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  • 51.
    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)
  • 52.
    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, p. 561-565Conference paper (Refereed)
  • 53.
    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, p. 40-42Article in journal (Refereed)
  • 54.
    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, p. 4352-4355Conference 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.

  • 55.
    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, p. 1022-1023Article in journal (Refereed)
  • 56.
    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, p. 10-14Article 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.

  • 57.
    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, p. 445-449Conference 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).

  • 58.
    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, p. 359-378Article in journal (Refereed)
  • 59.
    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)
  • 60.
    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, p. 783-793Article in journal (Refereed)
  • 61.
    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, p. 1022-1026Conference paper (Refereed)
  • 62. 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, p. 1057-1067Article in journal (Refereed)
  • 63. 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)
  • 64. 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, E-ISSN 1941-0476, Vol. 56, no 5, p. 1770-1778Article 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.

  • 65.
    Bengtsson, Ewert
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. 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.
    Wieslander, Håkan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Forslid, Gustav
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Wählby, Carolina
    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, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Hirsch, Jan-Michael
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Oral and Maxillofacial Surgery.
    Runow Stark, Christina
    Kecheril Sadanandan, Sajith
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    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, Division of Visual Information and Interaction.
    Detection of Malignancy-Associated Changes Due to Precancerous and Oral Cancer Lesions: A Pilot Study Using Deep Learning2018In: CYTO2018 / [ed] Andrea Cossarizza, 2018Conference paper (Refereed)
    Abstract [en]

    Background: The incidence of oral cancer is increasing and it is effecting younger individuals. PAP smear-based screening, visual, and automated, have been used for decades, to successfully decrease the incidence of cervical cancer. Can similar methods be used for oral cancer screening? We have carried out a pilot study using neural networks for classifying cells, both from cervical cancer and oral cancer patients. The results which were reported from a technical point of view at the 2017 IEEE International Conference on Computer Vision Workshop (ICCVW), were particularly interesting for the oral cancer cases, and we are currently collecting and analyzing samples from more patients. Methods: Samples were collected with a brush in the oral cavity and smeared on glass slides, stained, and prepared, according to standard PAP procedures. Images from the slides were digitized with a 0.35 micron pixel size, using focus stacks with 15 levels 0.4 micron apart. Between 245 and 2,123 cell nuclei were manually selected for analysis for each of 14 datasets, usually 2 datasets for each of the 6 cases, in total around 15,000 cells. A small region was cropped around each nucleus, and the best 2 adjacent focus layers in each direction were automatically found, thus creating images of 100x100x5 pixels. Nuclei were chosen with an aim to select well preserved free-lying cells, with no effort to specifically select diagnostic cells. We therefore had no ground truth on the cellular level, only on the patient level. Subsets of these images were used for training 2 sets of neural networks, created according to the ResNet and VGG architectures described in literature, to distinguish between cells from healthy persons, and those with precancerous lesions. The datasets were augmented through mirroring and 90 degrees rotations. The resulting networks were used to classify subsets of cells from different persons, than those in the training sets. This was repeated for a total of 5 folds. Results: The results were expressed as the percentage of cell nuclei that the neural networks indicated as positive. The percentage of positive cells from healthy persons was in the range 8% to 38%. The percentage of positive cells collected near the lesions was in the range 31% to 96%. The percentages from the healthy side of the oral cavity of patients with lesions ranged 37% to 89%. For each fold, it was possible to find a threshold for the number of positive cells that would correctly classify all patients as normal or positive, even for the samples taken from the healthy side of the oral cavity. The network based on the ResNet architecture showed slightly better performance than the VGG-based one. Conclusion: Our small pilot study indicates that malignancyassociated changes that can be detected by neural networks may exist among cells in the oral cavity of patients with precancerous lesions. We are currently collecting samples from more patients, and will present those results as well, with our poster at CYTO 2018.

