Author: Gansen Zhao
Publisher: Springer Nature
ISBN: 9819794129
Category :
Languages : en
Pages : 528
Book Description
Blockchain and Web3.0 Technology Innovation and Application
Author: Gansen Zhao
Publisher: Springer Nature
ISBN: 9819794129
Category :
Languages : en
Pages : 528
Book Description
Publisher: Springer Nature
ISBN: 9819794129
Category :
Languages : en
Pages : 528
Book Description
Advances in Computing and Data Sciences
Author: Mayank Singh
Publisher: Springer Nature
ISBN: 3031709063
Category :
Languages : en
Pages : 361
Book Description
Publisher: Springer Nature
ISBN: 3031709063
Category :
Languages : en
Pages : 361
Book Description
Digital Product Management in the Era of Data Economy, Artificial Intelligence, and Ecosystems
Author: Dimitri Petrik
Publisher: Springer Nature
ISBN: 3031715152
Category :
Languages : en
Pages : 109
Book Description
Publisher: Springer Nature
ISBN: 3031715152
Category :
Languages : en
Pages : 109
Book Description
Vehicular Networking
Author: Christoph Sommer
Publisher: Cambridge University Press
ISBN: 1107046718
Category : Computers
Languages : en
Pages : 373
Book Description
Learn about the basics and the future of vehicular networking research with this essential guide to in- and inter-vehicle communication.
Publisher: Cambridge University Press
ISBN: 1107046718
Category : Computers
Languages : en
Pages : 373
Book Description
Learn about the basics and the future of vehicular networking research with this essential guide to in- and inter-vehicle communication.
Cognitive Communications
Author: David Grace
Publisher: John Wiley & Sons
ISBN: 1118360338
Category : Technology & Engineering
Languages : en
Pages : 500
Book Description
This book discusses in-depth the concept of distributed artificial intelligence (DAI) and its application to cognitive communications In this book, the authors present an overview of cognitive communications, encompassing both cognitive radio and cognitive networks, and also other application areas such as cognitive acoustics. The book also explains the specific rationale for the integration of different forms of distributed artificial intelligence into cognitive communications, something which is often neglected in many forms of technical contributions available today. Furthermore, the chapters are divided into four disciplines: wireless communications, distributed artificial intelligence, regulatory policy and economics and implementation. The book contains contributions from leading experts (academia and industry) in the field. Key Features: Covers the broader field of cognitive communications as a whole, addressing application to communication systems in general (e.g. cognitive acoustics and Distributed Artificial Intelligence (DAI) Illustrates how different DAI based techniques can be used to self-organise the radio spectrum Explores the regulatory, policy and economic issues of cognitive communications in the context of secondary spectrum access Discusses application and implementation of cognitive communications techniques in different application areas (e.g. Cognitive Femtocell Networks (CFN) Written by experts in the field from both academia and industry Cognitive Communications will be an invaluable guide for research community (PhD students, researchers) in the areas of wireless communications, and development engineers involved in the design and development of mobile, portable and fixed wireless systems., wireless network design engineer. Undergraduate and postgraduate students on elective courses in electronic engineering or computer science, and the research and engineering community will also find this book of interest.
Publisher: John Wiley & Sons
ISBN: 1118360338
Category : Technology & Engineering
Languages : en
Pages : 500
Book Description
This book discusses in-depth the concept of distributed artificial intelligence (DAI) and its application to cognitive communications In this book, the authors present an overview of cognitive communications, encompassing both cognitive radio and cognitive networks, and also other application areas such as cognitive acoustics. The book also explains the specific rationale for the integration of different forms of distributed artificial intelligence into cognitive communications, something which is often neglected in many forms of technical contributions available today. Furthermore, the chapters are divided into four disciplines: wireless communications, distributed artificial intelligence, regulatory policy and economics and implementation. The book contains contributions from leading experts (academia and industry) in the field. Key Features: Covers the broader field of cognitive communications as a whole, addressing application to communication systems in general (e.g. cognitive acoustics and Distributed Artificial Intelligence (DAI) Illustrates how different DAI based techniques can be used to self-organise the radio spectrum Explores the regulatory, policy and economic issues of cognitive communications in the context of secondary spectrum access Discusses application and implementation of cognitive communications techniques in different application areas (e.g. Cognitive Femtocell Networks (CFN) Written by experts in the field from both academia and industry Cognitive Communications will be an invaluable guide for research community (PhD students, researchers) in the areas of wireless communications, and development engineers involved in the design and development of mobile, portable and fixed wireless systems., wireless network design engineer. Undergraduate and postgraduate students on elective courses in electronic engineering or computer science, and the research and engineering community will also find this book of interest.
