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  • 1.
    Abdalmoaty, Mohamed
    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.
    Coimbatore Anand, Sribalaji
    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.
    Privacy and Security in Network Controlled Systems via Dynamic Masking2023In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 56, no 2, p. 991-996Article in journal (Refereed)
    Abstract [en]

    In this paper, we propose a new architecture to enhance the privacy and security of networked control systems against malicious adversaries. We consider an adversary which first learns the system using system identification techniques (privacy), and then performs a data injection attack (security). In particular, we consider an adversary conducting zero-dynamics attacks (ZDA) which maximizes the performance cost of the system whilst staying undetected. Using the proposed architecture, we show that it is possible to (i) introduce significant bias in the system estimates obtained by the adversary: thus providing privacy, and (ii) efficiently detect attacks when the adversary performs a ZDA using the identified system: thus providing security. Through numerical simulations, we illustrate the efficacy of the proposed architecture

  • 2.
    Abdalmoaty, Mohamed
    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.
    Medvedev, Alexander
    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.
    Noise reduction in Laguerre-domain discrete delay estimation2022In: 2022 IEEE 61st Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 6254-6259Conference paper (Refereed)
    Abstract [en]

    This paper introduces a stochastic framework for a recently proposed discrete-time delay estimation method in Laguerre-domain, i.e. with the delay block input and output signals being represented by the corresponding Laguerre series. A novel Laguerre-domain disturbance model allowing the involved signals to be square-summable sequences is devised. The relation to two commonly used time-domain disturbance models is clarified. Furthermore, by forming the input signal in a certain way, the signal shape of an additive output disturbance can be estimated and utilized for noise reduction. It is demonstrated that a significant improvement in the delay estimation error is achieved when the noise sequence is correlated. The noise reduction approach is applicable to other Laguerre-domain problems than pure delay estimation.

  • 3.
    Abd-Elrady, E.
    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. AUTOMATIC CONTROL.
    A nonlinear approach to harmonic signal modeling2004In: Signal Processing, Vol. 84, no 1, p. 163-195Article in journal (Refereed)
  • 4.
    Abd-Elrady, E.
    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. AUTOMATIC CONTROL.
    An adaptive grid point algorithm for harmonic signal modeling,2001Report (Other scientific)
  • 5.
    Abd-Elrady, E.
    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. AUTOMATIC CONTROL.
    An adaptive grid point algorithm for harmonic signal modeling2002In: Proc. of The 15th IFAC World Congress on Automatic Control, Barcelona, Spain, July 21-26,, 2002Conference paper (Refereed)
  • 6.
    Abd-Elrady, E.
    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.
    An adaptive grid point algorithm for harmonic signal modeling2002In: Preprint of Reglermöte, Linköping, Sweden, May 29-30., 2002Conference paper (Other scientific)
  • 7.
    Abd-Elrady, E.
    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. AUTOMATIC CONTROL.
    Study of a nonlinear recursive method for harmonic signal modeling2001In: Proc. of The 20th IASTED International Conference on Modeling, Identification and Control, Innsbruck, Austria, Feb. 19-22,, 2001Conference paper (Refereed)
  • 8.
    Abd-Elrady, E
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. AUTOMATIC CONTROL.
    Schoukens, J
    Least squares periodic signal modeling using orbits of nonlinear ODE's and fully automated spectral analysis2005In: Automatica, Vol. 41, no 5, p. 857-862Article in journal (Refereed)
  • 9.
    Abd-Elrady, E
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. AUTOMATIC CONTROL.
    Schoukens, J
    Least squares periodic signal modeling using orbits of nonlinear ODE's and fully automated spectral analysis2004In: Proc 6th IFAC Symposium on Nonlinear Control Systems, 2004Conference paper (Refereed)
  • 10.
    Abd-Elrady, E
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. AUTOMATIC CONTROL.
    Schoukens, J
    Least squares periodic signal modeling using orbits of nonlinear ODE's and fully automated spectral analysis2004In: Preprint of Reglermöte, Gothenburg, Sweden, May 26-27, 2004Conference paper (Refereed)
  • 11.
    Abd-Elrady, E
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. AUTOMATIC CONTROL.
    Söderström, T
    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. AUTOMATIC CONTROL.
    Bias analysis in least squares estimation of periodic signals using nonlinear ODEs2004Report (Other scientific)
  • 12.
    Abd-Elrady, E
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. AUTOMATIC CONTROL.
    Söderström, T
    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. AUTOMATIC CONTROL.
    Bias analysis in LS estimation of periodic signals using nonlinear ODE's2005In: Proc IFAC 16th World Congress, 2005Conference paper (Refereed)
  • 13.
    Abd-Elrady, Emad
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. AUTOMATIC CONTROL.
    Convergence of the RPEM as applied to harmonic signal modeling2000Report (Other academic)
    Abstract [en]

    Arbitrary periodic signals can be estimated recursively by exploiting the fact that a sine wave passing through a static nonlinear function generates a spectrum of overtones. The estimated signal model is hence parameterized as a real wave with unknown period in cascade with a piecewise linear function. The driving periodic wave can be chosen depending on any prior knowledge. The performance of a recursive Gauss-Newton prediction error identification algorithm for joint estimation of the driving frequency and the parameters of the nonlinear output function is therefore studied. A theoretical analysis of local convergence to the true parameter vector as well as numerical examples are given. Furthermore, the Cramer-Rao bound (CRB) is calculated in this report.

