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  • 1.
    Jansson, Daniel
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Rosén, Olov
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Non-parametric analysis of eye-tracking data by anomaly detection2013In: Proc. 12th European Control Conference, IEEE , 2013, p. 632-637Conference paper (Refereed)
  • 2.
    Jansson, Daniel
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Rosén, Olov
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Parametric and nonparametric analysis of eye-tracking data by anomaly detection2015In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 23, no 4, p. 1578-1586Article in journal (Refereed)
    Abstract [en]

    An approach to smooth pursuit eye movement's analysis by means of stochastic anomaly detection is presented and applied to the problem of distinguishing between patients diagnosed with Parkinson's disease and normal controls. Both parametric Wiener model-based techniques and nonparametric modeling utilizing a description of the involved probability density functions in orthonormal bases are considered. The necessity of proper visual stimuli design for the accuracy of mathematical modeling is highlighted and a formal method for producing such stimuli is suggested. The efficacy of the approach is demonstrated on experimental data collected by means of a commercial video-based eye tracker.

  • 3.
    Medvedev, Alexander
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Zhusubaliyev, Zhanybai T.
    Southwest State Univ, Dept Comp Sci, 50 Years October Str 94, RU-305040 Kursk, Russia.
    Rosén, Olov
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Martins da Silva, Margarida
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Oscillations-free PID control of anesthetic drug delivery in neuromuscular blockade2019In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 171, p. 119-131Article in journal (Refereed)
    Abstract [en]

    Background and Objectives: The PID-control of drug delivery or the neuromuscular blockade (NMB) in closed-loop anesthesia is considered. The NMB system dynamics portrayed by a Wiener model can exhibit sustained nonlinear oscillations under realistic PID gains and for physiologically feasible values of the model parameters. Such oscillations, also repeatedly observed in clinical trials, lead to under- and overdosing of the administered drug and undermine patient safety. This paper proposes a tuning policy for the proportional PID gain that via bifurcation analysis ensures oscillations-free performance of the control loop. Online estimates of the Wiener model parameters are needed for the controller implementation and monitoring of the closed-loop proximity to oscillation.

    Methods: The nonlinear dynamics of the PID-controlled NMB system are studied by bifurcation analysis. A database of patient models estimated under PID-controlled neuromuscular blockade during general anesthesia is utilized, along with the corresponding clinical measurements. The performance of three recursive algorithms is compared in the application at hand: an extended Kalman filter, a conventional particle filter (PF), and a PF making use of an orthonormal basis to estimate the probability density function from the particle set.

    Results: It is shown that with a time-varying proportional PID gain, the type of equilibria of the closed-loop system remains the same as in the case of constant controller gains. The recovery time and frequency of oscillations are also evaluated in simulation over the database of patient models. Nonlinear identification techniques based on model linearization yield biased parameter estimates and thus introduce superfluous uncertainty. The bias and variance of the estimated models are related to the computational complexity of the identification algorithms, highlighting the superiority of the PFs in this safety-critical application.

    Conclusions: The study demonstrates feasibility of the proposed oscillation-free control strategy combining bifurcation theory based design and online parameter estimation by PF.

  • 4.
    Rosén, Olov
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Orthogonal basis particle filtering: an approach to parallelization of recursive estimationManuscript (preprint) (Other academic)
  • 5.
    Rosén, Olov
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Parallel Stochastic Estimation on Multicore Platforms2015Doctoral thesis, monograph (Other academic)
    Abstract [en]

    The main part of this thesis concerns parallelization of recursive Bayesian estimation methods, both linear and nonlinear such. Recursive estimation deals with the problem of extracting information about parameters or states of a dynamical system, given noisy measurements of the system output and plays a central role in signal processing, system identification, and automatic control. Solving the recursive Bayesian estimation problem is known to be computationally expensive, which often makes the methods infeasible in real-time applications and problems of large dimension. As the computational power of the hardware is today increased by adding more processors on a single chip rather than increasing the clock frequency and shrinking the logic circuits, parallelization is one of the most powerful ways of improving the execution time of an algorithm. It has been found in the work of this thesis that several of the optimal filtering methods are suitable for parallel implementation, in certain ranges of problem sizes. For many of the suggested parallelizations, a linear speedup in the number of cores has been achieved providing up to 8 times speedup on a double quad-core computer. As the evolution of the parallel computer architectures is unfolding rapidly, many more processors on the same chip will soon become available. The developed methods do not, of course, scale infinitely, but definitely can exploit and harness some of the computational power of the next generation of parallel platforms, allowing for optimal state estimation in real-time applications.

  • 6.
    Rosén, Olov
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Parallelization of stochastic estimation algorithms on multicore computational platforms2013Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The main part of this licentiate thesis concerns parallelization of recursive estimation methods, both linear and nonlinear. Recursive estimation deals with the problem of extracting information about parameters or states of a dynamical system, given noisy measurements of the system output and plays a central role in many applications of signal processing, system identification, and automatic control. Solving the recursive Bayesian estimation problem is known to be computationally expensive, which often makes the methods infeasible in real-time applications and for problems of large dimension. As the computational power of the hardware is today increased by adding more processors on a single chip rather than increasing the clock frequency and shrinking the logic circuits, parallelization is the most powerful way of improving the execution time of an algorithm. It has been found in this thesis that several of the optimal filtering methods are suitable for parallel implementation, in certain ranges of problem sizes. It has been concluded from the experiments that substantial improvements can be achieved by performing "tailor"-made parallelization, compared to straightforward implementations based on multi-threaded libraries. For many of the suggested parallelizations, a linear speedup in the number of cores has been achieved that have provided up to 8 times speedup on a double quad-core computer. As the evolution of the parallel computer architectures is unfolding rapidly, many more processors on the same chip will become available. The developed methods do not, of course, scale infinitely, but definitely can exploit and harness some of the computational power of the next generation of parallel platforms, allowing for optimal state estimation in real-time applications.

