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dc.contributor.authorAmaricai, Alexandru
dc.contributor.authorBahrami, Mohsem
dc.contributor.authorVasic, Bane
dc.date.accessioned2020-09-14T22:53:38Z
dc.date.available2020-09-14T22:53:38Z
dc.date.issued2019-07
dc.identifier.citationAmaricai, A., Bahrami, M., & Vasić, B. (2019, July). A Log-Likelihood Ratio based Generalized Belief Propagation. In IEEE EUROCON 2019-18th International Conference on Smart Technologies (pp. 1-6). IEEE.en_US
dc.identifier.doi10.1109/eurocon.2019.8861528
dc.identifier.urihttp://hdl.handle.net/10150/643347
dc.description.abstractIn this paper, we propose a reduced complexity Generalized Belief Propagation (GBP) that propagates messages in Log-Likelihood Ratio (LLR) domain. The key novelties of the proposed LLR-GBP are: (i) reduced fixed point precision for messages instead of computational complex floating point format, (ii) operations performed in logarithm domain, thus eliminating the need for multiplications and divisions, (iii) usage of message ratios that leads to simple hard decision mechanisms. We demonstrated the validity of LLR-GBP on reconstruction of images passed through binary-input two-dimensional Gaussian channels with memory and affected by additive white Gaussian noise.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rightsCopyright © 2019 IEEEen_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en_US
dc.sourceIEEE EUROCON 2019 -18th International Conference on Smart Technologies
dc.subjectProbabilistic inferenceen_US
dc.subjectgraphical modelsen_US
dc.subjectgeneralized belief propagation (GBP)en_US
dc.titleA Log-Likelihood Ratio based Generalized Belief Propagationen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizonaen_US
dc.identifier.journalPROCEEDINGS OF 18TH INTERNATIONAL CONFERENCE ON SMART TECHNOLOGIES (IEEE EUROCON 2019)en_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
refterms.dateFOA2020-09-14T22:53:39Z


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