Flexible and Cognitive Radio Access Technologies for 5G and Beyond
Author: Hüseyin Arslan
Publisher: Institution of Engineering and Technology
ISBN: 1839530790
Category : Technology & Engineering
Languages : en
Pages : 680
Book Description
Standards for 5G and beyond will require communication systems with a much more flexible and cognitive design to support a wide variety of services including smart vehicles, smart cities, smart homes, IoTs, and remote health. Although future 6G technologies may look like an extension of their 5G counterparts, new user requirements, completely new applications and use-cases, and networking trends will bring more challenging communication engineering problems. New communication paradigms in different layers will be required, in particular in the physical layer of future wireless communication systems.
Publisher: Institution of Engineering and Technology
ISBN: 1839530790
Category : Technology & Engineering
Languages : en
Pages : 680
Book Description
Standards for 5G and beyond will require communication systems with a much more flexible and cognitive design to support a wide variety of services including smart vehicles, smart cities, smart homes, IoTs, and remote health. Although future 6G technologies may look like an extension of their 5G counterparts, new user requirements, completely new applications and use-cases, and networking trends will bring more challenging communication engineering problems. New communication paradigms in different layers will be required, in particular in the physical layer of future wireless communication systems.
Machine Learning and Wireless Communications
Author: Yonina C. Eldar
Publisher: Cambridge University Press
ISBN: 1108967736
Category : Technology & Engineering
Languages : en
Pages : 560
Book Description
How can machine learning help the design of future communication networks – and how can future networks meet the demands of emerging machine learning applications? Discover the interactions between two of the most transformative and impactful technologies of our age in this comprehensive book. First, learn how modern machine learning techniques, such as deep neural networks, can transform how we design and optimize future communication networks. Accessible introductions to concepts and tools are accompanied by numerous real-world examples, showing you how these techniques can be used to tackle longstanding problems. Next, explore the design of wireless networks as platforms for machine learning applications – an overview of modern machine learning techniques and communication protocols will help you to understand the challenges, while new methods and design approaches will be presented to handle wireless channel impairments such as noise and interference, to meet the demands of emerging machine learning applications at the wireless edge.
Publisher: Cambridge University Press
ISBN: 1108967736
Category : Technology & Engineering
Languages : en
Pages : 560
Book Description
How can machine learning help the design of future communication networks – and how can future networks meet the demands of emerging machine learning applications? Discover the interactions between two of the most transformative and impactful technologies of our age in this comprehensive book. First, learn how modern machine learning techniques, such as deep neural networks, can transform how we design and optimize future communication networks. Accessible introductions to concepts and tools are accompanied by numerous real-world examples, showing you how these techniques can be used to tackle longstanding problems. Next, explore the design of wireless networks as platforms for machine learning applications – an overview of modern machine learning techniques and communication protocols will help you to understand the challenges, while new methods and design approaches will be presented to handle wireless channel impairments such as noise and interference, to meet the demands of emerging machine learning applications at the wireless edge.
