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  • 1.
    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, 163-195 p.Article in journal (Refereed)
  • 2.
    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)
  • 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.
    An adaptive grid point algorithm for harmonic signal modeling,2001Report (Other scientific)
  • 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 modeling2002In: Proc. of The 15th IFAC World Congress on Automatic Control, Barcelona, Spain, July 21-26,, 2002Conference paper (Refereed)
  • 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.
    Convergence of the RPEM as applied to harmonic signal modeling2000Report (Other scientific)
  • 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. 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)
  • 7.
    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)
  • 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, 857-862 p.Article 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.
    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)
  • 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 LS estimation of periodic signals using nonlinear ODE's2005In: Proc IFAC 16th World Congress, 2005Conference paper (Refereed)
  • 12.
    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.

  • 13.
    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.

  • 14.
    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)
  • 15.
    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)
  • 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 modeling based on Liénard's equation2004In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 49, no 10, 1773-1778 p.Article in journal (Refereed)
  • 17.
    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.

  • 18.
    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)
  • 19.
    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, 756-773 p.Article 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.

  • 20.
    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, 969-972 p.Conference 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.

  • 21. 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, 1191-1201 p.Article in journal (Refereed)
  • 22. 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, 94-102 p.Article in journal (Refereed)
  • 23. 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, 421-429 p.Article in journal (Refereed)
  • 24.
    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, 1833-1838 p.Article in journal (Refereed)
  • 25. 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)
  • 26. 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, 66-71 p.Conference paper (Refereed)
  • 27. 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, 2793-2798 p.Conference paper (Refereed)
  • 28. 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, 599-604 p.Conference paper (Refereed)
  • 29. 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, 1341-1346 p.Conference paper (Refereed)
  • 30. 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, 269-273 p.Conference paper (Refereed)
  • 31.
    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.))
  • 32. 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, Vol. 90, 18-35 p.Article in journal (Refereed)
  • 33.
    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, Petre
    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)
  • 34.
    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.

  • 35.
    Babu, Prabhu
    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.
    Gudmundson, Erik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Automatic cepstrum-based smoothing of the periodogram via cross-validation2008In: Proc. 16th European Signal Processing Conference, European Association for Signal Processing , 2008Conference paper (Refereed)
  • 36.
    Babu, Prabhu
    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.
    Gudmundson, Erik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Optimal Preconditioning for Interpolation of Missing Data in a Band-Limited Sequence2008In: Proc. 42nd Asilomar Conference on Signals, Systems and Computers, Piscataway, NJ: IEEE , 2008, 561-565 p.Conference paper (Refereed)
  • 37.
    Babu, Prabhu
    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.
    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.
    Li, Jian
    Linear Systems, Sparse Solutions, and Sudoku2010In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 17, no 1, 40-42 p.Article in journal (Refereed)
  • 38.
    Babu, Prabhu
    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.
    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.
    A combined linear programming-maximum likelihood approach to radial velocity data analysis for extrasolar planet detection2011In: ICASSP2011, the 36th International Conference on Acoustics, Speech and Signal Processing, Prague, Czech Republic, 2011, 4352-4355 p.Conference paper (Refereed)
    Abstract [en]

    In this paper we introduce a new technique for estimating the parameters of the Keplerian model commonly used in radial velocity data analysis for extrasolar planet detection. The unknown parameters in the Keplerian model, namely eccentricity e, orbital frequency f, periastron passage time T, longitude of periastron., and radial velocity amplitude K are estimated by a new approach named SPICE (a semi-parametric iterative covariance-based estimation technique). SPICE enjoys global convergence, does not require selection of any hyperparameters, and is computationally efficient (indeed computing the SPICE estimates boils down to solving a numerically efficient linear program (LP)). The parameter estimates obtained from SPICE are then refined by means of a relaxation-based maximum likelihood algorithm (RELAX) and the significance of the resultant estimates is determined by a generalized likelihood ratio test (GLRT). A real-life radial velocity data set of the star HD 9446 is analyzed and the results obtained are compared with those reported in the literature.

  • 39.
    Babu, Prabhu
    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.
    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.
    Comments on "Iterative Estimation of Sinusoidal Signal Parameters"2010In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 17, no 12, 1022-1023 p.Article in journal (Refereed)
  • 40.
    Babu, Prabhu
    et al.
    Department of Electronic and Computer Engineering, HKUST, Hong Kong.
    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.
    Connection between SPICE and Square-Root LASSO for sparse parameter estimation2014In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 95, 10-14 p.Article in journal (Refereed)
    Abstract [en]

    In this note we show that the sparse estimation technique named Square-Root LASSO (SR-LASSO) is connected to a previously introduced method named SPICE. More concretely we prove that the SR-LASSO with a unit weighting factor is identical to SPICE. Furthermore we show via numerical simulations that the performance of the SR-LASSO changes insignificantly when the weighting factor is varied. SPICE stands for sparse iterative covariance-based estimation and LASSO for least absolute shrinkage and selection operator.

