MARC Record from marc_columbia

LEADER: 05171mam a22003974a 4500
001 3426964
005 20221020072122.0
008 021024t20022002flua b 001 0 eng
010 $a 2002191160
020 $a1584883103 (alk. paper)
035 $a(OCoLC)ocm50906326
035 $9AVP8143CU
035 $a3426964
040 $aDLC$cDLC$dYDX$dOrLoB-B
042 $apcc
050 00 $aQ360$b.T62 2002
082 00 $a003/.54$221
100 1 $aTogneri, Roberto.$0http://id.loc.gov/authorities/names/n2002162783
245 10 $aFundamentals of information theory and coding design /$cRoberto Togneri, Christopher J.S. deSilva.
260 $aBoca Raton, Fla. :$bChapman & Hall/CRC,$c[2002], ©2002.
300 $axii, 385 pages :$billustrations ;$c25 cm.
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
490 1 $aDiscrete mathematics and its applications
504 $aIncludes bibliographical references and index.
505 00 $g1.$tEntropy and Information -- $tStructure -- $tStructure in Randomness -- $tFirst Concepts of Probability Theory -- $tSurprise and Entropy -- $tUnits of Entropy -- $tThe Minimum and Maximum Values of Entropy -- $tA Useful Inequality -- $tJoint Probability Distribution Functions -- $tConditional Probability and Bayes' Theorem -- $tConditional Probability Distributions and Conditional Entropy -- $tInformation Sources -- $tMemoryless Information Sources -- $tMarkov Sources and n-gram Models -- $tStationary Distributions -- $tThe Entropy of Markov Sources -- $tSequences of Symbols -- $tThe Adjoint Source of a Markov Source -- $tExtensions of Sources -- $tInfinite Sample Spaces -- $g2.$tInformation Channels -- $tWhat Are Information Channels? -- $tBSC and BEC Channels -- $tMutual Information -- $tNoiseless and Deterministic Channels -- $tCascaded Channels -- $tAdditivity of Mutual Information -- $tChannel Capacity: Maximum Mutual Information -- $tContinuous Channels and Gaussian Channels --
505 80 $tInformation Capacity Theorem -- $tRate Distortion Theory -- $g3.$tSource Coding -- $tInstantaneous Codes -- $tThe Kraft Inequality and McMillan's Theorem -- $tAverage Length and Compact Codes -- $tShannon's Noiseless Coding Theorem -- $tFano Coding -- $tHuffman Coding -- $tArithmetic Coding -- $tHigher-order Modelling -- $g4.$tData Compression -- $tBasic Concepts of Data Compression -- $tRun-length Coding -- $tThe CCITT Standard for Facsimile Transmission -- $tBlock-sorting Compression -- $tDictionary Coding -- $tStatistical Compression -- $tPrediction by Partial Matching -- $tImage Coding -- $g5.$tFundamentals of Channel Coding -- $tCode Rate -- $tDecoding Rules -- $tHamming Distance -- $tBounds on M, Maximal Codes and Perfect Codes -- $tError Probabilities -- $tShannon's Fundamental Coding Theorem -- $g6.$tError-Correcting Codes -- $tGroups -- $tRings and Fields -- $tLinear Spaces -- $tLinear Spaces over the Binary Field -- $tLinear Codes -- $tEncoding and Decoding --
505 80 $tCodes Derived from Hadamard Matrices -- $g7.$tCyclic Codes -- $tRings of Polynomials -- $tCyclic Codes -- $tEncoding and Decoding of Cyclic Codes -- $tEncoding and Decoding Circuits for Cyclic Codes -- $tThe Golay Code -- $tHamming Codes -- $tCyclic Redundancy Check Codes -- $tReed-Muller Codes -- $g8.$tBurst-Correcting Codes -- $tFinite Fields -- $tIrreducible Polynomials -- $tConstruction of Finite Fields -- $tBursts of Errors -- $tFire Codes -- $tMinimum Polynomials -- $tBose-Chaudhuri-Hocquenghem Codes -- $tOther Fields -- $tReed-Solomon Codes -- $g9.$tConvolutional Codes -- $tA Simple Example -- $tBinary Convolutional Codes -- $tDecoding Convolutional Codes -- $tThe Viterbi Algorithm -- $tSequential Decoding -- $tTrellis Modulation -- $tTurbo Codes.
520 1 $a"Without abandoning the theoretical foundations, Fundamentals of Information Theory and Coding Design presents working algorithms and implementation that can be used to design and create real systems. The emphasis is on the underlying concepts governing information theory and the mathematical basis for modern coding systems, but the authors also provide the practical details of important codes like Reed-Solomon, BCH, and Turbo codes.
520 8 $aAlso setting this book apart are discussions on the cascading of information channels and the additivity of information, the details of arithmetic coding, and the connection between coding of extensions and Markov modeling.".
520 8 $a"Complete, balanced coverage, an outstanding format, and a wealth of examples and exercises make this an outstanding text for upper-level students in computer science, mathematics, and engineering, and a valuable reference for telecommunications engineers and coding theory researchers."--BOOK JACKET.
650 0 $aInformation theory.$0http://id.loc.gov/authorities/subjects/sh85066289
650 0 $aCoding theory.$0http://id.loc.gov/authorities/subjects/sh85027654
700 1 $aDeSilva, Christopher J. S.$0http://id.loc.gov/authorities/names/n2002162784
830 0 $aCRC Press series on discrete mathematics and its applications.$0http://id.loc.gov/authorities/names/n95009449
852 00 $boff,eng$hQ360$i.T62 2002