| 1. |
- Gudmundson, Erik, 1976-
(författare)
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Signal Processing for Spectroscopic Applications
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Spectroscopic techniques allow for studies of materials and organisms on the atomic and molecular level. Examples of such techniques are nuclear magnetic resonance (NMR) spectroscopy—one of the principal techniques to obtain physical, chemical, electronic and structural information about molecules—and magnetic resonance imaging (MRI)—an important medical imaging technique for, e.g., visualization of the internal structure of the human body. The less well-known spectroscopic technique of nuclear quadrupole resonance (NQR) is related to NMR and MRI but with the difference that no external magnetic field is needed. NQR has found applications in, e.g., detection of explosives and narcotics. The first part of this thesis is focused on detection and identification of solid and liquid explosives using both NQR and NMR data. Methods allowing for uncertainties in the assumed signal amplitudes are proposed, as well as methods for estimation of model parameters that allow for non-uniform sampling of the data. The second part treats two medical applications. Firstly, new, fast methods for parameter estimation in MRI data are presented. MRI can be used for, e.g., the diagnosis of anomalies in the skin or in the brain. The presented methods allow for a significant decrease in computational complexity without loss in performance. Secondly, the estimation of blood flow velo-city using medical ultrasound scanners is addressed. Information about anomalies in the blood flow dynamics is an important tool for the diagnosis of, for example, stenosis and atherosclerosis. The presented methods make no assumption on the sampling schemes, allowing for duplex mode transmissions where B-mode images are interleaved with the Doppler emissions.
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| 2. |
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| 3. |
- Zachariah, Dave, et al.
(författare)
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Scalable and Passive Wireless Network Clock Synchronization in LOS Environments
- 2017
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Ingår i: IEEE Transactions on Wireless Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 1536-1276 .- 1558-2248. ; 16:6, s. 3536-3546
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Tidskriftsartikel (refereegranskat)abstract
- Clock synchronization is ubiquitous in wireless systems for communication, sensing, and control. In this paper, we design a scalable system in which an indefinite number of passively receiving wireless units can synchronize to a single master clock at the level of discrete clock ticks. Accurate synchronization requires an estimate of the node positions to compensate the time-of-flight transmission delay in line-of-sight environments. If such information is available, the framework developed here takes position uncertainties into account. In the absence of such information, as in indoor scenarios, we propose an auxiliary localization mechanism. Furthermore, we derive the Cramer-Rao bounds for the system, which show that it enables synchronization accuracy at sub-nanosecond levels. Finally, we develop and evaluate an online estimation method, which is statistically efficient.
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| 4. |
- Åhgren, Per, 1974-
(författare)
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On System Identification and Acoustic Echo Cancellation
- 2004
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The topic of acoustic echo cancellation has received a lot of interest over the years. Even though the topic is relatively old, the increasing demand for hands-free telephony has made it more important than ever before. Furthermore, in recent years new applications such as Internet Protocol (IP) telephony and stereophonic hands-free telephony have emerged that also require acoustic echo cancellation in order to function properly. This thesis is partly about acoustic echo cancellation which has the purpose to remove the acoustic echoes from the loudspeaker sound signals picked up by the microphones in hands-free telephony systems. If the attenuation of the echoes is small, as it is in a hands-free telephony setup, good acoustic echo cancellation is required for the setup to work well. Several methods are presented in this thesis, addressing various acoustic echo cancellation areas such as doubletalk detection, stereophonic acoustic echo cancellation and ordinary acoustic echo cancellation. While some of the results are basically extensions of existing acoustic echo cancellation algorithms, others are more innovative in the sense that they offer solutions to previously unsolved problems or outperform by far existing algorithms. The second part of this thesis is about system identification which is also a relatively old, but still very active topic. System identification deals with the problem of building mathematical models of dynamic systems and its applications are manifold. One particular area where system identification is needed is the aforementioned acoustic echo cancellation application, where models of the acoustic paths between the loudspeakers and the microphones need to be determined. This thesis presents some explanations and additions to two well-known system identification algorithms. Furthermore, it provides new solutions to two previously unsolved system identification problems.
