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Combined turbo detection and decoding for unknown ISI channels

IEEE TRANSACTIONS ON COMMUNICATIONS 2003年第1期51卷 79-85页

作者： Garcia-Frias, J Villasenor, JD Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90024 USA

We present two decoding structures which combine turbo detection and decoding, allowing communication in the presence of intersymbol interference (ISI). The first one treats the ISI as another constituent decoder which participates in the exchange of extrinsic information, and performs slightly worse than the second structure, which combines the trellis representing each one of the constituent encoders with the ISI trellis. We show that for both methods, it is possible to obtain good performance, even when no a priori information about the ISI channel is available to the decoder.

关键词： baum-welch algorithm intersymbol interference (ISI) channels iterative decoding joint parameter estimation and decoding turbo codes

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Comparing and evaluating HMM ensemble training algorithms using train and test and condition number criteria

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PATTERN ANALYSIS AND APPLICATIONS 2003年第4期6卷 327-336页

作者： Davis, RIA Lovell, BC Univ Queensland Sch Informat Technol & Elect Engn Intelligent Real Time Imaging & Sensing Grp Brisbane Qld 4072 Australia

Hidden Markov Models have many applications in signal processing and pattern recognition, but their convergence-based training algorithms are known to suffer from over-sensitivity to the initial random model choice. This paper describes the boundary between regions in which ensemble learning is superior to Rabiner's multiple-sequence baum-welch training method, and proposes techniques for determining the best method in any arbitrary situation. It also studies the suitability of the training methods using the condition number, a recently proposed diagnostic tool for testing the quality of the model. A new method for training Hidden Markov Models called the Viterbi Path Counting algorithm is introduced and is found to produce significantly better performance than current methods in a range of trials.

关键词： baum-welch algorithm ensemble learning hidden Markov model model structure multiple sequence Viterbi

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Localization of a linear structure object in medical images based on hidden Markov model

Localization of a linear structure object in medical images ...

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16th IEEE Symposium on Computer-Based Medical Systems

作者： Xu, T Xing, HC Nanjing Univ Aeronaut & Astronaut Dept Comp Sci & Engn Nanjing 210016 Peoples R China

ISBN: (纸本)0769519016

In this paper a novel approach to localization of a linear structure object (spine) in medical images using one-dimensional Hidden Markov Model is proposed Feature sequence of a linear structure object is extracted by using a horizontal-line sampling window with fixed width along the central axis of the object in training phase and the model training is performed through maximum likelihood estimation provided by the baum-welch algorithm. A specific localization method derived from Viterbi algorithm is presented for determining a feature sequence in a test image, from which the position of a linear structure object could be obtained. The use of heuristic information improves obviously the performance of localization and the computational complexity. The experiments demonstrate the effectiveness in localization application based on the simple feature expression and the proposed localization method.

关键词： hidden Markov models medical image processing physiological models algorithm theory feature extraction linear structure object baum-welch algorithm Viterbi algorithm one-dimensional hidden markov model horizontal-line sampling window training phase feature sequence test image heuristic information computational complexity localization application simple feature expression localization method medical images

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Hidden Gauss-Markov models for signal classification

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2002年第6期50卷 1355-1367页

作者： Ainsleigh, PL Kehtarnavaz, N Streit, RL USN Undersea Warfare Ctr Newport RI 02841 USA Univ Texas Dept Elect Engn Richardson TX 75080 USA

Continuous-state hidden Markov models (CS-HMMs) are developed as a tool for signal classification. Analogs of the baum, Viterbi, and baum-welch algorithms are formulated for this class of models. The CS-HMM algorithms are then specialized to hidden Gauss-Markov models (HGMMs) with linear Gaussian state-transition and output densities. A new Gaussian refactorization lemma is used to show that the baum and Viterbi algorithms for HGMMs are implemented by two different formulations of the fixed-interval Kalman smoother. The measurement likelihoods obtained from the forward pass of the HGMM baum algorithm and from the Kalman-filter innovation sequence are shown to be equal. A direct link between the baum-welch training algorithm and an existing expectation-maximization (EM) algorithm for Gaussian models is demonstrated. A new expression for the cross covariance between time-adjacent states in HGMMs is derived from the off-diagonal block of the conditional joint covariance matrix. A parameter invariance structure is noted for the HGMM likelihood function. CS-HMMs and HGMMs are extended to incorporate mixture densities for the a priori density of the initial state. Application of HGMMs to signal classification is demonstrated with a three-class test simulation.

