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@PHDTHESIS{Yao:203514,
      author       = {Yao, Yu},
      title        = {{M}odel-based {A}lgorithm {D}evelopment with {F}ocus on
                      {B}iosignal {P}rocessing},
      volume       = {45},
      school       = {Universität Wuppertal},
      type         = {Dr.},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2015-05434},
      isbn         = {978-3-95806-080-7},
      series       = {Schriften des Forschungszentrums Jülich. Reihe Information
                      / Information},
      pages        = {x, 169 S.},
      year         = {2015},
      note         = {Universität Wuppertal, Diss., 2015},
      abstract     = {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. [...]},
      cin          = {ZEA-2},
      cid          = {I:(DE-Juel1)ZEA-2-20090406},
      pnm          = {573 - Neuroimaging (POF3-573)},
      pid          = {G:(DE-HGF)POF3-573},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      url          = {https://juser.fz-juelich.de/record/203514},
}