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  • 151.
    Cedervall, Ylva
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
    Halvorsen, Kjartan
    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.
    Åberg, Anna Cristina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
    A longitudinal study of gait function and characteristics of gait disturbances in individuals with Alzheimer's disease2014In: Gait & Posture, ISSN 0966-6362, E-ISSN 1879-2219, Vol. 39, no 4, p. 1022-1027Article in journal (Refereed)
    Abstract [en]

    Walking in daily life places high demands on the interplay between cognitive and motor functions. A well-functioning dual-tasking ability is thus essential for walking safely. The aims were to study longitudinal changes in gait function during single- and dual-tasking over a period of two years among people with initially mild AD (n = 21). Data were collected on three occasions, twelve months apart. An optical motion capture system was used for three-dimensional gait analysis. Gait parameters were examined at comfortable gait speed during single-tasking, dual-tasking naming names, and naming animals. The dual-task cost for gait speed was pronounced at baseline (names 26%, animals 35%), and remained so during the study period. A significant (p < 0.05) longitudinal decline in gait speed and step length during single- and dual-tasking was observed, whereas double support time, step width and step height showed inconsistent results. Systematic visual examination of the motion capture files revealed that dual-tasking frequently resulted in gait disturbances. Three main characteristics of such disturbances were identified: Temporal disturbance, Spatial disturbance and Instability in single stance. These aberrant gait performances may affect gait stability and increase the risk of falling. Furthermore, the observed gait disturbances can contribute to understanding and explaining previous reported gait variability among individuals with AD. However, the role that dual-task testing and aberrant dual-task gait performance play in the identification of individuals with early signs of cognitive impairment and in predicting fall risk in AD remains to be studied.

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  • 152. Cheng, Q
    et al.
    Hua, YB
    Stoica, P
    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. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Asymptotic performance of optimal gain-and-phase estimators of sensor arrays2000Other (Other scientific)
    Abstract [en]

    For estimating angles of arrival, there are three well known algorithms: weighted noise subspace fitting (WNSF), unconditional maximum likelihood (UML), and conditional niaximum likelihood (CML). These algorithms can also be used for estimating/calibratin

  • 153.
    Chistiakova, Tatiana
    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.
    Ammonium based aeration control in wastewater treatment plants: Modelling and controller design2018Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Wastewater treatment involves many processes and methods which make a treatment plant a large-scaled and complex system. A fundamental challenge is how to maintain a high process efficiency while keeping the operational costs low. The variety in plant configurations, the nonlinear behaviour, the large time delays and saturations present in the system contribute to making automation and monitoring a demanding task.

    The biological part of a wastewater treatment process includes an aeration of the water and this process has been shown to often result in the highest energy consumption of the plant. Oxygen supply is a fundamental part of the activated sludge process used for aerobic microorganisms growing. The concentration of the dissolved oxygen should be high enough to maintain a sufficient level of biological oxidation. However, if the concentration is too high the process efficiency is significantly reduced leading to a too high energy consumption. Hence, there are two motivations behind the aeration control task: process efficiency and economy. One of the possible strategies to adjust the dissolved oxygen level in a nitrifying activated sludge process is to use ammonium feedback measurements.

    In this thesis, an activated sludge process is modelled and analysed in terms of dissolved oxygen to ammonium dynamics. First, the data obtained from a simplified Benchmark Simulation Model no.1 was used to identify the system. Both linear and nonlinear models were evaluated. A model with a Hammerstein structure where the nonlinearity was described by a Monod function was chosen for a more thorough study. Here, a feedback controller was designed to achieve L2-stability. The stability region was pre-computed to determine the maximum allowed time delay for the closed loop system. Finally, a feedforward controller was added to the system, and shown to significantly improve the disturbance rejection properties.

    List of papers
    1. Nonlinear system identification of the dissolved oxygen to effluent ammonia dynamics in an activated sludge process
    Open this publication in new window or tab >>Nonlinear system identification of the dissolved oxygen to effluent ammonia dynamics in an activated sludge process
    2017 (English)Conference paper, Published paper (Refereed)
    Series
    IFAC-PapersOnLine, ISSN 2405-8963 ; 50:1
    National Category
    Control Engineering Water Treatment
    Identifiers
    urn:nbn:se:uu:diva-334208 (URN)10.1016/j.ifacol.2017.08.365 (DOI)000423964800149 ()
    Conference
    IFAC 2017, July 9–14, Toulouse, France
    Available from: 2017-10-18 Created: 2017-11-21 Last updated: 2018-11-29Bibliographically approved
    2. Non-linear modelling of the dissolved oxygen to ammonium dynamics in a nitrifying activated sludge process
    Open this publication in new window or tab >>Non-linear modelling of the dissolved oxygen to ammonium dynamics in a nitrifying activated sludge process
    2017 (English)In: Proc. 12th IWA Specialized Conference on Instrumentation, Control and Automation, 2017, p. 85-93Conference paper, Published paper (Refereed)
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:uu:diva-334201 (URN)
    Conference
    ICA2017, June 11–14, Québec, Canada
    Available from: 2017-06-14 Created: 2017-11-21 Last updated: 2018-08-17Bibliographically approved
    3. Input–output stability design of an ammonium based aeration controller for wastewater treatment
    Open this publication in new window or tab >>Input–output stability design of an ammonium based aeration controller for wastewater treatment
    2018 (English)In: 2018 Annual American Control Conference (ACC), Institute of Electrical and Electronics Engineers (IEEE) , 2018, p. 2964-2971Conference paper, Published paper (Refereed)
    Abstract [en]

    Ammonium feedback is commonly used for controlling the aeration in wastewater treatment plants having biological nitrogen removal. The paper proposes the use of a PI controller tuning method based on a simplified identified model with Hammerstein structure. The Hammerstein model accounts for the delay of the process caused by the settler, the multiple bioreactors dynamics and the main nonlinear process effects. The controller tuning method exploits the Popov inequality for a pre-computation of (a subset of) the stability region of the closed loop system as a function of the PI-controller parameters, quantified in terms of the maximum loop delay of the system. The controller parameters are selected in the computed stability region. In the numerical study, the plant is identified as a Hammerstein model using a recently published method, here extended to identify multiple bioreactor tank dynamics. The identification data is obtained from a high fidelity simulator, which is also used for evaluation of the proposed controller tuning method. The results show that the proposed procedure results in a PI-controller tuning with predictable stability properties and a good performance.

