustainability and the EnvironmentRenewable Energy Sustainability and the Environmentbusiness.industrylcsh:TEnergy assessmentRenewable energyWeather Research and Forecasting Modelwave energy; resource assessment; WaveWatch III; SWAN; flexible mesh model; renewable energybusinessGeologyEnergy (miscellaneous)Energies; Volume 12; Issue 3; Pages: 333
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“Vendor-Affected” WLAN experimental results: A Pandora’s Box?

2008

Experimental results are typically envisioned as the ultimate validation reference for any theoretical and/or simulation modelling assumptions. However, in the case of Wireless LANs, the situation is not nearly as straightforward as it might seem. In this paper, we discuss to what (large) extent measurement results may depend on proprietary undocumented algorithms implemented in the vendor-specific card/driver employed. Specifically, we focus on the experimental study of IEEE 802.11 b/g outdoor links based on the widely used Atheros/MADWiFi card/driver pair. We show that unexpected performance divergences do emerge in two classes of comparative experiments run on a same outdoor link: broadc…

Focus (computing)S-boxComputer sciencebusiness.industryVendorBroadcastingComputer securitycomputer.software_genrePreamblePHYWireless lanUnicastbusinesscomputerMultipath propagationComputer network2008 International Conference on Telecommunications
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Exploiting EDCA for Feedback Channels in Hybrid VLC/WiFi Architectures

2021

In this paper, we consider integrating VLC and WiFi technologies in a scenario in which Light-Emitting-Diodes (LEDs), acting as network access points (APs) for ultra-dense Internet of Things applications, are deployed into an indoor lighting infrastructure. In such a scenario, RF-links can be exploited for complementing VLC-links in dealing with mobility and bidirectional communications, which can be problematic due to the limited coverage areas and self-generated interference of VLC APs. In particular, we consider the possibility of supporting a technology-based duplexing scheme, in which downlink and uplink transmissions are performed by means, respectively, of VLC and WiFi interfaces int…

Frame aggregationExploitSIMPLE (military communications protocol)Computer sciencebusiness.industryNode (networking)Bandwidth (signal processing)Telecommunications linkComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKSNetwork access pointInterference (wave propagation)businessComputer network
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