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Proceedings of the IEEE

Proceedings of the IEEE

Archives Papers: 442
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Brain–Computer Interface—A Brain-in-the-Loop Communication System
Xiaorong GaoYijun WangXiaogang ChenBingchuan LiuShangkai Gao
Keywords:Communication systemsDecodingArtificial intelligenceCommunications technologyWireless communicationNoise measurementEncodingReviewsElectroencephalographyMotorsBrain-computer interfacesCommunication SystemsScience And TechnologyWirelessComputing DevicesExternal DevicesBrain-computer Interface SystemBrain-computer Interface ApplicationsBrain-computer Interface TechnologyElectrodeBrain ActivityElectrical StimulationInternet Of ThingsPhysical SystemClosed-loop SystemMultiple UsersTranscranial Magnetic StimulationBrain SignalsControl DevicesVisual Evoked PotentialsNeurofeedback TrainingModern Communication TechnologiesModern CommunicationSteady-state Visual Evoked PotentialBidirectional SystemAcquisition DeviceBrain-computer Interfaces PerformanceLow-cost EquipmentChannel CapacityWireless TechnologiesArtificial intelligence (AI)bidirectional communicationbrain–computer interface (BCI)brain-in-the-loopcoadaptive communicationcommunication systemdecodingencodinghuman augmentationhuman intelligence (HI)metaverseneural rehabilitationneuromodulationsixth generation (6G)
Abstracts:The brain–computer interface (BCI) establishes a direct communication system between the brain and a computer or other external devices. Since the inception of BCI technology half a century ago, it has advanced rapidly and developed into an active area of frontier research in modern applied science and technology. This article provides a comprehensive survey on BCI with respect to a brain-in-the-loop communication system. In the present work, we first introduce the underlying architecture of the BCI system from the theoretical and methodological perspectives of communication systems. The key technologies are then detailed, including the construction of BCI system, brain-to-computer (B2C) communication, computer-to-brain (C2B) communication, and multiuser BCI systems. Additionally, this article discusses the various applications of BCI and the challenges they face. Finally, this article discusses BCI’s future development, with an emphasis on the convergence of human intelligence (HI) and artificial intelligence (AI), and the interaction of BCI with wireless communication and the metaverse.
Vehicle-to-Everything Cooperative Perception for Autonomous Driving
Tao HuangJianan LiuXi ZhouDinh C. NguyenMostafa Rahimi AzghadiYuxuan XiaQing-Long HanSumei Sun
Keywords:Vehicle-to-everythingCollaborationAutonomous vehiclesSurveysSensorsFeature extractionAccuracyArtificial intelligenceVehicle dynamicsSafetyAutonomous drivingAutonomous VehiclesCooperative PerceptionDetection AccuracyFeature FusionSituational AwarenessCommunication ConstraintsFeature MapsObject DetectionPoint CloudBounding BoxLight Detection And RangingReal-world ScenariosAttention ModulePerceptual TaskData FusionMultiple AgentsDomain AdaptationCooperative ControlGraph Neural NetworksMotion PredictionV2X CommunicationDedicated Short Range CommunicationPose ErrorDomain Adaptation TechniquesRelative PoseCorner CasesTraffic ScenariosSimulated DatasetsComplex ScenariosRaw DataArtificial intelligence (AI)autonomous drivingconnected and automated vehiclescooperative perception (CP)data fusiondeep learningmultiobject trackingobject detectionsegmentationvehicle-to-everything (V2X) communication
Abstracts:Achieving fully autonomous driving with enhanced safety and efficiency relies on vehicle-to-everything (V2X) cooperative perception (CP), which enables vehicles to share perception data, thereby enhancing situational awareness and overcoming the limitations of the sensing ability of individual vehicles. V2X CP plays a crucial role in extending the perception range, increasing detection accuracy, and supporting more robust decision-making and control in complex environments. This article provides a comprehensive survey of recent developments in V2X CP, introducing mathematical models that characterize the perception process under different collaboration strategies. Key techniques for enabling reliable perception sharing, such as agent selection, data alignment, and feature fusion, are examined in detail. In addition, major challenges are discussed, including differences in agents and models, uncertainty in perception outputs, and the impact of communication constraints such as transmission delay and data loss. This article concludes by outlining promising research directions, including privacy-preserving artificial intelligence methods, collaborative intelligence, and integrated sensing frameworks to support future advancements in V2X CP.
Drone-as-a-Service: Research Challenges and Directions
Ali HamdiBalsam AlkouzBabar ShahzaadAthman BouguettayaAzadeh Ghari NeiatFlora SalimDu Yong Kim
Keywords:DronesSurveysCostsSensorsInternet of ThingsComputer scienceComputational modelingService robotsProduct deliveryCloud computingResearch ChallengesCloud ComputingApplicability DomainUse Of DronesService ProvidersDeep LearningOperational CostsNormalized Difference Vegetation IndexDelivery ModelsPath PlanningInternet Of Things DevicesConsumption Of ServicesDelivery RouteLevel Of AutonomyUser ControlReal-time VideoDrop OffEdge ServerFlight RangeCloud LayerEristalisSafety AssuranceGoogle TrendsPick-up TimeCommunication CoverageUrban PlanningAutonomous DroneBattery LevelApplicationdrone-as-a-service (DaaS)functionsservice selection and composition
Abstracts:We conduct a survey on drones used as a service, denoted as drone-as-a-service (DaaS). We develop a novel taxonomy based on DaaS functions, research tasks, and application domains. We provide a discussion on drones and their associated capabilities based on their type of use. We propose a three-layered DaaS system architecture that vertically integrates cloud computing, drones, and services as a reference framework to compare existing drone service implementations. Additionally, we propose a representative uncertainty-aware DaaS model for delivery scenarios, illustrating how service definitions can incorporate both functional and nonfunctional attributes under dynamic environmental conditions. Finally, we identify and discuss future research directions and open problems related to the use of drones for service delivery.
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