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Journal articles on the topic 'Algorithme de Viterbi'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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1

Xu, Hui Hong. "Speaker Recognition Study Based on Optimized HMM Algorithm." Advanced Materials Research 765-767 (September 2013): 2809–12. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2809.

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The article has carried on the optimization to the HMM algorithms Viterbi algorithm and LBG algorithm, It can be proofed that the optimized algorithm improved the text dependent recognition efficiency throgh experiment.

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2

Viterbi, Andrew. "Viterbi algorithm." Scholarpedia 4, no. 1 (2009): 6246. http://dx.doi.org/10.4249/scholarpedia.6246.

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3

GOLOD, DANIIL, and DANIEL G. BROWN. "A TUTORIAL OF TECHNIQUES FOR IMPROVING STANDARD HIDDEN MARKOV MODEL ALGORITHMS." Journal of Bioinformatics and Computational Biology 07, no. 04 (August 2009): 737–54. http://dx.doi.org/10.1142/s0219720009004242.

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In this tutorial, we discuss two main algorithms for Hidden Markov Models or HMMs: the Viterbi algorithm and the expectation phase of the Baum–Welch algorithm, and we describe ways to improve their naïve implementations. For the Baum–Welch algorithm we first present an implementation of the expectation computations using constant space. We then discuss the classical implementation of this calculation and describe ways to reduce its space usage to logarithmic and [Formula: see text], with their respective CPU costs. We also note where each respective algorithm can be parallelized. For the Viterbi algorithm, we describe [Formula: see text] and logarithmic space algorithms which increase CPU use by a factor of two and by a logarithmic factor respectively. We also present two recent heuristics for decreasing space use, which in practice lead to logarithmic space use. Classical version of Viterbi cannot be parallelized by splitting sequence in several subsequences, but we show a parallelization that works if we are willing to pay a significant extra CPU cost. Finally we show a very simple parallelization trick which enables full usage of multiple CPUs/cores under the condition that they share memory.

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Yin, Yibo, Kainan Ma, and Ming Liu. "Temporal Convolutional Network Connected with an Anti-Arrhythmia Hidden Semi-Markov Model for Heart Sound Segmentation." Applied Sciences 10, no. 20 (October 11, 2020): 7049. http://dx.doi.org/10.3390/app10207049.

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Heart sound segmentation (HSS) is a critical step in heart sound processing, where it improves the interpretability of heart sound disease classification algorithms. In this study, we aimed to develop a real-time algorithm for HSS by combining the temporal convolutional network (TCN) and the hidden semi-Markov model (HSMM), and improve the performance of HSMM for heart sounds with arrhythmias. We experimented with TCN and determined the best parameters based on spectral features, envelopes, and one-dimensional CNN. However, the TCN results could contradict the natural fixed order of S1-systolic-S2-diastolic of heart sound, and thereby the Viterbi algorithm based on HSMM was connected to correct the order errors. On this basis, we improved the performance of the Viterbi algorithm when detecting heart sounds with cardiac arrhythmias by changing the distribution and weights of the state duration probabilities. The public PhysioNet Computing in Cardiology Challenge 2016 data set was employed to evaluate the performance of the proposed algorithm. The proposed algorithm achieved an F1 score of 97.02%, and this result was comparable with the current state-of-the-art segmentation algorithms. In addition, the proposed enhanced Viterbi algorithm for HSMM corrected 30 out of 30 arrhythmia errors after checking one by one in the dataset.

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Minh Quang, Nguyen. "VITERBI ALGORITHM FOR ENGLISH - VIETNAMESE SENTENCE RETRIEVAL IN EBMT." Journal of Science, Educational Science 60, no. 7A (2015): 3–9. http://dx.doi.org/10.18173/2354-1075.2015-0047.

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6

Kahina, Rekkal, and Abdesselam Bassou. "Improving the Performance of Viterbi Decoder using Window System." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 1 (February 1, 2018): 611. http://dx.doi.org/10.11591/ijece.v8i1.pp611-621.

