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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, 2012Ideal 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 +2 more
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Reliability modeling with hidden Markov and semi-Markov chains
2013 IEEE Integration of Stochastic Energy in Power Systems Workshop (ISEPS), 2013Abstract form only given. Semi-Markov processes and Markov renewal processes represent a class of stochastic processes that generalize Markov and renewal processes. As it is well known, for a discrete-time (respectively continuous-time) Markov process, the sojourn time in each state is geometrically (respectively exponentially) distributed. In the semi-
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Semi-continuous hidden Markov models for speech signals
Computer Speech & Language, 1989Abstract A semi-continuous hidden Markov model, which can be considered as a special form of continuous mixture hidden Markov model with the continuous output probability density functions sharing in a mixture Gaussian density codebook, is proposed in this paper.
X.D. Huang, M.A. Jack
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Speaker adaptation using semi-continuous hidden Markov models
Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. IV. Conference D: Architectures for Vision and Pattern Recognition,, 2003Presents a new approach to speaker adaptation based on semi-continuous hidden Markov models (SCHMM). The authors introduce a modification of the semi-continuous codebook updating which allows rapid speaker adaptation. The approach is based on the idea that phonetic information already incorporated in a trained model should be used to update the ...
S. Rieck +2 more
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Hidden Markov and semi-Markov models for count time series
2022Hidden Markov models (HMMs) are models in which the distributionthat generates an observation depends on the state of an underlying and unobserved Markov process. HMMs have been employed in a variety of areas, including signal processing, bioinformatics, environment and ecology, and are noted for their flexibility and computational efficiency.
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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), 2013Activity 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 +3 more
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Coupled Hidden Semi Markov Models for Activity Recognition
2007 IEEE Workshop on Motion and Video Computing (WMVC'07), 2007Recognizing 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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Implementation of hidden semi-Markov models
2011One of the most frequently used concepts applied to a variety of engineering and scientific studies over the recent years is that of a Hidden Markov Model (HMM). The Hidden semi-Markov model (HsMM) is contrived in such a way that it does not make any premise of constant or geometric distributions of a state duration.
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Antibody–drug conjugates: Smart chemotherapy delivery across tumor histologies
Ca-A Cancer Journal for Clinicians, 2022Paolo Tarantino +2 more
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An overview of real‐world data sources for oncology and considerations for research
Ca-A Cancer Journal for Clinicians, 2022Lynne Penberthy +2 more
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