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  • 1.
    Ahlström, Christer
    et al.
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Liljefeldt, Olle
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Hult, Peter
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Ask, Per
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, The Institute of Technology.
    Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction2005In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 12, no 12, p. 812-815Article in journal (Refereed)
    Abstract [en]

    Heart sounds (HS) obscure the interpretation of lung sounds (LS). This letter presents a new method to detect and remove this undesired disturbance. The HS detection algorithm is based on a recurrence time statistic that is sensitive to changes in a reconstructed state space. Signal segments that are found to contain HS are removed, and the arising missing parts are replaced with predicted LS using a nonlinear prediction scheme. The prediction operates in the reconstructed state space and uses an iterated integrated nearest trajectory algorithm. The HS detection algorithm detects HS with an error rate of 4% false positives and 8% false negatives. The spectral difference between the reconstructed LS signal and an LS signal with removed HS was 0.34/spl plusmn/0.25, 0.50/spl plusmn/0.33, 0.46/spl plusmn/0.35, and 0.94/spl plusmn/0.64 dB/Hz in the frequency bands 20-40, 40-70, 70-150, and 150-300 Hz, respectively. The cross-correlation index was found to be 99.7%, indicating excellent similarity between actual LS and predicted LS. Listening tests performed by a skilled physician showed high-quality auditory results.

  • 2.
    Ardeshiri, Tohid
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Granström, Karl
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Özkan, Emre
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Middle E Technical University, Turkey.
    Greedy Reduction Algorithms for Mixtures of Exponential Family2015In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 22, no 6, p. 676-680Article in journal (Refereed)
    Abstract [en]

    In this letter, we propose a general framework for greedy reduction of mixture densities of exponential family. The performances of the generalized algorithms are illustrated both on an artificial example where randomly generated mixture densities are reduced and on a target tracking scenario where the reduction is carried out in the recursion of a Gaussian inverse Wishart probability hypothesis density (PHD) filter.

  • 3.
    Ardeshiri, Tohid
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Özkan, Emre
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Orguner, Umut
    Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances2015In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 22, no 12, p. 2450-2454Article in journal (Refereed)
    Abstract [en]

    We present an adaptive smoother for linear state-space models with unknown process and measurement noise covariances. The proposed method utilizes the variational Bayes technique to perform approximate inference. The resulting smoother is computationally efficient, easy to implement, and can be applied to high dimensional linear systems. The performance of the algorithm is illustrated on a target tracking example.

  • 4.
    Chopra, Ribhu
    et al.
    IIT Guwahati, India.
    Murthy, Chandra R.
    IISc Bangalore, India.
    Suraweera, Himal A.
    Univ Peradeniya, Sri Lanka.
    Larsson, Erik G
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Analysis of Nonorthogonal Training in Massive MIMO Under Channel Aging With SIC Receivers2019In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 26, no 2, p. 282-286Article in journal (Refereed)
    Abstract [en]

    We analyze the effect of channel aging on the achievable rate of time division duplexed massive multiple input multiple output systems serving a number of users under aging channels, using nonorthogonal multiple access (NOMA) and orthogonal multiple access (OMA). Using the recently proposed shared uplink pilot based channel estimation for NOMA, we derive bounds on the channel estimation error variance for the two schemes. We then derive the achievable spectral efficiencies of the two schemes. Using numerical results, we show that, in slowly varying channels, using NOMA with shared pilots is preferable over OMA, while the reverse is true under fast varying channels.

  • 5.
    Fritsche, Carsten
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    The Marginal Bayesian Cramér–Rao Bound for Jump Markov Systems2016In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 23, no 5, p. 575-579Article in journal (Refereed)
    Abstract [en]

    In this letter, numerical algorithms for computing the marginal version of the Bayesian Cramér–Rao bound (M-BCRB) for jump Markov nonlinear systems and jump Markov linear Gaussian systems are proposed. Benchmark examples for both systems illustrate that the M-BCRB is tighter than three other recently proposed BCRBs

  • 6.
    Fritsche, Carsten
    et al.
    IFEN GmbH, Germany .
    Orguner, Umut
    Middle E Technical University, Turkey .
    Svensson, Lennart
    Chalmers, Sweden .
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    The Marginal Enumeration Bayesian Cramer-Rao Bound for Jump Markov Systems2014In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 21, no 4, p. 464-468Article in journal (Refereed)
    Abstract [en]

    A marginal version of the enumeration Bayesian Cramer-Rao Bound (EBCRB) for jump Markov systems is proposed. It is shown that the proposed bound is at least as tight as EBCRB and the improvement stems from better handling of the nonlinearities. The new bound is illustrated to yield tighter results than BCRB and EBCRB on a benchmark example.