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An efficient Viterbi decoder is introduced in this paper; it is called Viterbi decoder with window system. The simulation results, over Gaussian channels, are performed from rate 1/2, 1/3 and 2/3 joined to TCM encoder with memory in order of 2, 3. These results show that the proposed scheme outperforms the classical Viterbi by a gain of 1 dB. On the other hand, we propose a function called RSCPOLY2TRELLIS, for recursive systematic convolutional (RSC) encoder which creates the trellis structure of a recursive systematic convolutional encoder from the matrix “H”. Moreover, we present a comparison between the decoding algorithms of the TCM encoder like Viterbi soft and hard, and the variants of the MAP decoder known as BCJR or forward-backward algorithm which is very performant in decoding TCM, but depends on the size of the code, the memory, and the CPU requirements of the application.

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7

Muhammad, Shamsuddeen Hassan, and Abdulrasheed Mustapha. "A Form of List Viterbi Algorithm for Decoding Convolutional Codes." U.Porto Journal of Engineering 4, no. 2 (October 31, 2018): 42–48. http://dx.doi.org/10.24840/2183-6493_004.002_0004.

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Viterbi algorithm is a maximum likelihood decoding algorithm. It is used to decode convolutional code in several wireless communication systems, including Wi-Fi. The standard Viterbi algorithm gives just one decoded output, which may be correct or incorrect. Incorrect packets are normally discarded thereby necessitating retransmission and hence resulting in considerable energy loss and delay. Some real-time applications such as Voice over Internet Protocol (VoIP) telephony do not tolerate excessive delay. This makes the conventional Viterbi decoding strategy sub-optimal. In this regard, a modified approach, which involves a form of List Viterbi for decoding the convolutional code is investigated. The technique employed combines the bit-error correction capabilities of both the Viterbi algorithm and the Cyclic Redundancy Check (CRC) procedures. It first uses a form of ‘List Viterbi Algorithm’ (LVA), which generates a list of possible decoded output candidates after the trellis search. The CRC check is then used to determine the presence of correct outcome. Results of experiments conducted using simulation shows considerable improvement in bit-error performance when compared to classical approach.

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8

Lou, H. L. "Implementing the Viterbi algorithm." IEEE Signal Processing Magazine 12, no. 5 (1995): 42–52. http://dx.doi.org/10.1109/79.410439.

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9

Jin, Yuzi. "The Optimization of the Dynamic Mechanism of Jilin’s Economic Revitalization from the Perspective of Viterbi Algorithm’s Supply-Side Structural Reform." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, Supp02 (December 2020): 111–22. http://dx.doi.org/10.1142/s0218488520400188.

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Under the background of the rapid development of the Internet, the development information and social status that affect economic regeneration exist in the form of literal data in massive data information. The use of Viterbi algorithm can establish a digital model of information extraction, so as to quickly and accurately find the important content information in the Internet that influences the development of reform and economic rejuvenation. Therefore, based on the Viterbi algorithm, the use of mathematical models to help Jilin economic mechanism research from qualitative to quantitative, and continuously improve the scientific level of economic research. After deep analysis of the implementation principle and algorithm flow of Viterbi algorithm, and from the need of text information extraction, an optimization and update scheme is proposed to improve the effectiveness of the algorithm and the accuracy is calculated effectively based on the word feature detection. After simulation experiments show that Viterbi algorithm has a good application value in the field of Jilin economic reform research.

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10

Liu, Xia, Fei Long, Wenjie Zhang, and Lu Guo. "Modular Interacting Multiple Model Based on Extended Viterbi Algorithm for Maneuvering Target Tracking." Mathematical Problems in Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/374054.

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A new maneuvering target tracking algorithm is investigated, which is modeled as a class of Markov jump linear systems (MJLS). Drawing on the experience of combination idea of the extended Viterbi algorithm (EV) and the interacting multiple model algorithm (IMM), a modular interacting multiple model based on extended Viterbi (MIMMEV) is presented. The MIMMEV algorithm consists ofNindependent interacting multiple model-extended Viterbi (IMM-EV). Furthermore, these IMM-EV filters are independent and working in parallel in the MIMMEV algorithm. According to the derived probability, the estimated state of every moment is the weighted sum of each estimator at the corresponding time. Simulation results demonstrate that the proposed algorithm improves the tracking precision and reduces the computational burden compared with traditional IMM and IMM-EV.