  • 66.
    Bereza, Robert
    et al.
    KTH Royal Institute of Technology.
    Eriksson, Oscar
    KTH Royal Institute of Technology.
    Abdalmoaty, Mohamed R.-H.
    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.
    Broman, David
    KTH Royal Institute of Technology.
    Hjalmarsson, Håkan
    KTH Royal Institute of Technology.
    Stochastic Approximation for Identification of Non-Linear Differential-Algebraic Equations with Process Disturbances2022In: 2022 IEEE 61st Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 6712-6717Conference paper (Refereed)
    Abstract [en]

    Differential-algebraic equations, commonly used to model physical systems, are the basis for many equation-based object-oriented modeling languages. When systems described by such equations are influenced by unknown process disturbances, estimating unknown parameters from experimental data becomes difficult. This is because of problems with the existence of well-defined solutions and the computational tractability of estimators. In this paper, we propose a way to minimize a cost function-whose minimizer is a consistent estimator of the true parameters-using stochastic gradient descent. This approach scales significantly better with the number of unknown parameters than other currently available methods for the same type of problem. The performance of the method is demonstrated through a simulation study with three unknown parameters. The experiments show a significantly reduced variance of the estimator, compared to an output error method neglecting the influence of process disturbances. The proposed approach is also able to reduce the estimation bias of parameters that the output error method particularly struggles with.

  • 67. 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)
  • 68. 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, p. 27-43Article 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.

  • 69.
    Besson, O
    et al.
    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.
    On parameter estimation of MIMO flat-fading channels with frequency offsets2003In: IEEE Trans Signal Process, Vol. 51, p. 602-613Article in journal (Refereed)
  • 70.
    Besson, O
    et al.
    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.
    Training sequence selection for frequency offset estimation in frequency selective channels2003In: Digital Signal Processing, Vol. 13, p. 106-127Article in journal (Refereed)
  • 71. 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.
    Kamiya, Y
    Direction finding in the presence of an intermittent interference2002In: IEEE Trans Signal Processing, Vol. 50, p. 1554-1564Article in journal (Refereed)
  • 72. Bessson, 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.
    Data-aided frequency offset estimation in frequency selective channels: training sequence selection.2002In: ICASSP 2002, the 27th International Conference on Acoustics, Speech and Signal Processing, Orlando, USA, 2002Conference paper (Refereed)
  • 73.
    Bhikkaji, B
    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.
    Mahata, K
    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.
    Reduced order models for a two-dimensional heat diffusion system2004In: International Journal of Control, Vol. 77, no 18, p. 1532-1548Article in journal (Refereed)
  • 74.
    Bhikkaji, B
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. AUTOMATIC CONTROL.
    Mahata, K
    Söderström, T
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Reduced order models for a two-dimensional heat diffusion system2003Report (Other academic)
    Abstract [en]

    In this paper, a two-dimensional heat diffusion system, which is modeled by a partial differential equation (PDE) is considered. Finite order approximations, for the infinite order PDE model, are constructed first by a direct application of the standard finite difference approximation (FD) scheme. Using tools of linear algebra, the constructed FD approximate models are reduced to computationally simpler models without any loss of accuracy. Further, the reduced approximate models are modified by replacing its poles with their respective asymptotic limits. Numerical experiments suggest that the proposed modifications improve the accuracy of the approximate models.

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  • 75.
    Bhikkaji, B
    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 and variance of the parameter estimates for a one dimensional diffusion system2002In: Proc 15th IFAC World Congress, 2002Conference paper (Refereed)
  • 76.
    Bhikkaji, B
    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.
    Recursive algorithm for estimating parameters in a one dimensional heat diffusion system2002In: Reglermöte 2002 (National Conference on Control), 2002Conference paper (Refereed)
  • 77.
    Bhikkaji, B
    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
    Reduced order models for diffusion systems2001In: INTERNATIONAL JOURNAL OF CONTROL, ISSN 0020-7179, Vol. 74, no 15, p. 1543-1557Article in journal (Refereed)
    Abstract [en]

    Mathematical models for diffusion processes like heat propagation, dispersion of pollutants, etc. are normally partial differential equations which involve certain unknown parameters. To use these mathematical models as substitutes of the true system, one

