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Decoding of low-density parity-check codes is over finite-state binary Markov channel

IEEE TRANSACTIONS ON COMMUNICATIONS 2004年第11期52卷 1840-1843页

作者： Garcia-Frias, J Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA

We propose a modified algorithm for decoding of low-density parity-check codes over finite-state binary Markov channels. The proposed approach clearly outperforms systems in which the channel statistics are not exploi... 详细信息

关键词： baum-welch algorithm finite-state binary Markov channels Gilbert-Elliot channels low-density parity-check (LDPC) codes

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Enhanced Bayesian Gaussian hidden Markov mixture clustering for improved knowledge discovery

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PATTERN ANALYSIS AND APPLICATIONS 2024年第4期27卷 1-16页

作者： Ganesan, Anusha Paul, Anand Kim, Sungho Yeungnam Univ Dept Elect Engn Gyongsan 38541 South Korea Louisiana State Univ Dept Biostat & Data Sci Hlth Sci Ctr New Orleans LA 70112 USA

The hidden Markov model (HMM) is widely utilized in natural language processing, speech recognition, autonomous vehicular systems, and healthcare for tasks such as clustering, pattern recognition, predictive modeling, anomaly detection, and time-series forecasting. However, HMMs can be sensitive to initial states, compromising clustering reliability. To address this issue, we propose an innovative integration of an HMM with hybrid distance metric learning and a modified Bayesian Gaussian mixture model (BGMM) to enhance clustering performance and robustness. A significant challenge in HMM applications is determining the optimal number of hidden states. We address this using a k-fold cross-validation strategy. Implementing our Bayesian Gaussian Hidden Markov Mixture Clustering Model (BGH2MCM) on five diverse datasets, we categorize the observed data sequences according to underlying hidden state sequences. This approach yields superior outcomes to conventional techniques such as K-means, agglomerative clustering, density-based spatial clustering of applications with noise (DBSCAN), and the BGMM. We evaluate the efficiency of our model using silhouette, Davies-Bouldin, and Calinski-Harabasz scores, accuracy metrics, and computation time. Our results demonstrate that the BGH2MCM consistently achieves better clustering quality and computational efficiency, showing an average computation time 23% lower than agglomerative clustering with HMM, 22% less than DBSCAN with HMM, and 14% lower than K-means with the HMM and a BGMM-HMM across all datasets. This study highlights the potential of our BGH2MCM to improve data mining and knowledge discovery practices from complex, real-world datasets.

关键词： baum-welch algorithm Bayesian Bayesian Gaussian mixture model Clustering Cross-validation Distance metric Gaussian Hidden Markov model Mixture models Viterbi algorithm

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ACO-based BW algorithm for parameter estimation of hidden Markov models

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2011年第3-4期41卷 281-286页

作者： Wang, Qingmiao Ju, Shiguang Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Peoples R China Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Peoples R China

The conventional method for parameter estimation of HMMs uses the baum-welch (BW) algorithm. However, the BW algorithm is highly sensitive to initial values of the model parameters. In this paper, we propose an Ant Colony Optimisation (ACO)-based BW algorithm (ACO-BW) for estimating the parameters of HMMs. Our approach benefits from the properties of ACO algorithms and the BW algorithm by combination of both into a single procedure. The improved ACO algorithm provides a new model of artificial ants which are characterised by a relatively simple but efficient strategy of prey search. This is performed by parallel local searches on hunting sites with sensitivity to successful sites. The ACO-BW algorithm also maintains the monotonic convergence property of the BW algorithm. Experimental results show that ACO-BW obtains better values for the likelihood function as well as higher recognition accuracy than that of the HMMs trained by other existing methods.

关键词： HMM hidden Markov model baum-welch algorithm ACO ant colony optimisation pheromone continuous optimisation problem

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Hidden Markov model with missing emissions

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COMPUTATIONAL STATISTICS 2024年第2期39卷 385-403页

作者： Elkimakh, Karima Nasroallah, Abdelaziz Moroccan Sch Engn Sci EMSI LAMIGEP Lab Marrakech Morocco Cadi Ayyad Univ Libma Lab Fac Sci Semlalia Marrakech Morocco

In a Hidden Markov model (HMM), from hidden states, the model generates emissions that are visible. Generally, the problems to be solved by such models, are based on such emissions that are considered as observed data. In this work, we propose to study the case where some emissions are missing in a given emission sequence using different techniques, in particular a split technique which reduces the computational cost. Mainly we resolve the fundamental problems of an HMM with a lack of observations. The algorithms obtained following this approach are successfully tested through numerical examples.