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  • 14.
    Abd-Elrady, Emad
    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.
    Harmonic signal modeling based on the Wiener model structure2002Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The estimation of frequencies and corresponding harmonic overtones is a problem of great importance in many situations. Applications can, for example, be found in supervision of electrical power transmission lines, in seismology and in acoustics. Generally, a periodic function with an unknown fundamental frequency in cascade with a parameterized and unknown nonlinear function can be used as a signal model for an arbitrary periodic signal. The main objective of the proposed modeling technique is to estimate the fundamental frequency of the periodic function in addition to the parameters of the nonlinear function.

    The thesis is divided into four parts. In the first part, a general introduction to the harmonic signal modeling problem and different approaches to solve the problem are given. Also, an outline of the thesis and future research topics are introduced.

    In the second part, a previously suggested recursive prediction error method (RPEM) for harmonic signal modeling is studied by numerical examples to explore the ability of the algorithm to converge to the true parameter vector. Also, the algorithm is modified to increase its ability to track the fundamental frequency variations.

    A modified algorithm is introduced in the third part to give the algorithm of the second part a more stable performance. The modifications in the RPEM are obtained by introducing an interval in the nonlinear block with fixed static gain. The modifications that result in the convergence analysis are, however, substantial and allows a complete treatment of the local convergence properties of the algorithm. Moreover, the Cramér–Rao bound (CRB) is derived for the modified algorithm and numerical simulations indicate that the method gives good results especially for moderate signal to noise ratios (SNR).

    In the fourth part, the idea is to give the algorithm of the third part the ability to estimate the driving frequency and the parameters of the nonlinear output function parameterized also in a number of adaptively estimated grid points. Allowing the algorithm to automatically adapt the grid points as well as the parameters of the nonlinear block, reduces the modeling errors and gives the algorithm more freedom to choose the suitable grid points. Numerical simulations indicate that the algorithm converges to the true parameter vector and gives better performance than the fixed grid point technique. Also, the CRB is derived for the adaptive grid point technique.

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    fulltext
  • 15.
    Abd-Elrady, Emad
    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 Approaches to Periodic Signal Modeling2005Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Periodic signal modeling plays an important role in different fields. The unifying theme of this thesis is using nonlinear techniques to model periodic signals. The suggested techniques utilize the user pre-knowledge about the signal waveform. This gives these techniques an advantage as compared to others that do not consider such priors.

    The technique of Part I relies on the fact that a sine wave that is passed through a static nonlinear function produces a harmonic spectrum of overtones. Consequently, the estimated signal model can be parameterized as a known periodic function (with unknown frequency) in cascade with an unknown static nonlinearity. The unknown frequency and the parameters of the static nonlinearity are estimated simultaneously using the recursive prediction error method (RPEM). A treatment of the local convergence properties of the RPEM is provided. Also, an adaptive grid point algorithm is introduced to estimate the unknown frequency and the parameters of the static nonlinearity in a number of adaptively estimated grid points. This gives the RPEM more freedom to select the grid points and hence reduces modeling errors.

    Limit cycle oscillations problem are encountered in many applications. Therefore, mathematical modeling of limit cycles becomes an essential topic that helps to better understand and/or to avoid limit cycle oscillations in different fields. In Part II, a second-order nonlinear ODE is used to model the periodic signal as a limit cycle oscillation. The right hand side of the ODE model is parameterized using a polynomial function in the states, and then discretized to allow for the implementation of different identification algorithms. Hence, it is possible to obtain highly accurate models by only estimating a few parameters.

    In Part III, different user aspects for the two nonlinear approaches of the thesis are discussed. Finally, topics for future research are presented.

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  • 16.
    Abd-Elrady, Emad
    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.
    Söderström, Torsten
    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.
    Periodic signal analysis using orbits of nonlinear ODEs based on the Markov estimate2004Conference paper (Refereed)
  • 17.
    Abd-Elrady, Emad
    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.
    Söderström, Torsten
    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.
    Periodic signal modeling based on Liénard's equation2004In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 49, no 10, p. 1773-1778Article in journal (Refereed)
  • 18.
    Abd-Elrady, Emad
    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.
    Söderström, Torsten
    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.
    Periodic signal modeling based on Liénard's equation2003Report (Other academic)
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  • 19.
    Abrahamsson, Richard
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Estimation Problems in Array Signal Processing, System Identification, and Radar Imagery2006Doctoral thesis, monograph (Other academic)
    Abstract [en]

    This thesis is concerned with parameter estimation, signal processing, and applications.

    In the first part, imaging using radar is considered. More specifically, two methods are presented for estimation and removal of ground-surface reflections in ground penetrating radar which otherwise hinder reliable detection of shallowly buried landmines. Further, a study of two autofocus methods for synthetic aperture radar is presented. In particular, we study their behavior in scenarios where the phase errors leading to cross-range defocusing are of a spatially variant kind.