    Place, publisher, year, edition, pages
    Institute of Electrical and Electronics Engineers (IEEE), 2018
    Series
    Proceedings of the American Control Conference (ACC), E-ISSN 2378-5861
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:uu:diva-349422 (URN)10.23919/ACC.2018.8431151 (DOI)000591256603006 ()978-1-5386-5428-6 (ISBN)978-1-5386-5427-9 (ISBN)978-1-5386-5429-3 (ISBN)
    Conference
    ACC 2018, June 27–29, Milwaukee, WI
    Available from: 2018-08-16 Created: 2018-04-27 Last updated: 2022-06-14Bibliographically approved
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  • 154.
    Chistiakova, Tatiana
    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.
    Carlsson, Bengt
    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.
    Non-linear modelling of the dissolved oxygen to ammonium dynamics in a nitrifying activated sludge process2017In: Proc. 12th IWA Specialized Conference on Instrumentation, Control and Automation, 2017, p. 85-93Conference paper (Refereed)
  • 155.
    Chistiakova, Tatiana
    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.
    Carlsson, Bengt
    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.
    Zambrano, Jesús
    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.
    Samuelsson, Oscar
    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.
    Binary classifiers applied to detect DO sensor faults during washing events2015In: Proc. 2nd IWA Conference on New Developments in IT & Water, IWA Publishing, 2015Conference paper (Other academic)
    Abstract [en]

    In this paper, several classication techniques are applied for monitoring the status of DO sensors in wastewater treatment plants. In particular, DO sensors during washing events are studied and indication parameters from these events are used. The methods considered are the following: k-Nearest Neighbours, Radial Basis Function and Random Forest classiers. The result shows the comparison and the eligibility of the methods to detect a clogged DO-sensor.

  • 156.
    Chistiakova, Tatiana
    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.
    Mattsson, Per
    Carlsson, Bengt
    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.
    Nonlinear system identification of the dissolved oxygen to effluent ammonia dynamics in an activated sludge process2017Conference paper (Refereed)
  • 157.
    Chistiakova, Tatiana
    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.
    Mattsson, Per
    University of Gävle.
    Carlsson, Bengt
    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.
    Nonlinear system identification of the dissolved oxygen to effluent ammonium dynamics in an activated sludge process2018Report (Other academic)
    Abstract [en]

    Aeration of biological reactors in wastewater treatment plants is important to obtain a high removal of soluble organic matter as well as for nitrification but requires a significant use of energy. It is hence of importance to control the aeration rate, for example, by ammonium feedback control. The goal of this report is to model the dynamics from the set point of an existing dissolved oxygen controller to effluent ammonium using two types of system identification methods for a Hammerstein model, including a newly developed recursive variant. The models are estimated and evaluated using noise corrupted data from a complex mechanistic model (Activated Sludge Model no.1). The performances of the estimated nonlinear models are compared with an estimated linear model and it is shown that the nonlinear models give a significantly better fit to the data. The resulting models may be used for adaptive control (using the recursive Hammerstein variant), gain-scheduling control, L2 stability analysis, and model based fault detection.

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  • 158.
    Chistiakova, Tatiana
    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.
    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.
    Carlsson, Bengt
    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.
    Combined L2-stable feedback and feedforward aeration control in a wastewater treatment plant2020In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 28, no 3, p. 1017-1024Article in journal (Refereed)
    Abstract [en]

    A nitrifying activated sludge process with ammonium feedback and inflow feedforward control is studied. The feedback and feedforward measurements are subject to time delay and the control loop includes a saturation. A Hammerstein-based model is therefore identified, including process delays and the nonlinearity. A Monod-type nonlinearity is used, as motivated by the oxygen injection efficiency reduction, with increasing dissolved oxygen concentration. The proposed feedback control strategy includes tuning of a linear lag compensator that has a limited low-frequency gain which allows global stability to be established with the Popov criterion. The feedforward controller attenuates the disturbance and provides an overall improvement of the control strategy. The performance of the combined feedback and feedforward aeration controller is evaluated with a benchmark model.

  • 159.
    Chistiakova, Tatiana
    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.
    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.
    Carlsson, Bengt
    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.
    Input–output stability design of an ammonium based aeration controller for wastewater treatment2018In: 2018 Annual American Control Conference (ACC), Institute of Electrical and Electronics Engineers (IEEE) , 2018, p. 2964-2971Conference paper (Refereed)
    Abstract [en]

    Ammonium feedback is commonly used for controlling the aeration in wastewater treatment plants having biological nitrogen removal. The paper proposes the use of a PI controller tuning method based on a simplified identified model with Hammerstein structure. The Hammerstein model accounts for the delay of the process caused by the settler, the multiple bioreactors dynamics and the main nonlinear process effects. The controller tuning method exploits the Popov inequality for a pre-computation of (a subset of) the stability region of the closed loop system as a function of the PI-controller parameters, quantified in terms of the maximum loop delay of the system. The controller parameters are selected in the computed stability region. In the numerical study, the plant is identified as a Hammerstein model using a recently published method, here extended to identify multiple bioreactor tank dynamics. The identification data is obtained from a high fidelity simulator, which is also used for evaluation of the proposed controller tuning method. The results show that the proposed procedure results in a PI-controller tuning with predictable stability properties and a good performance.

