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  • 1. Birk, W
    et al.
    Johansson, A
    Medvedev, A
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Model-based control for a fine coal injection plant1999In: IEEE Control Systems Magazine, Vol. 19, no 1, p. 33-43Article in journal (Refereed)
    Abstract [en]

    Modeling, control, and gas leakage detection in the coal injection process are discussed. It is shown that by use of model-based methods, the flow and pressure of the coal injection vessel are reliably controlled. With the new control law, the coal mass flow can be used as a control parameter for the blast furnace. High injection rates can be used and more coke substituted, This is expected to yield a cost reduction in the iron production. An experimental comparison of the conventional control unit with the one suggested in this article shows that an improvement of the process efficiency can be reached by other means than increasing the capacity of the plant

  • 2.
    Björk, Marcus
    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.
    Estimation of dynamic models with output quantization applied to drug response modeling2010Conference paper (Other academic)
  • 3.
    Björk, Marcus
    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.
    Stoica, Peter
    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.
    Dynamic models with quantized output for modeling patient response to pharmacotherapy2010In: Proc. International Conference on Control Applications: CCA 2010, Piscataway, NJ: IEEE , 2010, p. 1029-1034Conference paper (Refereed)
    Abstract [en]

    This article presents a way of modeling patient response to a pharmacotherapy by means of dynamic models with quantized output. The proposed modeling technique is exemplified by treatment of Parkinson's disease with Duodopa ®, where the drug is continuously administered via duodenal infusion. Titration of Duodopa ® is currently performed manually by a nurse judging the patient's motor symptoms on a quantized scale and adjusting the drug flow provided by a portable computer-controlled infusion pump. The optimal drug flow value is subject to significant inter-individual variation and the titration process might take up to two weeks for some patients. In order to expedite the titration procedure via automation, as well as to find optimal dosing strategies, a mathematical model of this system is sought. The proposed model is of Wiener type with a linear dynamic block, cascaded with a static nonlinearity in the form of a non-uniform quantizer where the quantizer levels are to be identified. An identification procedure based on the prediction error method and the Gauss-Newton algorithm is suggested. The datasets available from titration sessions are scarce so that finding a parsimonious model is essential. A few different model parameterizations and identification algorithms were initially evaluated. The results showed that models with four parameters giving accurate predictions can be identified for some of the available datasets.

  • 4.
    Bro, Viktor
    et al.
    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, Automatic control.
    Constrained SPICE in Volterra-Laguerre modeling of human smooth pursuit2017In: 2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017), IEEE, 2017, p. 13-18Conference paper (Refereed)
    Abstract [en]

    The Volterra model is a well-established option in nonlinear black-box system identification. However, the estimated model is often over-parametrized. This paper presents an approach to reducing the number of parameters of a Volterra model with the kernels parametrized in the orthonormal basis of Laguerre functions by estimating it with a sparse estimation algorithm subject to constraints. The resulting parameter estimates are scrutinized for parameter redundancy and functional dependence by principal component analysis. The benefits of this approach are illustrated by identifying the human smooth pursuit system. Previous studies have suggested that the Volterra model structure is suitable for modeling the human smooth pursuit system both in health and disease. The data sets are obtained by eye tracking in a study performed on 7 test subjects diagnosed with Parkinson's disease and 22 healthy control subjects. In terms of output error, the reduced model has similar performance to that of the full model.

  • 5.
    Bro, Viktor
    et al.
    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, Automatic control.
    Nonlinear Dynamics of the Human Smooth Pursuit System in Health and Disease: Model Structure and Parameter Estimation2017In: IEEE 56th Annual Conference on Decision and Control (CDC), IEEE, 2017, p. 4692-4697Conference paper (Refereed)
    Abstract [en]