    In the subsequent part, array signal processing and optimal beamforming is regarded. In particular, the phenomenon of signal cancellation in adaptive beamformers due to array perturbations, signal correlated interferences and limited data for covariance matrix estimation is considered. For the general signal cancellation problem, a class of improved adaptive beamformers is suggested based on ridge-regression. Another set of methods is suggested to mitigate signal cancellation due to correlated signal and interferences based on a novel way of finding a characterization of the interference subspace from observed array data. Further, a new minimum variance beamformer is presented for high resolution non-parametric spatial spectrum estimation in cases where the impinging signals are correlated. Lastly, a multitude of enhanced covariance matrix estimators from the statistical literature are studied as an alternative to other robust adaptive beamforming methods. The methods are also applied to space-time adaptive processing where limited data for covariance matrix estimation is a common problem.

    In the third and final part the estimation of the parameters of a general bilinear problem is considered. The bilinear model is motivated by the application of identifying submarines from their electromagnetic signature and by the identification of a Hamerstein-Wiener model of a non-linear dynamic system. An efficient approximate maximum-likelihood method with closed form solution is suggested for estimating the bilinear model parameters.

  • 20.
    Abrahamsson, Richard
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Jacobsson, A
    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, Automatic control.
    A capon-like spatial spectrum estimator for correlated sources2004In: 12th European Signal Processing Conference: EUSIPCO 2004, 2004Conference paper (Refereed)
  • 21.
    Abrahamsson, Richard
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Kay, Steven M.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Estimation of the parameters of a bilinear model with applications to submarine detection and system identification2007In: Digital signal processing (Print), ISSN 1051-2004, E-ISSN 1095-4333, Vol. 17, no 4, p. 756-773Article in journal (Refereed)
    Abstract [en]

    In this work we study the problem of estimating the parameters of a bilinear model describing, e.g., the amplitude modulation of extremely low frequency electromagnetic (ELFE) signatures of submarines. A similar problem arises in estimation of a nonlinear dynamic system using a Hammerstein–Wiener model, where two nonlinear static blocks surround a linear dynamic block. For these purposes a new method is derived. It is also shown in the same context that a two-stage method for parameter estimation of Hammerstein–Wiener models can be interpreted as an approximate least squares method. We also show the similarities with the problem of weighted low-rank approximation and the fact that these problems can be solved exactly in finite time using solvers for global optimization of systems of polynomials based on self dual optimization.

  • 22.
    Abrahamsson, Richard
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Selén, Yngve
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Enhanced covariance matrix estimators in adaptive beamforming2007In: 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol II, Pts 1-3, 2007, p. 969-972Conference paper (Refereed)
    Abstract [en]

    In this paper a number of covariance matrix estimators suggested in the literature are compared in terms of their performance in the context of array signal processing. More specifically they are applied in adaptive beamforming which is known to be sensitive to errors in the covariance matrix estimate and where often only a limited amount of data is available for estimation. As many covariance matrix estimators have the form of diagonal loading or eigenvalue adjustments of the sample covariance matrix and as they sometimes offer robustness to array imperfections and finite sample error, they are compared to a recent robustified adaptive Capon beamforming (RCB) method which also has a diagonal loading interpretation. Some of the covariance estimators show a significant improvement over the sample covariance matrix and in some cases they match the performance of the RCB even when a priori knowledge, which is not available in practice, is used for choosing the user parameter of RCB.

  • 23. Abu-Rmileh, Amjad
    et al.
    Garcia-Gabin, Winston
    Zambrano, Darine
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    A robust sliding mode controller with internal model for closed-loop artificial pancreas2010In: Medical and Biological Engineering and Computing, ISSN 0140-0118, E-ISSN 1741-0444, Vol. 48, no 12, p. 1191-1201Article in journal (Refereed)
  • 24. Abu-Rmileh, Amjad
    et al.
    Garcia-Gabin, Winston
    Zambrano, Darine
    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.
    Internal model sliding mode control approach for glucose regulation in type 1 diabetes2010In: Biomedical Signal Processing and Control, ISSN 1746-8094, Vol. 5, no 2, p. 94-102Article in journal (Refereed)
  • 25. Agarwal, M
    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, Automatic control.
    Åhgren, P
    Common factor estimation and two applications in signal prosessing2004In: Signal Processing, Vol. 84, p. 421-429Article in journal (Refereed)
  • 26.
    Aghanavesi, Somayeh
    et al.
    Dalarna Univ, Dept Comp Engn, Falun, Sweden..
    Westin, Jerker
    Dalarna Univ, Dept Comp Engn, Falun, Sweden..
    Bergquist, Filip
    Univ Gothenburg, Dept Pharmacol, Inst Neurosci & Physiol, Gothenburg, Sweden..
    Nyholm, Dag
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Landtblom: Neurology.
    Askmark, Håkan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Landtblom: Neurology.
    Aquilonius, Sten-Magnus
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Landtblom: Neurology.
    Constantinescu, Radu
    Univ Gothenburg, Dept Clin Neurosci, Gothenburg, Sweden..
    Medvedev, Alexander
    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.
    Spira, Jack
    Sensidose AB, Sollentuna, Sweden..
    Ohlsson, Fredrik
    Chalmers Univ, Gothenburg, Sweden..
    Thomas, Ilias
    Dalarna Univ, Dept Stat, Falun, Sweden..
    Ericsson, Anders
    Irisity AB, Gothenburg, Sweden..
    Buvarp, Dongni Johansson
    Univ Gothenburg, Dept Clin Neurosci & Rehabil, Gothenburg, Sweden..
    Memedi, Mevludin
    Orebro Univ, Informat, Orebro, Sweden..
    A multiple motion sensors index for motor state quantification in Parkinson's disease2020In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 189, article id 105309Article in journal (Refereed)
    Abstract [en]

