Implementing the Viterbi algorithm

Lou, Hui‐Ling

doi:10.1109/79.410439

Cited by 184 publications

(53 citation statements)

References 30 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The expected code symbol along the branch from state to state is derived by the mapping and . In the additive white Gaussian noise channel the optimal distance measure is the squared Euclidean distance (2) However, given the preceding symbol constraints, it is shown in [5] that this measure simplifies to for for (3) and the complete branch metric is then written as (4) III. USING THE COMPLEMENTARY PROPERTY This discussion is restricted to rate 1/2 codes, that is , although the considerations can be generalized to codes.…”

Section: Notationmentioning

confidence: 99%

A Simplified Computational Kernel for Trellis-Based Decoding

Kamuf

Anderson²,

Öwall³

2004

IEEE Commun. Lett.

View full text Add to dashboard Cite

Link to publicationCitation for published version (APA): Kamuf, M., Anderson, J. B., & Öwall, V. (2004). A simplified computational kernel for trellis-based decoding. IEEE Communications Letters, 8(3), 156-158. DOI: 10.1109/LCOMM.2004 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Abstract-A simplified branch metric and add-compare-select (ACS) unit is presented for use in trellis-based decoding architectures. The simplification is based on a complementary property of best feedforward and some systematic feedback encoders. As a result, one adder is saved in every other ACS unit. Furthermore, only half the branch metrics have to be calculated. It is shown that this simplification becomes especially beneficial for rate 1/2 convolutional codes. Consequently, area and power consumption will be reduced in a hardware implementation.Index Terms-Add-compare-select (ACS), branch metric, logMAP, very large-scale integration (VLSI), Viterbi decoding.

show abstract

Section: Notationmentioning

confidence: 99%

A Simplified Computational Kernel for Trellis-Based Decoding

Kamuf

Anderson²,

Öwall³

2004

IEEE Commun. Lett.

View full text Add to dashboard Cite

show abstract

“…The Viterbi algorithm is a formal technique used for finding the single best state sequence. It provides an optimal solution for the problem of estimating the state sequence of HMM (Lou, 1995). For this study, the parameters of the HMM were first defined using an EM algorithm.…”

Section: Methodsmentioning

confidence: 99%

Use of Hidden Markov Models to Identify Background States Behind Risks of Cerebral Infarction and Ischemic Heart Disease

Morimoto¹

2017

JMR

View full text Add to dashboard Cite

Cold exposure is often said to trigger the incidence of cerebral infarctions and ischemic heart disease. This association between weather and human health has attracted considerable interest, and has been explored using standard statistical techniques such as regression models. Meteorological factors, such as temperature, are controlled by background systems, notably weather patterns. Therefore, it is reasonable to posit that the incidence of diseases is similarly influenced by a background system. The aim of this paper was to identify and construct these respective background systems. Possible background states or "hidden states", behind the incidence of diseases were derived using the EM and Viterbi algorithms with in the framework of hidden Markov models (HMM). A self-organizing map (SOM) enabled identification of weather patterns, considered as background states behind meteorological factors. These background states were then compared, and the hidden states behind the incidence of diseases were identified by six weather patterns. This finding indicates new evidence of the links between weather and human health, shedding light on the association between changes in the weather and the onset of disease.

show abstract

“…According to the existing literature, the first problem (i.e., the problem of finding the most likely explanation for an observation sequence) can be solved efficiently using the Viterbi algorithm (Forney, 1973;Lou, 1995). To adapt that algorithm for our purposes, we have to define the following structures:…”

Section: Hmm Approachmentioning

confidence: 99%

An Evaluation of the Challenges of Multilingualism in Data Warehouse Development

Dediź

Stanier

2016

Proceedings of the 18th International Conference on Enterprise Information Systems

View full text Add to dashboard Cite

ICEIS 2016 received 257 paper submissions from 42 countries in all continents, which makes it one of the largest conferences in the World in the area of Information Systems, thus demonstrating the success and global dimension of this conference. From these, 42 papers were selected for publication and presentation at the Conference as full papers. These numbers, leading to a full-paper acceptance ratio of 16%, show the intention of preserving a high quality forum for this conference, a quality that we intend to maintain in the future, for the next editions of this conference.The high number and high quality of the received papers imposed difficult choices in the selection process. To evaluate each submission, a double blind paper review was performed by the Program Committee, whose members are highly qualified researchers in ICEIS topic areas.All presented papers will be available at the SCITEPRESS Digital Library and will be submitted for indexation by Thomson Reuters Conference Proceedings Citation Index (ISI), INSPEC, DBLP, EI (Elsevier Index) and Scopus.Additionally, a short list of presented papers will be selected to be expanded into a forthcoming book of ICEIS 2016 Selected Papers to be published by Springer in the LNBIP Series.The technical program of the conference included a panel and 4 invited talks delivered by internationally distinguished speakers, namely: Claudia Loebbecke (University of Cologne, Germany), Sergio Gusmeroli (TXT e-solutions SPA, Italy), Wil Van Der Aalst (Technische Universiteit Eindhoven, Netherlands) and Jan Vom Brocke (University of Liechtenstein, Liechtenstein). Their participation positively contributes to reinforce the overall quality of the Conference and to provide a deeper understanding of the fields addressed by the conference.Moreover, ICEIS 2016 had a Doctoral Consortium on Enterprise Information Systems and 1 tutorial. We are thankful to the Conference Co-chairs (Olivier Camp and José Cordeiro) and Program Co-chairs (Slimane Hammoudi, Leszek Maciaszek and Michele M. Missikoff) for their dedication and hard work in organizing these events.We sincerely thank all the authors for their submissions and participation in ICEIS 2016. Furthermore, we would like to thank all the members of the program committee and reviewers, who helped us with their expertise, dedication and time. We would also like to thank the invited speakers for their excellent contribution in sharing their knowledge and vision and the workshop/special session chairs whose collaboration with ICEIS 2015 was much appreciated. Finally, we gratefully acknowledge the professional support of the ICEIS 2016 team for all organizational processes. XIIIWe hope that all colleagues find this a fruitful and inspiring conference. We hope to contribute to the development of the Enterprise Information Systems community and look forward to having additional research results presented at the next edition of ICEIS, details of which are available at http://www.iceis.org. Slimane Hammoudi University of Cologne, GermanyAbstract:...

show abstract

Implementing the Viterbi algorithm

Cited by 184 publications

References 30 publications

A Simplified Computational Kernel for Trellis-Based Decoding

A Simplified Computational Kernel for Trellis-Based Decoding

Use of Hidden Markov Models to Identify Background States Behind Risks of Cerebral Infarction and Ischemic Heart Disease

An Evaluation of the Challenges of Multilingualism in Data Warehouse Development

Contact Info

Product

Resources

About