Search results for "working"

showing 10 items of 2747 documents

Discovery of the proton emitting nucleus $^{159}$Re

2007

Fund. para Cienc. Tecnol., FCT, Minist. Cienc. Tecnol.;Fundacao Calouste Gulbenkian;Fundacao Luso-Americana

Si detectorsnuclear spinMeasured E pProtonHadron02 engineering and technologyrhenium[PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex]7. Clean energyNuclear physicsIsotopes of cadmium0202 electrical engineering electronic engineering information engineeringnuclei with mass number 150 to 189Enriched targetsradioactive decay periodsNuclideIsotopeChemistryNuclear structure020206 networking & telecommunicationsT1/213. Climate action020201 artificial intelligence & image processing23.50.+z; 27.70.+q; 21.10.Tg; 21.10.HwGas-filled recoil separatorNucleonRadioactive decayNuclear reactions 58Ni + 106Cd at 300 MeV beam energyproton emission decay
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Presence and Absence in Global Virtual Team Meetings : Physical, Virtual, and Social Dimensions

2016

Global virtual work is commonly organised around virtual meetings. As virtual team members often belong to multiple groups in their work, finding time for global team meetings may be challenging due to competing priorities and roles as well as different time zones and schedules. Furthermore, social presence does not automatically emerge based on physical or virtual presence. Therefore, the notions of being physically, virtually and socially present or absent in global team meetings are important, and affect the collaboration of global work groups. These different dimensions of presence and absence and their nature as fluctuating phenomena have not yet been scrutinised in virtual work litera…

Sickness absenceKnowledge managementbusiness.industryTeam meetingVirtual teamPublic relationsSocial dimensionvirtual meetingsVirtual presenceglobal workWork (electrical)absenceVirtual workläsnäolobusinessPsychologyWorking groupvirtual teams
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Dynamic Regret Analysis for Online Tracking of Time-varying Structural Equation Model Topologies

2020

Identifying dependencies among variables in a complex system is an important problem in network science. Structural equation models (SEM) have been used widely in many fields for topology inference, because they are tractable and incorporate exogenous influences in the model. Topology identification based on static SEM is useful in stationary environments; however, in many applications a time-varying underlying topology is sought. This paper presents an online algorithm to track sparse time-varying topologies in dynamic environments and most importantly, performs a detailed analysis on the performance guarantees. The tracking capability is characterized in terms of a bound on the dynamic re…

Signal Processing (eess.SP)0209 industrial biotechnologyComputer scienceComplex system020206 networking & telecommunicationsRegretTopology (electrical circuits)Network science02 engineering and technologyTracking (particle physics)Network topologyStructural equation modeling020901 industrial engineering & automationOptimization and Control (math.OC)FOS: Electrical engineering electronic engineering information engineeringFOS: Mathematics0202 electrical engineering electronic engineering information engineeringOnline algorithmElectrical Engineering and Systems Science - Signal ProcessingAlgorithmMathematics - Optimization and Control
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Location-Free Spectrum Cartography

2019

Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using spatially distributed sensor measurements. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radios to name a few. Since existing spectrum cartography techniques require accurate estimates of the sensor locations, their performance is drastically impaired by multipath affecting the positioning pilot signals, as occurs in indoor or dense urban scenarios. To overcome such a limitation, this paper introduces a novel paradigm for spectrum cartography, where estimation of spectral maps rel…

Signal Processing (eess.SP)Computer science020206 networking & telecommunications02 engineering and technologyVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420Open spectrumNetwork planning and designBase stationCognitive radioInterference (communication)Signal ProcessingFOS: Electrical engineering electronic engineering information engineering0202 electrical engineering electronic engineering information engineeringElectrical Engineering and Systems Science - Signal ProcessingElectrical and Electronic EngineeringVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550CartographyMultipath propagationPower controlIEEE Transactions on Signal Processing
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Non-cooperative Aerial Base Station Placement via Stochastic Optimization

2019

Autonomous unmanned aerial vehicles (UAVs) with on-board base station equipment can potentially provide connectivity in areas where the terrestrial infrastructure is overloaded, damaged, or absent. Use cases comprise emergency response, wildfire suppression, surveillance, and cellular communications in crowded events to name a few. A central problem to enable this technology is to place such aerial base stations (AirBSs) in locations that approximately optimize the relevant communication metrics. To alleviate the limitations of existing algorithms, which require intensive and reliable communications among AirBSs or between the AirBSs and a central controller, this paper leverages stochastic…

Signal Processing (eess.SP)Computer scienceQuality of serviceDistributed computing05 social sciences050801 communication & media studies020206 networking & telecommunications02 engineering and technologyNetwork utilityCellular communicationBase station0508 media and communicationsControl theoryOptimization and Control (math.OC)0202 electrical engineering electronic engineering information engineeringFOS: Electrical engineering electronic engineering information engineeringFOS: MathematicsStochastic optimizationUse caseElectrical Engineering and Systems Science - Signal ProcessingGradient descentMathematics - Optimization and Control
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Dynamic network identification from non-stationary vector autoregressive time series

2018

Learning the dynamics of complex systems features a large number of applications in data science. Graph-based modeling and inference underpins the most prominent family of approaches to learn complex dynamics due to their ability to capture the intrinsic sparsity of direct interactions in such systems. They also provide the user with interpretable graphs that unveil behavioral patterns and changes. To cope with the time-varying nature of interactions, this paper develops an estimation criterion and a solver to learn the parameters of a time-varying vector autoregressive model supported on a network of time series. The notion of local breakpoint is proposed to accommodate changes at individu…