    Aim: To construct a Treatment Response Index from Multiple Sensors (TRIMS) for quantification of motor state in patients with Parkinson's disease (PD) during a single levodopa dose. Another aim was to compare TRIMS to sensor indexes derived from individual motor tasks.

    Method: Nineteen PD patients performed three motor tests including leg agility, pronation-supination movement of hands, and walking in a clinic while wearing inertial measurement unit sensors on their wrists and ankles. They performed the tests repeatedly before and after taking 150% of their individual oral levodopa-carbidopa equivalent morning dose.Three neurologists blinded to treatment status, viewed patients' videos and rated their motor symptoms, dyskinesia, overall motor state based on selected items of Unified PD Rating Scale (UPDRS) part III, Dyskinesia scale, and Treatment Response Scale (TRS). To build TRIMS, out of initially 178 extracted features from upper- and lower-limbs data, 39 features were selected by stepwise regression method and were used as input to support vector machines to be mapped to mean reference TRS scores using 10-fold cross-validation method. Test-retest reliability, responsiveness to medication, and correlation to TRS as well as other UPDRS items were evaluated for TRIMS.

    Results: The correlation of TRIMS with TRS was 0.93. TRIMS had good test-retest reliability (ICC = 0.83). Responsiveness of the TRIMS to medication was good compared to TRS indicating its power in capturing the treatment effects. TRIMS was highly correlated to dyskinesia (R = 0.85), bradykinesia (R = 0.84) and gait (R = 0.79) UPDRS items. Correlation of sensor index from the upper-limb to TRS was 0.89.

    Conclusion: Using the fusion of upper- and lower-limbs sensor data to construct TRIMS provided accurate PD motor states estimation and responsive to treatment. In addition, quantification of upper-limb sensor data during walking test provided strong results.

  • 27.
    Agrawal, M
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. AUTOMATIC CONTROL.
    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, Automatic control.
    Besson, O
    Åhgren, 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, Automatic control.
    Estimation of nominal directions of arrival and angular spreads of distributed sources2003In: Signal Processing, Vol. 83, p. 1833-1838Article in journal (Refereed)
  • 28. Aguero, Juan Carlos
    et al.
    Goodwin, Graham
    Söderström, Torsten
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Yuz, Juan
    Sampled Data Errors in Variables Systems.2009In: SYSID 2009, IFAC 15th Symposium on System Identification, Saint-Malo, France, July 6-8, 2009., 2009Conference paper (Refereed)
  • 29. Agüero, Juan C.
    et al.
    Godoy, Boris I.
    Goodwin, Graham C.
    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.
    Scenario-based EM identification for FIR systems having quantized output data2009In: Proc. 15th IFAC Symposium on System Identification, International Federation of Automatic Control , 2009, p. 66-71Conference paper (Refereed)
  • 30. Agüero, Juan C.
    et al.
    Goodwin, Graham C.
    Lau, Katrina
    Wang, Meng
    Silva, Eduardo I.
    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.
    Three-degree of freedom adaptive power control for CDMA cellular systems2009In: Proc. 28th Global Telecommunications Conference, IEEE Communications Society, 2009, p. 2793-2798Conference paper (Refereed)
  • 31. Ahmed-Ali, Tarek
    et al.
    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.
    Schoukens, Maarten
    Giri, Fouad
    Sampled-data adaptive observer for state-affine systems with uncertain output equation2019In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 103, p. 96-105Article in journal (Refereed)
  • 32. Ahmed-Ali, Tarek
    et al.
    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.
    Schoukens, Maarten
    Giri, Fouad
    Sampled-Data Based State and Parameter Estimation for State-Affine Systems with Uncertain Output Equation2018Conference paper (Refereed)
    Abstract [en]

    The problem of sampled-data observer design is addressed for a class of state- and parameter-affine nonlinear systems. The main novelty in this class lies in the fact that the unknown parameters enter the output equation and the associated regressor is nonlinear in the output. Wiener systems belong to this class. The difficulty with this class of systems comes from the fact that output measurements are only available at sampling times causing the loss of the parameter-affine nature of the model (except at the sampling instants). This makes existing adaptive observers inapplicable to this class of systems. In this paper, a new sampled-data adaptive observer is designed for these systems and shown to be exponentially convergent under specific persistent excitation (PE) conditions that ensure system observability and identifiability. The new observer involves an inter-sample output predictor that is different from those in existing observers and features continuous trajectories of the state and parameter estimates.

