Information theory has recently attracted renewed attention because of key developments spawning challenging research problems." "The book is suitable for graduate students and research mathematicians interested in communications and network information theory."--Jacket.

Information theory has recently attracted renewed attention because of key developments spawning challenging research problems." "The book is suitable for graduate students and research mathematicians interested in communications and network information theory."--Jacket.

This book provides a theoretical and technical basis for advanced research on visual data compression and communication. It presents the results of the author's research on visual data compression and transmission. Studying scalable video coding (SVC), it considers the fundamental problem to be solved in SVC-motion compensation. It explores directional transforms, extends the current coding framework by visual synthesis and reconstruction, and explains how to apply compressive sensing to solve the compression problems in transmission. It also develops the pseudo-analog transmission for image and video.

In these papers associated with the workshop of December 2003, contributors describe their work in fountain codes for lossless data compression, an application of coding theory to universal lossless source coding performance bounds, expander graphs and codes, multilevel expander codes, low parity check lattices, sparse factor graph representations of Reed-Solomon and related codes. Interpolation multiplicity assignment algorithms for algebraic soft- decision decoding of Reed-Solomon codes, the capacity of two- dimensional weight-constrained memories, networks of two-way channels, and a new approach to the design of digital communication systems. Annotation :2005 Book News, Inc., Portland, OR (booknews.com).

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.

This comprehensive treatment of network information theory and its applications provides the first unified coverage of both classical and recent results. With an approach that balances the introduction of new models and new coding techniques, readers are guided through Shannon's point-to-point information theory, single-hop networks, multihop networks, and extensions to distributed computing, secrecy, wireless communication, and networking. Elementary mathematical tools and techniques are used throughout, requiring only basic knowledge of probability, whilst unified proofs of coding theorems are based on a few simple lemmas, making the text accessible to newcomers. Key topics covered include successive cancellation and superposition coding, MIMO wireless communication, network coding, and cooperative relaying. Also covered are feedback and interactive communication, capacity approximations and scaling laws, and asynchronous and random access channels. This book is ideal for use in the classroom, for self-study, and as a reference for researchers and engineers in industry and academia.

This book provides a broad understanding of the fundamental tools and methods from information theory and mathematical programming, as well as specific applications in 6G and beyond system designs. The contents focus on not only both theories but also their intersection in 6G. Motivations are from the multitude of new developments which will arise once 6G systems integrate new communication networks with AIoT (Artificial Intelligence plus Internet of Things). Design issues such as the intermittent connectivity, low latency, federated learning, IoT security, etc., are covered. This monograph provides a thorough picture of new results from information and optimization theories, as well as how their dialogues work to solve aforementioned 6G design issues.

The first unified treatment of the interface between information theory and emerging topics in data science, written in a clear, tutorial style. Covering topics such as data acquisition, representation, analysis, and communication, it is ideal for graduate students and researchers in information theory, signal processing, and machine learning.

The aim of this book is to provide the latest research findings, innovative research results, methods and development techniques from both theoretical and practical perspectives related to intelligent social networks and collaborative systems, intelligent networking systems, mobile collaborative systems, secure intelligent cloud systems, etc., and to reveal synergies among various paradigms in the multi-disciplinary field of intelligent collaborative systems. It presents the Proceedings of the 9th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2017), held on August 24–26, 2017 in Toronto, Canada. With the rapid evolution of the Internet, we are currently experiencing a shift from the traditional sharing of information and applications as the main purpose of the Web to an emergent paradigm that puts people at the very centre of networks and exploits the value of people’s connections, relations and collaborations. Social networks are also pla ying a major role in the dynamics and structure of intelligent Web-based networking and collaborative systems. Virtual campuses, virtual communities and organizations effectively leverage intelligent networking and collaborative systems by tapping into a broad range of formal and informal electronic relations, such as business-to-business, peer-to-peer and many types of online collaborative learning interactions, including the emerging e-learning systems. This has resulted in entangled systems that need to be managed efficiently and autonomously. In addition, the latest and powerful technologies based on Grid and wireless infrastructure as well as Cloud computing are now greatly enhancing collaborative and networking applications, but are also facing new issues and challenges. The principal objective of the research and development community is to stimulate research that leads to the creation of responsive environments for networking and, in the longer-term, the developmen t of adaptive, secure, mobile, and intuitive intelligent systems for collaborative work and learning.

This interdisciplinary text offers theoretical and practical results of information theoretic methods used in statistical learning. It presents a comprehensive overview of the many different methods that have been developed in numerous contexts.