  • 78.
    Bhikkaji, B
    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.
    Mahata, K
    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 recursive algorithm for estimating parameters in a one dimensional diffusion system2003In: 13th IFAC Symposium on System Identification, 2003Conference paper (Refereed)
  • 79.
    Bhikkaji, Bharath
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Model Reduction and Parameter Estimation for Diffusion Systems2004Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Diffusion is a phenomenon in which particles move from regions of higher density to regions of lower density. Many physical systems, in fields as diverse as plant biology and finance, are known to involve diffusion phenomena. Typically, diffusion systems are modeled by partial differential equations (PDEs), which include certain parameters. These parameters characterize a given diffusion system. Therefore, for both modeling and simulation of a diffusion system, one has to either know or determine these parameters. Moreover, as PDEs are infinite order dynamic systems, for computational purposes one has to approximate them by a finite order model. In this thesis, we investigate these two issues of model reduction and parameter estimation by considering certain specific cases of heat diffusion systems.

    We first address model reduction by considering two specific cases of heat diffusion systems. The first case is a one-dimensional heat diffusion across a homogeneous wall, and the second case is a two-dimensional heat diffusion across a homogeneous rectangular plate. In the one-dimensional case we construct finite order approximations by using some well known PDE solvers and evaluate their effectiveness in approximating the true system. We also construct certain other alternative approximations for the one-dimensional diffusion system by exploiting the different modal structures inherently present in it. For the two-dimensional heat diffusion system, we construct finite order approximations first using the standard finite difference approximation (FD) scheme, and then refine the FD approximation by using its asymptotic limit.

    As for parameter estimation, we consider the same one-dimensional heat diffusion system, as in model reduction. We estimate the parameters involved, first using the standard batch estimation technique. The convergence of the estimates are investigated both numerically and theoretically. We also estimate the parameters of the one-dimensional heat diffusion system recursively, initially by adopting the standard recursive prediction error method (RPEM), and later by using two different recursive algorithms devised in the frequency domain. The convergence of the frequency domain recursive estimates is also investigated.

    List of papers
    1. Reduced order models for diffusion systems
    Open this publication in new window or tab >>Reduced order models for diffusion systems
    2001 In: International Journal of Control, Vol. 75, no 15, p. 1543-1557Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-91720 (URN)
    Available from: 2004-05-06 Created: 2004-05-06Bibliographically approved
    2. Reduced order models for diffusion systems using singular perturbations
    Open this publication in new window or tab >>Reduced order models for diffusion systems using singular perturbations
    2001 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 33, no 8, p. 769-781Article in journal (Refereed) Published
    Abstract [en]