  • 33.
    Albaba, Adnan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Online Model-Based Beat-by-beat Heart Rate Estimation2020In: 2020 American Control Conference (ACC), 2020, p. 539-544Conference paper (Refereed)
    Abstract [en]

    A method for estimating the instantaneous heart rate (HR) using the morphological features of one electrocardiogram (ECG) cycle (beat) at a time is proposed. This work is not aimed at introducing an alternative way for HR estimation, but rather illustrates the utility of model-based ECG analysis in online individualized monitoring of the heart function. The HR estimation problem is reduced to fitting one parameter, whose value is related to the nine parameters of a realistic nonlinear model of the ECG and estimated from data by nonlinear least-squares optimization. The method feasibility is evaluated on synthetic ECG signals as well as signals acquired from MIT-BIH databases at Physionet website. Moreover, the performance of the method was tested under realistic free-moving conditions using a wearable ECG and HR monitor with encouraging results.

  • 34.
    Albaba, Adnan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Patient-Specific Electrocardiogram Monitoring by Model-Based Stochastic Anomaly Detection2020In: 2020 European Control Conference (ECC), 2020, p. 735-740Conference paper (Refereed)
    Abstract [en]

    A novel model-based method for patient-specific detection of deformed electrocardiogram (ECG) beats is proposed and tested. Five parameters of a patient-specific nonlinear ECG model are estimated from data by nonlinear least-squares optimization. The normal variability of the model parameters is captured by estimated probability density functions. A binary classifier, based on stochastic anomaly detection methods, along with a pre-tuned classification threshold, is employed for detecting the abnormal ECG beats. We demonstrate the utility of the proposed approach by validating it on annotated arrhythmia data recorded under clinical conditions.

  • 35.
    Alenlöv, Johan
    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.
    Olsson, Jimmy
    Particle-based adaptive-lag online marginal smoothing in general state-space models2019In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 67, no 21, p. 5571-5582Article in journal (Refereed)
  • 36. Almeida, Juliana
    et al.
    Martins da Silva, Margarida
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Mendonça, Teresa
    Rocha, Paula
    A compartmental model-based control strategy for NeuroMuscular Blockade level2011In: Proc. 18th IFAC World Congress, International Federation of Automatic Control , 2011, p. 599-604Conference paper (Refereed)
  • 37. Almeida, Juliana
    et al.
    Martins da Silva, Margarida
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    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.
    Mendonça, Teresa
    Contributions to the initialization of online identification algorithms for anæsthesia: the NeuroMuscular Blockade case study2010In: Proc. 18th Mediterranean Conference on Control and Automation, Piscataway, NJ: IEEE , 2010, p. 1341-1346Conference paper (Refereed)
  • 38. Alonso, Hugo
    et al.
    Mendonça, Teresa
    Lemos, João M.
    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.
    A simple model for the identification of drug effects2009In: Proc. 6th International Symposium on Intelligent Signal Processing, Piscataway, NJ: IEEE , 2009, p. 269-273Conference paper (Refereed)
  • 39.
    Andersson, Carl
    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 learning applied to system identification: A probabilistic approach2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Machine learning has been applied to sequential data for a long time in the field of system identification. As deep learning grew under the late 00's machine learning was again applied to sequential data but from a new angle, not utilizing much of the knowledge from system identification. Likewise, the field of system identification has yet to adopt many of the recent advancements in deep learning. This thesis is a response to that. It introduces the field of deep learning in a probabilistic machine learning setting for problems known from system identification.

    Our goal for sequential modeling within the scope of this thesis is to obtain a model with good predictive and/or generative capabilities. The motivation behind this is that such a model can then be used in other areas, such as control or reinforcement learning. The model could also be used as a stepping stone for machine learning problems or for pure recreational purposes.

    Paper I and Paper II focus on how to apply deep learning to common system identification problems. Paper I introduces a novel way of regularizing the impulse response estimator for a system. In contrast to previous methods using Gaussian processes for this regularization we propose to parameterize the regularization with a neural network and train this using a large dataset. Paper II introduces deep learning and many of its core concepts for a system identification audience. In the paper we also evaluate several contemporary deep learning models on standard system identification benchmarks. Paper III is the odd fish in the collection in that it focuses on the mathematical formulation and evaluation of calibration in classification especially for deep neural network. The paper proposes a new formalized notation for calibration and some novel ideas for evaluation of calibration. It also provides some experimental results on calibration evaluation.