  • 7.
    Gustafsson, Oscar
    et al.
    Linköping University, Department of Electrical Engineering, Electronics System. Linköping University, The Institute of Technology.
    Qureshi, Fahad
    Linköping University, Department of Electrical Engineering, Electronics System. Linköping University, The Institute of Technology.
    Addition Aware Quantization for Low Complexity and High Precision Constant Multiplication2010In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 17, no 2, p. 173-176Article in journal (Refereed)
    Abstract [en]

    Multiplication by constants can be efficiently realized using shifts, additions, and subtractions. In this work we consider how to select a fixed-point value for a real valued, rational, or floating-point coefficient to obtain a low-complexity realization. It is shown that the process, denoted addition aware quantization, often can determine coefficients that has as low complexity as the rounded value, but with a smaller approximation error by searching among coefficients with a longer wordlength.

  • 8.
    Johansson, Håkan
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Harris, Fred
    San Diego State University, CA 92182 USA.
    Polyphase Decomposition of Digital Fractional-Delay Filters2015In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 22, no 8, p. 1021-1025Article in journal (Refereed)
    Abstract [en]

    This letter shows that, for arbitrary M-fold polyphase decompositions, the polyphase components of digital fractional-delay (FD) filters correspond to FD filters as well, but with different delays and gains. For even values of, the components also have additional and different phase offsets. The letter also discusses the application of these results to the optimization of high-order filters with few unknowns.

  • 9.
    Larsson, Erik G.
    Department of Electrical and Computer Engineering, The George Washington University, USA.
    Cramér–Rao Bound Analysis of Distributed Positioning in Sensor Networks2004In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 11, no 3, p. 334-337Article in journal (Refereed)
    Abstract [en]

    In future applications of sensor networking technology, it is envisioned that nodes will be able to determine their geographical position by measuring the range differences between one another in a collaborative fashion. The aim of this letter is to provide quantitative expressions that can be used both to facilitate an understanding for why such distributed positioning works and to assess the ultimately achievable accuracy in practice. Specifically, we compute the Crame´r-Rao bound on the positioning accuracy, under different assumptions on the network synchronization. Numerical examples illustrate our results.

  • 10.
    Larsson, Erik G.
    et al.
    Department of Electrical and Computer Engineering, University of Florida, USA.
    Li, Jian
    Department of Electrical and Computer Engineering, University of Florida, USA.
    Preamble design for multiple-antenna OFDM-based WLANs with null subcarriers2001In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 8, no 11, p. 285-288Article in journal (Refereed)
    Abstract [en]

    The so-called orthogonal frequency division multiplexing (OFDM) technique has received considerable interest, especially in the area of wireless local area networks (WLANs). One way of meeting the demands for increased data rates in WLANs is to provide the transmitter and receiver with multiple antennas. In this letter, we consider the estimation of the channel and the design of optimal preambles (training sequences) for an OFDM system with two transmit and multiple receive antennas.

  • 11.
    Larsson, Erik G.
    et al.
    Department of Electrical and Computer Engineering, The George Washington University, Washington, USA.
    Stoica, Petre
    Department of Systems and Control, Uppsala University, Sweden.
    Mean-Square-Error Optimality of Orthogonal Space–Time Block Codes2003In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 10, no 11, p. 327-330Article in journal (Refereed)
    Abstract [en]

    Linear space-time block coding (STBC) is a conceptually simple transmission technique for channels with multiple transmit and receive antennas. We study a subclass of linear STBC, namely orthogonal STBC (OSTBC), with the primary goal of contributing toward a complete understanding of OSTBC. In particular, we prove that OSTBC is optimal in a minimum mean-square-error (MSE) sense, provided that a zero-forcing detector is used at the receiver. As a by-product, we also obtain a concise characterization of linear and orthogonal STBCs as well as the relationship between them. The MSE optimality of OSTBC can be used as a way of introducing this coding scheme from first principles.