Signal Processing (eess.SP)Dynamic network analysisTheoretical computer scienceComputer scienceStationary vectorComplex systemBehavioral patternInference020206 networking & telecommunications02 engineering and technologySolver01 natural sciences010104 statistics & probabilityComplex dynamicsAutoregressive model0202 electrical engineering electronic engineering information engineeringFOS: Electrical engineering electronic engineering information engineering0101 mathematicsElectrical Engineering and Systems Science - Signal Processing
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A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks

2019

International audience; In a resource-constrained Wireless Sensor Networks (WSNs), the optimization of the sampling and the transmission rates of each individual node is a crucial issue. A high volume of redundant data transmitted through the network will result in collisions, data loss, and energy dissipation. This paper proposes a novel data reduction scheme, that exploits the spatial-temporal correlation among sensor data in order to determine the optimal sampling strategy for the deployed sensor nodes. This strategy reduces the overall sampling/transmission rates while preserving the quality of the data. Moreover, a back-end reconstruction algorithm is deployed on the workstation (Sink)…

Signal Processing (eess.SP)FOS: Computer and information sciencesAdaptive samplingGeneral Computer ScienceComputer sciencespatial-temporal correlationReal-time computing02 engineering and technologyData loss[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]data reconstructionQA76Computer Science - Networking and Internet Architecture[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]FOS: Electrical engineering electronic engineering information engineering0202 electrical engineering electronic engineering information engineeringGeneral Materials ScienceElectrical Engineering and Systems Science - Signal ProcessingNetworking and Internet Architecture (cs.NI)General EngineeringSampling (statistics)020206 networking & telecommunicationsReconstruction algorithmDissipation[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationWireless sensor networks[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]data reduction020201 artificial intelligence & image processing[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET]lcsh:Electrical engineering. Electronics. Nuclear engineering[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]lcsh:TK1-9971Wireless sensor networkData reduction
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Inference of Spatio-Temporal Functions over Graphs via Multi-Kernel Kriged Kalman Filtering

2018

Inference of space-time varying signals on graphs emerges naturally in a plethora of network science related applications. A frequently encountered challenge pertains to reconstructing such dynamic processes, given their values over a subset of vertices and time instants. The present paper develops a graph-aware kernel-based kriged Kalman filter that accounts for the spatio-temporal variations, and offers efficient online reconstruction, even for dynamically evolving network topologies. The kernel-based learning framework bypasses the need for statistical information by capitalizing on the smoothness that graph signals exhibit with respect to the underlying graph. To address the challenge o…

Signal Processing (eess.SP)FOS: Computer and information sciencesComputer Science - Machine LearningComputational complexity theoryComputer scienceInferenceMachine Learning (stat.ML)Network scienceMultikernel02 engineering and technologyNetwork topologyLinear spanMachine Learning (cs.LG)Kernel (linear algebra)Matrix (mathematics)Statistics - Machine LearningFOS: Electrical engineering electronic engineering information engineering0202 electrical engineering electronic engineering information engineeringElectrical and Electronic EngineeringElectrical Engineering and Systems Science - Signal Processing020206 networking & telecommunicationsKalman filterSignal Processing020201 artificial intelligence & image processingLaplace operatorAlgorithm
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Channel Gain Cartography via Mixture of Experts

2020

In order to estimate the channel gain (CG) between the locations of an arbitrary transceiver pair across a geographic area of interest, CG maps can be constructed from spatially distributed sensor measurements. Most approaches to build such spectrum maps are location-based, meaning that the input variable to the estimating function is a pair of spatial locations. The performance of such maps depends critically on the ability of the sensors to determine their positions, which may be drastically impaired if the positioning pilot signals are affected by multi-path channels. An alternative location-free approach was recently proposed for spectrum power maps, where the input variable to the maps…

Signal Processing (eess.SP)FOS: Computer and information sciencesComputer Science - Machine LearningJ.2Computer scienceFeature extractionComputingMilieux_LEGALASPECTSOFCOMPUTING02 engineering and technologycomputer.software_genreMachine Learning (cs.LG)Channel gain0203 mechanical engineeringFOS: Electrical engineering electronic engineering information engineering0202 electrical engineering electronic engineering information engineeringElectrical Engineering and Systems Science - Signal ProcessingVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550Location awareness020206 networking & telecommunications020302 automobile design & engineeringFunction (mathematics)Power (physics)Mixture of expertsVariable (computer science)TransceivercomputerAlgorithmGLOBECOM 2020 - 2020 IEEE Global Communications Conference
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Propagation Channels for mmWave Vehicular Communications : State-of-the-art and Future Research Directions

2018

Vehicular communications essentially support automotive applications for safety and infotainment. For this reason, industry leaders envision an enhanced role for vehicular communications in the fifth generation of mobile communications technology. Over the years, the number of vehicle- mounted sensors has increased steadily, which potentially leads to more volume of critical data communications in a short time. Also, emerging applications such as remote/autonomous driving and infotainment such as high-definition movie streaming require data-rates on the order of multiple Gb/s. Such high data rates require a large system bandwidth, but very limited bandwidth is available in the sub-6 GHz cel…

Signal Processing (eess.SP)FOS: Computer and information sciencesComputer scienceComputer Science - Information Theoryfrequency bandsAutomotive industry02 engineering and technologyFOS: Electrical engineering electronic engineering information engineering0202 electrical engineering electronic engineering information engineeringajoneuvotElectrical and Electronic EngineeringElectrical Engineering and Systems Science - Signal ProcessingHigh data rateta213business.industryInformation Theory (cs.IT)Bandwidth (signal processing)020206 networking & telecommunicationsMicrowave transmissionwireless communicationComputer Science ApplicationsvehiclesExtremely high frequencylangaton viestintätaajuusalueetCellular frequenciesMobile telephonybusinessTelecommunicationsCommunication channelIEEE Wireless Communications
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