000 | 03543nam a22005415i 4500 | ||
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001 | 978-3-030-54762-2 | ||
003 | DE-He213 | ||
005 | 20240423125329.0 | ||
007 | cr nn 008mamaa | ||
008 | 201013s2020 sz | s |||| 0|eng d | ||
020 |
_a9783030547622 _9978-3-030-54762-2 |
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024 | 7 |
_a10.1007/978-3-030-54762-2 _2doi |
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050 | 4 | _aTK5101-5105.9 | |
072 | 7 |
_aTJK _2bicssc |
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_aTEC041000 _2bisacsh |
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_aTJK _2thema |
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082 | 0 | 4 |
_a621.382 _223 |
100 | 1 |
_aYuan, Xiaojun. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aTurbo Message Passing Algorithms for Structured Signal Recovery _h[electronic resource] / _cby Xiaojun Yuan, Zhipeng Xue. |
250 | _a1st ed. 2020. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2020. |
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300 |
_aXI, 105 p. 30 illus., 20 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aSpringerBriefs in Computer Science, _x2191-5776 |
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505 | 0 | _aIntroduction -- Turbo Message Passing for Compressed Sensing -- Turbo Message Passing for Affine Rank Minimization -- Turbo Message Passing for Compressed Robust Principal Component Analysis -- Learned Turbo Message Passing Algorithms -- Future Research Directions -- Conclusion. | |
520 | _aThis book takes a comprehensive study on turbo message passing algorithms for structured signal recovery, where the considered structured signals include 1) a sparse vector/matrix (which corresponds to the compressed sensing (CS) problem), 2) a low-rank matrix (which corresponds to the affine rank minimization (ARM) problem), 3) a mixture of a sparse matrix and a low-rank matrix (which corresponds to the robust principal component analysis (RPCA) problem). The book is divided into three parts. First, the authors introduce a turbo message passing algorithm termed denoising-based Turbo-CS (D-Turbo-CS). Second, the authors introduce a turbo message passing (TMP) algorithm for solving the ARM problem. Third, the authors introduce a TMP algorithm for solving the RPCA problem which aims to recover a low-rank matrix and a sparse matrix from their compressed mixture. With this book, we wish to spur new researches on applying message passing to various inference problems. Provides an in depth look into turbo message passing algorithms for structured signal recovery Includes efficient iterative algorithmic solutions for inference, optimization, and satisfaction problems through message passing Shows applications in areas such as wireless communications and computer vision. | ||
650 | 0 | _aTelecommunication. | |
650 | 0 | _aSignal processing. | |
650 | 0 | _aComputer networks . | |
650 | 1 | 4 | _aCommunications Engineering, Networks. |
650 | 2 | 4 | _aSignal, Speech and Image Processing. |
650 | 2 | 4 | _aComputer Communication Networks. |
700 | 1 |
_aXue, Zhipeng. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030547615 |
776 | 0 | 8 |
_iPrinted edition: _z9783030547639 |
830 | 0 |
_aSpringerBriefs in Computer Science, _x2191-5776 |
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856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-54762-2 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c176779 _d176779 |