  • 160.
    Chistiakova, Tatiana
    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.
    Zambrano, Jesús
    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.
    Carlsson, Bengt
    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.
    Application of machine learning methods for fault detection in wastewater treatment plants2014In: Reglermöte, 2014Conference paper (Other academic)
  • 161.
    Chockalingam, Sabarathinam
    et al.
    Delft Univ Technol, Fac Technol Policy & Management, Delft, Netherlands.;Inst Energy Technol, Dept Risk & Secur, Halden, Norway..
    Pieters, Wolter
    Delft Univ Technol, Fac Technol Policy & Management, Delft, Netherlands.;Radboud Univ Nijmegen, Behav Sci Inst, Nijmegen, Netherlands..
    Teixeira, André
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems. 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.
    van Gelder, Pieter
    Delft Univ Technol, Fac Technol Policy & Management, Delft, Netherlands..
    Probability elicitation for Bayesian networks to distinguish between intentional attacks and accidental technical failures2023In: Journal of Information Security and Applications, ISSN 2214-2134, E-ISSN 2214-2126, Vol. 75, article id 103497Article in journal (Refereed)
    Abstract [en]

    Both intentional attacks and accidental technical failures can lead to abnormal behaviour in components of industrial control systems. In our previous work, we developed a framework for constructing Bayesian Network (BN) models to enable operators to distinguish between those two classes, including knowledge elicitation to construct the directed acyclic graph of BN models. In this paper, we add a systematic method for knowledge elicitation to construct the Conditional Probability Tables (CPTs) of BN models, thereby completing a holistic framework to distinguish between attacks and technical failures. In order to elicit reliable probabilities from experts, we need to reduce the workload of experts in probability elicitation by reducing the number of conditional probabilities to elicit and facilitating individual probability entry. We utilise DeMorgan models to reduce the number of conditional probabilities to elicit as they are suitable for modelling opposing influences i.e., combinations of influences that promote and inhibit the child event. To facilitate individual probability entry, we use probability scales with numerical and verbal anchors. We demonstrate the proposed approach using an example from the water management domain.

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  • 162. Christensen, M
    et al.
    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.
    Jakobsson, Andreas
    Jensen, S
    Multi-pitch estimation2008In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 88, no 4, p. 972-983Article in journal (Refereed)
    Abstract [en]

    In this paper, we formulate the multi-pitch estimation problem and propose a number of methods to estimate the set of fundamental frequencies. The proposed methods, based on the nonlinear least-squares (NLS), Multiple Signal Classification (MUSIC) and the Capon principles, estimate the multiple fundamental frequencies via a number of one-dimensional searches. We also propose an iterative method based on the Expectation Maximization (EM) algorithm. The statistical properties of the methods are evaluated via Monte Carlo simulations for both the single- and multi-pitch cases.

  • 163. Christensen, Mads
    et al.
    Stoica, Peter
    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. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Jakobsson, Andreas
    Jensen, Søren Holdt
    The Multi-Pitch Estimation Problem: Some New Solutions2007In: International Conference on Acoustics, Speech, and Signal Processing: April 15-20, 2007, Honolulu, Hawaii, USA, 2007Conference paper (Refereed)
  • 164. 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)
  • 165. 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)
  • 166. 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)
  • 167. 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)
  • 168. 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.

  • 169. 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.

  • 170. 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)
  • 171.
    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.

  • 172. 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.

  • 173. 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)
  • 174.
    Coimbatore Anand, Sribalaji
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems.
    Teixeira, André
    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, Technology, Department of Electrical Engineering, Signals and Systems. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Risk-based Security Measure Allocation Against Actuator Attacks2023In: IEEE Open Journal of Control Systems, E-ISSN 2694-085X, p. 1-12Article in journal (Refereed)
  • 175.
    Coimbatore Anand, Sribalaji
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems.
    Teixeira, André M. H.
    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.
    Risk-averse controller design against data injection attacks on actuators for uncertain control systems2022In: 2022 AMERICAN CONTROL CONFERENCE (ACC), IEEE, 2022, p. 5037-5042Conference paper (Refereed)
    Abstract [en]

    In this paper, we consider the optimal controller design problem against data injection attacks on actuators for an uncertain control system. We consider attacks that aim at maximizing the attack impact while remaining stealthy in the finite horizon. To this end, we use the Conditional Value-at-Risk to characterize the risk associated with the impact of attacks. The worst-case attack impact is characterized using the recently proposed output-to-output l(2)-gain (OOG). We formulate the design problem and observe that it is non-convex and hard to solve. Using the framework of scenariobased optimization and a convex proxy for the OOG, we propose a convex optimization problem that approximately solves the design problem with probabilistic certificates. Finally, we illustrate the results through a numerical example.

  • 176.
    Coimbatore Anand, Sribalaji
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems.
    Teixeira, André M. H.
    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.
    Stealthy Cyber-Attack Design Using Dynamic Programming2021In: 2021 60th IEEE Conference On Decision And Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 3474-3479Conference paper (Refereed)
    Abstract [en]

    This paper addresses the issue of data injection attacks on control systems. We consider attacks which aim at maximizing system disruption while staying undetected in the finite horizon. The maximum possible disruption caused by such attacks is formulated as a non-convex optimization problem whose dual problem is a convex semi-definite program. We show that the duality gap is zero using S-lemma. To determine the optimal attack vector, we formulate a soft-constrained optimization problem using the Lagrangian dual function. The framework of dynamic programming for indefinite cost functions is used to solve the soft-constrained optimization problem and determine the attack vector. Using the Karush-Kuhn-Tucker conditions, we also provide necessary and sufficient conditions under which the obtained attack vector is optimal to the primal problem. Finally, we illustrate the results through numerical examples.