    Oculomotor tests (OMT) are administered to quantify symptoms in neurological and mental diseases. Eye movements in response to displayed visual stimuli are registered by an digital video-based eye tracker and processed. Stimuli of simple signal form, e.g. sine waves, are traditionally used in medical practice to test the performance of the oculomotor system in smooth pursuit (SP). The calculated SP gain and the phase shift at the frequency in question are then presented as the test outcome. This paper revisits the problem of quantifying the SP dynamics from eye-tracking data by means of nonlinear system identification. First, a sparse Volterra-Laguerre (VL) model is estimated from an OMT with sufficiently exciting (in frequency and amplitude) stimuli. Then the structure and initial parameter estimates of a polynomial Wiener model (WM) are obtained from the kernel estimates of the VL model. Finally, the parameter distributions of the WM are inferred by a particle filter (PF). In the proposed approach, the performance of the PF is improved by the individualized sparse model structure. Experimental data show that the latter captures the alternations in the SP dynamics due to aging and in Parkinson’s disease.

  • 6. Churilov, Alexander
    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.
    Mattsson, Per
    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.
    Periodical solutions in a pulse-modulated model of endocrine regulation with time-delay2014In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 59, no 3, p. 728-733Article in journal (Refereed)
  • 7. Churilov, Alexander
    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.
    Shepeljavyi, Alexander
    A state observer for continuous oscillating systems under intrinsic pulse-modulated feedback2012In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 48, no 6, p. 1117-1122Article in journal (Refereed)
  • 8. Churilov, Alexander
    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.
    Shepeljavyi, Alexander
    Further Results on a State Observer for Continuous Oscillating Systems under Intrinsic Pulsatile Feedback2011In: Proc. 50th Conference on Decision and Control, Piscataway, NJ: IEEE , 2011Conference paper (Refereed)
  • 9. Churilov, Alexander
    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.
    Shepeljavyi, Alexander
    State observer for continuous oscillating systems with pulsatile feedback2011In: Proc. 18th IFAC World Congress, International Federation of Automatic Control , 2011Conference paper (Refereed)
  • 10. Churilov, Alexander
    et al.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Shepelyavyi, Alexander
    Mathematical model of non-basal testosterone regulation in the male by pulse modulated feedback2009In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 45, no 1, p. 78-85Article in journal (Refereed)
    Abstract [en]

    A parsimonious mathematical model of pulse modulated regulation of non-basal testosterone secretion in the male is developed. The model is of the third differential order, reflecting the three most significant hormones in the regulation loop, but is yet shown to be capable of sustaining periodic solutions with one or two pulses of gonadotropin-releasing hormone (GnRH) in each period. Lack of stable periodic solutions is otherwise a main shortcoming of existing low-order hormone regulation models. Existence and stability of periodic solutions are studied. The periodic mode with two GnRH pulses in the least period has not been described in medical literature, but is found to explain experimental data well.

  • 11. Churilov, Alexander N.
    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.
    An impulse-to-impulse discrete-time mapping for a time-delay impulsive system2014In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 50, no 8, p. 2187-2190Article in journal (Refereed)
    Abstract [en]

    It is shown that an impulsive system with a time-delay in the continuous part can be equivalently represented by discrete dynamics under less restrictive conditions on the time-delay value than considered previously.

  • 12. Churilov, Alexander N.
    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.
    Discrete-time map for an impulsive Goodwin oscillator with a distributed delay2016In: MCSS. Mathematics of Control, Signals and Systems, ISSN 0932-4194, E-ISSN 1435-568X, Vol. 28, no 1, article id 9Article in journal (Refereed)
  • 13.
    Churilov, Alexander N.
    et al.
    St Petersburg State Univ, Dept Math & Mech, St Petersburg, Russia..
    Medvedev, Alexander
    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, Kursk, Russia..
    Delay-induced Dynamical Phenomena in Impulsive Goodwin's Oscillator: What We Know So Far2015In: 2015 54Th Ieee Conference On Decision And Control (CDC), 2015, p. 590-595Conference paper (Refereed)
    Abstract [en]