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| 5. |
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| 6. |
- Abrahamsson, Richard, et al.
(författare)
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Enhanced covariance matrix estimators in adaptive beamforming
- 2007
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Ingår i: 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol II, Pts 1-3. ; , s. 969-972
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Konferensbidrag (refereegranskat)abstract
- In this paper a number of covariance matrix estimators suggested in the literature are compared in terms of their performance in the context of array signal processing. More specifically they are applied in adaptive beamforming which is known to be sensitive to errors in the covariance matrix estimate and where often only a limited amount of data is available for estimation. As many covariance matrix estimators have the form of diagonal loading or eigenvalue adjustments of the sample covariance matrix and as they sometimes offer robustness to array imperfections and finite sample error, they are compared to a recent robustified adaptive Capon beamforming (RCB) method which also has a diagonal loading interpretation. Some of the covariance estimators show a significant improvement over the sample covariance matrix and in some cases they match the performance of the RCB even when a priori knowledge, which is not available in practice, is used for choosing the user parameter of RCB.
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| 7. |
- Abrahamsson, Richard, et al.
(författare)
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Estimation of the parameters of a bilinear model with applications to submarine detection and system identification
- 2007
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Ingår i: Digital signal processing (Print). - : Elsevier BV. - 1051-2004 .- 1095-4333. ; 17:4, s. 756-773
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Tidskriftsartikel (refereegranskat)abstract
- In this work we study the problem of estimating the parameters of a bilinear model describing, e.g., the amplitude modulation of extremely low frequency electromagnetic (ELFE) signatures of submarines. A similar problem arises in estimation of a nonlinear dynamic system using a Hammerstein–Wiener model, where two nonlinear static blocks surround a linear dynamic block. For these purposes a new method is derived. It is also shown in the same context that a two-stage method for parameter estimation of Hammerstein–Wiener models can be interpreted as an approximate least squares method. We also show the similarities with the problem of weighted low-rank approximation and the fact that these problems can be solved exactly in finite time using solvers for global optimization of systems of polynomials based on self dual optimization.
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| 8. |
- Abrahamsson, Richard, 1976-
(författare)
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Estimation Problems in Array Signal Processing, System Identification, and Radar Imagery
- 2006
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis is concerned with parameter estimation, signal processing, and applications. In the first part, imaging using radar is considered. More specifically, two methods are presented for estimation and removal of ground-surface reflections in ground penetrating radar which otherwise hinder reliable detection of shallowly buried landmines. Further, a study of two autofocus methods for synthetic aperture radar is presented. In particular, we study their behavior in scenarios where the phase errors leading to cross-range defocusing are of a spatially variant kind. In the subsequent part, array signal processing and optimal beamforming is regarded. In particular, the phenomenon of signal cancellation in adaptive beamformers due to array perturbations, signal correlated interferences and limited data for covariance matrix estimation is considered. For the general signal cancellation problem, a class of improved adaptive beamformers is suggested based on ridge-regression. Another set of methods is suggested to mitigate signal cancellation due to correlated signal and interferences based on a novel way of finding a characterization of the interference subspace from observed array data. Further, a new minimum variance beamformer is presented for high resolution non-parametric spatial spectrum estimation in cases where the impinging signals are correlated. Lastly, a multitude of enhanced covariance matrix estimators from the statistical literature are studied as an alternative to other robust adaptive beamforming methods. The methods are also applied to space-time adaptive processing where limited data for covariance matrix estimation is a common problem. In the third and final part the estimation of the parameters of a general bilinear problem is considered. The bilinear model is motivated by the application of identifying submarines from their electromagnetic signature and by the identification of a Hamerstein-Wiener model of a non-linear dynamic system. An efficient approximate maximum-likelihood method with closed form solution is suggested for estimating the bilinear model parameters.
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| 9. |
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| 10. |
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| 11. |
- Azevedo, Flavio, et al.
(författare)
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Social and moral psychology of COVID-19 across 69 countries
- 2023
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Ingår i: Scientific Data. - : NATURE PORTFOLIO. - 2052-4463. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables.
