Results 191 to 200 of about 7,217 (228)

Unsupervised classification of Blanding's turtle (Emydoidea blandingii) behavioural states from multi-sensor biologger data. [PDF]

PLoS One
Adderley-Heron K, Chow-Fraser P.
europepmc   +1 more source

A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools. [PDF]

Front Syst Biol
Canonaco F, Gaudillo J, Astrologo N, Stella F, Acerbi E.   +4 more
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Machine condition recognition via hidden semi-Markov model

Computers & Industrial Engineering, 2021
Abstract In intelligent manufacturing systems, machines are subject to condition deterioration.Identifying machine condition is crucial for making practical decisions in production management. This paper studies the machine condition recognition problem in wafer fabrication.
Wenhui Yang, Lu Chen
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Hidden semi-Markov model for anomaly detection

Applied Mathematics and Computation, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tan, Xiaobin, Xi, Hongsheng
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Hidden semi-Markov model based speech synthesis

Interspeech 2004, 2004
In the present paper, a hidden-semi Markov model (HSMM) based speech synthesis system is proposed. In a hidden Markov model (HMM) based speech synthesis system which we have proposed, rhythm and tempo are controlled by state duration probability distributions modeled by single Gaussian distributions.
Heiga Zen, Keiichi Tokuda, Takashi Masuko, Takao Kobayashi, Tadashi Kitamura   +4 more
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Online identification of hidden Semi-Markov models

3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the, 2004
Hidden Markov models (HMM) are a powerful tool in signal modelling. In an HMM, the probability that signal leaves a state is constant, and hence the duration that signal stays in each state has an exponential distribution. However, this exponential density is not appropriate for a large class of physical signals.
M. Azimi, P. Nasiopoulos, R.K. Ward
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Asynchronous Brain Computer Interface using Hidden Semi-Markov Models

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012
Ideal Brain Computer Interfaces need to perform asynchronously and at real time. We propose Hidden Semi-Markov Models (HSMM) to better segment and classify EEG data. The proposed HSMM method was tested against a simple windowed method on standard datasets. We found that our HSMM outperformed the simple windowed method.
Gareth, Oliver, Peter, Sunehag, Tom, Gedeon   +2 more
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ECG segmentation algorithm based on bidirectional hidden semi-Markov model

Computers in Biology and Medicine, 2022
Accurate segmentation of electrocardiogram (ECG) waves is crucial for cardiovascular diseases (CVDs). In this study, a bidirectional hidden semi-Markov model (BI-HSMM) based on the probability distributions of ECG waveform duration was proposed for ECG wave segmentation.
Rui, Huo, Liting, Zhang, Feifei, Liu, Ying, Wang, Yesong, Liang, Shoushui, Wei   +5 more
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Activity recognition using logical hidden semi-Markov models

2013 10th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 2013
Activity recognition is challenging and valuable in both real and virtual world. As important directed graphical models, hidden Markov models and their extensions are widely used to solve probabilistic activity recognition problems. In this paper, logical hidden semi-Markov models (LHSMMs) which combine logical hidden Markov models (LHMMs), a ...
Ya-Bing Zha, Shi-Guang Yue, Quan-Jun Yin, Xiao-Cheng Liu   +3 more
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Coupled Hidden Semi Markov Models for Activity Recognition

2007 IEEE Workshop on Motion and Video Computing (WMVC'07), 2007
Recognizing human activity from a stream of sensory observations is important for a number of applications such as surveillance and human-computer interaction. Hidden Markov Models (HMMs) have been proposed as suitable tools for modeling the variations in the observations for the same action and for discriminating among different actions.
Pradeep Natarajan, Ramakant Nevatia
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