    Impulsive Goodwin's oscillator model is introduced to capture the dynamics of sustained periodic processes in endocrine systems controlled by episodic pulses of hormones. The model is hybrid and comprises a continuous subsystem describing the hormone concentrations operating under a discrete pulse-modulated feedback implemented by firing neurons. Time delays appear in mathematical models of endocrine systems due to the significant transport phenomena but also because of the time necessary to produce releasable hormone quantities. From a biological point of view, the neural control should be robust against the time delay to ensure the loop functionality over a wide range of inter-individual variability. The paper provides an overview of the currently available results and contributes a generalization of a Poincare mapping approach to study complex dynamics of impulsive Goodwin oscillator. Both pointwise and distributed time delays are considered in a general framework based on the Poincare mapping. Bifurcation analysis is utilized to illustrate the analytical results.

  • 14. Churilov, Alexander N.
    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.
    Zhusubaliyev, Zhanybai T.
    Discrete-time mapping for an impulsive Goodwin oscillator with three delays2017In: International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, ISSN 0218-1274, Vol. 27, no 12, article id 1750182Article in journal (Refereed)
    Abstract [en]

    A popular biomathematics model of the Goodwin oscillator has been previously generalized to a more biologically plausible construct by introducing three time delays to portray the transport phenomena arising due to the spatial distribution of the model states. The present paper addresses a similar conversion of an impulsive version of the Goodwin oscillator that has found application in mathematical modeling, e.g. in endocrine systems with pulsatile hormone secretion. While the cascade structure of the linear continuous part pertinent to the Goodwin oscillator is preserved in the impulsive Goodwin oscillator, the static nonlinear feedback of the former is substituted with a pulse modulation mechanism thus resulting in hybrid dynamics of the closed-loop system. To facilitate the analysis of the mathematical model under investigation, a discrete mapping propagating the continuous state variables through the firing times of the impulsive feedback is derived. Due to the presence of multiple time delays in the considered model, previously developed mapping derivation approaches are not applicable here and a novel technique is proposed and applied. The mapping captures the dynamics of the original hybrid system and is instrumental in studying complex nonlinear phenomena arising in the impulsive Goodwin oscillator. A simulation example is presented to demonstrate the utility of the proposed approach in bifurcation analysis.

  • 15. Churilov, Alexander N.
    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.
    Zhusubaliyev, Zhanybai T.
    Impulsive Goodwin oscillator with large delay: Periodic oscillations, bistability, and attractors2016In: Nonlinear Analysis: Hybrid Systems, ISSN 1751-570X, E-ISSN 1878-7460, Vol. 21, p. 171-183Article in journal (Refereed)
  • 16.
    Cubo, Rubén
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Fahlström, Markus
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. Uppsala Univ Hosp, Dept Biomed Technol Med Phys & IT, Uppsala, Sweden.
    Jiltsova, Elena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology.
    Andersson, Helena
    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.
    Semi-Individualized electrical models in deep brain stimulation: A variability analysis2017In: 2017 IEEE Conference on Control Technology and Applications (CCTA), IEEE, 2017, p. 517-522Conference paper (Refereed)
    Abstract [en]

    Deep Brain Stimulation (DBS) is a well-established treatment in neurodegenerative diseases, e.g. Parkinson's Disease. It consists of delivering electrical stimuli to a target in the brain via a chronically implanted lead. To expedite the tuning of DBS stimuli to best therapeutical effect, mathematical models have been developed during recent years. The electric field produced by the stimuli in the brain for a given lead position is evaluated by numerically solving a Partial Differential Equation with the medium conductivity as a parameter. The latter is patient- and target-specific but difficult to measure in vivo. Estimating brain tissue conductivity through medical imaging is feasible but time consuming due to registration, segmentation and post-processing. On the other hand, brain atlases are readily available and processed. This study analyzes how alternations in the conductivity due to inter-patient variability or lead position uncertainties affect both the stimulation shape and the activation of a given target. Results suggest that stimulation shapes are similar, with a Dice's Coefficient between 93.2 and 98.8%, with a higher similarity at lower depths. On the other hand, activation shows a significant variation of 17 percentage points, with most of it being at deeper positions as well. It is concluded that, as long as the lead is not too deep, atlases can be used for conductivity maps with acceptable accuracy instead of fully individualized though medical imaging models.