    In this paper, we consider a special case of the one dimensional heat diffusion across a homogeneous wall. This physical system is modeled by a linear partial differential equation, which can be thought of as an infinite dimensional dynamic system. To simulate this physical system, one has to approximate the underlying infinite order system by a finite order approximation. In this paper we first construct a simple and straight forward approximate finite order model for the true system. The proposed approximate models may require large model order to approximate the true system dynamics in the high frequency regions. To avoid the usage of higher order models, we use a scheme similar to singular perturbations to further reduce the model order.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:uu:diva-91721 (URN)10.1016/S0378-7788(01)00071-8 (DOI)000170948400002 ()
    Available from: 2004-05-06 Created: 2004-05-06 Last updated: 2022-09-14Bibliographically approved
    3. Reduced order model for a two-dimensional diffusion system
    Open this publication in new window or tab >>Reduced order model for a two-dimensional diffusion system
    In: International Journal of ControlArticle in journal (Refereed) Submitted
    Identifiers
    urn:nbn:se:uu:diva-91722 (URN)
    Available from: 2004-05-06 Created: 2004-05-06Bibliographically approved
    4. Reduced order models for diffusion systems via Collocation methods
    Open this publication in new window or tab >>Reduced order models for diffusion systems via Collocation methods
    2000 In: Proc of 12th IFAC Symposium on System Identification, Santa Barbara, CA, USA, JuneArticle in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-91723 (URN)
    Available from: 2004-05-06 Created: 2004-05-06Bibliographically approved
    5. Bias and variance of parameter estimates of a one-dimensional heat diffusion system
    Open this publication in new window or tab >>Bias and variance of parameter estimates of a one-dimensional heat diffusion system
    2002 In: Proc of 15th IFAC Congress, Barcelona, Spain, JulyArticle in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-91724 (URN)
    Available from: 2004-05-06 Created: 2004-05-06Bibliographically approved
    6. Recursive algorithm for estimating parameters in a one-dimensional heat diffusion system
    Open this publication in new window or tab >>Recursive algorithm for estimating parameters in a one-dimensional heat diffusion system
    2002 In: Proc of Reglermöte, Linköping, SwedenArticle in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-91725 (URN)
    Available from: 2004-05-06 Created: 2004-05-06Bibliographically approved
    7. Recursive algorithms for estimating parameters in a one-dimensional diffusion system: derivation and implementation
    Open this publication in new window or tab >>Recursive algorithms for estimating parameters in a one-dimensional diffusion system: derivation and implementation
    In: International Journal of Adaptive Control and Signal ProcessingArticle in journal (Refereed) Submitted
    Identifiers
    urn:nbn:se:uu:diva-91726 (URN)
    Available from: 2004-05-06 Created: 2004-05-06Bibliographically approved
    8. Recursive algorithms for estimating parameters in a one-dimensional diffusion system: analysis
    Open this publication in new window or tab >>Recursive algorithms for estimating parameters in a one-dimensional diffusion system: analysis
    In: International Journal of ControlArticle in journal (Refereed) Submitted
    Identifiers
    urn:nbn:se:uu:diva-91727 (URN)
    Available from: 2004-05-06 Created: 2004-05-06Bibliographically approved
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    FULLTEXT01
  • 80.
    Bhikkaji, Bharath
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Model reduction for diffusion systems2000Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Diffusion phenomena has been studied with a lot of interest, for a long time, due to its historical and practical significance. In the recent days it has thrown a lot of interest among control engineers, as more and more practical systems, varying from stock markets to environmental pollution, have been observed to involve diffusion.

    Diffusion systems are normally modeled by linear partial differential equations (LPDEs) of the form

    (1)   ∂T(x,t)/∂t = £T(x,t),

    where £ is a second order linear spatial differential operator and T(x,t) is the physical quantity, whose variations in the spatial domain cause diffusion. To characterise diffusion phenomena, one has to obtain the solution of (1) either analytically or numerically. Note that, since (1) involves a second order spatial operator and a first order time derivative, one needs at least two boundary conditions in the spatial domain, x, and an initial condition at time t = 0, for determining T(x,t).

    LPDEs of the type (1) can be interpreted as infinite order linear time invariant (LTI) systems with inputs as boundary conditions. To compute the solution of (1) numerically, one has to approximate, explicitly or implicitly, the underlying infinite order system by a finite order system. Any numerical scheme, which computes the solution of (1), essentially approximates the underlying infinite order LTI system by a finite order LTI system. The efficiency of the approximation, for a given problem, varies for the different numerical schemes.

    In this thesis, we make an attempt to explore more about diffusion systems in general. As a starting point, we consider a simple case of one-dimensional heat diffusion across a homogeneous region. The resulting LPDE is first shown explicitly to be an infinite order dynamical system. An approximate solution is computed from a finite order approximation of the true infinite order dynamical system. In this thesis, we first construct the finite order approximations using certain standard PDE solvers based on Chebyshev polynomials. From these finite order approximations we choose the best one, from a model reduction perspective, and use it as a benchmark model. We later construct two more approximate models, by exploiting the given structure of the problem and we show by simulations that these models perform better than the chosen benchmark.