  • 177.
    Coimbatore Anand, Sribalaji
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems.
    Teixeira, André M. H.
    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.
    Ahlén, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems.
    Risk assessment and optimal allocation of security measures under stealthy false data injection attacks2022In: 2022 IEEE Conference on Control Technology and Applications (CCTA), Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 1347-1353Conference paper (Refereed)
    Abstract [en]

    This paper firstly addresses the problem of risk assessment under false data injection attacks on uncertain control systems. We consider an adversary with complete system knowledge, injecting stealthy false data into an uncertain control system. We then use the Value-at-Risk to characterize the risk associated with the attack impact caused by the adversary. The worst-case attack impact is characterized by the recently proposed output-to-output gain. We observe that the risk assessment problem corresponds to an infinite non-convex robust optimization problem. To this end, we use dissipative system theory and the scenario approach to approximate the risk-assessment problem into a convex problem and also provide probabilistic certificates on approximation. Secondly, we con-sider the problem of security measure allocation. We consider an operator with a constraint on the security budget. Under this constraint, we propose an algorithm to optimally allocate the security measures using the calculated risk such that the resulting Value-at-risk is minimized. Finally, we illustrate the results through a numerical example. The numerical example also illustrates that the security allocation using the Value-at-risk, and the impact on the nominal system may have different outcomes: thereby depicting the benefit of using risk metrics.

  • 178.
    Cooper, Samson B.
    et al.
    Univ Bristol, Dept Mech Engn, Queens Bldg, Bristol, Avon, England.;AB Dynam Ltd, Simulat & Lab Testing, Bradford On Avon, Wilts, England..
    Tiels, Koen
    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. Eindhoven Univ Technol, Dept Mech Engn, POB 337, NL-5600 MB Eindhoven, Netherlands..
    Titurus, Branislav
    Univ Bristol, Dept Aerosp Engn, Queens Bldg, Bristol, Avon, England..
    Di Maio, Dario
    Univ Bristol, Dept Mech Engn, Queens Bldg, Bristol, Avon, England..
    Polynomial nonlinear state space identification of an aero-engine structure2020In: Computers & structures, ISSN 0045-7949, E-ISSN 1879-2243, Vol. 238, article id 106299Article in journal (Refereed)
    Abstract [en]

    Most nonlinear identification problems often require prior knowledge or an initial assumption of the mathematical law (model structure) and data processing to estimate the nonlinear parameters present in a system, i.e. they require the functional form or depend on a proposition that the measured data obey a certain nonlinear function. However, obtaining prior knowledge or performing nonlinear characterisation can be difficult or impossible for certain identification problems due to the individualistic nature of practical nonlinearities. For example, joints between substructures of large aerospace design frequently feature complex physics at local regions of the structure, making a physically motivated identification in terms of nonlinear stiffness and damping impossible. As a result, black-box models which use no prior knowledge can be regarded as an effective method. This paper explores the pragmatism of a black-box approach based on Polynomial Nonlinear State Space (PNLSS) models to identify the nonlinear dynamics observed in a large aerospace component. As a first step, the Best Linear Approximation (BLA), noise and nonlinear distortion levels are estimated over different amplitudes of excitation using the Local Polynomial Method (LPM). Next, a linear state space model is estimated on the non-parametric BLA using the frequency domain subspace identification method. Nonlinear model terms are then constructed in the form of multivariate polynomials in the state variables while the parameters are estimated through a nonlinear optimisation routine. Further analyses were also conducted to determine the most suitable monomial degree and type required for the nonlinear identification procedure. Practical application is carried out on an Aero-Engine casing assembly with multiple joints, while model estimation and validation is achieved using measured sine-sweep and broadband data obtained from the experimental campaign.

  • 179.
    Cubo, Rubén
    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.
    Jltsova, Elena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurosurgery.
    Andersson, Helena
    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.
    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.
    Calculating Directional Deep Brain Stimulation Settings by Constrained OptimizationIn: Article in journal (Refereed)
    Abstract [en]

    Objective: Deep Brain Stimulation (DBS) consists of delivering electrical stimuli to a brain target via an implanted lead to treat neurodegenerative conditions. Individualized stimulation is vital to ensure therapeutic results, since DBS may otherwise become ineffective or cause undesirable side effects. Since the DBS pulse generator is battery-driven, power consumption incurred by the stimulation is important. In this study, target coverage and power consumption are compared over a patient population for clinical and model-based patient-specific settings calculated by constrained optimization. Methods: Brain models for five patients undergoing bilateral DBS were built. Mathematical optimization of activated tissue volume was utilized to calculate stimuli amplitudes, with and without specifying the volumes, where stimulation was not allowed to avoid side effects. Power consumption was estimated using measured impedance values and battery life under both clinical and optimized settings. Results: It was observed that clinical settings are generally less aggressive than the ones suggested by unconstrained model-based optimization, especially under asymmetrical stimulation. The DBS settings satisfying the constraints were close to the clinical values. Conclusion: The use of mathematical models to suggest optimal patient-specific DBS settings that observe technological and safety constraints can save time in clinical practice. It appears though that the considered anatomy-related safety constraints depend on the patient and further research is needed in this regard. Power consumption is important to consider since it increases with the square of the stimuli amplitude and critically affects battery life. Significance: This work highlights the need of specifying the brain volumes to be avoided by stimulation while optimizing the DBS amplitude, in contrast to minimizing general stimuli overspill, and applies the technique to a cohort of patients. It also stresses the importance of taking power consumption into account.

  • 180.
    Cubo, Rubén
    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.
    Mathematical modeling for optimization of Deep Brain Stimulation2016Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Deep Brain Stimulation (DBS) consists of sending mild electric stimuli to the brain via a chronically implanted lead. The therapy is used to alleviate the symptoms of different neurological diseases, such as Parkinson's Disease. However, its underlying biological mechanism is currently unknown. DBS patients undergo a lengthy trial-and-error procedure in order to tune the stimuli so that the treatment achieves maximal therapeutic benefits while limiting side effects that are often present with large stimulation values.

    The present licentiate thesis deals with mathematical modeling for DBS, extending it towards optimization. Mathematical modeling is motivated by the difficulty of obtaining in vivo measurements from the brain, especially in humans. It is expected to facilitate the optimization of the stimuli delivered to the brain and be instrumental in evaluating the performance of novel lead designs. Both topics are discussed in this thesis.