Gaussian Processes for Machine Learning
Author: Carl Edward Rasmussen
Publisher: MIT Press
ISBN: 026218253X
Category : Computers
Languages : en
Pages : 266
Book Description
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
Publisher: MIT Press
ISBN: 026218253X
Category : Computers
Languages : en
Pages : 266
Book Description
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
Optimization Algorithms on Matrix Manifolds
Author: P.-A. Absil
Publisher: Princeton University Press
ISBN: 1400830249
Category : Mathematics
Languages : en
Pages : 240
Book Description
Many problems in the sciences and engineering can be rephrased as optimization problems on matrix search spaces endowed with a so-called manifold structure. This book shows how to exploit the special structure of such problems to develop efficient numerical algorithms. It places careful emphasis on both the numerical formulation of the algorithm and its differential geometric abstraction--illustrating how good algorithms draw equally from the insights of differential geometry, optimization, and numerical analysis. Two more theoretical chapters provide readers with the background in differential geometry necessary to algorithmic development. In the other chapters, several well-known optimization methods such as steepest descent and conjugate gradients are generalized to abstract manifolds. The book provides a generic development of each of these methods, building upon the material of the geometric chapters. It then guides readers through the calculations that turn these geometrically formulated methods into concrete numerical algorithms. The state-of-the-art algorithms given as examples are competitive with the best existing algorithms for a selection of eigenspace problems in numerical linear algebra. Optimization Algorithms on Matrix Manifolds offers techniques with broad applications in linear algebra, signal processing, data mining, computer vision, and statistical analysis. It can serve as a graduate-level textbook and will be of interest to applied mathematicians, engineers, and computer scientists.
Publisher: Princeton University Press
ISBN: 1400830249
Category : Mathematics
Languages : en
Pages : 240
Book Description
Many problems in the sciences and engineering can be rephrased as optimization problems on matrix search spaces endowed with a so-called manifold structure. This book shows how to exploit the special structure of such problems to develop efficient numerical algorithms. It places careful emphasis on both the numerical formulation of the algorithm and its differential geometric abstraction--illustrating how good algorithms draw equally from the insights of differential geometry, optimization, and numerical analysis. Two more theoretical chapters provide readers with the background in differential geometry necessary to algorithmic development. In the other chapters, several well-known optimization methods such as steepest descent and conjugate gradients are generalized to abstract manifolds. The book provides a generic development of each of these methods, building upon the material of the geometric chapters. It then guides readers through the calculations that turn these geometrically formulated methods into concrete numerical algorithms. The state-of-the-art algorithms given as examples are competitive with the best existing algorithms for a selection of eigenspace problems in numerical linear algebra. Optimization Algorithms on Matrix Manifolds offers techniques with broad applications in linear algebra, signal processing, data mining, computer vision, and statistical analysis. It can serve as a graduate-level textbook and will be of interest to applied mathematicians, engineers, and computer scientists.
Programming Heterogeneous MPSoCs
Author: Jerónimo Castrillón Mazo
Publisher: Springer Science & Business Media
ISBN: 3319006754
Category : Technology & Engineering
Languages : en
Pages : 243
Book Description
This book provides embedded software developers with techniques for programming heterogeneous Multi-Processor Systems-on-Chip (MPSoCs), capable of executing multiple applications simultaneously. It describes a set of algorithms and methodologies to narrow the software productivity gap, as well as an in-depth description of the underlying problems and challenges of today’s programming practices. The authors present four different tool flows: A parallelism extraction flow for applications written using the C programming language, a mapping and scheduling flow for parallel applications, a special mapping flow for baseband applications in the context of Software Defined Radio (SDR) and a final flow for analyzing multiple applications at design time. The tool flows are evaluated on Virtual Platforms (VPs), which mimic different characteristics of state-of-the-art heterogeneous MPSoCs.
Publisher: Springer Science & Business Media
ISBN: 3319006754
Category : Technology & Engineering
Languages : en
Pages : 243
Book Description
This book provides embedded software developers with techniques for programming heterogeneous Multi-Processor Systems-on-Chip (MPSoCs), capable of executing multiple applications simultaneously. It describes a set of algorithms and methodologies to narrow the software productivity gap, as well as an in-depth description of the underlying problems and challenges of today’s programming practices. The authors present four different tool flows: A parallelism extraction flow for applications written using the C programming language, a mapping and scheduling flow for parallel applications, a special mapping flow for baseband applications in the context of Software Defined Radio (SDR) and a final flow for analyzing multiple applications at design time. The tool flows are evaluated on Virtual Platforms (VPs), which mimic different characteristics of state-of-the-art heterogeneous MPSoCs.