  • 81.
    Bhikkaji, Bharath
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. AUTOMATIC CONTROL.
    Söderström, Torsten
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. AUTOMATIC CONTROL.
    Reduced order models for diffusion systems using singular perturbations2000Report (Other academic)
    Abstract [en]

    In this paper, we consider a special case of the one dimensional heat diffusion across a homogeneous wall. This physical system is modeled by a linear partial differential equation, which can be thought of as an infinite dimensional dynamic system. To simulate this physical system, one has to approximate the underlying infinite order system by a finite order approximation. In this paper we first construct a simple and straightforward approximate finite order model for the true system. The proposed approximate models may require large model order to approximate the true system dynamics in the high frequency regions. To avoid the usage of higher order models, we use a scheme similar to singular perturbations to further reduce the model order.

    Download full text (pdf)
    fulltext
  • 82.
    Bhikkaji, Bharath
    et al.
    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, Automatic control.
    Reduced order models for diffusion systems using singular perturbations2001In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 33, no 8, p. 769-781Article in journal (Refereed)
    Abstract [en]

    In this paper, we consider a special case of the one dimensional heat diffusion across a homogeneous wall. This physical system is modeled by a linear partial differential equation, which can be thought of as an infinite dimensional dynamic system. To simulate this physical system, one has to approximate the underlying infinite order system by a finite order approximation. In this paper we first construct a simple and straight forward approximate finite order model for the true system. The proposed approximate models may require large model order to approximate the true system dynamics in the high frequency regions. To avoid the usage of higher order models, we use a scheme similar to singular perturbations to further reduce the model order.

  • 83.
    Bijl, Hildo
    et al.
    Delft University of Technology.
    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.
    Optimal controller/observer gains of discounted-cost LQG systems2019In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 101, p. 471-474Article in journal (Refereed)
    Abstract [en]

    The linear-quadratic-Gaussian (LQG) control paradigm is well-known in literature. The strategy of minimizing the cost function is available, both for the case where the state is known and where it is estimated through an observer. The situation is different when the cost function has an exponential discount factor, also known as a prescribed degree of stability. In this case, the optimal control strategy is only available when the state is known. This paper builds onward from that result, deriving an optimal control strategy when working with an estimated state. Expressions for the resulting optimal expected cost are also given. 

  • 84. Bijl, Hildo
    et al.
    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. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Artificial Intelligence.
    van Wingerden, Jan-Willem
    Verhaegen, Michel
    System identification through online sparse Gaussian process regression with input noise2017In: IFAC Journal of Systems and Control, ISSN 2468-6018, Vol. 2, p. 1-11Article in journal (Refereed)
  • 85. Bijl, Hildo
    et al.
    van Wingerden, Jan-Willem
    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.
    Verhaegen, Michel
    Mean and variance of the LQG cost function2016In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 67, p. 216-223Article in journal (Refereed)
    Abstract [en]

    Linear Quadratic Gaussian (LQG) systems are well-understood and methods to minimize the expected cost are readily available. Less is known about the statistical properties of the resulting cost function. The contribution of this paper is a set of analytic expressions for the mean and variance of the LQG cost function. These expressions are derived using two different methods, one using solutions to Lyapunov equations and the other using only matrix exponentials. Both the discounted and the non-discounted cost function are considered, as well as the finite-time and the infinite-time cost function. The derived expressions are successfully applied to an example system to reduce the probability of the cost exceeding a given threshold.

  • 86.
    Binggeli, Christian
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Observational Astronomy.
    Zackrisson, Erik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Observational Astronomy.
    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.
    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.
    Jensen, Hannes
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Observational Astronomy.
    Shimizu, Ikko
    Osaka Univ, Dept Earth & Space Sci, Theoret Astrophys, 1-1 Machikaneyama, Toyonaka, Osaka 5600043, Japan.
    Lyman continuum leakage versus quenching with the James Webb Space Telescope: the spectral signatures of quenched star formation activity in reionization-epoch galaxies2018In: Monthly notices of the Royal Astronomical Society, ISSN 0035-8711, E-ISSN 1365-2966, Vol. 479, no 1, p. 368-376Article in journal (Refereed)
    Abstract [en]