    List of papers
    1. Efficient parallel implementation of state estimation algorithms on multicore platforms
    Open this publication in new window or tab >>Efficient parallel implementation of state estimation algorithms on multicore platforms
    2013 (English)In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 21, no 1, p. 107-120Article in journal (Refereed) Published
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:uu:diva-184672 (URN)10.1109/TCST.2011.2176735 (DOI)000318576100009 ()
    Available from: 2012-12-20 Created: 2012-11-12 Last updated: 2017-12-07Bibliographically approved
    2. Parallelization of the Kalman filter on multicore computational platforms
    Open this publication in new window or tab >>Parallelization of the Kalman filter on multicore computational platforms
    2013 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 21, no 9, p. 1188-1194Article in journal (Refereed) Published
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:uu:diva-207009 (URN)10.1016/j.conengprac.2013.03.008 (DOI)000322295600004 ()
    Available from: 2013-06-13 Created: 2013-09-09 Last updated: 2018-08-17Bibliographically approved
    3. Parallel recursive Bayesian estimation on multicore computational platforms using orthogonal basis functions
    Open this publication in new window or tab >>Parallel recursive Bayesian estimation on multicore computational platforms using orthogonal basis functions
    2014 (English)In: Proc. American Control Conference: ACC 2014, American Automatic Control Council , 2014, p. 622-627Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    American Automatic Control Council, 2014
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:uu:diva-229018 (URN)10.1109/ACC.2014.6858950 (DOI)000346492601029 ()978-1-4799-3272-6 (ISBN)
    Conference
    ACC 2014, June 4–6, Portland, OR
    Available from: 2014-06-06 Created: 2014-07-25 Last updated: 2015-07-24Bibliographically approved
    4. Non-parametric anomaly detection in trajectorial data
    Open this publication in new window or tab >>Non-parametric anomaly detection in trajectorial data
    2013 (English)Manuscript (preprint) (Other academic)
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:uu:diva-229019 (URN)
    Available from: 2013-04-19 Created: 2014-07-25 Last updated: 2014-07-25Bibliographically approved
  • 7.
    Rosén, Olov
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Martins da Silva, Margarida
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Nonlinear estimation of a parsimonious Wiener model for the neuromuscular blockade in closed-loop anesthesia2014In: Proc. 19th IFAC World Congress, International Federation of Automatic Control , 2014, p. 9258-9264Conference paper (Refereed)
  • 8.
    Rosén, Olov
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    An on-line algorithm for anomaly detection in trajectory data2012In: Proc. American Control Conference: ACC 2012, American Automatic Control Council , 2012, p. 1117-1122Conference paper (Refereed)
  • 9.
    Rosén, Olov
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Efficient Parallel Implementation of a Kalman Filter for Single Output Systems on Multicore Computational Platforms2011In: Proc. 50th Conference on Decision and Control, Piscataway, NJ: IEEE , 2011Conference paper (Refereed)
  • 10.
    Rosén, Olov
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Efficient parallel implementation of state estimation algorithms on multicore platforms2013In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 21, no 1, p. 107-120Article in journal (Refereed)
  • 11.
    Rosén, Olov
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Parallel recursive Bayesian estimation on multicore computational platforms using orthogonal basis functions2014In: Proc. American Control Conference: ACC 2014, American Automatic Control Council , 2014, p. 622-627Conference paper (Refereed)
  • 12.
    Rosén, Olov
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Parallel recursive estimation using Monte Carlo and orthogonal series expansions2015In: Proc. American Control Conference: ACC 2015, American Automatic Control Council , 2015, p. 3905-3910Conference paper (Refereed)
  • 13.
    Rosén, Olov
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Parallelization of the Kalman filter for banded systems on multicore computational platforms2012In: Proc. 51st Conference on Decision and Control, Piscataway, NJ: IEEE, 2012, p. 2022-2027Conference paper (Refereed)
  • 14.
    Rosén, Olov
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Ekman, Mats
    Speedup and tracking accuracy evaluation of parallel particle filter algorithms implemented on a multicore architecture2010In: Proc. International Conference on Control Applications: CCA 2010, Piscataway, NJ: IEEE , 2010, p. 440-445Conference paper (Refereed)
  • 15.
    Rosén, Olov
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Jansson, Daniel
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Non-parametric anomaly detection in trajectorial data2013Manuscript (preprint) (Other academic)
  • 16.
    Rosén, Olov
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Wigren, Torbjörn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Parallelization of the Kalman filter on multicore computational platforms2013In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 21, no 9, p. 1188-1194Article in journal (Refereed)
  • 17. Wahlberg, Fredrik
    et al.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Rosén, Olov
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    A LEGO-Based Mobile Robotic Platform for Evaluation of Parallel Control and Estimation Algorithms2011In: Proc. 50th Conference on Decision and Control, Piscataway, NJ: IEEE , 2011, p. 4548-4553Conference paper (Refereed)
    Abstract [en]

    An inexpensive robotic system intended for educational use in parallel algorithms for embedded control and signal processing is described. The hardware platform is comprised of a state-of-the-art multi-core system in a wireless network with several mobile LEGO robots that collect data from their environment. The setup covers a broad range of real-time cooperative and parallel problems arising in sensor networks, robotics, surveillance and high-performance embedded applications. As an illustration, a bearings-only tracking problem, estimating both mobile robots positions and the position of a non-cooperating target by using parallel particle filtering, is solved on the proposed platform. In order to improve the estimation accuracy and to adjust to changes in the environment and movements of the target, a controller positioning the mobile robots is utilized.

1 - 17 of 17
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