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  • 1.
    Beek, Jaap van de
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Sandell, Magnus
    Luleå tekniska universitet.
    Börjesson, Per Ola
    Luleå tekniska universitet.
    ML estimation of time and frequency offset in OFDM systems1997In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 45, no 7, p. 1800-1805Article in journal (Refereed)
    Abstract [en]

    We present the joint likelihood (ML) symbol-time and carrier-frequency offset estimator in orthogonal frequency-division multiplexing (OFDM) systems. Redundant information contained within the cyclic prefix enables this estimation without additional pilots. Simulations show that the frequency estimator may be used in tracking mode and the time estimator in an acquisition mode.

  • 2.
    Beek, Jaap van de
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Sandell, Magnus
    Luleå tekniska universitet.
    Börjesson, Per-Ola
    Luleå tekniska universitet.
    ML estimation of time and frequecy offset in OFDM systems1998In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 45, no 7, p. 1800-1805Article in journal (Refereed)
  • 3. Ericsson, Stefan
    et al.
    Grip, Niklas
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    Efficient wavelet prefilters with optimal time-shifts2005In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 53, no 7, p. 2451-2461Article in journal (Refereed)
    Abstract [en]

    A wavelet prefilter maps sample values of an analyzed signal to the scaling function coefficient input of standard discrete wavelet transform (DWT) algorithms. The prefilter is the inverse of a certain postfilter convolution matrix consisting of integer sample values of a noninteger-shifted wavelet scaling function. For the prefilter and the DWT algorithms to have similar computational complexity, it is often necessary to use a "short enough" approximation of the prefilter. In addition to well-known quadrature formula and identity matrix prefilter approximations, we propose a Neumann series approximation, which is a band matrix truncation of the optimal prefilter, and derive simple formulas for the operator norm approximation error. This error shows a dramatic dependence on how the postfilter noninteger shift is chosen. We explain the meaning of this shift in practical applications, describe how to choose it, and plot optimally shifted prefilter approximation errors for 95 different Daubechies, Symlet, and B-spline wavelets. Whereas the truncated inverse is overall superior, the Neumann filters are by far the easiest ones to compute, and for some short support wavelets, they also give the smallest approximation error. For example, for Daubechies 1-5 wavelets, the simplest Neumann prefilter provide an approximation error reduction corresponding to 100-10 000 times oversampling in a nonprefiltered system.

  • 4. Krishnamurthy, Vikram
    et al.
    Dey, Subhrakanti
    Leblanc, James
    Blind equalization of IIR channels using hidden Markov models and extended least squares1995In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 43, no 12, p. 2994-3006Article in journal (Refereed)
    Abstract [en]

    In this paper, we present a blind equalization algorithm for noisy IIR channels when the channel input is a finite state Markov chain. The algorithm yields estimates of the IIR channel coefficients, channel noise variance, transition probabilities, and state of the Markov chain. Unlike the optimal maximum likelihood estimator which is computationally infeasible since the computing cost increases exponentially with data length, our algorithm is computationally inexpensive. Our algorithm is based on combining a recursive hidden Markov model (HMM) estimator with a relaxed SPR (strictly positive real) extended least squares (ELS) scheme. In simulation studies we show that the algorithm yields satisfactory estimates even in low SNR. We also compare the performance of our scheme with a truncated FIR scheme and the constant modulus algorithm (CMA) which is currently a popular algorithm in blind equalization.

  • 5. Lundberg, Magnus
    et al.
    Muhammad, Khurram
    Texas Instruments, Dallas.
    Roy, Kaushik
    Purdue University.
    Wilson, Sarah Kate
    A novel approach to high-level switching activity modeling with applications to low-power DSP system synthesis2001In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 49, no 12, p. 3157-3167Article in journal (Refereed)
    Abstract [en]

    We address high-level synthesis of low-power digital signal processing (DSP) systems by using efficient switching activity models. We present a technology-independent hierarchical scheme that can be easily integrated into current communications/DSP CAD tools for comparing the relative power/performance of two competing DSP designs without specific knowledge of transistor-level details. The basic building blocks considered for such systems are a full adder, a half adder, and a one-bit delay. Estimates of the switching activity at the output of these primitives are used to model the activity in more complex building blocks of DSP systems. The presented hierarchical method is very fast and simple. The accuracy of estimates obtained using the proposed approach is shown to be within 4% of the results obtained using extensive bit-level simulations. Our approach shows that the choice of multiplier/multiplicand is important when using array multipliers in a datapath. If the input signal with smaller mean square value is chosen as the multiplicand, almost 20% savings in switching activity can be achieved. This observation is verified by an analog simulation using a 16 × 16 bit array multiplier implemented in a 0.6-μ process with 3.3 V supply voltage.

  • 6.
    Ovacikli, Kubilay
    et al.
    Rubico Vibration Analysis AB.
    Pääjärvi, Patrik
    Rubico Vibration Analysis AB.
    Leblanc, James
    Swedish Rifle AB.
    Carlson, Johan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Recovering Periodic Impulsive Signals Through Skewness Maximization2016In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 64, no 6, p. 1586-1596Article in journal (Refereed)
    Abstract [en]

    Maximizing the skewness of a measured signal by adaptive filtering to reveal hidden periodic impulses is proposed as a pre-processing method. Periodic impulsive signals are modelled by harmonically related sinusoids to prove that amplitude and phase distortion from a transfer function, effects of sinusoidal interferences and noise can be compensated for by a linear filter. The convergence behaviour of the skewness maximization algorithm is analysed to show that it is possible to recover the original harmonic structure with an unknown fundamental frequency by achieving maximum skewness in the given signal. It is shown that maximizing the skewness always results in a sub-space containing only a single harmonic family. Defect detection in rolling element bearings is presented as an application example and as a comparative study against kurtosis maximization.