    List of papers
    1. Data-driven impulse response regularization via deep learning
    Open this publication in new window or tab >>Data-driven impulse response regularization via deep learning
    2018 (English)Conference paper, Published paper (Refereed)
    Series
    IFAC-PapersOnLine, ISSN 2405-8963 ; 51:15
    National Category
    Signal Processing
    Identifiers
    urn:nbn:se:uu:diva-366186 (URN)10.1016/j.ifacol.2018.09.081 (DOI)000446599200002 ()
    Conference
    SYSID 2018, July 9–11, Stockholm, Sweden
    Available from: 2018-10-08 Created: 2018-11-22 Last updated: 2022-04-04Bibliographically approved
    2. Deep convolutional networks in system identification
    Open this publication in new window or tab >>Deep convolutional networks in system identification
    Show others...
    2019 (English)In: Proc. 58th IEEE Conference on Decision and Control, IEEE, 2019, p. 3670-3676Conference paper, Published paper (Refereed)
    Abstract [en]

    Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system identification communities. It has recently been shown that convolutional architectures are at least as capable as recurrent architectures when it comes to sequence modeling tasks. Inspired by these results we explore the explicit relationships between the recently proposed temporal convolutional network (TCN) and two classic system identification model structures; Volterra series and block-oriented models. We end the paper with an experimental study where we provide results on two real-world problems, the well-known Silverbox dataset and a newer dataset originating from ground vibration experiments on an F-16 fighter aircraft.

    Place, publisher, year, edition, pages
    IEEE, 2019
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:uu:diva-397528 (URN)10.1109/CDC40024.2019.9030219 (DOI)000560779003058 ()978-1-7281-1398-2 (ISBN)
    Conference
    CDC 2019, December 11–13, Nice, France
    Funder
    Swedish Foundation for Strategic Research , RIT15-0012Swedish Research Council, 621-2016-06079
    Available from: 2020-03-12 Created: 2019-11-21 Last updated: 2022-04-04Bibliographically approved
    3. Evaluating model calibration in classification
    Open this publication in new window or tab >>Evaluating model calibration in classification
    Show others...
    2019 (English)In: 22nd International Conference on Artificial Intelligence and Statistics, 2019, p. 3459-3467Conference paper, Published paper (Refereed)
    Series
    Proceedings of Machine Learning Research, ISSN 2640-3498 ; 89
    National Category
    Probability Theory and Statistics
    Identifiers
    urn:nbn:se:uu:diva-397519 (URN)000509687903053 ()
    Conference
    AISTATS 2019, April 16–18, Naha, Japan
    Available from: 2019-04-25 Created: 2019-11-21 Last updated: 2023-04-26Bibliographically approved
    Download full text (pdf)
    fulltext
  • 40.
    Andersson, Carl
    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 probabilistic models for sequential and hierarchical data2022Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Consider the problem where we want a computer program capable of recognizing a pedestrian on the road. This could be employed in a car to automatically apply the brakes to avoid an accident. Writing such a program is immensely difficult but what if we could instead use examples and let the program learn what characterizes a pedestrian from the examples. Machine learning can be described as the process of teaching a model (computer program) to predict something (the presence of a pedestrian) with help of data (examples) instead of through explicit programming.

    This thesis focuses on a specific method in machine learning, called deep learning. This method can arguably be seen as sole responsible for the recent upswing of machine learning in academia as well as in society at large. However, deep learning requires, in human standards, a huge amount of data to perform well which can be a limiting factor.  In this thesis we describe different approaches to reduce the amount of data that is needed by encoding some of our prior knowledge about the problem into the model. To this end we focus on sequential and hierarchical data, such as speech and written language.

    Representing sequential output is in general difficult due to the complexity of the output space. Here, we make use of a probabilistic approach focusing on sequential models in combination with a deep learning structure called the variational autoencoder. This is applied to a range of different problem settings, from system identification to speech modeling.

    The results come in three parts. The first contribution focus on applications of deep learning to typical system identification problems, the intersection between the two areas and how they can benefit from each other. The second contribution is on hierarchical data where we promote a multiscale variational autoencoder inspired by image modeling. The final contribution is on verification of probabilistic models, in particular how to evaluate the validity of a probabilistic output, also known as calibration.

    List of papers
    1. Data-driven impulse response regularization via deep learning
    Open this publication in new window or tab >>Data-driven impulse response regularization via deep learning
    2018 (English)Conference paper, Published paper (Refereed)
    Series
    IFAC-PapersOnLine, ISSN 2405-8963 ; 51:15
    National Category
    Signal Processing
    Identifiers
    urn:nbn:se:uu:diva-366186 (URN)10.1016/j.ifacol.2018.09.081 (DOI)000446599200002 ()
    Conference
    SYSID 2018, July 9–11, Stockholm, Sweden
    Available from: 2018-10-08 Created: 2018-11-22 Last updated: 2022-04-04Bibliographically approved
    2. Deep convolutional networks in system identification
    Open this publication in new window or tab >>Deep convolutional networks in system identification
    Show others...
    2019 (English)In: Proc. 58th IEEE Conference on Decision and Control, IEEE, 2019, p. 3670-3676Conference paper, Published paper (Refereed)
    Abstract [en]

    Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system identification communities. It has recently been shown that convolutional architectures are at least as capable as recurrent architectures when it comes to sequence modeling tasks. Inspired by these results we explore the explicit relationships between the recently proposed temporal convolutional network (TCN) and two classic system identification model structures; Volterra series and block-oriented models. We end the paper with an experimental study where we provide results on two real-world problems, the well-known Silverbox dataset and a newer dataset originating from ground vibration experiments on an F-16 fighter aircraft.