关键词： baum-welch algorithm continuous-state HMM EM algorithm fixed-interval smoother forward-backward algorithm hidden Markov model Kalman filter maximum likelihood classification mixture density

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High-order extensions of the double chain Markov model

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STOCHASTIC MODELS 2002年第2期18卷 193-227页

作者： Berchtold, A Univ Lausanne Inst Appl Math SSP BFSH2 CH-1015 Lausanne Switzerland Univ Washington Seattle WA 98195 USA

The Double Chain Markov Model is a fully Markovian model for the representation of time-series in random environments. In this article, we show that it can handle transitions of high-order between both a set of observations and a set of hidden states. In order to reduce the number of parameters, each transition matrix can be replaced by a Mixture Transition Distribution model. We provide a complete derivation of the algorithms needed to compute the model. Three applications, the analysis of a sequence of DNA, the song of the wood pewee, and the behavior of young monkeys show that this model is of great interest for the representation of data that can be decomposed into a finite set of patterns.

关键词： double chain Markov model (DCMM) high-order transitions mixture transition distribution model (MTD) forward-backward algorithm baum-welch algorithm viterbi algorithm DNA pewee behavior

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Turbo decoding of Gilbert-Elliot channels

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IEEE TRANSACTIONS ON COMMUNICATIONS 2002年第3期50卷 357-363页

作者： Garcia-Frias, J Villasenor, JD Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90024 USA

We describe parallel concatenated codes for communication over finite-state binary Markov channels. We present encoder design techniques and decoder processing modifications that utilize the a priori statistics of the channel and show that the resulting codes allow reliable communication at rates which are above the capacity of a memoryless channel with the same stationary bit error probability as the Markov channel. These codes outperform systems based on the traditional approach of using a channel interleaver to create a channel which is assumed to be memoryless. In addition, we introduce a joint estimation/decoding method that allows the estimation of the parameters of the hidden Markov model when they are not known a priori.

关键词： baum-welch algorithm finite-state binary Markov channels Gilbert-Elliot channels iterative decoding turbo codes

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Optimisation of HMM topology and its model parameters by genetic algorithms

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PATTERN RECOGNITION 2001年第2期34卷 509-522页

作者： Kwong, S Chau, CW Man, KF Tang, KS City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

Hidden Markov model (HMM) is currently the most popular approach to speech recognition. However, the problems of finding a good HMM model and its optimised model parameters are still of great interest to the researchers in this area. In our previous work, we have successfully applied the genetic algorithm (GA) to the HMM training process to obtain the optimised model parameters (Chau et al. Proc. ICASSP (1997) 1727) of the HMM models. In this paper, we further extend our work and propose a new training method based on GA and baum-welch algorithms to obtain an HMM model with optimised number of states in the HMM models and its model parameters. In this work, we are not only able to overcome the shortcomings of the slow convergence speed of the simple GA-HMM approach. In addition, this method also finds better number of states in the HMM topology as well as its model parameters. From our experiments with the 100 words extracted from the TIMIT corpus, our method is able to find the optimal topology in all cases. In addition, the HMMs trained by our GA HMM training have a better recognition capability than the HMMs trained by the baum-welch algorithm. In addition, 290 words are randomly selected from the TMIIT database for testing the recognition performances of both approaches, it is found that the GA-HMM approach has a recognition rate of 95.86% while the baum-welch method has a recognition rate of 93.1%. This implies that the HMMs trained by our GA-HMM method are more optimised than the HMMs trained by the baum-welch method. (C) 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

