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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This SpringerBrief discusses the applications of spare representation in wireless communications, with a particular focus on the most recent developed compressive sensing (CS) enabled approaches. With the help of sparsity property, sub-Nyquist sampling can be achieved in wideband cognitive radio networks by adopting compressive sensing, which is illustrated in this brief, and it starts with a comprehensive overview of compressive sensing principles. Subsequently, the authors present a complete framework for data-driven compressive spectrum sensing in cognitive radio networks, which guarantees robustness, low-complexity, and security.Particularly, robust compressive spectrum sensing, low-complexity compressive spectrum sensing, and secure compressive sensing based malicious user detection are proposed to address the various issues in wideband cognitive radio networks. Correspondingly, the real-world signals and data collected by experiments carried out during TV white space pilot trial enables data-driven compressive spectrum sensing. The collected data are analysed and used to verify our designs and provide significant insights on the potential of applying compressive sensing to wideband spectrum sensing.This SpringerBrief provides readers a clear picture on how to exploit the compressive sensing to process wireless signals in wideband cognitive radio networks. Students, professors, researchers, scientists, practitioners, and engineers working in the fields of compressive sensing in wireless communications will find this SpringerBrief very useful as a short reference or study guide book. Industry managers, and government research agency employees also working in the fields of compressive sensing in wireless communications will find this SpringerBrief useful as well.…

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Hardback. Zustand: New. Comprehensive overview of the principles, theories, and techniques behind deep learning enabled semantic communications Deep Learning Enabled Semantic Communications explores the synergy between deep learning and semantic communication, particularly in the context of advancing 6G networks. It provides a focused introduction to the subject, systematically covering deep learning enabled semantic communication systems and task-oriented semantic transmission paradigms in wireless communication. The book reviews various aspects of semantic communications, including information theory, multimodal technologies, semantic noise, and semantic sensing. It explores cutting-edge semantic communication architectures, highlighting their advantages over traditional approaches and their potential to drive the future of intelligent information industry. The book also details applications of deep learning-based semantic communication systems across various sources, including text, speech, images, and videos, comprehensively addressing system design, performance optimization, and measurement metrics. The book is divided into eight main parts, which cover foundational knowledge, system design, multimodal and multitask-oriented semantic communication systems, joint semantic sensing and sampling, semantic noise suppression, and generative AI enabled systems. Written by a diverse group of experts in academia and research institutions, Deep Learning Enabled Semantic Communications includes information on: Fundamental knowledge about deep learning and semantic communications, including the history, neural networks, and semantic information theory Compression of multimodal inputs, extraction of global semantic information, and the design of neural networks to boost the capability of handling lengthy speech Incorporation of different sources to extract semantic features and serve diverse intelligent tasks at the receiver Introduction of semantic impairments in communications to uncover how to design robust systems Joint design of data sampling, compression, and coding schemes under the guidance of semantic information Framework of generative semantic communications to detail the principles of incorporating generative models into semantic communications Deep Learning Enabled Semantic Communications is an essential learning resource and reference for graduate and undergraduate students pursuing degrees in wireless communications, signal processing, or deep learning as well as engineers in the telecommunications and IT industries focusing on wireless communication techniques.…

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book discusses the evolving designs and applications of multimedia content delivery and focuses on computing-based methods. It offers readers an in-depth understanding of how computational resources at both the source and the destination of the networking continuum can be exploited. This enhances the overall performance of multimedia data networking.This book also presents novel designs and applications focusing on information delivery based on computing. It starts with an overview of the multimedia computational communications as well as spanning topics. The topics range in experience evaluation using electroencephalography, semantic knowledge bases with the next generation of multiple access, end-to-end semantic communication framework and cloud-edge-end intelligent coordination computing. The authors believe this book offers readers a clear picture of the current state and the next steps in multimedia computational communication networks.Graduate students majoring in the areas of communication networks, computer science and engineering and electrical engineering will find this book useful as a secondary text or reference book.Professionals and researchers working in computational transmission solutions for multimedia communication networks will find this book to be a valuable resource as well.…

Hardback. Zustand: New. Comprehensive overview of the principles, theories, and techniques behind deep learning enabled semantic communications Deep Learning Enabled Semantic Communications explores the synergy between deep learning and semantic communication, particularly in the context of advancing 6G networks. It provides a focused introduction to the subject, systematically covering deep learning enabled semantic communication systems and task-oriented semantic transmission paradigms in wireless communication. The book reviews various aspects of semantic communications, including information theory, multimodal technologies, semantic noise, and semantic sensing. It explores cutting-edge semantic communication architectures, highlighting their advantages over traditional approaches and their potential to drive the future of intelligent information industry. The book also details applications of deep learning-based semantic communication systems across various sources, including text, speech, images, and videos, comprehensively addressing system design, performance optimization, and measurement metrics. The book is divided into eight main parts, which cover foundational knowledge, system design, multimodal and multitask-oriented semantic communication systems, joint semantic sensing and sampling, semantic noise suppression, and generative AI enabled systems. Written by a diverse group of experts in academia and research institutions, Deep Learning Enabled Semantic Communications includes information on: Fundamental knowledge about deep learning and semantic communications, including the history, neural networks, and semantic information theory Compression of multimodal inputs, extraction of global semantic information, and the design of neural networks to boost the capability of handling lengthy speech Incorporation of different sources to extract semantic features and serve diverse intelligent tasks at the receiver Introduction of semantic impairments in communications to uncover how to design robust systems Joint design of data sampling, compression, and coding schemes under the guidance of semantic information Framework of generative semantic communications to detail the principles of incorporating generative models into semantic communications Deep Learning Enabled Semantic Communications is an essential learning resource and reference for graduate and undergraduate students pursuing degrees in wireless communications, signal processing, or deep learning as well as engineers in the telecommunications and IT industries focusing on wireless communication techniques.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book discusses the evolving designs and applications of multimedia content delivery and focuses on computing-based methods. It offers readers an in-depth understanding of how computational resources at both the source and the destination of the networking continuum can be exploited. This enhances the overall performance of multimedia data networking.This book also presents novel designs and applications focusing on information delivery based on computing. It starts with an overview of the multimedia computational communications as well as spanning topics. The topics range in experience evaluation using electroencephalography, semantic knowledge bases with the next generation of multiple access, end-to-end semantic communication framework and cloud-edge-end intelligent coordination computing. The authors believe this book offers readers a clear picture of the current state and the next steps in multimedia computational communication networks.Graduate students majoring in the areas of communication networks, computer science and engineering and electrical engineering will find this book useful as a secondary text or reference book.Professionals and researchers working in computational transmission solutions for multimedia communication networks will find this book to be a valuable resource as well.…