关键词： Hidden Markov model Markov chain Forward and backward probabilities Viterbi algorithm baum-welch algorithm Monte Carlo simulation Missing observations Qualitative data

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Text Information Extraction based on Genetic algorithm and Hidden Markov Model

Text Information Extraction based on Genetic Algorithm and H...

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1st International Workshop on Education Technology and Computer Science

作者： Li, Rong Zheng, Jia-heng Pei, Chun-qin Xinzhou Teachers Coll Dept Comp Xinzhou 034000 Shanxi Peoples R China Shanxi Univ Sch Comp & Informat Technol Taiyuan 030006 Shanxi Peoples R China

ISBN: (纸本)9780769535579

Since the traditional training method of BAM for text information extraction is sensitive to the initial model parameters and easy to converge to a local optimal model in practice a novel hybrid model of genetic algorithm (GA) and hidden Markov model (HMM) for text information extraction is presented. During the parameter training phase, the hybrid method combines GA and baum-welch algorithm to optimize HMM parameters globally. In the selection process of the HMM initial parameters, the hybrid method adopts GA which uses real number matrix encoding as the representation of the chromosomes and the likelihood values as the fitness values, and then utilizes a modified baum-welch algorithm to reevaluate parameters and construct HMM. And during the information extraction phase, an improved Viterbi algorithm is presented to obtain the optimal state sequence of test sample for text information extraction. Experimental results show that the new algorithm improves the performance in precision and recall.

关键词： text information extraction genetic algorithm hidden markov model baum-welch algorithm Viterbi algorithm

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Audio-visual emotion recognition with multilayer boosted HMM

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Journal of Beijing Institute of Technology 2013年第1期22卷 89-93页

作者：吕坤贾云得张欣 School of Software Beijing Institute of Technology Beijing Lab of Intelligent Information Technology School of Computer ScienceBeijing Institute of Technology

Emotion recognition has become an important task of modern human-computer interac- tion. A multilayer boosted HMM （ MBHMM ） classifier for automatic audio-visual emotion recognition is presented in this paper. A modified baum-welch algorithm is proposed for component HMM learn- ing and adaptive boosting （AdaBoost） is used to train ensemble classifiers for different layers （cues）. Except for the first layer, the initial weights of training samples in current layer are decided by recognition results of the ensemble classifier in the upper layer. Thus the training procedure using current cue can focus more on the difficult samples according to the previous cue. Our MBHMM clas- sifier is combined by these ensemble classifiers and takes advantage of the complementary informa- tion from multiple cues and modalities. Experimental results on audio-visual emotion data collected in Wizard of Oz scenarios and labeled under two types of emotion category sets demonstrate that our approach is effective and promising.

关键词： emotion recognition audio-visual fusion baum-welch algorithm multilayer boostedHMM Wizard of Oz scenario

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Compound Attack Prediction Method Based on Improved algorithm of Hidden Markov Model

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JOURNAL OF WEB ENGINEERING 2020年第7-8期19卷 1213-1237页

作者： Zhao, Dongmei Wang, Hongbin Geng, Shixun Hebei Normal Univ Coll Comp & Cyber Secur Shijiazhuang Hebei Peoples R China Hebei Key Lab Network & Informat Secur Shijiazhuang Hebei Peoples R China

Network attacks are developing in the direction of concealment, complexity, multi-step, etc., making it difficult to identify and predict. In order to solve the problems such as the difficulty of determining the matching degree of the network attack, the difficulty of predicting the attack intention, and the incorrect calculation of the alarm intent sequence due to the incorrect alarm information, a hidden Markov model based on improved algorithm composite attack prediction is proposed. Firstly, in order to improve the learning ability and adaptability of the algorithm, an improved baum-welch algorithm is proposed to train the hidden Markov model (HMM) and generate new HMMs. Then use the Forward algorithm to calculate the HMM with the maximum probability of generating a pre-processed alarm message sequence. When the alarm message sequence is misreported, the attack intent sequence obtained by the classic Viterbi algorithm may be biased. This paper improves the Viterbi algorithm to make the extracted attack intention sequence more accurate. Finally, simulation results show that the model can effectively extract attack intention sequence and improve the accuracy of compound attack prediction.