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| 12. |
- Babu, Prabhu, et al.
(författare)
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A combined linear programming-maximum likelihood approach to radial velocity data analysis for extrasolar planet detection
- 2011
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Ingår i: ICASSP2011, the 36th International Conference on Acoustics, Speech and Signal Processing, Prague, Czech Republic. - 9781457705397 ; , s. 4352-4355
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 13. |
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| 14. |
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| 15. |
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| 16. |
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| 17. |
- Babu, Prabhu, et al.
(författare)
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Connection between SPICE and Square-Root LASSO for sparse parameter estimation
- 2014
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Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 95, s. 10-14
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 18. |
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| 19. |
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| 20. |
- Babu, Prabhu, et al.
(författare)
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Multiple Hypothesis Testing-Based Cepstrum Thresholding for Nonparametric Spectral Estimation
- 2022
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Ingår i: IEEE Signal Processing Letters. - : IEEE. - 1070-9908 .- 1558-2361. ; 29, s. 2367-2371
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Tidskriftsartikel (refereegranskat)abstract
- In this letter we revisit the problem of smoothed nonparametric spectral estimation via cepstrum thresholding. We formulate the problem of cepstrum thresholding as a multiple hypothesis testing problem and use the false discovery rate (FDR) and familywise error rate (FER) procedures to threshold the cepstral coefficients. We compare the FDR and FER approaches with a previously proposed individual hypothesis testing approach and show that the cepstrum thresholding based on FDR and FER can yield spectral estimates with lower mean square error (MSE).
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| 21. |
- Babu, Prabhu, et al.
(författare)
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Multiple-hypothesis testing rules for high-dimensional model selection and sparse-parameter estimation
- 2023
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Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 213
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Tidskriftsartikel (refereegranskat)abstract
- We consider the problem of model selection for high-dimensional sparse linear regression models. We pose the model selection problem as a multiple-hypothesis testing problem and employ the methods of false discovery rate (FDR) and familywise error rate (FER) to solve it. We also present the reformulation of the FDR/FER-based approaches as criterion-based model selection rules and establish their relation to the extended Bayesian Information Criterion (EBIC), which is a state-of-the-art high-dimensional model selection rule. We use numerical simulations to show that the proposed FDR/FER method is well suited for high-dimensional model selection and performs better than EBIC.
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| 22. |
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| 23. |
- Babu, Prabhu, et al.
(författare)
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Sparse spectral-line estimation for nonuniformly sampled multivariate time series : SPICE, LIKES and MSBL
- 2012
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Ingår i: 2012 Proceedings Of The 20th European Signal Processing Conference (EUSIPCO). - 9781467310680 ; , s. 445-449
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Konferensbidrag (refereegranskat)abstract
- 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).
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| 24. |
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| 25. |
- Babu, Prabhu, 1984-
(författare)
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Spectral Analysis of Nonuniformly Sampled Data and Applications
- 2012
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Signal acquisition, signal reconstruction and analysis of spectrum of the signal are the three most important steps in signal processing and they are found in almost all of the modern day hardware. In most of the signal processing hardware, the signal of interest is sampled at uniform intervals satisfying some conditions like Nyquist rate. However, in some cases the privilege of having uniformly sampled data is lost due to some constraints on the hardware resources. In this thesis an important problem of signal reconstruction and spectral analysis from nonuniformly sampled data is addressed and a variety of methods are presented. The proposed methods are tested via numerical experiments on both artificial and real-life data sets.The thesis starts with a brief review of methods available in the literature for signal reconstruction and spectral analysis from non uniformly sampled data. The methods discussed in the thesis are classified into two broad categories - dense and sparse methods, the classification is based on the kind of spectra for which they are applicable. Under dense spectral methods the main contribution of the thesis is a non-parametric approach named LIMES, which recovers the smooth spectrum from non uniformly sampled data. Apart from recovering the spectrum, LIMES also gives an estimate of the covariance matrix. Under sparse methods the two main contributions are methods named SPICE and LIKES - both of them are user parameter free sparse estimation methods applicable for line spectral estimation. The other important contributions are extensions of SPICE and LIKES to multivariate time series and array processing models, and a solution to the grid selection problem in sparse estimation of spectral-line parameters.The third and final part of the thesis contains applications of the methods discussed in the thesis to the problem of radial velocity data analysis for exoplanet detection. Apart from the exoplanet application, an application based on Sudoku, which is related to sparse parameter estimation, is also discussed.
