Details

Title

FPGA implementation of logarithmic versions of Baum-Welch and Viterbi algorithms for reduced precision hidden Markov models

Journal title

Bulletin of the Polish Academy of Sciences: Technical Sciences

Yearbook

2017

Numer

No 6

Publication authors

Divisions of PAS

Nauki Techniczne

Publisher

Polish Academy of Sciences

Date

2017

Identifier

ISSN 0239-7528, eISSN 2300-1917

References

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Jun Li (2009), The fast evaluation of hidden Markov models on GPU International Conference on Intelligent Computing and Intelligent Systems Shanghai, IEEE, 31, 426, doi.org/10.1109/icicisys.2009.5357649 ; Pietras (2016), Hidden Markov models with affix based observation in the field of syntactic analysis In for eds, Soft Computing Artificial Intelligence Multimedia Security, 534, doi.org/10.1007/9783319484297_2 ; Singh (2014), Dynamic classification of ballistic missiles using neural networks and hidden Markov models, Applied Soft Computing, 19, 280. ; Churbanov (2008), Implementing EM and Viterbi algorithms for hidden Markov model in linear memory, BMC Bioinformatics, 22, 1, doi.org/10.1186/147121059224 ; Varma (2010), Easing the verification bottleneck using high level synthesis th Test Santa, VLSI Symposium, 24, 253, doi.org/10.1109/VTS.2010.5469565 ; Anisha (null), hybrid Parts Of Speech tagger for Malayalam language International Conference on Advances in Communications and, Computing, 26, 1502, doi.org/10.1109/ICACCI.2015.7275825 ; Hsieh (2014), An HMM - based eye movement detection system using EEG brain - computer interface International Symposium on Circuits and Systems Melbourne VIC, IEEE, 13, 662, doi.org/10.1109/ISCAS.2014.6865222 ; Behnam (1509), Stats - calculus pose descriptor feeding a discrete HMM low - latency detection and recognition system for skeletal actions arXiv preprint arXiv, null, 09014. ; Rabiner (1989), tutorial on hidden Markov models and selected applications in speech recognition of the, Proceedings IEEE, 15, 77, doi.org/10.1016/b9780080515847.500279 ; Narasimhan (2006), Online decoding of Markov models under latency constraints in pp, null, 657, doi.org/10.1145/1143844.1143927 ; Taehwan (2011), HMM - based underwater target classification with synthesized active sonar signals, Trans, 9, 2039, doi.org/10.1587/transfun.e94.a.2039 ; Atef (2016), Reconfigurable hardware accelerator for profile hidden Markov models for and, Arabian Journal Science Engineering, 18, 3267. ; Majumder (2014), Hardware accelerators in computational biology application potential challenges Test, IEEE Design, 21, 8, doi.org/10.1109/MDAT.2013.2290118 ; Mannini (2014), Online decoding of hidden Markov models for gait event detection using foot - mounted gyroscopes of, IEEE Journal Health, 18, 1122, doi.org/10.1109/jbhi.2013.2293887 ; Sun (2009), Accelerating HMMer on FPGAs using systolic array based architecture Proceedings of the rd International and Distributed Processing, IEEE Parallel Symposium, 20, doi.org/10.1109/ipdps.2009.5160927 ; Vinyals (2008), Hardware - independent fast logarithm approximation with adjustable accuracy International on, IEEE Symposium Multimedia, 28, 61, doi.org/10.1109/ISM.2008.83 ; Manandhar (null), Multiple - instance hidden Markov model for gpr - based landmine detection on and, IEEE Transactions Geoscience Remote Sensing, 53, 1737. ; Loughlin (2014), vivado high level synthesis case studies Signals and Systems Conference Limerick, null, 25, 352, doi.org/10.1049/cp.2014.0713 ; Lyu (null), vision based sense and avoid system for small unmanned helicopter International Conference on Unmanned Aircraft Systems Denver, null, 14, 2015, doi.org/10.1109/ICUAS.2015.7152339 ; 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DOI

10.1515/bpasts-2017-0101

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