    Place, publisher, year, edition, pages
    IEEE, 2019
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:uu:diva-397528 (URN)10.1109/CDC40024.2019.9030219 (DOI)000560779003058 ()978-1-7281-1398-2 (ISBN)
    Conference
    CDC 2019, December 11–13, Nice, France
    Funder
    Swedish Foundation for Strategic Research , RIT15-0012Swedish Research Council, 621-2016-06079
    Available from: 2020-03-12 Created: 2019-11-21 Last updated: 2022-04-04Bibliographically approved
    3. Learning deep autoregressive models for hierarchical data
    Open this publication in new window or tab >>Learning deep autoregressive models for hierarchical data
    2021 (English)In: IFAC PapersOnLine, Elsevier BV Elsevier, 2021, Vol. 54, no 7, p. 529-534Conference paper, Published paper (Refereed)
    Abstract [en]

    We propose a model for hierarchical structured data as an extension to the stochastic temporal convolutional network. The proposed model combines an autoregressive model with a hierarchical variational autoencoder and downsampling to achieve superior computational complexity. We evaluate the proposed model on two different types of sequential data: speech and handwritten text. The results are promising with the proposed model achieving state-of-the-art performance.

    Place, publisher, year, edition, pages
    ElsevierElsevier BV, 2021
    Keywords
    Deep learning, variational autoencoders, nonlinear systems
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:uu:diva-457738 (URN)10.1016/j.ifacol.2021.08.414 (DOI)000696396200091 ()
    Conference
    19th IFAC Symposium on System Identification (SYSID), JUL 13-16, 2021, Padova, ITALY
    Funder
    Swedish Research CouncilKjell and Marta Beijer Foundation
    Available from: 2021-11-12 Created: 2021-11-12 Last updated: 2024-01-15Bibliographically approved
    4. Evaluating model calibration in classification
    Open this publication in new window or tab >>Evaluating model calibration in classification
    Show others...
    2019 (English)In: 22nd International Conference on Artificial Intelligence and Statistics, 2019, p. 3459-3467Conference paper, Published paper (Refereed)
    Series
    Proceedings of Machine Learning Research, ISSN 2640-3498 ; 89
    National Category
    Probability Theory and Statistics
    Identifiers
    urn:nbn:se:uu:diva-397519 (URN)000509687903053 ()
    Conference
    AISTATS 2019, April 16–18, Naha, Japan
    Available from: 2019-04-25 Created: 2019-11-21 Last updated: 2023-04-26Bibliographically approved
    Download full text (pdf)
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  • 41.
    Andersson, Carl
    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.
    Horta Ribeiro, Antônio
    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. Univ Fed Minas Gerais, Grad Program Elect Engn, Ave Antonio Carlos 6627, BR-31270901 Belo Horizonte, MG, Brazil.
    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.
    Wahlström, Niklas
    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.
    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.
    Deep convolutional networks in system identification2019In: Proc. 58th IEEE Conference on Decision and Control, IEEE, 2019, p. 3670-3676Conference paper (Refereed)
    Abstract [en]

    Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system identification communities. It has recently been shown that convolutional architectures are at least as capable as recurrent architectures when it comes to sequence modeling tasks. Inspired by these results we explore the explicit relationships between the recently proposed temporal convolutional network (TCN) and two classic system identification model structures; Volterra series and block-oriented models. We end the paper with an experimental study where we provide results on two real-world problems, the well-known Silverbox dataset and a newer dataset originating from ground vibration experiments on an F-16 fighter aircraft.

  • 42.
    Andersson, Carl
    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.
    Wahlström, Niklas
    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.
    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.
    Data-driven impulse response regularization via deep learning2018Conference paper (Refereed)
  • 43.
    Andersson, Helena
    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.
    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.
    The impact of deep brain stimulation on a simulated neuron: Inhibition, excitation, and partial recovery2018In: Proc. 16th European Control Conference, IEEE, 2018, p. 2034-2039Conference paper (Refereed)
  • 44.
    Anubhab, Ghosh
    et al.
    KTH Royal Institute of Technology.
    Abdalmoaty, Mohamed
    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.
    Chatterjee, Saikat
    KTH Royal Institute of Technology.
    Hjalmarsson, Håkan
    KTH Royal Institute of Technology.
    DeepBayes -- an estimator for parameter estimation in stochastic nonlinear dynamical modelsManuscript (preprint) (Other academic)
    Abstract [en]

    Stochastic nonlinear dynamical systems are ubiquitous in modern, real-world applications. Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains a challenging problem. The majority of existing methods employ maximum likelihood or Bayesian estimation. However, these methods suffer from some limitations, most notably the substantial computational time for inference coupled with limited flexibility in application. In this work, we propose DeepBayes estimators that leverage the power of deep recurrent neural networks in learning an estimator. The method consists of first training a recurrent neural network to minimize the mean-squared estimation error over a set of synthetically generated data using models drawn from the model set of interest. The a priori trained estimator can then be used directly for inference by evaluating the network with the estimation data. The deep recurrent neural network architectures can be trained offline and ensure significant time savings during inference. We experiment with two popular recurrent neural networks -- long short term memory network (LSTM) and gated recurrent unit (GRU). We demonstrate the applicability of our proposed method on different example models and perform detailed comparisons with state-of-the-art approaches. We also provide a study on a real-world nonlinear benchmark problem. The experimental evaluations show that the proposed approach is asymptotically as good as the Bayes estimator. 