    First, an analysis of numerical accuracy is presented in order to verify the DBS models utilized in this study. Then a performance comparison between a state-of-the-art lead and a novel field-steering lead using clinical settings is provided. Afterwards, optimization schemes using intersection of volumes and electric field control are described, together with some simplification tools, in order to speed up the computations involved in the modeling.

    List of papers
    1. Accuracy of the Finite Element Method in Deep Brain Stimulation Modelling
    Open this publication in new window or tab >>Accuracy of the Finite Element Method in Deep Brain Stimulation Modelling
    2014 (English)In: Proc. International Conference on Control Applications: CCA 2014, Piscataway, NJ: IEEE , 2014, p. 1479-1484Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    Piscataway, NJ: IEEE, 2014
    National Category
    Control Engineering Medical Equipment Engineering
    Identifiers
    urn:nbn:se:uu:diva-238211 (URN)10.1109/CCA.2014.6981533 (DOI)000366055800214 ()978-1-4799-7409-2 (ISBN)
    Conference
    CCA 2014, October 8–10, Antibes, France
    Funder
    EU, European Research Council, 247035
    Available from: 2014-10-10 Created: 2014-12-10 Last updated: 2018-03-29Bibliographically approved
    2. Target coverage and selectivity in field steering brain stimulation
    Open this publication in new window or tab >>Target coverage and selectivity in field steering brain stimulation
    2014 (English)In: Proc. 36th International Conference of the IEEE Engineering in Medicine and Biology Society, Piscataway, NJ: IEEE , 2014, p. 522-525Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    Piscataway, NJ: IEEE, 2014
    National Category
    Control Engineering Medical Equipment Engineering
    Identifiers
    urn:nbn:se:uu:diva-252475 (URN)10.1109/EMBC.2014.6943643 (DOI)000350044700130 ()978-1-4244-7929-0 (ISBN)
    Conference
    EMBC 2014, August 26–30, Chicago, IL
    Funder
    EU, European Research Council, 247035
    Available from: 2014-08-30 Created: 2015-05-07 Last updated: 2016-04-16Bibliographically approved
    3. Model-based optimization of lead configurations in Deep Brain Stimulation
    Open this publication in new window or tab >>Model-based optimization of lead configurations in Deep Brain Stimulation
    2015 (English)In: 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, Published paper (Refereed)
    Place, publisher, year, edition, pages
    International Academy, Research and Industry Association (IARIA), 2015
    National Category
    Control Engineering Medical Equipment Engineering
    Identifiers
    urn:nbn:se:uu:diva-238214 (URN)978-1-61208-446-6 (ISBN)
    Conference
    SPWID 2015, June 21–26, Brussels, Belgium
    Funder
    EU, European Research Council, 247035
    Available from: 2015-06-26 Created: 2014-12-10 Last updated: 2016-04-17Bibliographically approved
    4. Electric field modeling and spatial control in Deep Brain Stimulation
    Open this publication in new window or tab >>Electric field modeling and spatial control in Deep Brain Stimulation
    2015 (English)In: Proc. 54th Conference on Decision and Control, Piscataway, NJ: IEEE , 2015, p. 3846-3851Conference paper, Published 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.

    Place, publisher, year, edition, pages
    Piscataway, NJ: IEEE, 2015
    National Category
    Control Engineering Medical Equipment Engineering
    Identifiers
    urn:nbn:se:uu:diva-284317 (URN)10.1109/CDC.2015.7402817 (DOI)000381554504006 ()9781479978847 (ISBN)
    Conference
    CDC 2015, December 15–18, Osaka, Japan
    Funder
    EU, European Research Council, 247035
    Available from: 2015-12-18 Created: 2016-04-16 Last updated: 2018-03-29Bibliographically approved
    Download full text (pdf)
    fulltext
  • 181.
    Cubo, Rubén
    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.
    Model-based optimization for individualized deep brain stimulation2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Deep Brain Stimulation (DBS) is an established therapy that is predominantly  utilized in treating the symptoms of neurodegenerative diseases such as Parkinson's Disease and Essential Tremor, crippling diseases like Chronic Pain and Epilepsy, and psychiatric diseases such as Schizophrenia and Depression. Due to its invasive nature, DBS is considered as a last resort therapy.DBS is performed by transmitting electric pulses through an electrode implanted in the brain of the patient.

    The stimulation is driven by a battery-powered Implanted Pulse Generator. The brain is a very delicate and complex organ and, therefore, accurate positioning the electrode is vital. To achieve a satisfactory therapeutical result, the stimulation targets a certain predefined brain structure that depends on the disease.

    The effect of DBS depends on the individual, the chosen stimulating contact(s), and the pulse parameters, i.e. amplitude, frequency, width, and shape. Tuning these parameters to the best effect is currently done by a lengthy trial-and-error process. Insufficient stimulation does not properly alleviate the symptoms of the disease, while overstimulation or stimulation off target is prone to side effects.

    This work envisions assisting physicians in DBS therapy by utilizing model-based estimation and optimization, maximizing stimulation of the target and minimizing stimulation in potentially problematic areas of the brain. This work focuses on amplitude and contact selection. Because of inter-patient differences, individualized models based on clinical imaging have to be created. Alternatively, semi-individualized models can be designed using atlases that save time but potentially introduce inaccuracies. Other optimization  applications to DBS are proposed in the thesis, e.g. fault alleviation and electrode design.

    Electrical properties of the brain can change over time and alter the stimulation spread. A system identification approach has been proposed to quantify these changes.

    The main aim of DBS is to alleviate the symptoms of the disease and quantifying symptoms is important. The ultimate vision of this work is to design a closed-loop system that can deliver optimal stimulation to the brain while automatically adapting to changes in the brain and the severity of symptoms.