  • 17.
    Cubo, Rubén
    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.
    Jiltsova, Elena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurosurgery.
    Fahlström, Markus
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
    Andersson, Helena
    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.
    Optimization of deep brain stimulation by means of a patient-specific mathematical model2016Conference paper (Refereed)
  • 18.
    Cubo, Rubén
    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.
    Accuracy of the Finite Element Method in Deep Brain Stimulation Modelling2014In: Proc. International Conference on Control Applications: CCA 2014, Piscataway, NJ: IEEE , 2014, p. 1479-1484Conference paper (Refereed)
  • 19.
    Cubo, Rubén
    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.
    Individualization of a surrounding tissue model in Deep Brain Stimulation2017In: Proc. 56th Conference on Decision and Control, Piscataway, NJ: IEEE, 2017, p. 5919-5924Conference paper (Refereed)
  • 20.
    Cubo, Rubén
    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.
    Andersson, Helena
    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.
    Deep Brain Stimulation therapies: a control-engineering perspective2017In: Proc. American Control Conference: ACC 2017, IEEE, 2017, p. 104-109Conference paper (Refereed)
    Abstract [en]

    Deep Brain Stimulation (DBS) is an established therapy for treating e.g. Parkinson's disease, essential tremor, as well as epilepsy. In DBS, chronic pulsatile electrical stimulation is administered to a certain target area of the brain through a surgically implanted lead. The stimuli parameters have to be properly tuned in order to achieve therapeutical effect that in most cases is alleviation of motor symptoms. Tuning of DBS currently is a tedious task since it is performed manually by medical personnel in a trial-and-error manner. It can be dramatically improved and expedited by means of recently developed mathematical models together with control and estimation technology. This paper presents a control engineering perspective on DBS, viewing it as a control system for minimizing the severity of the symptoms through coordinated manipulation of the stimuli parameters. The DBS model structure comprises a stimuli model, an activation model, and a symptoms model. Each of those is individualized from patient data obtained through medical imaging, electrical measurements, and objective symptom quantification. The proposed approach is illustrated by simulation and clinical data from an individualized DBS model being developed by the authors.

  • 21.
    Cubo, Rubén
    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.
    Åström, Mattias
    Model-based optimization of individualized Deep Brain Stimulation therapy2016In: IEEE Design & Test, ISSN 2168-2356, Vol. 33, no 4, p. 74-81Article in journal (Refereed)
  • 22.
    Cubo, Rubén
    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.
    Åström, Mattias
    Stimulation field coverage and target structure selectivity in field steering brain stimulation2014Conference paper (Refereed)
  • 23.
    Cubo, Rubén
    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.
    Åström, Mattias
    Linkoping Univ, Dept Biomed Engn, S-58183 Linkoping, Sweden; Medtron Eindhoven Design Ctr, Medtron Neuromodulat, Eindhoven, Netherlands.
    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.
    Electric field modeling and spatial control in Deep Brain Stimulation2015In: Proc. 54th Conference on Decision and Control, Piscataway, NJ: IEEE , 2015, p. 3846-3851Conference paper (Refereed)
    Abstract [en]

    Deep Brain Stimulation (DBS) is an established treatment, in e.g. Parkinson's Disease, whose underlying biological mechanisms are unknown. In DBS, electrical stimulation is delivered through electrodes surgically implanted into certain regions of the brain of the patient. Mathematical models aiming at a better understanding of DBS and optimization of its therapeutical effect through the simulation of the electrical field propagating in the brain tissue have been developed in the past decade. The contribution of the present study is twofold: First, an analytical approximation of the electric field produced by an emitting contact is suggested and compared to the numerical solution given by a Finite Element Method (FEM) solver. Second, the optimal stimulation settings are evaluated by fitting the field distribution to a target one to control the spread of the stimulation. Optimization results are compared to those of a geometric approach, maximizing the intersection between the target and the activated volume in the brain tissue and reducing the stimulated area beyond said target. Both methods exhibit similar performance with respect to the optimal stimuli, with the electric field control approach being faster and more versatile.

