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| Hauptseite > Publikationsdatenbank > Model-based Algorithm Development with Focus on Biosignal Processing > print |
| Hauptseite > Publikationsdatenbank > Model-based Algorithm Development with Focus on Biosignal Processing > print |
| 001 | 203514 | ||
| 005 | 20250129092500.0 | ||
| 020 | _ | _ | |a 978-3-95806-080-7 |
| 024 | 7 | _ | |2 Handle |a 2128/9194 |
| 024 | 7 | _ | |2 ISSN |a 1866-1777 |
| 037 | _ | _ | |a FZJ-2015-05434 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |0 P:(DE-Juel1)144822 |a Yao, Yu |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Model-based Algorithm Development with Focus on Biosignal Processing |f 2013-07-02 - 2015-07-01 |
| 260 | _ | _ | |a Jülich |b Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag |c 2015 |
| 300 | _ | _ | |a x, 169 S. |
| 336 | 7 | _ | |0 PUB:(DE-HGF)11 |2 PUB:(DE-HGF) |a Dissertation / PhD Thesis |b phd |m phd |s 1442235988_14017 |
| 336 | 7 | _ | |0 PUB:(DE-HGF)3 |2 PUB:(DE-HGF) |a Book |m book |
| 336 | 7 | _ | |0 2 |2 EndNote |a Thesis |
| 336 | 7 | _ | |2 DRIVER |a doctoralThesis |
| 336 | 7 | _ | |2 BibTeX |a PHDTHESIS |
| 336 | 7 | _ | |2 DataCite |a Output Types/Dissertation |
| 336 | 7 | _ | |2 ORCID |a DISSERTATION |
| 490 | 0 | _ | |a Schriften des Forschungszentrums Jülich. Reihe Information / Information |v 45 |
| 502 | _ | _ | |a Universität Wuppertal, Diss., 2015 |b Dr. |c Universität Wuppertal |d 2015 |
| 520 | _ | _ | |a In recent years, the development of cheap and robust sensors combined with the ever increasing availability of the internet led to a revolution in information technology, giving rise to an amount of data, which was unimaginable just a decade ago. This explosion in data lead to an increased demand for algorithms for processing this data. However, an often overlooked aspect is that with ever sophisticated algorithms there is associated a demand for equally sophisticated mathematical modelling. In this thesis, we explore the interaction between algorithm design and modelling. Although, the models and methods discussed here are not limited to any single domain of application, we will base our discussion on example applications from the domain of biomedical engineering. This is because the analysis of physiological time series is characterised by two problems which help to highlightthe importance of modelling. First, the high noise level of biological signals requires strong regularization, which can be provided via a model. Second, in many medical applications the value of interest is not directly observable. Thus, these latent variables have to be estimated, e.g. with the help of a model. In the course of our discussion, we will encounter two major modalities. The first one is Ballistocardiography (BCG), a modality often used in home monitoring applications, which is based on simple pressure sensors, yielding a scalar signal. The second modality is functional magnetic resonance imaging (fMRI), a complex and highly sophisticated method, capable of generating images of brain functionality. [...] |
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| 914 | 1 | _ | |y 2015 |
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