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Matczak, Grzegorz, and Przemyslaw Mazurek. "History Dependent Viterbi Algorithm for Navigation Purposes of Line Following Robot." Image Processing & Communications 20, no. 4 (December 1, 2015): 5–11. http://dx.doi.org/10.1515/ipc-2015-0039.

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Abstract Line following robots are applied in numerous applications and the best performance could be obtained if the forward looking camera is applied. Variable light and line conditions influence the line estimation and quality of the robot navigation. Proposed History Dependent Viterbi Algorithm and Viterbi Algorithm are compared. Obtained results using Monte Carlo tests show improved performance of the proposed algorithm for assumed model.

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12

Eusse, Juan Fernando, Nahri Moreano, Alba Cristina Magalhaes Alves de Melo, and Ricardo Pezzuol Jacobi. "A Protein Sequence Analysis Hardware Accelerator Based on Divergences." International Journal of Reconfigurable Computing 2012 (2012): 1–19. http://dx.doi.org/10.1155/2012/201378.

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The Viterbi algorithm is one of the most used dynamic programming algorithms for protein comparison and identification, based on hidden markov Models (HMMs). Most of the works in the literature focus on the implementation of hardware accelerators that act as a prefilter stage in the comparison process. This stage discards poorly aligned sequences with a low similarity score and forwards sequences with good similarity scores to software, where they are reprocessed to generate the sequence alignment. In order to reduce the software reprocessing time, this work proposes a hardware accelerator for the Viterbi algorithm which includes the concept of divergence, in which the region of interest of the dynamic programming matrices is delimited. We obtained gains of up to 182x when compared to unaccelerated software. The performance measurement methodology adopted in this work takes into account not only the acceleration achieved by the hardware but also the reprocessing software stage required to generate the alignment.

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13

Baldi, Pierre, and Yves Chauvin. "Smooth On-Line Learning Algorithms for Hidden Markov Models." Neural Computation 6, no. 2 (March 1994): 307–18. http://dx.doi.org/10.1162/neco.1994.6.2.307.

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A simple learning algorithm for Hidden Markov Models (HMMs) is presented together with a number of variations. Unlike other classical algorithms such as the Baum-Welch algorithm, the algorithms described are smooth and can be used on-line (after each example presentation) or in batch mode, with or without the usual Viterbi most likely path approximation. The algorithms have simple expressions that result from using a normalized-exponential representation for the HMM parameters. All the algorithms presented are proved to be exact or approximate gradient optimization algorithms with respect to likelihood, log-likelihood, or cross-entropy functions, and as such are usually convergent. These algorithms can also be casted in the more general EM (Expectation-Maximization) framework where they can be viewed as exact or approximate GEM (Generalized Expectation-Maximization) algorithms. The mathematical properties of the algorithms are derived in the appendix.

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14

Xie, Qiwei, Qian Long, and Seiichi Mita. "Integration of optical flow and Multi-Path-Viterbi algorithm for stereo vision." International Journal of Wavelets, Multiresolution and Information Processing 15, no. 03 (February 16, 2017): 1750022. http://dx.doi.org/10.1142/s0219691317500229.

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This paper proposes a novel stereo matching algorithm to solve environment sensing problems. It integrates a non-convex optical flow and Viterbi process. The non-convex optical flow employs a new adaptive weighted non-convex Total Generalized Variation (TGV) model, which can obtain sharp disparity maps. Structural similarity, total variation constraint, and a specific merging strategy are combined with the 4 bi-directional Viterbi process to improve the robustness. In the fusion of the optical flow and Viterbi process, a new occlusion processing method is incorporated in order to get more sharp disparity and more robust result. Extensive experiments are conducted to compare this algorithm with other state-of-the-art methods. Experimental results show the superiority of our algorithm.

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Wei, L. "Iterative Viterbi Algorithm: Implementation Issues." IEEE Transactions on Wireless Communications 3, no. 2 (March 2004): 382–86. http://dx.doi.org/10.1109/twc.2003.821149.