  • 24.
    Cubo, Rubén
    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.
    Åström, Mattias
    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.
    Model-based optimization of lead configurations in Deep Brain Stimulation2015In: Proc. 1st International Conference on Smart Portable, Wearable, Implantable and Disability-oriented Devices and Systems, International Academy, Research and Industry Association (IARIA), 2015, p. 14-19Conference paper (Refereed)
  • 25.
    Cubo, Rubén
    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.
    Åström, Mattias
    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.
    Optimization-based contact fault alleviation in deep brain stimulation leads2018In: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210, Vol. 26, no 1, p. 69-76Article in journal (Refereed)
  • 26.
    Cubo, Rubén
    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.
    Åström, Mattias
    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.
    Target coverage and selectivity in field steering brain stimulation2014In: Proc. 36th International Conference of the IEEE Engineering in Medicine and Biology Society, Piscataway, NJ: IEEE , 2014, p. 522-525Conference paper (Refereed)
  • 27.
    Dimitrakopoulos, Konstantinos
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Ellmer, Christoph
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Lindström, Martin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    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.
    Tremor Quantification through Event-based Movement Trajectory Modeling2017In: 2017 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA 2017), IEEE, 2017, p. 542-547Conference paper (Refereed)
    Abstract [en]

    A simple non-intrusive approach to tremor quantification utilizing the repetitive nature of the phenomenon is proposed and implemented on a portable device equipped with a fused off-the-shelf sensor platform measuring 3D acceleration. The device can be automatically activated when picked up from a stationary position and acceleration measurements are performed for a certain time interval. This usage scenario naturally arises e.g. when a person lifts the cellular phone from a surface to the ear to make or answer a call. The relatively slow and damped voluntary movement is separated by filtering from the involuntary and repetitive tremor manifestations in the device position. Extreme points of the tremor signal are detected and the time stamps of the corresponding events are used to estimate of the momentary tremor amplitude and frequency. Kalman filtering of the estimates is applied further to obtain their smoothed versions.

  • 28.
    Evestedt, Magnus
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. Systemteknik.
    Medvedev, Alexander
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. Systemteknik.
    Cavity depth and diameter estimation in the converter process water model2004In: Association for Iron & Steel Technology Conference Proceedings, 2004Conference paper (Other scientific)
  • 29.
    Evestedt, Magnus
    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.
    Cavity Shape Dynamical Modelling and Estimation in a Water Model of the Steel Converter Process2007In: Journal of the Japanese Society for Experimental Mechanics, ISSN 1346-4930, Vol. 7, p. s93-s98Article in journal (Refereed)
  • 30.
    Evestedt, Magnus
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. Systemteknik.
    Medvedev, Alexander
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. Systemteknik.
    Gas jet impinging on liquid surface: Cavity shape modelling and video based estimation2005In: Proceedings of the 16th IFAC World Congress, 2005Conference paper (Refereed)
  • 31.
    Evestedt, Magnus
    et al.
    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, Automatic control.
    Model-based Slopping Warning in the LD Steel Converter Process2009In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 19, no 6, p. 1000-1010Article in journal (Refereed)
    Abstract [en]

    The most prevalent steel-making process is the basic oxygen steel-making (BOS) process. Problems arise when the layer of foaming slag created on the surface of the molten metal exceeds the height of the vessel and overflows, causing metal loss, process disruption and environmental pollution. This phenomenon is commonly referred to as slopping. A method for automatic slopping detection is described in this contribution. The sound signal from a microphone located in the off-gas funnel is processed to obtain an estimate of the slag level in the converter. A model describing the relationship between off-gas flow rate, pressure and the slag level estimate is updated recursively in time. The output error is fed to a change detector yielding a warning system with three alarm levels indicating the persistence of slopping symptoms. The algorithm was tested on data from 100 heats at SSAB Oxelosund. Slopping was correctly detected in 80% of the blows.