  • 12.
    Larsson, Erik K.
    et al.
    Department of Systems of Control, Uppsala University, Sweden.
    Larsson, Erik G.
    Department of Electrical and Computer Engineering, The George Washington University, USA.
    The CRB for Parameter Estimation in IrregularlySampled Continuous-Time ARMA Systems2004In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 11, no 2, p. 197-200Article in journal (Refereed)
    Abstract [en]

    We derive novel and compact formulas for the Cramer-Rao bound (CRB) associated with the identification of the parameters in a continuous-time autoregressive moving-average (ARMA) model, given nonuniformly sampled data. Our approach is based on a state-space formulation of the ARMA model, which facilitates a derivation of the CRB in closed form. Numerical examples illustrate our results.

  • 13.
    Lenz, Reiner
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Siegel Descriptors for Image Processing2016In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 23, no 5, p. 625-628Article in journal (Refereed)
    Abstract [en]

    We introduce the Siegel upper half-space with its symplectic geometry as a framework for low-level image processing. We characterize properties of images with the help of six parameters: two spatial coordinates, the pixel value, and the three parameters of a symmetric positive-definite (SPD) matrix such as the metric tensor. We construct a mapping of these parameters into the Siegel upper half-space. From the general theory, it is known that there is a distance on this space that is preserved by the symplectic transformations. The construction provides a mapping that has relatively simply transformation properties under spatial rotations, and the distance values can be computed with the help of closed-form expressions which allow an efficient implementation. We illustrate the properties of this geometry by considering a special case where we compute for every pixel its symplectic distance to its four spatial neighbors and we show how spatial distances, pixel value changes, and texture properties are described in this unifying symplectic framework.

  • 14.
    Miandji, Ehsan
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Emadi, Mohammad
    Qualcomm Technologies Inc., San Jose, CA, USA.
    Unger, Jonas
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Ehsan, Afshari
    Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.
    On Probability of Support Recovery for Orthogonal Matching Pursuit Using Mutual Coherence2017In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 24, no 11, p. 1646-1650Article in journal (Refereed)
    Abstract [en]

    In this paper we present a new coherence-based performance guarantee for the Orthogonal Matching Pursuit (OMP) algorithm. A lower bound for the probability of correctly identifying the support of a sparse signal with additive white Gaussian noise is derived. Compared to previous work, the new bound takes into account the signal parameters such as dynamic range, noise variance, and sparsity. Numerical simulations show significant improvements over previous work and a closer match to empirically obtained results of the OMP algorithm.

  • 15.
    Mishra, Deepak
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Johansson, Håkan
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Efficacy of Hybrid Energy Beamforming With Phase Shifter Impairments and Channel Estimation Errors2019In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 26, no 1, p. 99-103Article in journal (Refereed)
    Abstract [en]

    Hybrid energy beamforming (HEB) can reduce the hardware cost, energy consumption, and space constraints associated with massive antenna array transmitter (TX). With a single radio frequency chain having N digitally controlled phase shifter pairs, one per antenna element, theoretically achieving the same performance as a fully digital beamforming architecture with N RF chains, this letter investigates the practical efficacy of the HEB. First adopting the proposed analog phase shifter impairments model and exploiting the channel reciprocity along with the available statistical information, we present a novel approach to obtain an accurate minimum mean-square error estimate for the wireless channel between TX and energy receiver (RX). Then, tight analytical approximation for the global optimal time allocation between uplink channel estimation and downlink energy transfer operations is derived to maximize the mean net harvested energy at RX. Numerical results, validating the analysis and presenting key design insights, show that with an average improvement of 58% over the benchmark scheme, the optimized HEB can help in practically realizing the fully digital array gains.