关键词： hidden Markov model genetic algorithm baum-welch algorithm speech recognition pattern recognition

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Joint turbo decoding and estimation of hidden Markov sources

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2001年第9期19卷 1671-1679页

作者： Garcia-Frias, J Villasenor, JD Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90024 USA

We describe a joint source-channel scheme for modifying a turbo decoder in order to exploit the statistical characteristics of hidden Markov sources. The basic idea is to treat the trellis describing the hidden Markov source as another constituent decoder which exchanges information with the other constituent decoder blocks. The source block uses as extrinsic information the probability of the input bits that is provided by the constituent decoder blocks. On the other hand, it produces a new estimation of such a probability which will be used as extrinsic information by the constituent turbo decoders. The proposed joint source-channel decoding technique leads to significantly improved performance relative to systems in which source statistics are not exploited and avoids the need to perform any explicit source coding prior to transmission. Lack of a priori knowledge of the source parameters does not degrade the performance of the system, since these parameters can be jointly estimated with turbo decoding.

关键词： baum-welch algorithm hidden Markov sources iterative decoding joint source-channel coding turbo codes

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Training hidden Markov models with multiple observations - A combinatorial method

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2000年第4期22卷 371-377页

作者： Li, XL Parizeau, M Plamondon, R CADlink Technol Corp Ottawa ON K1H 1E1 Canada Univ Laval Dept Genie Elect & Genie Informat St Foy PQ G1K 7P4 Canada Ecole Polytech Montreal PQ H3C 3A7 Canada

Hidden Markov models (HMMs) are stochastic models capable of statistical learning and classification. They have been applied in speech recognition and handwriting recognition because of their great adaptability and Versatility in handling sequential signals. On the other hand, as these models have a complex structure and also because the involved data sets usually contain uncertainty, it is difficult to analyze the multiple observation training problem without certain assumptions. For many years researchers have used Levinson's training equations in speech and handwriting applications, simply assuming that all observations are independent of each other. This paper presents a formal treatment of HMM multiple observation training without imposing the above assumption. In this treatment, the multiple observation probability is expressed as a combination of individual observation probabilities without losing generality. This combinatorial method gives one more freedom in making different dependence-independence assumptions. By generalizing baum's auxiliary function into this framework and building up an associated objective function using the Lagrange multiplier method, it is proven that the derived training equations guarantee the maximization of the objective function. Furthermore, we show that Levinson's training equations can be easily derived as a special case in this treatment.

关键词： hidden Markov model forward-backward procedure baum-welch algorithm multiple observation training

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A hidden Markov model-based algorithm for fault diagnosis with partial and imperfect tests

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS 2000年第4期30卷 463-473页

作者： Ying, J Kirubarajan, T Pattipati, KR Patterson-Hine, A Univ Connecticut Dept Elect & Syst Engn Storrs CT 06269 USA NASA Ames Res Ctr Moffett Field CA 94035 USA

ln this paper, we present a hidden Markov model (HMM) based algorithm for fault diagnosis in systems with partial and imperfect tests. The HMM-based algorithm Ends the most likely state evolution, given a sequence of uncertain test outcomes over time. We also present a method to estimate online the HMM parameters, namely, the state transition probabilities, the instantaneous probabilities of test outcomes given the system state and the initial state distribution, that are fundamental to HMM-based adaptive fault diagnosis, The efficacy of parameter estimation method is demonstrated by comparing the diagnostic accuracies of an algorithm with complete knowledge of HMM parameters with those of an adaptive one. In addition, the advantages of using the HMM approach over a Hamming-distance based fault diagnosis technique are quantified. Tradeoffs in computational complexity versus performance of the diagnostic algorithm are also discussed.

关键词： baum-welch algorithm fault diagnosis Hamming distance hidden Markov models imperfect tests Viterbi decoding

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