  • 45.
    Anubhab, Ghosh
    et al.
    KTH Royal Institute of Technology.
    Fontcuberta, Aleix Espuña
    Abdalmoaty, Mohamed
    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.
    Chatterjee, Saikat
    KTH Royal Institute of Technology.
    Time-Varying Normalizing Flows for Dynamical Signals2022Conference paper (Refereed)
    Abstract [en]

    We develop a time-varying normalizing flow (TVNF) for explicit generative modeling of dynamical signals. Being explicit, it can generate samples of dynamical signals, and compute the likelihood of a (given) dynamical signal sample. In the proposed model, signal flow in the layers of the normalizing flow is a function of time, which is realized using an encoded representation that is the output of a recurrent neural network (RNN). Given a set of dynamical signals, the parameters of TVNF are learned according to a maximum-likelihood approach in conjunction with gradient descent (backpropagation). Use of the proposed model is illustrated for a toy application scenario-maximum-likelihood based speech-phone classification task.

  • 46.
    Artman, Henrik
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. HUMAN-COMPUTER INTERACTION.
    Holmlid, S
    Gulliksen, Jan
    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. HUMAN-COMPUTER INTERACTION.
    Beställ användbarhet vid IT-utveckling!2001Other (Other (popular scientific, debate etc.))
  • 47. Arvidsson, Åke
    et al.
    Rydén, Tobias
    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.
    Load transients in pooled cellular core network nodes2015In: Performance evaluation (Print), ISSN 0166-5316, E-ISSN 1872-745X, Vol. 90, p. 18-35Article in journal (Refereed)
  • 48. Aslani, Mohammad
    et al.
    Seipel, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. 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 Visual Information and Interaction. University of Gävle.
    A fast instance selection method for support vector machines in building extraction2020In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 97, article id 106716Article in journal (Refereed)
    Abstract [en]

    Training support vector machines (SVMs) for pixel-based feature extraction purposes from aerial images requires selecting representative pixels (instances) as a training dataset. In this research, locality-sensitive hashing (LSH) is adopted for developing a new instance selection method which is referred to as DR.LSH. The intuition of DR.LSH rests on rapidly finding similar and redundant training samples and excluding them from the original dataset. The simple idea of this method alongside its linear computational complexity make it expeditious in coping with massive training data (millions of pixels). DR.LSH is benchmarked against two recently proposed methods on a dataset for building extraction with 23,750,000 samples obtained from the fusion of aerial images and point clouds. The results reveal that DR.LSH outperforms them in terms of both preservation rate and maintaining the generalization ability (classification loss). The source code of DR.LSH can be found in https://github.com/mohaslani/DR.LSH.

    Download full text (pdf)
    fulltext
  • 49.
    Aubry, Augusto
    et al.
    CNR, IREA.
    De Maio, Antonio
    Universit`a degli Studi di Napoli “Federico II”.
    Piezzo, Marco
    Universit`a degli Studi di Napoli “Federico II”.
    Naghsh, Mohammad Mahdi
    Isfahan University of Technology.
    Soltanalian, Mojtaba
    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.
    Stoica, Peter
    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.
    Cognitive Radar Waveform Design for Spectral Coexistence in Signal-Dependent Interference2014Conference paper (Refereed)
  • 50.
    Babu, Prabhu
    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.
    Spectral Analysis of Nonuniformly Sampled Data and Applications2012Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Signal acquisition, signal reconstruction and analysis of spectrum of the signal are the three most important steps in signal processing and they are found in almost all of the modern day hardware. In most of the signal processing hardware, the signal of interest is sampled at uniform intervals satisfying some conditions like Nyquist rate. However, in some cases the privilege of having uniformly sampled data is lost due to some constraints on the hardware resources. In this thesis an important problem of signal reconstruction and spectral analysis from nonuniformly sampled data is addressed and a variety of methods are presented. The proposed methods are tested via numerical experiments on both artificial and real-life data sets.

    The thesis starts with a brief review of methods available in the literature for signal reconstruction and spectral analysis from non uniformly sampled data. The methods discussed in the thesis are classified into two broad categories - dense and sparse methods, the classification is based on the kind of spectra for which they are applicable. Under dense spectral methods the main contribution of the thesis is a non-parametric approach named LIMES, which recovers the smooth spectrum from non uniformly sampled data. Apart from recovering the spectrum, LIMES also gives an estimate of the covariance matrix. Under sparse methods the two main contributions are methods named SPICE and LIKES - both of them are user parameter free sparse estimation methods applicable for line spectral estimation. The other important contributions are extensions of SPICE and LIKES to multivariate time series and array processing models, and a solution to the grid selection problem in sparse estimation of spectral-line parameters.

    The third and final part of the thesis contains applications of the methods discussed in the thesis to the problem of radial velocity data analysis for exoplanet detection. Apart from the exoplanet application, an application based on Sudoku, which is related to sparse parameter estimation, is also discussed.

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