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FOREMAN, LINDSEY A. "Generalisation of the Viterbi algorithm." IMA Journal of Management Mathematics 4, no. 4 (1992): 351–67. http://dx.doi.org/10.1093/imaman/4.4.351.

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17

Reeve, J. S. "A parallel Viterbi decoding algorithm." Concurrency and Computation: Practice and Experience 13, no. 2 (2001): 95–102. http://dx.doi.org/10.1002/cpe.539.

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18

Ann Wen, Kuei, and Jau Yien Lee. "Parallel processing for Viterbi algorithm." Electronics Letters 24, no. 17 (1988): 1098. http://dx.doi.org/10.1049/el:19880745.

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19

Sarkar, Swagata, Sanjana R, Rajalakshmi S, and Harini T J. "Simulation and detection of tamil speech accent using modified mel frequency cepstral coefficient algorithm." International Journal of Engineering & Technology 7, no. 3.3 (June 8, 2018): 426. http://dx.doi.org/10.14419/ijet.v7i2.33.14202.

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Automatic Speech reconstruction system is a topic of interest of many researchers. Since many online courses are come into the picture, so recent researchers are concentrating on speech accent recognition. Many works have been done in this field. In this paper speech accent recognition of Tamil speech from different zones of Tamilnadu is addressed. Hidden Markov Model (HMM) and Viterbi algorithms are very popularly used algorithms. Researchers have worked with Mel Frequency Cepstral Coefficients (MFCC) to identify speech as well as speech accent. In this paper speech accent features are identified by modified MFCC algorithm. The classification of features is done by back propagation algorithm.

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20

Kumar, Ghanendra, and Chakres Kumar. "Effect of NAVA for Dense Optical Communication System." Science & Technology Journal 7, no. 1 (January 1, 2019): 78–81. http://dx.doi.org/10.22232/stj.2019.07.01.10.

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Viterbi Algorithm (VA) is basically an algorithm that is implemented for the detection & estimation of a sequence of symbols in digital communication and signal processing. It calculates a survivor path with minimum metric value, but cannot detect any error. The VA is scrutinized to upgrade the transmission process and provide the plate form in terms of the existence, linear phase noise as well as non-linear phase noise. In contradiction to non-adaptive MLSD (Maximum Likelihood Sequence Detection), the Viterbi algorithm renders the exemplary performance.

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Trogh, Jens, David Plets, Luc Martens, and Wout Joseph. "Advanced Real-Time Indoor Tracking Based on the Viterbi Algorithm and Semantic Data." International Journal of Distributed Sensor Networks 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/271818.

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A real-time indoor tracking system based on the Viterbi algorithm is developed. This Viterbi principle is used in combination with semantic data to improve the accuracy, that is, the environment of the object that is being tracked and a motion model. The starting point is a fingerprinting technique for which an advanced network planner is used to automatically construct the radio map, avoiding a time consuming measurement campaign. The developed algorithm was verified with simulations and with experiments in a building-wide testbed for sensor experiments, where a median accuracy below 2 m was obtained. Compared to a reference algorithm without Viterbi or semantic data, the results indicated a significant improvement: the mean accuracy and standard deviation improved by, respectively, 26.1% and 65.3%. Thereafter a sensitivity analysis was conducted to estimate the influence of node density, grid size, memory usage, and semantic data on the performance.

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Sung-Hyun, Yang, Keshav Thapa, M. Humayun Kabir, and Lee Hee-Chan. "Log-Viterbi algorithm applied on second-order hidden Markov model for human activity recognition." International Journal of Distributed Sensor Networks 14, no. 4 (April 2018): 155014771877254. http://dx.doi.org/10.1177/1550147718772541.