  • 7.
    Pezeshki, Ali
    et al.
    Program in Applied and Computational Mathematics, Princeton University.
    Veen, Barry D. Van
    University of Wisconsin.
    Scharf, Louis L.
    Colorado State University.
    Cox, Harry
    Lockheed Martin–Orincon Defense.
    Nordenvaad, Magnus Lundberg
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Eigenvalue beamforming using a multirank MVDR beamformer and subspace selection2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 5, p. 1954-1967Article in journal (Refereed)
    Abstract [en]

    We derive eigenvalue beamformers to resolve an unknown signal of interest whose spatial signature lies in a known subspace, but whose orientation in that subspace is otherwise unknown. The unknown orientation may be fixed, in which case the signal covariance is rank-1, or it may be random, in which case the signal covariance is multirank. We present a systematic treatment of such signal models and explain their relevance for modeling signal uncertainties. We then present a multirank generalization of the MVDR beamformer. The idea is to minimize the power at the output of a matrix beamformer, while enforcing a data dependent distortionless constraint in the signal subspace, which we design based on the type of signal we wish to resolve. We show that the eigenvalues of an error covariance matrix are fundamental for resolving signals of interest. Signals with rank-1 covariances are resolved by the largest eigenvalues of the error covariance, while signals with multirank covariances are resolved by the smallest eigenvalues. Thus, the beamformers we design are eigenvalue beamformers, which extract signal information from eigenmodes of an error covariance. We address the tradeoff between angular resolution of eigenvalue beamformers and the fraction of the signal power they capture.

  • 8.
    Renbi, Abdelghani
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Carlson, Johan E.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Impact of PCB manufacturing process variations on trace impedance2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 39, no 1, p. 20-24Article in journal (Refereed)
    Abstract [en]

    This paper demonstrates statistically the impact of PCB manufacturing variations on the characteristic impedance. Moreover, it shows that the characteristics of the PCBs vary across different suppliers. These differences cannot be tolerated in some applications where the characteristic impedance is restricted to be within a specific range. We sampled 3 x 20 PCBs, each batch of twenty is ordered from a different manufacturer: The sampling consisted of measuring the phase shift between the reflected and the incident signals when injecting a ISO MHz sinewave into a PCB trace. The trace is selected to be the same for all samples. All the PCBs are ordered to be identical and designed for 50 devices. Our conclusion was drawn after running the T-tests to assess statistically the significance of the difference occurring between the PCBs. Based on the computed P-values all three batches are different from each other in the mean of the measured phase shift with 95 % confidence. The difference between the measured and the expected characteristic impedance is found as 3 %, 10 % and 20 %for these three manufacturers. We also witnessed board- to-board variations even within the same batch and from the same supplier due to the process instability by looking at the probability density of having the same phase shift that is equal to the mean. Some samples showed 2.6 % to 3.5 % difference above the mean.

  • 9.
    Svensson, Lennart
    et al.
    Chalmers University of Technology.
    Lundberg, Magnus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On posterior distributions for signals in Gaussian noise with unknown covariance matrix2005In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 53, no 9, p. 3554-3571Article in journal (Refereed)
    Abstract [en]

    A Bayesian approach to estimate parameters of signals embedded in complex Gaussian noise with unknown color is presented. The study specifically focuses on a Bayesian treatment of the unknown noise covariance matrix making up a nuisance parameter in such problems. By integrating out uncertainties regarding the noise color, an enhanced ability to estimate both the signal parameters as well as properties of the error is exploited. Several noninformative priors for the covariance matrix, such as the reference prior, the Jeffreys prior, and modifications to this, are considered. Some of the priors result in analytical solutions, whereas others demand numerical approximations. In the linear signal model, connections are made between the standard Adaptive Maximum Likelihood (AML) estimate and a Bayesian solution using the Jeffreys prior. With adjustments to the Jeffreys prior, correspondence to the regularized solution is also established. This in turn enables a formal treatment of the regularization parameter. Simulations indicate that significant improvements, compared to the AML estimator, can be obtained by considering both the derived regularized solutions as well as the one obtained using the reference prior. The simulations also indicate the possibility of enhancing the predictions of properties of the error as uncertainties in the noise color are acknowledged.

  • 10.
    Svensson, Lennart
    et al.
    Chalmers University of Technology.
    Nordenvaad, Magnus Lundberg
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    The reference prior for complex covariance matrices with efficient implementation strategies2010In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 58, no 1, p. 53-66Article in journal (Refereed)
    Abstract [en]

    The paper derives the reference prior for complex covariance matrices. The reference prior is a noninformative prior that circumvents some of the weaknesses of common alternatives in multidimensional settings. As a consequence, inference based on this prior renders well-behaving solutions that in many cases outperform traditionally used approaches. The main obstacle is that inference based on this prior require integration over high-dimensional spaces which have no closed form solutions. A focus of the paper is therefore to discuss efficient implementation strategies based on Markov chain Monte Carlo methods. It is identified that certain structures can be treated analytically both for the case where the parameter of interest is the covariance matrix itself but also for cases in which the covariance matrix is a nuisance parameter that characterizes noise color. Evaluation in both these settings also verify the superior performance obtained by using the proposed prior as compared to traditional techniques to treat unknown covariance matrices.

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