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| 26. |
- Barral, Joelle K., et al.
(författare)
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A Robust Methodology for In Vivo T1 Mapping
- 2010
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Ingår i: Magnetic Resonance in Medicine. - : Wiley. - 0740-3194 .- 1522-2594. ; 64:4, s. 1057-1067
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Tidskriftsartikel (refereegranskat)abstract
- In this article, a robust methodology for in vivo T(1) mapping is presented. The approach combines a gold standard scanning procedure with a novel fitting procedure. Fitting complex data to a five-parameter model ensures accuracy and precision of the T(1) estimation. A reduced-dimension nonlinear least squares method is proposed. This method turns the complicated multi-parameter minimization into a straightforward one-dimensional search. As the range of possible T(1) values is known, a global grid search can be used, ensuring that a global optimal solution is found. When only magnitude data are available, the algorithm is adapted to concurrently restore polarity. The performance of the new algorithm is demonstrated in simulations and phantom experiments. The new algorithm is as accurate and precise as the conventionally used Levenberg-Marquardt algorithm but much faster. This gain in speed makes the use of the five-parameter model viable. In addition, the new algorithm does not require initialization of the search parameters. Finally, the methodology is applied in vivo to conventional brain imaging and to skin imaging. T(1) values are estimated for white matter and gray matter at 1.5T and for dermis, hypodermis, and muscle at 1.5T, 3T, and 7T. Magn Reson Med 64:1057-1067, 2010.
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| 27. |
- Barral, Joëlle K., et al.
(författare)
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Skin T1 Mapping at 1.5T, 3T, and 7T
- 2009
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Ingår i: Proceedings of the ISMRM 2009, Honolulu, Hawaii, USA.
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Konferensbidrag (refereegranskat)
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| 28. |
- Beck, Amir, et al.
(författare)
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Exact and approximate solutions of source localization problems
- 2008
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Ingår i: IEEE Transactions on Signal Processing. - 1053-587X .- 1941-0476. ; 56:5, s. 1770-1778
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 29. |
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| 30. |
- Björk, Marcus, 1985-
(författare)
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Contributions to Signal Processing for MRI
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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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| 31. |
- Björk, Marcus, 1985-, et al.
(författare)
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Dynamic models with quantized output for modeling patient response to pharmacotherapy
- 2010
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Ingår i: Proc. International Conference on Control Applications. - Piscataway, NJ : IEEE. - 9781424453627 ; , s. 1029-1034
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 32. |
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| 33. |
- Björk, Marcus, et al.
(författare)
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Magnitude-constrained sequence design with application in MRI
- 2014
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Ingår i: Proc. 39th IEEE International Conference on Acoustics, Speech, and Signal Processing. - Piscataway, NJ : IEEE. - 9781479928934 ; , s. 4943-4947
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 34. |
- Björk, Marcus, 1985-, et al.
(författare)
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New approach to phase correction in multi-echo T2 relaxometry
- 2014
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Ingår i: Journal of magnetic resonance. - : Elsevier BV. - 1090-7807 .- 1096-0856. ; 249, s. 100-107
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 35. |
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| 36. |
- Björk, Marcus, et al.
(författare)
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Parameter estimation approach to banding artifact reduction in balanced steady-state free precession
- 2014
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Ingår i: Magnetic Resonance in Medicine. - : Wiley. - 1522-2594 .- 0740-3194. ; 72:3, s. 880-892
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Tidskriftsartikel (refereegranskat)abstract
- 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 paper. 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 Cramér-Rao bound (CRB), 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 T1 and T2 from phase-cycled bSSFP is difficult, since the CRB is high at common SNR. 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.
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| 37. |
- Björk, Marcus, et al.