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Recognition of human activities is getting into the limelight among researchers in the field of pervasive computing, ambient intelligence, robotic, and monitoring such as assistive living, elderly care, and health care. Many platforms, models, and algorithms have been developed and implemented to recognize the human activities. However, existing approaches suffer from low-activity accuracy and high time complexity. Therefore, we proposed probabilistic log-Viterbi algorithm on second-order hidden Markov model that facilitates our algorithm by reducing the time complexity with increased accuracy. Second-order hidden Markov model is efficient relevance between previous two activities, current activity, and current observation that incorporate more information into recognition procedure. The log-Viterbi algorithm converts the products of a large number of probabilities into additions and finds the most likely activity from observation sequence under given model. Therefore, this approach maximizes the probability of activity recognition with improved accuracy and reduced time complexity. We compared our proposed algorithm among other famous probabilistic models such as Naïve Bayes, condition random field, hidden Markov model, and hidden semi-Markov model using three datasets in the smart home environment. The recognition possibility of our proposed method is significantly better in accuracy and time complexity than early proposed method. Moreover, this improved algorithm for activity recognition is much effective for almost all the dynamic environments such as assistive living, elderly care, healthcare applications, and home automation.

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23

Shi, Xiaoxiao. "A Method of Optimizing Network Topology Structure Combining Viterbi Algorithm and Bayesian Algorithm." Wireless Communications and Mobile Computing 2021 (May 8, 2021): 1–12. http://dx.doi.org/10.1155/2021/5513349.

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With Internet entering all walks of life, development of internet and usage expansion demand better performance, especially the application of 5G network that adopts NAS networking mode. Some of the network bandwidth cannot fully support the current network demand, which causes network fluctuations and other concerns. In this paper, a method for optimizing the topological structure of the bottom layer of the communication network is proposed that has outage performance close to optimal routing scheme. In specific, path in areas with poor network conditions is first optimized using Viterbi algorithm. Then, network element nodes on the path are optimized using Bayes recommendation algorithm for reasonable flow distribution. Dual planning of improved Viterbi algorithm is used to realize the main and standby path planning, and then, Bayesian recommendation algorithm based on the average value is used to optimize the network elements. Therefore, it is very efficient to realize overall performance optimization.

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Lu, Ying, Quan Yuan Xu, and Zhi Gang Liu. "Realization of the Viterbi Algorithm in MLSE with Intersymbol Interference." Applied Mechanics and Materials 214 (November 2012): 208–12. http://dx.doi.org/10.4028/www.scientific.net/amm.214.208.

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Optimum receiver model for channels with Intersymbol Interference (ISI) and Additive White Gaussian Noise (AWGN) are introduced to deduce the Viterbi algorithm in the Maximum-Likelihood Sequence Estimation (MLSE). Finally, we use Matlab to simulate the algorithm in three different channels and analyze the experiment results. Analyses show that the Viterbi algorithm is applicable for any channel which is optimum from a probability of error viewpoint; the MLSE for channels with ISI has a computational complexity that grows exponentially with the length of channels time dispersion L; the loss of performance is negligible when the decoding delay achieves 5L.

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Chen, B., and C. E. W. Sundberg. "List Viterbi algorithms for continuous transmission." IEEE Transactions on Communications 49, no. 5 (May 2001): 784–92. http://dx.doi.org/10.1109/26.923802.

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Seshadri, N., and C. E. W. Sundberg. "List Viterbi decoding algorithms with applications." IEEE Transactions on Communications 42, no. 2/3/4 (February 1994): 313–23. http://dx.doi.org/10.1109/tcomm.1994.577040.

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Sujatha, R., and T. M. Rajalaxmi. "Hierarchical Fuzzy Hidden Markov Chain for Web Applications." International Journal of Information Technology & Decision Making 15, no. 01 (January 2016): 83–118. http://dx.doi.org/10.1142/s0219622015500376.

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Fuzzy sets, a scheme for handling nonstatistical vague concepts, provide a natural basis for the theory of possibility space. In this paper, on possibility space, a hierarchical generalization of the fuzzy hidden Markov chain (HFHMC) which is named as FHMC is proposed. For the proposed model, three problems which naturally arise in any kind of hidden Markov models (HMMs) are discussed. To solve these problems, generalized Baum–Welch and generalized Viterbi algorithms are formulated; further it is observed that the generalized Viterbi algorithm itself solves the first two problems namely the likelihood of a given observation sequence and finding the most likelihood state sequence, which exhibits that the time complexity involved in the computation of two problems reduces to a single problem. In order to ensure the ease of models use, the proposed model is applied to our institution website and simulation is performed to analyze the accessibility of the website among the users.

