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008			230422s2010 sz | s |||| 0|eng d
020			_a9783031016752
024	7		_a10.1007/978-3-031-01675-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5102.92 _b2010 EB
100	1		_aBi, Dongsheng _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688214
245	1	0	_aJoint Source Channel Coding Using Arithmetic Codes _cby Bi Dongsheng, Khalid Sayood, Michael Hoffman
250			_a1st edition 2010
264		1	_aCham _bSpringer International Publishing _c2010
300			_a1 recurso en línea (VIII, 69 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Communications _x1932-1708
505	0		_aIntroduction -- Arithmetic Codes -- Arithmetic Codes with Forbidden Symbols -- Distance Property and Code Construction -- Conclusion.
520			_aBased on the encoding process, arithmetic codes can be viewed as tree codes and current proposals for decoding arithmetic codes with forbidden symbols belong to sequential decoding algorithms and their variants. In this monograph, we propose a new way of looking at arithmetic codes with forbidden symbols. If a limit is imposed on the maximum value of a key parameter in the encoder, this modified arithmetic encoder can also be modeled as a finite state machine and the code generated can be treated as a variable-length trellis code. The number of states used can be reduced and techniques used for decoding convolutional codes, such as the list Viterbi decoding algorithm, can be applied directly on the trellis. The finite state machine interpretation can be easily migrated to Markov source case. We can encode Markov sources without considering the conditional probabilities, while using the list Viterbi decoding algorithm which utilizes the conditional probabilities. We can also use context-based arithmetic coding to exploit the conditional probabilities of the Markov source and apply a finite state machine interpretation to this problem. The finite state machine interpretation also allows us to more systematically understand arithmetic codes with forbidden symbols. It allows us to find the partial distance spectrum of arithmetic codes with forbidden symbols. We also propose arithmetic codes with memories which use high memory but low implementation precision arithmetic codes. The low implementation precision results in a state machine with less complexity. The introduced input memories allow us to switch the probability functions used for arithmetic coding. Combining these two methods give us a huge parameter space of the arithmetic codes with forbidden symbols. Hence we can choose codes with better distance properties while maintaining the encoding efficiency and decoding complexity. A construction and search method is proposed and simulation results show that we can achieve a similar performance as turbo codes when we apply this approach to rate 2/3 arithmetic codes. Table of Contents: Introduction / Arithmetic Codes / Arithmetic Codes with Forbidden Symbols / Distance Property and Code Construction / Conclusion.
988			_aSynthesis Collection of Technology_2010
650		7	_2embne _9666753 _aCodificación, Teoría de la
650		7	_2embne _9147136 _aCódigos correctores de errores (Teoría de la información)
700	1		_aSayood, Khalid _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686768
700	1		_aHoffman, Michael W. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688215 _q(Michael Ward)
776	0	8	_iPrinted edition: _z9783031005473
776	0	8	_iPrinted edition: _z9783031028038
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01675-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _dz _eIG _zSI