  • 16.
    Mishra, Deepak
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Johansson, Håkan
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Optimal Least Squares Estimator and Precoder for Energy Beamforming Over IQ-Impaired Channels2019In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 26, no 8, p. 1207-1211Article in journal (Refereed)
    Abstract [en]

    Usage of low-cost hardware in large antenna arrays and low-power wireless devices in Internet of Things (IoT) has led to the degradation of practical beamforming gains due to the underlying hardware impairments, such as in-phase and quadrature-phase imbalance (IQI). To address this timely concern, we present a new nontrivial closed-form expression for the globally optimal least squares estimator (LSE) for the IQI-influenced channel between a multiantenna transmitter and single-antenna IoT device. Thereafter, to maximize the realistic transmit beamforming gains, a novel precoder design is derived that accounts for the underlying IQI for maximizing received power in both single and multiuser settings. Finally, the simulation results, demonstrating a significant -8 dB improvement in the mean squared error of the proposed LSE over existing benchmarks, show that the optimal precoder designing is more critical than accurately estimating IQI-impaired channels. Also, the proposed jointly optimal LSE and beamformer outperforms the existing designs by providing 24% enhancement in mean signal power received under IQI.

  • 17.
    Nurminen, Henri
    et al.
    Tampere University of Technology, Finland.
    Ardeshiri, Tohid
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Piche, Robert
    Tampere University of Technology, Finland.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Robust Inference for State-Space Models with Skewed Measurement Noise2015In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 22, no 11, p. 1898-1902Article in journal (Refereed)
    Abstract [en]

    Filtering and smoothing algorithms for linear discrete-time state-space models with skewed and heavy-tailed measurement noise are presented. The algorithms use a variational Bayes approximation of the posterior distribution of models that have normal prior and skew-t-distributed measurement noise. The proposed filter and smoother are compared with conventional low-complexity alternatives in a simulated pseudorange positioning scenario. In the simulations the proposed methods achieve better accuracy than the alternative methods, the computational complexity of the filter being roughly 5 to 10 times that of the Kalman filter.

  • 18.
    Pham, Tuan D
    Aizu Research Cluster for Medical Engineering and Informatics, Center for Advanced Information Science and Technology, The University of Aizu, Aizuwakamatsu, Japan.
    Estimating Parameters of Optimal Average and Adaptive Wiener Filters for Image Restoration with Sequential Gaussian Simulation2015In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 22, no 11, p. 1950-1954Article in journal (Refereed)
    Abstract [en]

    Filtering additive white Gaussian noise in images using the best linear unbiased estimator (BLUE) is technically sound in a sense that it is an optimal average filter derived from the statistical estimation theory. The BLUE filter mask has the theoretical advantage in that its shape and its size are formulated in terms of the image signals and associated noise components. However, like many other noise filtering problems, prior knowledge about the additive noise needs to be available, which is often obtained using training data. This paper presents the sequential Gaussian simulation in geostatistics for measuring signal and noise variances in images without the need of training data for the BLUE filter implementation. The simulated signal variance and the BLUE average can be further used as parameters of the adaptive Wiener filter for image restoration.

  • 19.
    Rui, Rafael
    et al.
    University of Federal Rio Grande do Sul, Brazil.
    Ardeshiri, Tohid
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. University of Cambridge, England.
    Nurminen, Henri
    Tampere University of Technology, Finland.
    Bazanella, Alexandre
    University of Federal Rio Grande do Sul, Brazil.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    State Estimation for a Class of Piecewise Affine State-Space Models2017In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 24, no 1, p. 61-65Article in journal (Refereed)
    Abstract [en]

    n/a

  • 20.
    Stoica, Petre
    et al.
    Department of Systems and Control, Uppsala University, Sweden.
    Larsson, Erik G.
    Department of Systems and Control, Uppsala University, Sweden.
    Gershman, Alex B.
    Communications Research Laboratory, Department of Electrical and Computer Engineering, McMaster University, Canada.
    The stochastic CRB for array processing: a textbook derivation2001In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 8, no 5, p. 148-150Article in journal (Refereed)
    Abstract [en]

    The stochastic Cramer-Rao bound (CRB) for direction estimation in array processing applications was indirectly derived some ten years ago as the (asymptotic) covariance matrix of the maximum likelihood (ML) estimator. Attempts to obtain the stochastic CRB directly via the CRB theory fell short of providing a simple derivation and consequently, no direct derivation of this useful performance bound was available in the open literature. we correct this situation by providing a textbook-like direct derivation of the stochastic CRB.

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