  • 41.
    Babu, Prabhu
    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.
    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.
    Sparse spectral-line estimation for nonuniformly sampled multivariate time series: SPICE, LIKES and MSBL2012In: 2012 Proceedings Of The 20th European Signal Processing Conference (EUSIPCO), 2012, 445-449 p.Conference paper (Refereed)
    Abstract [en]

    In this paper we deal with the problem of spectral-line analysis ofnonuniformly sampled multivariate time series for which we introduce two methods: the first method named SPICE (sparse iterativecovariance based estimation) is based on a covariance fitting framework whereas the second method named LIKES (likelihood-basedestimation of sparse parameters) is a maximum likelihood technique. Both methods yield sparse spectral estimates and they donot require the choice of any hyperparameters. We numericallycompare the performance of SPICE and LIKES with that of the recently introduced method of multivariate sparse Bayesian learning(MSBL).

  • 42.
    Babu, Prabhu
    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.
    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.
    Spectral analysis of nonuniformly sampled data — a review2010In: Digital signal processing (Print), ISSN 1051-2004, E-ISSN 1095-4333, Vol. 20, no 2, 359-378 p.Article in journal (Refereed)
  • 43.
    Babu, Prabhu
    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.
    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.
    Li, Jian
    University of Florida.
    Modeling radial velocity signals for exoplanet search applications2010In: The 7th International Conference on Informatics in Control, Automation and Robotics, Madeira, Portugal, 2010Conference paper (Refereed)
  • 44.
    Babu, Prabhu
    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.
    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.
    Li, Jian
    Chen, Zhaofu
    Ge, Jian
    Analysis of radial velocity data by a novel adaptive approach2010In: Astronomical Journal, ISSN 0004-6256, E-ISSN 1538-3881, Vol. 139, no 2, 783-793 p.Article in journal (Refereed)
  • 45.
    Babu, Prabhu
    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.
    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.
    Marzetta, Thomas L.
    An IQML type algorithm for AR parameter estimation from noisy covariance sequences2009In: Proc. 17th European Signal Processing Conference, European Association for Signal Processing , 2009, 1022-1026 p.Conference paper (Refereed)
  • 46. Barral, Joëlle K.
    et al.
    Gudmundson, Erik
    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.
    Stikov, Nikola
    Etezadi-Amoli, Maryam
    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.
    Nishimura, Dwight G.
    A Robust Methodology for In Vivo T1 Mapping2010In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 64, no 4, 1057-1067 p.Article in journal (Refereed)
  • 47. Barral, Joëlle K.
    et al.
    Stikov, Nikola
    Gudmundson, Erik
    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.
    Nishimura, Dwight G.
    Skin T1 Mapping at 1.5T, 3T, and 7T2009In: Proceedings of the ISMRM 2009, Honolulu, Hawaii, USA, 2009Conference paper (Refereed)
  • 48. Beck, Amir
    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.
    Li, Jian
    Exact and approximate solutions of source localization problems2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, Vol. 56, no 5, 1770-1778 p.Article in journal (Refereed)
    Abstract [en]

    We consider least squares (LS) approaches for locating a radiating source from range measurements (which we call R-LS) or from range-difference measurements (RD-LS) collected using an array of passive sensors. We also consider LS approaches based on squared range observations (SR-LS) and based on squared range-difference measurements (SRD-LS). Despite the fact that the resulting optimization problems are nonconvex, we provide exact solution procedures for efficiently computing the SR-LS and SRD-LS estimates. Numerical simulations suggest that the exact SR-LS and SRD-LS estimates outperform existing approximations of the SR-LS and SRD-LS solutions as well as approximations of the R-LS and RD-LS solutions which are based on a semidefinite relaxation.

  • 49. Besson, O
    et al.
    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.
    Channel and frequency offsets estimation in MIMO time-selective channels2002In: 2nd IEEE Sensor Array and Multichannel Signal Processing Workshop, Rosslyn ,USA, 2002Conference paper (Refereed)
  • 50. Besson, O
    et al.
    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.
    Exponential signals with time-varying amplitude: Parameter estimation via polar decomposition1998In: Signal Processing, Vol. 66, no 1, 27-43 p.Article in journal (Refereed)
    Abstract [en]

    This paper addresses the estimation of the center frequency of complex exponential signals with time-varying amplitude. A method which requires few assumptions regarding the signal’s envelope is proposed. It is based on the polar decomposition of a certain covariance matrix. The polar decomposition, a generalization to matrices of the complex number representation z=reiθ with r>0, is particularly suitable for the application considered. The notion of truncated polar decomposition is introduced. Simple schemes for estimating the signal’s frequency are presented, based on these decompositions. In contrast to most existing methods, the methods presented herein do not rely on any assumed structure for the time-varying amplitude, and they are shown to possess good performances in a large class of signals. The effectiveness and robustness of our approach are demonstrated on real radar data.

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