Searched for: subject%3A%22hidden%255C+Markov%255C+model%22
(1 - 19 of 19)
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Firtina, Can (author), Pillai, Kamlesh (author), Kalsi, Gurpreet S. (author), Suresh, Bharathwaj (author), Cali, Damla Senol (author), Kim, Jeremie S. (author), Shahroodi, Taha (author), Cavlak, Meryem Banu (author), Lindegger, Joël (author)
Profile hidden Markov models (pHMMs) are widely employed in various bioinformatics applications to identify similarities between biological sequences, such as DNA or protein sequences. In pHMMs, sequences are represented as graph structures, where states and edges capture modifications (i.e., insertions, deletions, and substitutions) by...
journal article 2024
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Budel, G.J.A. (author), Frasincar, Flavius (author), Boekestijn, David (author)
Sequence data mining has become an increasingly popular research topic as the availability of data has grown rapidly over the past decades. Sequence clustering is a type of method within this field that is in high demand in the industry, but the sequence clustering problem is non-trivial and, as opposed to static cluster analysis,...
journal article 2024
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de Boer, Wouter (author)
Autonomous robots are often successfully deployed in controlled environments. Operation in uncontrolled situations remains challenging; it is hypothesized that the detection of abstract discrete states (ADS) can improve operation in these circumstances. ADS are high-level system states that are not directly detectable and influence system...
master thesis 2023
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Wijnands, R. (author), Dauwels, J.H.G. (author), Serra, Ines (author), Kruizinga, P. (author), Badura, Aleksandra (author), Hunyadi, Borbala (author)
Functional ultrasound (fUS) is a novel neuroimaging technique that measures brain hemodynamics through a time series of Doppler images. The measured spatiotemporal hemodynamic changes reflect changes in neural activity through the neurovascular coupling (NVC). Often, such image time series is used to analyze dynamic functional connectivity ...
conference paper 2023
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Wang, Hanyu (author), Chen, Dengkai (author), Huang, Y. (author), Zhang, Yahan (author), Qiao, Yidan (author), Xiao, Jianghao (author), Xie, Ning (author), Fan, Hao (author)
This study aimed to enhance the real-time performance and accuracy of vigilance assessment by developing a hidden Markov model (HMM). Electrocardiogram (ECG) signals were collected and processed to remove noise and baseline drift. A group of 20 volunteers participated in the study. Their heart rate variability (HRV) was measured to train...
journal article 2023
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Mignacco, Chiara (author)
Since their introduction in 1993, particle filters are amongst the most popular algorithms for performing Bayesian inference on state space models that do not admit an analytical solution. In this thesis, we will present several particle filtering algorithms adapted to a class of models known as Piecewise Deterministic Markov Processes (PDMP), i...
master thesis 2022
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Wijnands, Ruben (author)
In recent years, the increase in brain research led to the development of large-scale brain imaging techniques. With large-scale brain imaging techniques, such as functional magnetic resonance imaging (fMRI), functional connectivity analyses have shown altered connectivity patterns in humans and mice with neurobiological disorders, such as...
master thesis 2022
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van Wijk, Robert (author), Lazcano, Andrea Michelle Rios (author), Akutain, Xabier Carrera (author), Shyrokau, B. (author)
Modern Advanced Driver Assistance Systems (ADAS) are limited in their ability to consider the driver's intention, resulting in unnatural guidance and low customer acceptance. In this research, we focus on a novel data-driven approach to predict driver steering torque. In particular, driver behavior is modeled by learning the parameters of a...
journal article 2022
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van Wijk, Robert (author)
Current commercial Driver Steering Assistance Systems (DSAS) focus on path-tracking performance without taking into account driver intentions. Improved driver-automation interaction can be achieved by sharing vehicle lateral control through torques. Furthermore, integrating a driver steering-torque model allows to better match driver intentions....
master thesis 2021
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Harleman, Bram (author)
Research has shown that GLOSA systems can help reduce the emission of $CO_2$ by motor vehicles and improve flow on urban road networks. However, existing GLOSA systems only work in combination with a limited selection of Traffic Signal Controllers (TSCs) and therefore have not been widely implemented. This research describes and evaluates the...
master thesis 2021
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Clay, Thomas A. (author), Joo, Rocío (author), Weimerskirch, Henri (author), Phillips, Richard A. (author), den Ouden, O.F.C. (author), Basille, Mathieu (author), Clusella-Trullas, Susana (author), Assink, Jelle D. (author), Patrick, Samantha C. (author)
In a highly dynamic airspace, flying animals are predicted to adjust foraging behaviour to variable wind conditions to minimize movement costs. Sexual size dimorphism is widespread in wild animal populations, and for large soaring birds which rely on favourable winds for energy-efficient flight, differences in morphology, wing loading and...
journal article 2020
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Yu, Rui (author)
The development of intelligent vehicle and autonomous driving asked a higher requirement of ADAS on its functionality. Currently, ADAS systems are able to detect and segment urban and highway driving scenes. They cannot, in general, extract ’meaning’ from this segmentation yet. Learning the intention of other road users will help ADAS understand...
master thesis 2019
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Richa, Eduardo (author)
The detection of unusual behavior plays a crucial role in the prevention of illegal and harmful activities such as smuggling, piracy, arms trading, human trafficking and illegal immigration. Also for military applications, it is useful to detect anomalous behavior to provide an alert for potential threats, especially with the more recent...
master thesis 2018
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Feng, R. (author), Luthi, S.M. (author), Gisolf, A. (author), Angerer, Erika (author)
In this paper, geological prior information is incorporated in the classification of reservoir lithologies after the adoption of Markov random fields (MRFs). The prediction of hidden lithologies is based on measured observations, such as seismic inversion results, which are associated with the latent categorical variables, based on the...
journal article 2018
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van Santvoort, Sander (author)
Hidden Markov models are suited for modelling processes whose state is not<br/>directly visible, but the outputs of which depend on its state. As such they can be used to model degradation of machines. Hidden semi Markov models extend hidden Markov models by removing the inherent geometric constraint imposed on hidden Markov models.<br/>Using...
master thesis 2018
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Janssen, Walter (author)
In this MSc. thesis steady-state disaggregation methods for the Non-Intrusive Load Monitoring are researched. The main target is to find methods that have the potential to be practically applicable. These methods are characterized by the fact that they use data that is collected by smart meters, since smart meters are more and more the standard...
master thesis 2018
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Alberts, J.S.C. (author)
This thesis discusses dimension reduction of the risk drivers that determine embedded option values by using the class of State Space Hidden Markov Models. As embedded options are typically valued by nested Monte Carlo simulations, this dimension reduction leads to a major reduction in computing time. This is especially important for insurance...
master thesis 2016
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Brouwer, J. (author)
Een studie naar Sequentiele Monte Carlo Methods toegepast op Hidden Markov Models. De Sequential Importance Sampling en Sequential Importance Resampling filters worden bestudeerd en de gebreken zoals weight degeneration en sample impoverishment worden aangetoond en bestreden. Afsluitend een model geconstrueerd voor Stochastische Volatiliteit.
bachelor thesis 2011
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Bouarfa, L. (author), Jonker, P.P. (author), Dankelman, J. (author)
Recognizing and understanding surgical high-level tasks from sensor readings is important for surgical workflow analysis. Surgical high-level task recognition is also a challenging task in ubiquitous computing because of the inherent uncertainty of sensor data and the complexity of the operating room environment. In this paper, we present a...
journal article 2010
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