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- Bartl-Pokorny, K. D., et al.
(author)
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Eye Tracking in Basic Research and Clinical Practice
- 2013
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In: Klinische Neurophysiologie. - : Georg Thieme Verlag KG. - 1434-0275 .- 1439-4081. ; 44:3, s. 193-198
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Journal article (peer-reviewed)abstract
- Eye tracking is a non-invasive technique based on infrared video technology that is used to analyse eye movements. Such analyses might provide insights into perceptual and cognitive capacities. It is a method widely used in various disciplines, such as ophthalmology, neurology, psychiatry and neuropsychology for basic science, but also clinical practice. For example, recent studies on children who were later diagnosed with autism spectrum disorders revealed early abnormal eye movement patterns in socio-communicative settings; children with dyslexia appeared also to have peculiar eye movement patterns, expressed in longer fixation durations and smaller saccades while reading. Current research using eye tracking systems in combination with neurophysiological and brain imaging techniques will add to a better understanding of cognitive, linguistic and socio-communicative development and in the near future possibly also lead to a broader clinical application of this method.
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- Falck, Tillmann, et al.
(author)
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Least-Squares Support Vector Machines for the identification of Wiener-Hammerstein systems
- 2012
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In: Control Engineering Practice. - : Elsevier BV. - 0967-0661 .- 1873-6939. ; 20:11, s. 1165-1174
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Journal article (peer-reviewed)abstract
- This paper considers the identification of Wiener-Hammerstein systems using Least-Squares Support Vector Machines based models. The power of fully black-box NARX-type models is evaluated and compared with models incorporating information about the structure of the systems. For the NARX models it is shown how to extend the kernel-based estimator to large data sets. For the structured model the emphasis is on preserving the convexity of the estimation problem through a suitable relaxation of the original problem. To develop an empirical understanding of the implications of the different model design choices, all considered models are compared on an artificial system under a number of different experimental conditions. The obtained results are then validated on the Wiener-Hammerstein benchmark data set and the final models are presented. It is illustrated that black-box models are a suitable technique for the identification of Wiener-Hammerstein systems. The incorporation of structural information results in significant improvements in modeling performance.
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- Falck, Tillmann, et al.
(author)
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Segmentation of Time Series from Nonlinear Dynamical Systems
- 2011
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In: Proceedings of the 18th IFAC World Congress. - 9783902661937 ; , s. 13209-13214
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Conference paper (peer-reviewed)abstract
- Segmentation of time series data is of interest in many applications, as for example in change detection and fault detection. In the area of convex optimization, the sum-of-norms regularization has recently proven useful for segmentation. Proposed formulations handle linear models, like ARX models, but cannot handle nonlinear models. To handle nonlinear dynamics, we propose integrating the sum-of-norms regularization with a least squares support vector machine (LS-SVM) core model. The proposed formulation takes the form of a convex optimization problem with the regularization constant trading off the fit and the number of segments.
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