关键词： Network security hidden Markov model compound attack prediction attack intention baum-welch algorithm forward algorithm Viterbi algorithm

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Online handwriting Farsi character and number recognition based on hand movement direction using Hidden Markov Models

Online handwriting Farsi character and number recognition ba...

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International Congress on Technology, Communication and Knowledge (ICTCK)

作者： Sadrnezhad, Zohre Nekouie, Atefeh Jahan, Majid Vafaei Islamic Azad Univ Mashhad Comp Software Mashhad Iran Islamic Azad Univ Mashhad Dept Comp Software Mashhad Iran

ISBN: (纸本)9781479980215

Online handwriting recognition has many applications and the recognition with high accuracy is essential. In this paper, we introduce a method for online handwriting Farsi character and number recognition using Hidden Markov Models (HMM). First we recognize handwriting direction then we get some statistical and formatting features. The letters are classified by means of these features and then we use HMM for the recognition. We have some movements outside of the main body at the beginning or the end letters and number, so in this paper we put a Noise State at the beginning and end hmm and put an accepting state at the end to increase the recognition accuracy. We use baum-welch algorithm to introduce HMM and then give some samples. Note that the test results demonstrate the scalability of the proposed model and the recognition accuracy for numbers is 99.22% and for letters is 95.91%.

关键词： baum-welch algorithm Forward algorithm online handwriting Farsi character and numbers recognition Hidden Markov Model (HMM)

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Narrowband PLC Channel Modeling using USRP and PSK Modulations

Narrowband PLC Channel Modeling using USRP and PSK Modulatio...

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18th IEEE International Symposium on Power Line Communications and its Applications (ISPLC)

作者： Familua, A. D. Ogunyanda, K. Swart, T. G. Ferreira, H. C. Van Olst, Rex Cheng, L. Univ Witwatersrand Sch Elect & Informat Engn Private Bag 3 ZA-2050 Johannesburg South Africa Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Auckland Pk South Africa

ISBN: (纸本)9781479949809

The indoor narrowband power line communication (NB-PLC) suffers from noise impairments, which emanate from several end-user electrical devices connected across the PLC channel. These noise impairments result into burst errors, which consequently lead to data corruption. Therefore, in order to implement robust communication techniques that will thrive on the noisy PLC channel, a full knowledge and modeling of the noise that exists on the NB-PLC channel is inevitable. This paper thus reports a First-order Markov modeling of NB-PLC channel noise, based on experimental measurements. For the modeling, BPSK, DBPSK, QPSK and DQPSK modulation schemes were implemented using Universal Software Radio Peripheral (USRP). The resulting channel models are useful for improving the robustness of the above modulation schemes as well as designing forward error correction techniques for mitigating the effect of noise impairments. The results are also useful in optimizing NB-PLC system design, thereby, enhancing the accuracy and improving the overall PLC system performance.

关键词： baum-welch algorithm CENELEC Fritchman model noise measurement power line communications USRP

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Identifying P2P network activities on encrypted traffic 13

Identifying P2P network activities on encrypted traffic

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13th IEEE International Conference on Trust Security and Privacy in Computing and Communications (TrustCom)

作者： Wang, Xiaolei Yang, Yuexiang He, Jie Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

ISBN: (纸本)9781479965137

Peer-to-Peer (P2P) traffic has always been a dominant portion of current Internet traffic and become more and more difficult to manage for Internet Service Producers (ISP) and network administrators. Although many methods have been proposed to classify different types of P2P applications and achieved satisfied performance, research on identifying network activities of a certain P2P application is still lacking to the best of our knowledge, which is urgently required in the context of forensic investigation for illegal P2P applications. In this paper, a novel approach based on Hidden Markov Model is proposed to identify network activities on the encrypted traffic, based on analysis of the time series characteristics and statistical properties of network traffic. After presenting a general model of network activities, TeamViewer is selected as a case study to verify the effectiveness of the approach to identify different activities. According to experiments using real network traces, our approach proves to be effective in identifying different activities of a P2P application with a high true positive 99.1% and low negligible false positive 3.6%.

关键词： Peer-to-Peer time series characteristics statistical properties Hidden Markov Model(HMM) TeamViewer baum-welch algorithm Viterbi algorithm

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