    In this paper, we study the effects of a recent drop in star formation rate (SFR) on the spectra of epoch of reionization (EoR) galaxies, and the resulting degeneracy with the spectral features produced by extreme Lyman continuum leakage. In order to study these effects in the wavelength range relevant for the upcoming James Webb Space Telescope (JWST), we utilize synthetic spectra of simulated EoR galaxies from cosmological simulations together with synthetic spectra of partially quenched mock galaxies. We find that rapid declines in the SFR of EoR galaxies could seriously affect the applicability of methods that utilize the equivalent width of Balmer lines and the ultraviolet spectral slope to assess the escape fraction of EoR galaxies. In order to determine if the aforementioned degeneracy can be avoided by using the overall shape of the spectrum, we generate mock NIRCam observations and utilize a classification algorithm to identify galaxies that have undergone quenching. We find that while there are problematic cases, JWST/NIRCam or NIRSpec should be able to reliably identify galaxies with redshifts z similar to 7 that have experienced a significant decrease in the SFR (by a factor of 10-100) in the past 50-100 Myr with a success rate greater than or similar to 85 per cent. We also find that uncertainties in the dust-reddening effects on EoR galaxies significantly affect the performance of the results of the classification algorithm. We argue that studies that aim to characterize the dust extinction law most representative in the EoR would be extremely useful.

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

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

  • 88. Birk, Wolfgang
    et al.
    Medvedev, Alexander
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    A note on gramain-based interaction measures2003In: Proceedings of European Control Conference, 2003Conference paper (Refereed)
  • 89.
    Biton, Shany
    et al.
    Faculty of Biomedical Engineering, Technion-IIT , Haifa, Israel.
    Gendelman, Sheina
    Faculty of Biomedical Engineering, Technion-IIT , Haifa, Israel.
    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. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Artificial Intelligence.
    Miana, Gabriela
    Telehealth Center, Hospital das Clínicas , Belo Horizonte, Brazil;Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais , Belo Horizonte, Brazil.
    Moreira, Carla
    Telehealth Center, Hospital das Clínicas , Belo Horizonte, Brazil.
    Ribeiro, Antonio Luiz P
    Telehealth Center, Hospital das Clínicas , Belo Horizonte, Brazil;Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais , Belo Horizonte, Brazil.
    Behar, Joachim A
    Faculty of Biomedical Engineering, Technion-IIT , Haifa, Israel.
    Atrial fibrillation risk prediction from the 12-lead electrocardiogram using digital biomarkers and deep representation learning2021In: The European Heart Journal - Digital Health, E-ISSN 2634-3916, Vol. 2, no 4, p. 576-585Article in journal (Refereed)
  • 90.
    Björk, Marcus
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Contributions to Signal Processing for MRI2015Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Magnetic Resonance Imaging (MRI) is an important diagnostic tool for imaging soft tissue without the use of ionizing radiation. Moreover, through advanced signal processing, MRI can provide more than just anatomical information, such as estimates of tissue-specific physical properties.

    Signal processing lies at the very core of the MRI process, which involves input design, information encoding, image reconstruction, and advanced filtering. Based on signal modeling and estimation, it is possible to further improve the images, reduce artifacts, mitigate noise, and obtain quantitative tissue information.

    In quantitative MRI, different physical quantities are estimated from a set of collected images. The optimization problems solved are typically nonlinear, and require intelligent and application-specific algorithms to avoid suboptimal local minima. This thesis presents several methods for efficiently solving different parameter estimation problems in MRI, such as multi-component T2 relaxometry, temporal phase correction of complex-valued data, and minimizing banding artifacts due to field inhomogeneity. The performance of the proposed algorithms is evaluated using both simulation and in-vivo data. The results show improvements over previous approaches, while maintaining a relatively low computational complexity. Using new and improved estimation methods enables better tissue characterization and diagnosis.

    Furthermore, a sequence design problem is treated, where the radio-frequency excitation is optimized to minimize image artifacts when using amplifiers of limited quality. In turn, obtaining higher fidelity images enables improved diagnosis, and can increase the estimation accuracy in quantitative MRI.