    List of papers
    1. Accuracy of the Finite Element Method in Deep Brain Stimulation Modelling
    Open this publication in new window or tab >>Accuracy of the Finite Element Method in Deep Brain Stimulation Modelling
    2014 (English)In: Proc. International Conference on Control Applications: CCA 2014, Piscataway, NJ: IEEE , 2014, p. 1479-1484Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    Piscataway, NJ: IEEE, 2014
    National Category
    Control Engineering Medical Equipment Engineering
    Identifiers
    urn:nbn:se:uu:diva-238211 (URN)10.1109/CCA.2014.6981533 (DOI)000366055800214 ()978-1-4799-7409-2 (ISBN)
    Conference
    CCA 2014, October 8–10, Antibes, France
    Funder
    EU, European Research Council, 247035
    Available from: 2014-10-10 Created: 2014-12-10 Last updated: 2018-03-29Bibliographically approved
    2. Optimization of lead design and electrode configuration in Deep Brain Stimulation
    Open this publication in new window or tab >>Optimization of lead design and electrode configuration in Deep Brain Stimulation
    2016 (English)In: International Journal on Advances in Life Sciences, E-ISSN 1942-2660, Vol. 8, p. 76-86Article in journal (Refereed) Published
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:uu:diva-305224 (URN)
    Available from: 2016-06-30 Created: 2016-10-13 Last updated: 2024-04-26Bibliographically approved
    3. Electric field modeling and spatial control in Deep Brain Stimulation
    Open this publication in new window or tab >>Electric field modeling and spatial control in Deep Brain Stimulation
    2015 (English)In: Proc. 54th Conference on Decision and Control, Piscataway, NJ: IEEE , 2015, p. 3846-3851Conference paper, Published 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.

    Place, publisher, year, edition, pages
    Piscataway, NJ: IEEE, 2015
    National Category
    Control Engineering Medical Equipment Engineering
    Identifiers
    urn:nbn:se:uu:diva-284317 (URN)10.1109/CDC.2015.7402817 (DOI)000381554504006 ()9781479978847 (ISBN)
    Conference
    CDC 2015, December 15–18, Osaka, Japan
    Funder
    EU, European Research Council, 247035
    Available from: 2015-12-18 Created: 2016-04-16 Last updated: 2018-03-29Bibliographically approved
    4. Optimization-based contact fault alleviation in deep brain stimulation leads
    Open this publication in new window or tab >>Optimization-based contact fault alleviation in deep brain stimulation leads
    2018 (English)In: 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) Published
    National Category
    Medical Engineering
    Identifiers
    urn:nbn:se:uu:diva-342456 (URN)10.1109/TNSRE.2017.2769707 (DOI)000422939000008 ()29324404 (PubMedID)
    Available from: 2017-11-03 Created: 2018-02-26 Last updated: 2018-03-29Bibliographically approved
    5. Semi-Individualized electrical models in deep brain stimulation: A variability analysis
    Open this publication in new window or tab >>Semi-Individualized electrical models in deep brain stimulation: A variability analysis
    Show others...
    2017 (English)In: 2017 IEEE Conference on Control Technology and Applications (CCTA), IEEE, 2017, p. 517-522Conference paper, Published 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.

    Place, publisher, year, edition, pages
    IEEE, 2017
    Keywords
    bioelectric phenomena, biological tissues, biomedical electrodes, brain, diseases, neurophysiology, partial differential equations, patient treatment, DBS stimuli, Parkinson disease, Partial Differential Equation, brain atlases, brain tissue conductivity, chronically implanted lead, deep brain stimulation, electric field, electrical stimuli, interpatient variability, medical imaging models, neurodegenerative diseases, semiIndividualized electrical models, variability analysis, Brain modeling, Computational modeling, Conductivity, Lead, Mathematical model, Satellite broadcasting
    National Category
    Control Engineering Other Medical Engineering
    Identifiers
    urn:nbn:se:uu:diva-347344 (URN)10.1109/CCTA.2017.8062514 (DOI)000426981500084 ()978-1-5090-2183-3 (ISBN)978-1-5090-2182-6 (ISBN)978-1-5090-2181-9 (ISBN)
    Conference
    1st Annual IEEE Conference on Control Technology and Applications, 27-30 Aug. 2017, Mauna Lani, HI, USA.
    Available from: 2018-03-29 Created: 2018-03-29 Last updated: 2018-08-17Bibliographically approved
    6. Calculating Directional Deep Brain Stimulation Settings by Constrained Optimization
    Open this publication in new window or tab >>Calculating Directional Deep Brain Stimulation Settings by Constrained Optimization
    Show others...
    (English)In: Article in journal (Refereed) Submitted
    Abstract [en]

    Objective: Deep Brain Stimulation (DBS) consists of delivering electrical stimuli to a brain target via an implanted lead to treat neurodegenerative conditions. Individualized stimulation is vital to ensure therapeutic results, since DBS may otherwise become ineffective or cause undesirable side effects. Since the DBS pulse generator is battery-driven, power consumption incurred by the stimulation is important. In this study, target coverage and power consumption are compared over a patient population for clinical and model-based patient-specific settings calculated by constrained optimization. Methods: Brain models for five patients undergoing bilateral DBS were built. Mathematical optimization of activated tissue volume was utilized to calculate stimuli amplitudes, with and without specifying the volumes, where stimulation was not allowed to avoid side effects. Power consumption was estimated using measured impedance values and battery life under both clinical and optimized settings. Results: It was observed that clinical settings are generally less aggressive than the ones suggested by unconstrained model-based optimization, especially under asymmetrical stimulation. The DBS settings satisfying the constraints were close to the clinical values. Conclusion: The use of mathematical models to suggest optimal patient-specific DBS settings that observe technological and safety constraints can save time in clinical practice. It appears though that the considered anatomy-related safety constraints depend on the patient and further research is needed in this regard. Power consumption is important to consider since it increases with the square of the stimuli amplitude and critically affects battery life. Significance: This work highlights the need of specifying the brain volumes to be avoided by stimulation while optimizing the DBS amplitude, in contrast to minimizing general stimuli overspill, and applies the technique to a cohort of patients. It also stresses the importance of taking power consumption into account.