  • 32.
    Evestedt, Magnus
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. Systemteknik.
    Medvedev, Alexander
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. Systemteknik.
    Stationary behavior of an anti-windup scheme for recursive parameter estimation under lack of excitation2006In: Automatica, Vol. 42, no 1, p. 151-157Article in journal (Refereed)
  • 33.
    Evestedt, Magnus
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. Systemteknik.
    Medvedev, Alexander
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. Systemteknik.
    Stationary behaviour of an anti-windup scheme for recursive parameter estimation under lack of excitation2005In: Proceedings of the 16th IFAC World Congress, 2005Conference paper (Refereed)
  • 34.
    Evestedt, Magnus
    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.
    Comparative study of three recursive parameter estimation algorithms with application to acoustic echo cancellation2004In: Proc. Reglermöte, 2004Conference paper (Other academic)
  • 35.
    Evestedt, Magnus
    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.
    Windup properties of recursive parameter estimation algorithms in acoustic echo cancellation2008In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 16, no 11, p. 1372-1378Article in journal (Refereed)
  • 36.
    Evestedt, Magnus
    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.
    Windup properties of recursive parameter estimation algorithms in acoustic echo cancellation2005Conference paper (Refereed)
  • 37.
    Hidayat, Egi
    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.
    Identification of a pulsatile endocrine model from hormone concentration data2012In: 2012 IEEE INTERNATIONAL CONFERENCE ON CONTROL APPLICATIONS (CCA), 2012, p. 356-363Conference paper (Refereed)
    Abstract [en]

    This paper presents two approaches to estimate parameters of a mathematical model of a bipartite endocrine axis. Secretion of one of the involved hormones is stimulated by the concentration of another one, with the latter secreted in a pulsatile manner. The system output can be modeled as the response of a linear time-invariant system to a train of Dirac delta functions with unknown weights and fired at unknown instants. The hormone mechanism in question appears often in animal and human endocrine systems, e. g. in the regulation of testosterone in the human male. The model has been introduced elsewhere and makes use of pulse-modulated feedback for describing pulsatile endocrine regulation. The first identification approach is based on the mathematical machinery of constrained nonlinear least-squares minimization, while the second one is based on Laguerre domain identification of continuous time-delay systems. Both algorithm perform reasonably well on actual biological data yielding accurate fitting of luteinizing hormone concentration profiles.

  • 38.
    Hidayat, Egi
    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.
    Laguerre domain identification of continuous linear time delay systems from impulse response data2011In: Proc. 18th IFAC World Congress, International Federation of Automatic Control , 2011Conference paper (Refereed)
  • 39.
    Hidayat, Egi
    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.
    Laguerre domain identification of continuous linear time-delay systems from impulse response data2012In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 48, no 11, p. 2902-2907Article in journal (Refereed)
    Abstract [en]

    An expression for the Laguerre spectrum of the impulse response of a linear continuous time-invariant system with input or output delay is derived. A discrete state-space description of the time-delay system in the Laguerre shift operator is obtained opening up for the use of conventional identification techniques. A method for Laguerre domain identification of continuous time-delay systems from impulse response data is then proposed. Linear time-invariant systems resulting from cascading finite-dimensional dynamics with pure time delays are considered. Subspace identification is utilized for estimation of finitedimensional dynamics. An application to blind identification of a mathematical model of an endocrine system with pulsatile regulation is also provided.