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  • 91.
    Björk, Marcus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Berglund, Johan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
    Kullberg, Joel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
    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.
    Signal Modeling and the Cramér-Rao Bound for Absolute Magnetic Resonance Thermometry in Fat Tissue2011In: Proc. 45th Asilomar Conference on Signals, Systems, and Computers, 2011, p. 80-84Conference paper (Refereed)
    Abstract [en]

    Magnetic Resonance Imaging of tissues with both fat and water resonances allows for absolute temperature mapping through parametric modeling. The fat resonance is used as a reference to determine the absolute water resonance frequency which is linearly related to the temperature. The goal of thispaper is to assess whether or not resonance frequency based absolute temperature mapping is feasible in fat tissue. This is done by examining identifiability conditions and analyzing the obtainable performance in terms of the Cramér-Rao Bound of the temperature estimates. We develop the model by including multiple fat peaks, since even small fat resonances can be significant compared to the small water component in fat tissue. It is showed that a high signal to noise ratio is needed for practical use on a 1.5 T scanner, and that higher field strengths can improve the bound significantly. It is also shown that the choice of sampling interval is important to avoid aliasing. In sum, this type of magnetic resonance thermometry is feasible for fat tissuein applications where high field strength is used or when high signal to noise ratio can be obtained.

  • 92.
    Björk, Marcus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Gudmundson, Erik
    Centre for Mathematical Sciences, Lund University.
    Barral, Joëlle
    Department of Bioengineering, Stanford University.
    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.
    Signal Processing Algorithms for Removing Banding Artifacts in MRI2011In: Proceedings of the 19th European Signal Processing Conference (EUSIPCO-2011), 2011, p. 1000-1004Conference paper (Refereed)
    Abstract [en]

    In magnetic resonance imaging (MRI), the balanced steady-state free precession (bSSFP) pulse sequence has shown to be of great interest, due to its relatively high signal-to-noise ratio in a short scan time. However, images acquired with this pulse sequence suffer from banding artifacts due to off-resonance effects. These artifacts typically appear as black bands covering parts of the image and they severely degrade the image quality. In this paper, we present a fast two-step algorithm for estimating the unknowns in the signal model and removing the banding artifacts. The first step consists of rewriting the model in such a way that it becomes linear in the unknowns (this step is named Linearization for Off-Resonance Estimation, or LORE). In the second step, we use a Gauss-Newton iterative optimization with the parameters obtained by LORE as initial guesses. We name the full algorithm LORE-GN. Using both simulated and in vivo data, we show the performance gain associated with using LORE-GN as compared to general methods commonly employed in similar cases.

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  • 93.
    Björk, Marcus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Ingle, R. Reeve
    Electrical Engineering, Stanford University, Stanford, California, United States.
    Barral, Joëlle K.
    HeartVista, Inc., Los Altos, California, USA.
    Gudmundson, Erik
    Centre for Mathematical Sciences, Lund University.
    Nishimura, Dwight G.
    Electrical Engineering, Stanford University, Stanford, California, United States.
    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.
    Optimality of Equally-Spaced Phase Increments for Banding Removal in bSSFP2012In: Proceedings of the ISMRM 20th annual meeting, 2012Conference paper (Refereed)
  • 94.
    Björk, Marcus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Ingle, R. Reeve
    Gudmundson, Erik
    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.
    Barral, Joëlle K.
    Parameter estimation approach to banding artifact reduction in balanced steady-state free precession2014In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 72, no 3, p. 880-892Article in journal (Refereed)
    Abstract [en]

    Purpose: The balanced steady-state free precession (bSSFP) pulse sequence has shown to be of great interest due to its high signal-to-noise ratio efficiency. However, bSSFP images often suffer from banding artifacts due to off-resonance effects, which we aim to minimize in this article. Methods: We present a general and fast two-step algorithm for 1) estimating the unknowns in the bSSFP signal model from multiple phase-cycled acquisitions, and 2) reconstructing band-free images. The first step, linearization for off-resonance estimation (LORE), solves the nonlinear problem approximately by a robust linear approach. The second step applies a Gauss-Newton algorithm, initialized by LORE, to minimize the nonlinear least squares criterion. We name the full algorithm LORE-GN. Results: We derive the Cramer-Rao bound, a theoretical lower bound of the variance for any unbiased estimator, and show that LORE-GN is statistically efficient. Furthermore, we show that simultaneous estimation of T-1 and T-2 from phase-cycled bSSFP is difficult, since the Cramer-Rao bound is high at common signal-to-noise ratio. Using simulated, phantom, and in vivo data, we illustrate the band-reduction capabilities of LORE-GN compared to other techniques, such as sum-of-squares. Conclusion: Using LORE-GN we can successfully minimize banding artifacts in bSSFP.

