Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 60,87
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
EUR 58,24
Cantidad disponible: 2 disponibles
Añadir al carritoPAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 61,86
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New.
Librería: California Books, Miami, FL, Estados Unidos de America
EUR 64,18
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New.
Librería: Majestic Books, Hounslow, Reino Unido
EUR 61,33
Cantidad disponible: 3 disponibles
Añadir al carritoCondición: New. pages cm.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 54,43
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New.
Idioma: Inglés
Publicado por Taylor and Francis Ltd, GB, 2024
ISBN 10: 1032041382 ISBN 13: 9781032041384
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
EUR 72,84
Cantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: New. Deep Learning (DL) is an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart and efficient resource allocation and intelligent distributed resource allocation methods. This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: (I) DL for vehicle safety and security: This part covers the use of DL algorithms for vehicle safety or security.(II) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. This part covers how Intelligent vehicle networks require a flexible selection of the best path across all vehicles, adaptive sending rate control based on bandwidth availability and timely data downloads from a roadside base-station. (III) DL for vehicle control: The myriad operations that require intelligent control for each individual vehicle are discussed in this part. This also includes emission control, which is based on the road traffic situation, the charging pile load is predicted through DL andvehicle speed adjustments based on the camera-captured image analysis. (IV) DL for information management: This part covers some intelligent information collection and understanding. We can use DL for energy-saving vehicle trajectory control based on the road traffic situation and given destination information; we can also natural language processing based on DL algorithm for automatic internet of things (IoT) search during driving. (V) Other applications. This part introduces the use of DL models for other vehicle controls. Autonomous vehicles are becoming more and more popular in society. The DL and its variants will play greater roles in cognitive vehicle communications and control. Other machine learning models such as deep reinforcement learning will also facilitate intelligent vehicle behavior understanding and adjustment. This book will become a valuable reference to your understanding of this critical field.
Librería: Chiron Media, Wallingford, Reino Unido
EUR 58,54
Cantidad disponible: 2 disponibles
Añadir al carritopaperback. Condición: New.
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 72,62
Cantidad disponible: 3 disponibles
Añadir al carritoCondición: New. pages cm First edition Includes bibliographical references and index.
Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 62,18
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New. In.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 62,10
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
EUR 61,24
Cantidad disponible: 1 disponibles
Añadir al carritoPaperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.
Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 70,01
Cantidad disponible: 2 disponibles
Añadir al carritoCondición: New.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 72,37
Cantidad disponible: 3 disponibles
Añadir al carritoCondición: New. pages cm.
Librería: Kennys Bookstore, Olney, MD, Estados Unidos de America
EUR 85,37
Cantidad disponible: 2 disponibles
Añadir al carritoCondición: New.
EUR 54,45
Cantidad disponible: 2 disponibles
Añadir al carritoCondición: NEW.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 90,88
Cantidad disponible: 2 disponibles
Añadir al carritoPaperback. Condición: Brand New. 356 pages. 10.00x7.00x10.00 inches. In Stock.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 142,07
Cantidad disponible: 2 disponibles
Añadir al carritoCondición: New.
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 140,91
Cantidad disponible: 4 disponibles
Añadir al carritoCondición: New. 1st edition NO-PA16APR2015-KAP.
Idioma: Inglés
Publicado por Taylor and Francis Ltd, GB, 2024
ISBN 10: 1032041382 ISBN 13: 9781032041384
Librería: Rarewaves.com UK, London, Reino Unido
EUR 67,44
Cantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: New. Deep Learning (DL) is an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart and efficient resource allocation and intelligent distributed resource allocation methods. This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: (I) DL for vehicle safety and security: This part covers the use of DL algorithms for vehicle safety or security.(II) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. This part covers how Intelligent vehicle networks require a flexible selection of the best path across all vehicles, adaptive sending rate control based on bandwidth availability and timely data downloads from a roadside base-station. (III) DL for vehicle control: The myriad operations that require intelligent control for each individual vehicle are discussed in this part. This also includes emission control, which is based on the road traffic situation, the charging pile load is predicted through DL andvehicle speed adjustments based on the camera-captured image analysis. (IV) DL for information management: This part covers some intelligent information collection and understanding. We can use DL for energy-saving vehicle trajectory control based on the road traffic situation and given destination information; we can also natural language processing based on DL algorithm for automatic internet of things (IoT) search during driving. (V) Other applications. This part introduces the use of DL models for other vehicle controls. Autonomous vehicles are becoming more and more popular in society. The DL and its variants will play greater roles in cognitive vehicle communications and control. Other machine learning models such as deep reinforcement learning will also facilitate intelligent vehicle behavior understanding and adjustment. This book will become a valuable reference to your understanding of this critical field.
EUR 75,30
Cantidad disponible: 1 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. Deep Learning and Its Applications for Vehicle Networks | Fei Hu (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2024 | CRC Press | EAN 9781032041384 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 146,90
Cantidad disponible: 2 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
EUR 147,85
Cantidad disponible: 1 disponibles
Añadir al carritoHardback. Condición: New. New copy - Usually dispatched within 4 working days.
Librería: California Books, Miami, FL, Estados Unidos de America
EUR 164,79
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 147,13
Cantidad disponible: 2 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 147,84
Cantidad disponible: 2 disponibles
Añadir al carritoCondición: New.
Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 164,96
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New. In.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 219,13
Cantidad disponible: 2 disponibles
Añadir al carritoHardcover. Condición: Brand New. 360 pages. 10.00x7.00x1.00 inches. In Stock.
Idioma: Inglés
Publicado por Taylor & Francis Ltd, London, 2024
ISBN 10: 1032041382 ISBN 13: 9781032041384
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 64,13
Cantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: new. Paperback. Deep Learning (DL) is an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart and efficient resource allocation and intelligent distributed resource allocation methods. This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: (I) DL for vehicle safety and security: This part covers the use of DL algorithms for vehicle safety or security.(II) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. This part covers how Intelligent vehicle networks require a flexible selection of the best path across all vehicles, adaptive sending rate control based on bandwidth availability and timely data downloads from a roadside base-station. (III) DL for vehicle control: The myriad operations that require intelligent control for each individual vehicle are discussed in this part. This also includes emission control, which is based on the road traffic situation, the charging pile load is predicted through DL andvehicle speed adjustments based on the camera-captured image analysis. (IV) DL for information management: This part covers some intelligent information collection and understanding. We can use DL for energy-saving vehicle trajectory control based on the road traffic situation and given destination information; we can also natural language processing based on DL algorithm for automatic internet of things (IoT) search during driving. (V) Other applications. This part introduces the use of DL models for other vehicle controls. Autonomous vehicles are becoming more and more popular in society. The DL and its variants will play greater roles in cognitive vehicle communications and control. Other machine learning models such as deep reinforcement learning will also facilitate intelligent vehicle behavior understanding and adjustment. This book will become a valuable reference to your understanding of this critical field. This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: 1. DL for vehicle safety and security, 2. DL for effective vehicle communications, 3. DL for vehicle control, 4. DL for information management, 5. Other applications. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 71,74
Cantidad disponible: 2 disponibles
Añadir al carritoPaperback. Condición: Brand New. 356 pages. 10.00x7.00x10.00 inches. In Stock. This item is printed on demand.