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Autor(en): 
  • Xuemin Shen
  • Jie Gao
  • Conghao Zhou
  • Data-centric AI: Integrated Computation and Communications for Extended Reality 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Vorankündigung
    Veröffentlichung:  ANGEKÜNDIGT (August 2026)  
    Genre:  Naturwissensch., Medizin, Technik 
     
    Augmented Reality / Communications Engineering, Networks / Computer Communication Networks / Computing Offloading / Data-centric Artificial Intelligence / Device Pose Tracking / Digital Twin / Drahtlostechnologie
    ISBN:  9783032274861 
    EAN-Code: 
    9783032274861 
    Verlag:  Springer International Publishing 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Seiten:  188 
    Illustration:  X, 188 p. 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Keine Bewertung vor Veröffentlichung möglich.
    Inhalt:
    This book explores how integrated computation and communication can be achieved to support scalable and user-centric Extended Reality (XR) service provisions. Leveraging the digital twin technique as a key enabler, this book aims to introduce a novel data-centric AI framework to support XR from the perspective of communication and networking. Specifically, the authors present architectural designs and algorithmic solutions of data-centric AI that support the cross-layer and intelligent collection, processing, and analysis of XR user data, thereby enabling user-centric service provision. The book presents a digital twin-based framework that encompasses conceptual architecture, workflow, and operation functions to support diverse XR modules involving both communication and computation. In addition, the book explores the role of data-centric AI in XR resource provisioning, with a particular focus on how data-centric AI enhances both the quality and quantity of data available for AI model training and decision-making. Various learning paradigms, including supervised learning and reinforcement learning, are examined to demonstrate how AI enhances the efficiency and adaptability of resource management

      



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