    Keywords
    Neuromodulation, Deep Brain Stimulation, inverse problems
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:uu:diva-347345 (URN)
    Available from: 2018-03-29 Created: 2018-03-29 Last updated: 2021-01-12
    7. Online tissue conductivity estimation in Deep Brain Stimulation
    Open this publication in new window or tab >>Online tissue conductivity estimation in Deep Brain Stimulation
    2020 (English)In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 28, no 1, p. 149-162Article in journal (Refereed) Published
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:uu:diva-347346 (URN)10.1109/TCST.2018.2862397 (DOI)000505786600012 ()
    Available from: 2018-08-16 Created: 2018-03-29 Last updated: 2020-01-29Bibliographically approved
    Download full text (pdf)
    fulltext
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  • 182.
    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.
    Fahlström, Markus
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
    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, Automatic control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Calculating Deep Brain Stimulation Amplitudes and Power Consumption by Constrained Optimization2019In: Journal of Neural Engineering, ISSN 1741-2560, E-ISSN 1741-2552, Vol. 16, no 1, article id 016020Article in journal (Refereed)
    Abstract [en]

    Objective: Deep brain stimulation (DBS) consists of delivering electrical stimuli to a brain target via an implanted lead to treat neurological and psychiatric conditions. Individualized stimulation is vital to ensure therapeutic results, since DBS may otherwise become ineffective or cause undesirable side effects. Since the DBS pulse generator is battery-driven, power consumption incurred by the stimulation is important. In this study, target coverage and power consumption are compared over a patient population for clinical and model-based patient-specific settings calculated by constrained optimization.

    Approach: Brain models for five patients undergoing bilateral DBS were built. Mathematical optimization of activated tissue volume was utilized to calculate stimuli amplitudes, with and without specifying the volumes, where stimulation was not allowed to avoid side effects. Power consumption was estimated using measured impedance values and battery life under both clinical and optimized settings.

    Results: It was observed that clinical settings were generally less aggressive than the ones suggested by unconstrained model-based optimization, especially under asymmetrical stimulation. The DBS settings satisfying the constraints were close to the clinical values.

    Significance: The use of mathematical models to suggest optimal patient-specific DBS settings that observe technological and safety constraints can save time in clinical practice. It appears though that the considered safety constraints based on brain anatomy depend on the patient and further research into it is needed. This work highlights the need of specifying the brain volumes to be avoided by stimulation while optimizing the DBS amplitude, in contrast to minimizing general stimuli overspill, and applies the technique to a cohort of patients. It also stresses the importance of considering power consumption in DBS optimization, since it increases with the square of the stimuli amplitude and also critically affects battery life through pulse frequency and duty cycle.

  • 183.
    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.

  • 184.
    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)
  • 185.
    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)
  • 186.
    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)
  • 187.
    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.
    Online tissue conductivity estimation in Deep Brain Stimulation2020In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 28, no 1, p. 149-162Article in journal (Refereed)
  • 188.
    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.

  • 189.
    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)
  • 190.
    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)
  • 191.
    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.

  • 192.
    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)
  • 193.
    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 of lead design and electrode configuration in Deep Brain Stimulation2016In: International Journal on Advances in Life Sciences, E-ISSN 1942-2660, Vol. 8, p. 76-86Article in journal (Refereed)
    Download full text (pdf)
    fulltext
  • 194.
    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)
  • 195.
    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)
  • 196. Dahlin, Johan
    et al.
    Lindsten, Fredrik
    Schön, Thomas B.
    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.
    Particle Metropolis–Hastings using gradient and Hessian information2015In: Statistics and computing, ISSN 0960-3174, E-ISSN 1573-1375, Vol. 25, no 1, p. 81-92Article in journal (Refereed)
    Abstract [en]

    Particle Metropolis–Hastings (PMH) allows for Bayesian parameter inference in nonlinear state space models by combining Markov chain Monte Carlo (MCMC) and particle filtering. The latter is used to estimate the intractable likelihood. In its original formulation, PMH makes use of a marginal MCMC proposal for the parameters, typically a Gaussian random walk. However, this can lead to a poor exploration of the parameter space and an inefficient use of the generated particles. We propose a number of alternative versions of PMH that incorporate gradient and Hessian information about the posterior into the proposal. This information is more or less obtained as a byproduct of the likelihood estimation. Indeed, we show how to estimate the required information using a fixed-lag particle smoother, with a computational cost growing linearly in the number of particles. We conclude that the proposed methods can: (i) decrease the length of the burn-in phase, (ii) increase the mixing of the Markov chain at the stationary phase, and (iii) make the proposal distribution scale invariant which simplifies tuning.

  • 197.
    Dahlin, Johan
    et al.
    Department of Computer and Information Science, Linköping University, Linköping, Sweden.
    Schön, Thomas B.
    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.
    Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models2019In: Journal of Statistical Software, E-ISSN 1548-7660, Vol. 88, no CN2, p. 1-41Article in journal (Refereed)
    Abstract [en]

    This tutorial provides a gentle introduction to the particle Metropolis-Hastings (PMH) algorithm for parameter inference in nonlinear state-space models together with a software implementation in the statistical programming language R. We employ a step-by-step approach to develop an implementation of the PMH algorithm (and the particle filter within) together with the reader. This final implementation is also available as the package pmhtutorial in the CRAN repository. Throughout the tutorial, we provide some intuition as to how the algorithm operates and discuss some solutions to problems that might occur in practice. To illustrate the use of PMH, we consider parameter inference in a linear Gaussian state-space model with synthetic data and a nonlinear stochastic volatility model with real-world data.

    Download full text (pdf)
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  • 198.
    Dai, Liang
    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 using Convexification and Recursion2016Doctoral thesis, monograph (Other academic)
    Abstract [en]

    System identification studies how to construct mathematical models for dynamical systems from the input and output data, which finds applications in many scenarios, such as predicting future output of the system or building model based controllers for regulating the output the system.