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Turin, W. "Union Bounds on Viterbi Algorithm Performance." AT&T Technical Journal 64, no. 10 (December 1985): 2375–85. http://dx.doi.org/10.1002/j.1538-7305.1985.tb00008.x.

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Benvenuto, N., and R. Marchesani. "The Viterbi algorithm for sparse channels." IEEE Transactions on Communications 44, no. 3 (March 1996): 287–89. http://dx.doi.org/10.1109/26.486320.

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Maymon, Shay, and Yonina C. Eldar. "The Viterbi Algorithm for Subset Selection." IEEE Signal Processing Letters 22, no. 5 (May 2015): 524–28. http://dx.doi.org/10.1109/lsp.2014.2360881.

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31

Bouloutas, A., G. W. Hart, and M. Schwartz. "Two extensions of the Viterbi algorithm." IEEE Transactions on Information Theory 37, no. 2 (March 1991): 430–36. http://dx.doi.org/10.1109/18.75270.

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Miller, Casey, Bobby R. Hunt, Michael W. Marcellin, and Mark A. Neifeld. "Image restoration with the Viterbi algorithm." Journal of the Optical Society of America A 17, no. 2 (February 1, 2000): 265. http://dx.doi.org/10.1364/josaa.17.000265.

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Schlegel, C. B., and M. A. Herro. "A burst-error-correcting Viterbi algorithm." IEEE Transactions on Communications 38, no. 3 (March 1990): 285–91. http://dx.doi.org/10.1109/26.48885.

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Wang, Haocheng, Yafeng Wang, and Yue Hu. "Bidirectional Viterbi decoding algorithm for OvTDM." China Communications 17, no. 7 (July 2020): 183–92. http://dx.doi.org/10.23919/j.cc.2020.07.013.

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Aftelak, S. B., and A. P. Clark. "Adaptive reduced-state Viterbi algorithm detector." Journal of the Institution of Electronic and Radio Engineers 56, no. 5 (1986): 197. http://dx.doi.org/10.1049/jiere.1986.0079.

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Sidorenko, Vladimir, Wenhui Li, Onur Günlü, and Gerhard Kramer. "Skew Convolutional Codes." Entropy 22, no. 12 (December 2, 2020): 1364. http://dx.doi.org/10.3390/e22121364.

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A new class of convolutional codes, called skew convolutional codes, that extends the class of classical fixed convolutional codes, is proposed. Skew convolutional codes can be represented as periodic time-varying convolutional codes but have a description as compact as fixed convolutional codes. Designs of generator and parity check matrices, encoders, and code trellises for skew convolutional codes and their duals are shown. For memoryless channels, one can apply Viterbi or BCJR decoding algorithms, or a dualized BCJR algorithm, to decode skew convolutional codes.

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Han, Ke, Zhong Liang Deng, and Lian Ming Xu. "The Viterbi Decoding Scheme for FPGA." Applied Mechanics and Materials 63-64 (June 2011): 835–40. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.835.

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This paper analyzes the principle of Viterbi algorithm which can be used in the norm of the mobile communication system. Then a new Viterbi decoding scheme of (2, 1, 7) convolutional code is presented for FPGA implementation. To take advantage of the FPGA, a new branch weight algorithm and uniform state weight memories is used. At last, a new decoding circuit which can work on 35MHz and can achieve 120 kbs in decoding speed was designed. To use the design of survival path exchange register module, it can decrease the power consumption and the RAM size.

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Lember, Jüri, and Alexey Koloydenko. "ADJUSTED VITERBI TRAINING." Probability in the Engineering and Informational Sciences 21, no. 3 (July 2007): 451–75. http://dx.doi.org/10.1017/s0269964807000083.