  • 95.
    Björk, Marcus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Estimation of dynamic models with output quantization applied to drug response modeling2010Conference paper (Other academic)
  • 96.
    Björk, Marcus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Dynamic models with quantized output for modeling patient response to pharmacotherapy2010In: Proc. International Conference on Control Applications: CCA 2010, Piscataway, NJ: IEEE , 2010, p. 1029-1034Conference paper (Refereed)
    Abstract [en]

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

  • 97.
    Björk, Marcus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    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.
    Fast denoising techniques for transverse relaxation time estimation in MRI2013In: Proc. 21st European Signal Processing Conference, 2013Conference paper (Refereed)
  • 98.
    Björk, Marcus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    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.
    Magnitude-constrained sequence design with application in MRI2014In: Proc. 39th IEEE International Conference on Acoustics, Speech, and Signal Processing, Piscataway, NJ: IEEE , 2014, p. 4943-4947Conference paper (Refereed)
    Abstract [en]

    In this paper we present an algorithm for sequence design with magnitude constraints. We formulate the design problem in a general setting, but also illustrate its relevance to parallel excitation MRI. The formulated non-convex design optimization criterion is minimized locally by means of a cyclic algorithm, consisting of two simple algebraic sub-steps. Since the algorithm truly minimizes the criterion, the obtained sequence designs are guaranteed to improve upon the estimates provided by a previous method, which is based on the heuristic principle of the Iterative Quadratic Maximum Likelihood algorithm. The performance of the proposed algorithm is illustrated in two numerical examples.

  • 99.
    Björk, Marcus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    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.
    New approach to phase correction in multi-echo T2 relaxometry2014In: Journal of magnetic resonance, ISSN 1090-7807, E-ISSN 1096-0856, Vol. 249, p. 100-107Article in journal (Refereed)
    Abstract [en]

    Estimation of the transverse relaxation time, T-2, from multi-echo spin-echo images is usually performed using the magnitude of the noisy data, and a least squares (LS) approach. The noise in these magnitude images is Rice distributed, which can lead to a considerable bias in the LS-based T-2 estimates. One way to avoid this bias problem is to estimate a real-valued and Gaussian distributed dataset from the complex data, rather than using the magnitude. In this paper, we propose two algorithms for phase correction which can be used to generate real-valued data suitable for LS-based parameter estimation approaches. The first is a Weighted Linear Phase Estimation algorithm, abbreviated as WELPE. This method provides an improvement over a previously published algorithm, while simplifying the estimation procedure and extending it to support multi-coil input. The algorithm fits a linearly parameterized function to the multi-echo phase-data in each voxel and, based on this estimated phase, projects the data onto the real axis. The second method is a maximum likelihood estimator of the true decaying signal magnitude, which can be efficiently implemented when the phase variation is linear in time. The performance of the algorithms is demonstrated via Monte Carlo simulations, by comparing the accuracy of the estimates. Furthermore, it is shown that using one of the proposed algorithms enables more accurate T-2 estimates; in particular, phase corrected data significantly reduces the estimation bias in multi-component T-2 relaxometry example, compared to when using magnitude data. WELPE is also applied to a 32-echo in vivo brain dataset, to show its practical feasibility.

  • 100.
    Björk, Marcus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Zachariah, Dave
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Kullberg, Joel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
    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 multicomponent T2 relaxometry algorithm for myelin water imaging of the brain2016In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 75, no 1, p. 390-402Article in journal (Refereed)
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