    Among many other methods, convex optimization is becoming an increasingly useful tool for solving system identification problems. The reason is that many identification problems can be formulated as, or transformed into convex optimization problems. This transformation is commonly referred to as the convexification technique. The first theme of the thesis is to understand the efficacy of the convexification idea by examining two specific examples. We first establish that a l1 norm based approach can indeed help in exploiting the sparsity information of the underlying parameter vector under certain persistent excitation assumptions. After that, we analyze how the nuclear norm minimization heuristic performs on a low-rank Hankel matrix completion problem. The underlying key is to construct the dual certificate based on the structure information that is available in the problem setting.        

    Recursive algorithms are ubiquitous in system identification. The second theme of the thesis is the study of some existing recursive algorithms, by establishing new connections, giving new insights or interpretations to them. We first establish a connection between a basic property of the convolution operator and the score function estimation. Based on this relationship, we show how certain recursive Bayesian algorithms can be exploited to estimate the score function for systems with intractable transition densities. We also provide a new derivation and interpretation of the recursive direct weight optimization method, by exploiting certain structural information that is present in the algorithm. Finally, we study how an improved randomization strategy can be found for the randomized Kaczmarz algorithm, and how the convergence rate of the classical Kaczmarz algorithm can be studied by the stability analysis of a related time varying linear dynamical system.

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  • 199.
    Dai, Liang
    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.
    On some sparsity related problems and the randomized Kaczmarz algorithm2014Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis studies several problems related to recovery and estimation. Specifically, these problems are about sparsity and low-rankness, and the randomized Kaczmarz algorithm. This thesis includes four papers referred to as Paper A, Paper B, Paper C, and Paper D.

    Paper A considers how to make use of the fact that the solution to an overdetermined system is sparse. This paper presents a three-stage approach to accomplish the task. We show that this strategy, under the assumptions as made in the paper, achieves the oracle property.

    In Paper B, a Hankel-matrix completion problem arising in system theory is studied. The use of the nuclear norm heuristic for this basic problem is considered. Theoretical justification for the case of a single real pole is given. Results show that for the case of a single real pole, the nuclear norm heuristic succeeds in the matrix completion task. Numerical simulations indicate that this result does not always carry over to the case of two real poles.

    Paper C discusses a screening approach for improving the computational performance of the Basis Pursuit De-Noising problem. The key ingredient for this work is to make use of an efficient ellipsoid update algorithm. The results of the experiments show that the proposed scheme can improve the overall time complexity for solving the problem.

    Paper D studies the choice of the probability distribution for implementing the row-projections in the randomized Kaczmarz algorithm. This relates to an open question in the recent literature. The result proves that a probability distribution resulting in a faster convergence of the algorithm can be found by solving a related Semi-Definite Programming optimization problem.

    List of papers
    1. Sparse estimation from noisy observations of an overdetermined linear system
    Open this publication in new window or tab >>Sparse estimation from noisy observations of an overdetermined linear system
    2014 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 50, no 11, p. 2845-2851Article in journal (Refereed) Published
    National Category
    Signal Processing
    Identifiers
    urn:nbn:se:uu:diva-226192 (URN)10.1016/j.automatica.2014.08.018 (DOI)000345727700010 ()
    Available from: 2014-10-07 Created: 2014-06-12 Last updated: 2017-12-05Bibliographically approved
    2. On the nuclear norm heuristic for a Hankel matrix completion problem
    Open this publication in new window or tab >>On the nuclear norm heuristic for a Hankel matrix completion problem
    2015 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 51, p. 268-272Article in journal (Refereed) Published
    Abstract [en]

    This note addresses the question if and why the nuclear norm heuristic can recover an impulse response generated by a stable single-real-pole system, if elements of the upper-triangle of the associated Hankel matrix are given. Since the setting is deterministic, theories based on stochastic assumptions for low-rank matrix recovery do not apply in the considered situation. A 'certificate' which guarantees the success of the matrix completion task is constructed by exploring the structural information of the hidden matrix. Experimental results and discussions regarding the nuclear norm heuristic applied to a more general setting are also given.

    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:uu:diva-226193 (URN)10.1016/j.automatica.2014.10.045 (DOI)000348015500032 ()
    Available from: 2014-10-29 Created: 2014-06-12 Last updated: 2017-12-05Bibliographically approved
    3. An ellipsoid based, two-stage screening test for BPDN
    Open this publication in new window or tab >>An ellipsoid based, two-stage screening test for BPDN
    2012 (English)In: Proc. 20th European Signal Processing Conference, IEEE , 2012, p. 654-658Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    IEEE, 2012
    National Category
    Signal Processing
    Identifiers
    urn:nbn:se:uu:diva-183856 (URN)978-1-4673-1068-0 (ISBN)
    Conference
    EUSIPCO 2012, August 27-31, Bucharest, Romania
    Available from: 2012-08-31 Created: 2012-11-05 Last updated: 2014-06-12Bibliographically approved
    4. On the randomized Kaczmarz algorithm
    Open this publication in new window or tab >>On the randomized Kaczmarz algorithm
    2014 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 21, no 3, p. 330-333Article in journal (Refereed) Published
    National Category
    Signal Processing
    Identifiers
    urn:nbn:se:uu:diva-211501 (URN)10.1109/LSP.2013.2294376 (DOI)000331299200004 ()
    Available from: 2014-01-31 Created: 2013-11-25 Last updated: 2017-12-06Bibliographically approved
    Download full text (pdf)
    fulltext
  • 200.
    Dai, Liang
    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.
    Pelckmans, Kristiaan
    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 ellipsoid based, two-stage screening test for BPDN2012In: Proc. 20th European Signal Processing Conference, IEEE , 2012, p. 654-658Conference paper (Refereed)
1234567 151 - 200 of 1530
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