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Viterbi training (VT) provides a fast but inconsistent estimator of hidden Markov models (HMM). The inconsistency is alleviated with a little extra computation when we enable VT to asymptotically fix the true values of the parameters. This relies on infinite Viterbi alignments and associated with them limiting probability distributions. First in a sequel, this article is a proof of concept; it focuses on mixture models, an important but special case of HMM where the limiting distributions can be calculated exactly. A simulated Gaussian mixture shows that our central algorithm (VA1) can significantly improve the accuracy of VT with little extra cost. Next in the sequel, we present elsewhere a theory of the adjusted VT for the general HMMs, where the limiting distributions are more challenging to find. Here, we also present another, more advanced correction to VT and verify its fast convergence and high accuracy; its computational feasibility requires additional investigation.

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Nishida, Noriki, and Hideki Nakayama. "Unsupervised Discourse Constituency Parsing Using Viterbi EM." Transactions of the Association for Computational Linguistics 8 (July 2020): 215–30. http://dx.doi.org/10.1162/tacl_a_00312.

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In this paper, we introduce an unsupervised discourse constituency parsing algorithm. We use Viterbi EM with a margin-based criterion to train a span-based discourse parser in an unsupervised manner. We also propose initialization methods for Viterbi training of discourse constituents based on our prior knowledge of text structures. Experimental results demonstrate that our unsupervised parser achieves comparable or even superior performance to fully supervised parsers. We also investigate discourse constituents that are learned by our method.

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Huang, Qin, Qiang Xiao, Li Quan, Zulin Wang, and Shafei Wang. "Trimming Soft-Input Soft-Output Viterbi Algorithms." IEEE Transactions on Communications 64, no. 7 (July 2016): 2952–60. http://dx.doi.org/10.1109/tcomm.2016.2569588.

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ZHAO, Xiaoping. "Gabor Order Tracking Based on Viterbi Algorithm." Journal of Mechanical Engineering 45, no. 11 (2009): 247. http://dx.doi.org/10.3901/jme.2009.11.247.

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Yoon, Sang-Hun, and Jun-Mo Jung. "Viterbi-based Decoding Algorithm for DBO-CSS." Journal of information and communication convergence engineering 9, no. 6 (December 31, 2011): 657–60. http://dx.doi.org/10.6109/ijice.2011.9.6.657.

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Kavcic, A., and J. M. F. Moura. "The Viterbi algorithm and Markov noise memory." IEEE Transactions on Information Theory 46, no. 1 (2000): 291–301. http://dx.doi.org/10.1109/18.817531.

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McGinty, N. C., R. A. Kennedy, and P. Hocher. "Parallel trellis Viterbi algorithm for sparse channels." IEEE Communications Letters 2, no. 5 (May 1998): 143–45. http://dx.doi.org/10.1109/4234.673661.

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Nohre, Ragnar. "Deformed template matching by the Viterbi algorithm." Pattern Recognition Letters 17, no. 14 (December 1996): 1423–28. http://dx.doi.org/10.1016/s0167-8655(96)00107-9.

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Chang, C. Y., and K. Yao. "Systolic array processing of the Viterbi algorithm." IEEE Transactions on Information Theory 35, no. 1 (1989): 76–86. http://dx.doi.org/10.1109/18.42179.

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Viterbi, A. J. "A personal history of the Viterbi algorithm." IEEE Signal Processing Magazine 23, no. 4 (July 2006): 120–42. http://dx.doi.org/10.1109/msp.2006.1657823.

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Abbasfar, A., and K. Yao. "Survivor memory reduction in the Viterbi algorithm." IEEE Communications Letters 9, no. 4 (2005): 352–54. http://dx.doi.org/10.1109/lcomm.2005.04004.

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Abbasfar, A., and Kung Yao. "Survivor memory reduction in the viterbi algorithm." IEEE Communications Letters 9, no. 4 (April 2005): 352–54. http://dx.doi.org/10.1109/lcomm.2005.1413631.

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Kuei-Ann Wen, Ting-Shiun Wen, and Jhing-Fa Wang. "A new transform algorithm for Viterbi decoding." IEEE Transactions on Communications 38, no. 6 (June 1990): 764–72. http://dx.doi.org/10.1109/26.57468.

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