  • 40.
    Hidayat, Egi
    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.
    Nordström, Karin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Physiology.
    Identification of the elementary motion detector model in fly motion vision from intracellularly recorded neural data2014Article in journal (Other academic)
  • 41.
    Hidayat, Egi
    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.
    Nordström, Karin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Physiology.
    Identification of the Reichardt elementary motion detector model2015In: Signal and Image Analysis for Biomedical and Life Sciences, Springer, 2015, p. 83-105Chapter in book (Refereed)
    Abstract [en]

    The classical Hassenstein-Reichardt mathematical elementary motion detector (EMD) model is treated analytically. The EMD is stimulated with drifting sinusoidal gratings, which are often used in motion vision research, thus enabling direct comparison with neural responses from motion-sensitive neurones in the fly brain. When sinusoidal gratings are displayed on a cathode ray tube monitor, they are modulated by the refresh rate of the monitor. This generates a pulsatile signature of the visual stimulus, which is also seen in the neural response. Such pulsatile signals make a Laguerre domain identification method for estimating the parameters of a single EMD suitable, allowing estimation of both finite and infinite-dimensional dynamics. To model the response of motion-sensitive neurones with large receptive fields, a pool of spatially distributed EMDs is considered, with the weights of the contributing EMDs fitted to the neural data by a sparse estimation method. Such an EMD-array is more reliably estimated by stimulating with multiple sinusoidal gratings, since these provide higher spatial excitation than a single sinusoidal grating. Consequently, a way of designing the visual stimuli for a certain order of spatial resolution is suggested.

  • 42.
    Hidayat, Egi
    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.
    Nordström, Karin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Physiology.
    Laguerre Domain Identification of the Elementary Motion Detector Model in Insect Vision2013In: Adaptation and Learning in Control and Signal Processing, International Federation of Automatic Control , 2013, p. 623-628Conference paper (Refereed)
    Abstract [en]

    A Laguerre domain approach to the identification of the so-called Elementary Motion Detector (EMD) that is hypothesized to constitute the basis of biological motion vision in nearly all animals is proposed. Despite the vast popularity of the EMD concept in both biology and biologically inspired computer vision, the problem of estimating the dynamics of the EMD from experimental data has been poorly addressed. The choice of the Laguerre domain for the representation of the input and the output of the EMD is motivated by the pulse-modulated character of the visual stimuli produced by the CRT displays that are often used in animal experiments. An analytical expression for the Laguerre spectrum of the EMD output given the Laguerre spectrum of the input is derived and a parameter estimation algorithm of the system dynamics is developed. The feasibility of the approach is illustrated by simulation using actual visual stimuli from fly electrophysiology.

  • 43.
    Hidayat, Egi
    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.
    Nordström, Karin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Physiology.
    On identification of elementary motion detectors2013In: Computational Models for Life Sciences: CMLS 2013, Melville, NY: American Institute of Physics (AIP), 2013, p. 14-23Conference paper (Refereed)
    Abstract [en]

    The classical mathematical elementary motion detector (EMD) model stimulated with sinusoidal and pulsatile input signals is treated analytically. Drifting sinusoidal gratings are often used in insect vision research, enabling direct comparison with biological data. When displayed on a cathode ray tube monitor, the sinusoidal grating is modulated by the refresh rate of the monitor. Due to the resulting pulsatile nature of the visual stimuli and the corresponding biological response, a Laguerre domain identification method for estimating the dynamics of a single EMD appears to be suitable. A pool of spatially distributed EMDs is considered as the model for the measured neural output. The weights of the contributing EMDs are evaluated by a sparse optimization method to fit the experimental data.

  • 44.
    Hidayat, Egi
    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.
    Soltanalian, Mojtaba
    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.
    Nordström, Karin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Physiology.
    Spatial excitation properties of sinusoidal grating stimuli in the identification of a layer of motion detectors2014Article in journal (Other academic)
  • 45.
    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.
    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 non-parametric stochastic anomaly detection in analysis of eye-tracking data2013In: Proc. 52nd Conference on Decision and Control, Piscataway, NJ: IEEE , 2013, p. 2532-2537Conference paper (Refereed)
  • 46.
    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.
    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.
    Visual stimulus design in parameter estimation of the human smooth pursuit system from eye-tracking data2013In: Proc. American Control Conference: ACC 2013, American Automatic Control Council , 2013, p. 887-892Conference paper (Refereed)
  • 47.
    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.
    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.
    Volterra modeling of the smooth pursuit system with application to motor symptoms characterization in Parkinson's disease2014In: 2014 European Control Conference (ECC), IEEE , 2014, p. 1856-1861Conference paper (Refereed)
    Abstract [en]

