Journal ArticleDOI
A hidden semi-Markov model with missing data and multiple observation sequences for mobility tracking
Shun-Zheng Yu,Hisashi Kobayashi +1 more
Reads0
Chats0
TLDR
A new and computationally efficient forward-backward algorithm is proposed for HSMM with missing observations and multiple observation sequences, and the required computational amount for the forward and backward variables is reduced to O(D), where D is the maximum allowed duration in a state.Citations
More filters
Journal ArticleDOI
Hidden semi-Markov models
TL;DR: An overview of HSMMs is presented, including modelling, inference, estimation, implementation and applications, which has been applied in thirty scientific and engineering areas, including speech recognition/synthesis, human activity recognition/prediction, handwriting recognition, functional MRI brain mapping, and network anomaly detection.
Patent
Information processing device, information processing method, and program
Hirotaka Suzuki,Masato Ito +1 more
TL;DR: In this article, an information processing device including a display section configured to display a first object in a virtual three-dimensional space having a depth direction of a display screen, an operation part configured to acquire an operation for moving the first object on the display screen in accordance with the acquired operation, to execute, when a region of the first objects overlaps a first overlap determination region, a first process to one or both of thefirst and second objects, and to execute when the region of an object overlaps the second overlap determination regions, a second process to either the first or the second
Journal ArticleDOI
PM2.5 concentration prediction using hidden semi-Markov model-based times series data mining
TL;DR: A novel framework and methodology based on hidden semi-Markov models (HSMMs) for high PM"2".
Journal ArticleDOI
Practical implementation of an efficient forward-backward algorithm for an explicit-duration hidden Markov model
Shun-Zheng Yu,Hisashi Kobayashi +1 more
TL;DR: This correspondence addresses several practical problems in implementing a forward-backward (FB) algorithm for an explicit-duration hidden Markov model by redefined the FB variables in terms of posterior probabilities to avoid possible underflows that may occur in practice.
Journal ArticleDOI
Mobility Tracking in Cellular Networks Using Particle Filtering
Lyudmila Mihaylova,Donka Angelova,S. Honary,David Bull,Cedric Nishan Canagarajah,Branko Ristic +5 more
TL;DR: This paper proposes Monte Carlo techniques for mobility tracking in wireless communication networks by means of received signal strength indications that allow for accurate estimation of mobile station's position and speed.
References
More filters
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Journal ArticleDOI
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
TL;DR: The upper bound is obtained for a specific probabilistic nonsequential decoding algorithm which is shown to be asymptotically optimum for rates above R_{0} and whose performance bears certain similarities to that of sequential decoding algorithms.
Journal ArticleDOI
The viterbi algorithm
TL;DR: This paper gives a tutorial exposition of the Viterbi algorithm and of how it is implemented and analyzed, and increasing use of the algorithm in a widening variety of areas is foreseen.
Related Papers (5)
An efficient forward-backward algorithm for an explicit-duration hidden Markov model
Shun-Zheng Yu,Hisashi Kobayashi +1 more