Search results for "Electronics Engineering"

showing 3 items of 3 documents

Depth Enhancement by Fusion for Passive and Active Sensing

2012

This paper presents a general refinement procedure that enhances any given depth map obtained by passive or active sensing. Given a depth map, either estimated by triangulation methods or directly provided by the sensing system, and its corresponding 2-D image, we correct the depth values by separately treating regions with undesired effects such as empty holes, texture copying or edge blurring due to homogeneous regions, occlusions, and shadowing. In this work, we use recent depth enhancement filters intended for Time-of-Flight cameras, and adapt them to alternative depth sensing modalities, both active using an RGB-D camera and passive using a dense stereo camera. To that end, we propose …

Homogeneous regionsComputer scienceActive SensingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONSensing systemsTime-of-flight camerasPassive and active sensing: Electrical & electronics engineering [C06] [Engineering computing & technology]Depth mapTriangulation methodComputer vision: Computer science [C05] [Engineering computing & technology]: Ingénierie électrique & électronique [C06] [Ingénierie informatique & technologie]Signal processingStereo camerasPassive sensingbusiness.industrySensorsPassive filtersTriangulation (computer vision)Depth enhancementData fusionSensor fusionCameras: Sciences informatiques [C05] [Ingénierie informatique & technologie]Depth sensingSpecial treatmentsDepth valueRGB color modelComputer visionArtificial intelligenceEnhanced Data Rates for GSM EvolutionDepth MapbusinessDepth measurementsStereo cameraStereo cameras
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Can't simply roll it out: Evaluating a real-world virtual reality intervention to reduce driving under the influence.

2020

Driving under the influence (DUI) increases the risk of crashes. Emerging technologies, such as virtual reality (VR), represent potentially powerful and attractive tools for the prevention of risky behaviours, such as DUI. Therefore, they are embraced in prevention efforts with VR interventions primed to grow in popularity in near future. However, little is known about the actual effectiveness of such DUI-targeting VR interventions. To help fill the knowledge gap, this study explored the effects of one VR intervention as delivered in the real world. Using pre and post test design, including an intervention group (n = 98) and a control group (n = 39), the intervention evaluation examined you…

MaleMan-Computer InterfaceEpidemiologyApplied psychologyPsychological interventionTransportationIntentionSurveysChi Square TestsComputer ArchitectureElectronics EngineeringMathematical and Statistical TechniquesMedicine and Health SciencesPublic and Occupational HealthComputer EngineeringDriving Under the InfluenceMultidisciplinaryOrganic CompoundscelebritiesTraumatic Injury Risk FactorsStatisticsQVirtual RealityRSoftware EngineeringTransportation Infrastructurecelebrities.reason_for_arrestChemistryResearch DesignPhysical SciencesEngineering and TechnologyMedicineFemalePsychologyQuasi-experimentResearch ArticleAdultAutomobile DrivingComputer and Information SciencesAdolescentEmerging technologiesScienceVirtual realityResearch and Analysis MethodsCivil EngineeringComputer SoftwareIntervention (counseling)HumansStatistical MethodsStatistical Hypothesis TestingDriving under the influenceSurvey ResearchTest designOrganic ChemistryChemical CompoundsPopularityRoadsHigh Fidelity Simulation TrainingMedical Risk FactorsAlcoholsMathematicsUser InterfacesPLoS ONE
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Adapting to Dynamic LEO-B5G Systems : Meta-Critic Learning Based Efficient Resource Scheduling

2022

Low earth orbit (LEO) satellite-assisted communications have been considered as one of key elements in beyond 5G systems to provide wide coverage and cost-efficient data services. Such dynamic space-terrestrial topologies impose exponential increase in the degrees of freedom in network management. In this paper, we address two practical issues for an over-loaded LEO-terrestrial system. The first challenge is how to efficiently schedule resources to serve the massive number of connected users, such that more data and users can be delivered/served. The second challenge is how to make the algorithmic solution more resilient in adapting to dynamic wireless environments.To address them, we first…

Signal Processing (eess.SP)FOS: Computer and information sciencesdynamic environmentComputer Science - Machine Learningreinforcement learningmeta-critic learningComputer Science - Artificial Intelligence5G-tekniikkaresursointiMachine Learning (cs.LG): Electrical & electronics engineering [C06] [Engineering computing & technology]LEO satelliteslangaton tiedonsiirtoresources allocationalgoritmitFOS: Electrical engineering electronic engineering information engineeringElectrical and Electronic EngineeringElectrical Engineering and Systems Science - Signal Processing: Ingénierie électrique & électronique [C06] [Ingénierie informatique & technologie]Applied MathematicstietoliikennesatelliititComputer Science ApplicationsArtificial Intelligence (cs.AI)koneoppiminenresource schedulinglangattomat verkot
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