(författare)
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Signal Modeling and the Cramér-Rao Bound for Absolute Magnetic Resonance Thermometry in Fat Tissue
- 2011
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Ingår i: Proc. 45th Asilomar Conference on Signals, Systems, and Computers. ; , s. 80-84
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 38. |
- Björk, Marcus, 1985-, et al.
(författare)
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Signal Processing Algorithms for Removing Banding Artifacts in MRI
- 2011
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Ingår i: Proceedings of the 19th European Signal Processing Conference (EUSIPCO-2011). ; , s. 1000-1004, s. 1000-1004
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Konferensbidrag (refereegranskat)abstract
- 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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| 39. |
- Boman, Katarina
(författare)
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Low-angle estimation : Models, methods and bounds
- 2000
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this work we study the performance of elevation estimators and lower bounds on the estimation error variance for a low angle target in a smooth sea scenario using an array antenna. The article is structured around some key assumptions on multipath knowledge, signal parameterization and noise covariance, giving the reader a framework in which Maximum Likelihood estimators exploiting different á priori information can be found.The crucial factor that determines the estimator accuracy is the multipath modeling, and there are three alternative levels of knowledge that can be used: 1) two unknown target locations 2) the target and its corresponding sea-reflection are related via simple geometry 3) the sea-reflection coefficient is known as a function of grazing angle.A compact expression for the Cramér–Rao lower bound is derived, including all special cases of the key assumptions. We prove that the Cramér–Rao bound is highly dependent on the multipath model, while it is the same for the different signal parameterizations and that it is independent of the noise covariance. However, the Cramér–Rao bound is sometimes too optimistic and not achievable. The tighter Barankin bound is derived to predict the threshold behavior seen at low SNR. At high SNR the Barankin bound coincides with the Cramér–Rao bound. Simulations show that the Maximum Likelihood methods are statistically efficient and achieve the theoretical lower bound on error variance, in case of high enough SNR.The bounds are also useful tools to design an improved array structure that can give better performance than the standard uniform linear array structure. The influence of the number of sensors and the number of snapshots on the error variance is also studied, showing the rate of improvement with more sensors or snapshots. Finally we discuss the use of multiple frequencies, which is mainly a tool for suppressing ambiguities. We show for which signal models it provides improved performance.
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| 40. |
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| 41. |
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| 44. |
- Cheng, Yuanbo, et al.
(författare)
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Interval Design for Signal Parameter Estimation From Quantized Data
- 2022
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 70, s. 6011-6020
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Tidskriftsartikel (refereegranskat)abstract
- We consider the problem of optimizing the quantization intervals (or thresholds) of low-resolution analog-to-digital converters (ADCs) via the minimization of a Cramer-Rao bound (CRB)-based metric. The interval design is formulated as a dynamic programming problem. A computationally efficient global algorithm, referred to as the interval design for enhanced accuracy (IDEA) algorithm, is presented to solve this optimization problem. If the realization in hardware of a quantizer with optimized intervals is difficult, it can be approximated by a design whose practical implementation is feasible. Furthermore, the optimized quantizer can also be useful in signal compression applications, in which case no approximation should be necessary. As an additional contribution, we establish the equivalence between the Lloyd-Max type of quantizer and a low signal-to-noise ratio version of our IDEA quantizer, and show that it holds true if and only if the noise is Gaussian. Furthermore, IDEA quantizers for several typical signals, for instance normally distributed signals, are provided. Finally, a number of numerical examples are presented to demonstrate that the use of IDEA quantizers can enhance the parameter estimation performance.
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| 45. |
- Christensen, M, et al.
(författare)
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Multi-pitch estimation
- 2008
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Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 88:4, s. 972-983
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we formulate the multi-pitch estimation problem and propose a number of methods to estimate the set of fundamental frequencies. The proposed methods, based on the nonlinear least-squares (NLS), Multiple Signal Classification (MUSIC) and the Capon principles, estimate the multiple fundamental frequencies via a number of one-dimensional searches. We also propose an iterative method based on the Expectation Maximization (EM) algorithm. The statistical properties of the methods are evaluated via Monte Carlo simulations for both the single- and multi-pitch cases.
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