    A new way of modeling the Smooth Pursuit System (SPS) in humans by means of Volterra series expansion is suggested and utilized together with Gaussian Mixture Models (GMMs) to successfully distinguish between healthy controls and Parkinson patients based on their eye movements. To obtain parsimonious Volterra models, orthonormal function expansion of the Volterra kernels in Laguerre functions with the coefficients estimated by SParse Iterative Covariance-based Estimation (SPICE) is used. A combination of these two techniques is shown to greatly reduce the number of model parameters without significant performance loss. In fact, the resulting models outperform the Wiener models of previous research despite the significantly lower number of model parameters. Furthermore, the results of this study indicate that the nonlinearity of the system is likely to be dynamical in nature, rather than static which was previously presumed. The difference between the SPS in healthy controls and Parkinson patients is shown to lie largely in the higher order dynamics of the system. Finally, without the model reduction provided by SPICE, the GMM estimation fails, rendering the model unable to separate healthy controls from Parkinson patients.

  • 48.
    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.
    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.
    Axelson, Hans
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Clinical Neurophysiology.
    Nyholm, Dag
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology.
    Stochastic anomaly detection in eye-tracking data for quantification of motor symptoms in Parkinson's disease2015In: Signal and Image Analysis for Biomedical and Life Sciences, Springer, 2015, p. 63-82Chapter in book (Refereed)
    Abstract [en]

    Two methods for distinguishing between healthy controls and patients diagnosed with Parkinson's disease by means of recorded smooth pursuit eye movements are presented and evaluated. Both methods are based on the principles of stochastic anomaly detection and make use of orthogonal series approximation for probability distribution estimation. The first method relies on the identification of a Wiener model of the smooth pursuit system and attempts to find statistically significant differences between the estimated parameters in healthy controls and patients with Parkinson's disease. The second method applies the same statistical method to distinguish between the gaze trajectories of healthy and Parkinson subjects tracking visual stimuli. Both methods show promising results, where healthy controls and patients with Parkinson's disease are effectively separated in terms of the considered metric. The results are preliminary because of the small number of participating test subjects, but they are indicative of the potential of the presented methods as diagnosing or staging tools for Parkinson's disease.

  • 49.
    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.
    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.
    Axelson, Hans
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Clinical Neurophysiology.
    Nyholm, Dag
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology.
    Stochastic anomaly detection in eye-tracking data for quantification of motor symptoms in Parkinson's disease2013In: International Symposium on Computational Models for Life Sciences: CMLS 2013, Melville, NY: American Institute of Physics (AIP), 2013, p. 98-107Conference paper (Refereed)
    Abstract [en]

    Two methods for distinguishing between healthy controls and patients diagnosed with Parkinson's disease by means of recorded smooth pursuit eye movements are presented and evaluated. Both methods are based on the principles of stochastic anomaly detection and make use of orthogonal series approximation for probability distribution estimation. The first method relies on the identification of a Wiener-type model of the smooth pursuit system and attempts to find statistically significant differences between the estimated parameters in healthy controls and patientts with Parkinson's disease. The second method applies the same statistical method to distinguish between the gaze trajectories of healthy and Parkinson subjects attempting to track visual stimuli. Both methods show promising results, where healthy controls and patients with Parkinson's disease are effectively separated in terms of the considered metric. The results are preliminary because of the small number of participating test subjects, but they are indicative of the potential of the presented methods as diagnosing or staging tools for Parkinson's disease.

  • 50.
    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.
    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.
    Stoica, Peter
    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.
    Axelson, Hans W.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Clinical Neurophysiology.
    Mathematical modeling and grey-box identification of the human smooth pursuit mechanism2010In: Proc. International Conference on Control Applications: CCA 2010, Piscataway, NJ: IEEE , 2010, p. 1023-1028Conference paper (Refereed)
12 1 - 50 of 100
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