000			03524nam a22004695i 4500
001			978-0-387-79234-7
003			DE-He213
005			20251006084421.0
007			cr nn 008mamaa
008			100301s2008 xxu| s |||| 0|eng d
020			_a9780387792347
020			_a99780387792347
024	7		_a10.1007/978-0-387-79234-7 _2doi
082	0	4	_a621.382 _223
100	1		_aYeung, Raymond W. _eauthor.
245	1	0	_aInformation Theory and Network Coding _h[electronic resource] / _cby Raymond W. Yeung.
264		1	_aBoston, MA : _bSpringer US, _c2008.
300			_aXXII, 582p. 130 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aInformation Technology Transmission Processing and Storage
505	0		_aThe Science of Information -- The Science of Information -- Fundamentals of Network Coding -- Information Measures -- Information Measures -- Zero-Error Data Compression -- Weak Typicality -- Strong Typicality -- Discrete Memoryless Channels -- Rate-Distortion Theory -- The Blahut-Arimoto Algorithms -- Differential Entropy -- Continuous-Valued Channels -- Markov Structures -- Information Inequalities -- Shannon-Type Inequalities -- Beyond Shannon-Type Inequalities -- Entropy and Groups -- Fundamentals of Network Coding -- The Max-Flow Bound -- Single-Source Linear Network Coding: Acyclic Networks -- Single-Source Linear Network Coding: Cyclic Networks -- Multi-source Network Coding.
520			_aInformation Theory and Network Coding consists of two parts: Components of Information Theory, and Fundamentals of Network Coding Theory. Part I is a rigorous treatment of information theory for discrete and continuous systems. In addition to the classical topics, there are such modern topics as the I-Measure, Shannon-type and non-Shannon-type information inequalities, and a fundamental relation between entropy and group theory. With information theory as the foundation, Part II is a comprehensive treatment of network coding theory with detailed discussions on linear network codes, convolutional network codes, and multi-source network coding. Other important features include: Derivations that are from the first principle A large number of examples throughout the book Many original exercise problems Easy-to-use chapter summaries Two parts that can be used separately or together for a comprehensive course Information Theory and Network Coding is for senior undergraduate and graduate students in electrical engineering, computer science, and applied mathematics. This work can also be used as a reference for professional engineers in the area of communications.
650		0	_aENGINEERING.
650		0	_aDATA STRUCTURES (COMPUTER SCIENCE).
650		0	_aDISTRIBUTION (PROBABILITY THEORY).
650		0	_aTELECOMMUNICATION.
650	1	4	_aENGINEERING.
650	2	4	_aCOMMUNICATIONS ENGINEERING, NETWORKS.
650	2	4	_aDATA STRUCTURES, CRYPTOLOGY AND INFORMATION THEORY.
650	2	4	_aPROBABILITY THEORY AND STOCHASTIC PROCESSES.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387792330
830		0	_aInformation Technology Transmission Processing and Storage
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-79234-7 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-ENG
942			_2ddc _cER
999			_c59156 _d59156