| 1. |
- Glentis, G. -O, et al.
(författare)
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Efficient spectral analysis in the missing data case using sparse ML methods
- 2014
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Ingår i: European Signal Processing Conference. - 2219-5491.
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Konferensbidrag (refereegranskat)abstract
- Given their wide applicability, several sparse high-resolution spectral estimation techniques and their implementation have been examined in the recent literature. In this work, we further the topic by examining a computationally efficient implementation of the recent SMLA algorithms in the missing data case. The work is an extension of our implementation for the uniformly sampled case, and offers a notable computational gain as compared to the alternative implementations in the missing data case.
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| 2. |
- Adalbjörnsson, Stefan Ingi, et al.
(författare)
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High resolution sparse estimation of exponentially decaying two-dimensional signals
- 2014
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Ingår i: European Signal Processing Conference. - 2219-5491.
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Konferensbidrag (refereegranskat)abstract
- In this work, we consider the problem of high-resolution estimation of the parameters detailing a two-dimensional (2-D) signal consisting of an unknown number of exponentially decaying sinusoidal components. Interpreting the estimation problem as a block (or group) sparse representation problem allows the decoupling of the 2-D data structure into a sum of outer-products of 1-D damped sinusoidal signals with unknown damping and frequency. The resulting non-zero blocks will represent each of the 1-D damped sinusoids, which may then be used as non-parametric estimates of the corresponding 1-D signals; this implies that the sought 2-D modes may be estimated using a sequence of 1-D optimization problems. The resulting sparse representation problem is solved using an iterative ADMM-based algorithm, after which the damping and frequency parameter can be estimated by a sequence of simple 1-D optimization problems.
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| 3. |
- Adalbjörnsson, Stefan Ingi, et al.
(författare)
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Sparse Estimation Of Spectroscopic Signals
- 2011
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Ingår i: European Signal Processing Conference. - 2219-5491. ; 2011, s. 333-337
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Konferensbidrag (refereegranskat)abstract
- This work considers the semi-parametric estimation of sparse spec- troscopic signals, aiming to form a detailed spectral representation of both the frequency content and the spectral line widths of the oc- curring signals. Extending on the recent FOCUSS-based SLIM al- gorithm, we propose an alternative prior for a Bayesian formulation of this sparse reconstruction method, exploiting a proposed suitable prior for the noise variance. Examining three common models for spectroscopic signals, the introduced technique allows for reliable estimation of the characteristics of these models. Numerical sim- ulations illustrate the improved performance of the proposed tech- nique.
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| 4. |
- Akesson, Maria, et al.
(författare)
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Highly Accurate and Noise-Robust Phase Delay Estimation using Multitaper Reassignment
- 2023
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Ingår i: 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings. - 2219-5491. - 9789464593600 ; , s. 1763-1767
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Konferensbidrag (refereegranskat)abstract
- The recently developed Phase-Scaled Reassignment (PSR) can estimate phase-difference between two oscillating transient signals with high accuracy. However, in low signal-to-noise ratios (SNRs) the performance of commonly applied reassignment techniques is known to deteriorate. In order to reduce variance in low SNR, we propose a multitaper PSR (mtPSR) method for phase-difference estimation between Gaussian transient signals. Three possible variations of this method are investigated and evaluated, mtPSR1, mtPSR2, and mtPSR3. All three variations are shown to outperform state-of-the-art methods and improve estimation accuracy in low SNR. The mtPSR1 is superior in terms of computational efficiency while the mtPSR3 achieves the highest accuracy. The mtPSR technique is also shown to be robust to model assumptions. An example of phase delay estimates of the electrical signals measured from the brain reveals promising results.
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| 5. |
- Akesson, Maria, et al.
(författare)
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Phase Reassignment with Efficient Estimation of Phase Difference
- 2022
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Ingår i: 30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings. - 2219-5491. - 9789082797091 ; 2022-August, s. 2126-2130
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Konferensbidrag (refereegranskat)abstract
- The recently developed Matched Phase Reassignment (MPR) gives a time-frequency local measure of phase difference between short oscillatory transient signals. However, the resulting phase estimate is not satisfactory as it has poor resolution for high oscillatory frequencies. The MPR is also sensitive to high noise levels and is computationally cumbersome. In this paper, a novel reassignment method for phase difference estimation is proposed and evaluated. In comparison to the MPR the accuracy is increased and the computational time is reduced. Simulations show that the proposed technique also outperforms state-of-the-art methods in terms of efficiency. An illustrative example of phase difference estimates of the electrical signals measured from the brain is included.
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| 6. |
- Andersson, Matilda, et al.
(författare)
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Augmentation Strategies for Self-Supervised Representation Learning from Electrocardiograms
- 2023
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Ingår i: 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings. - 2219-5491. - 9789464593600 ; , s. 1075-1079
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Konferensbidrag (refereegranskat)abstract
- In this paper, we investigate the effects of different augmentation strategies in self-supervised representation learning from electrocardiograms. Our study examines the impact of random resized crop and time out on downstream performance. We also consider the importance of the signal length. Furthermore, instead of using two augmented copies of the sample as a positive pair, we suggest augmenting only one. The second signal is kept as the original signal. These different augmentation strategies are investigated in the context of pre-training and fine-tuning, following the different self-supervised learning frameworks BYOL, SimCLR, and VICReg. We formulate the downstream task as a multi-label classification task using a public dataset containing ECG recordings and annotations. In our experiments, we demonstrate that self-supervised learning can consistently outperform classical supervised learning when configured correctly. These findings are of particular importance in the medical domain, as the medical labeling process is particularly expensive, and clinical ground truth is often difficult to define. We are hopeful that our findings will be a catalyst for further research into augmentation strategies in self-supervised learning to improve performance in the detection of cardiovascular disease.
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| 7. |
- Angelopoulos, Kostas, et al.
(författare)
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Efficient Time Recursive Coherence Spectrum Estimation
- 2012
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Ingår i: Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European. - 2076-1465 .- 2219-5491. - 9781467310680 ; , s. 425-429
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Konferensbidrag (refereegranskat)abstract
- The coherence spectrum is of notable interest as a bivariate spectral measure in a variety of application, and the topic has lately attracted notable interest with the recent formulation of several high-resolution data adaptive estimators. In this work, we present computationally efficient time recursive implementations of the recent iterative adaptive approach (IAA) estimator, examining both the case of complete data sets and when some observations are missing. The algorithms continues the recent development of exploiting the estimators’ inherently low displacement rank of the necessary products of Toeplitz-like matrices, extending these to time-updating formulations for the IAA-based coherence estimation algorithm. Numerical simulations together with theoretical complexity measures illustrate the performance of the proposed algorithm.
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| 8. |
- Ask, Erik, et al.
(författare)
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A Unifying Approach to Minimal Problems in Collinear and Planar TDOA Sensor Network Self-Calibration
- 2014
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Ingår i: European Signal Processing Conference. - 2219-5491.
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Konferensbidrag (refereegranskat)abstract
- This work presents a study of sensor network calibration from time-difference-of-arrival (TDOA) measurements for cases when the dimensions spanned by the receivers and the transmitters differ. This could for example be if receivers are restricted to a line or plane or if the transmitting objects are moving linearly in space. Such calibration arises in several applications such as calibration of (acoustic or ultrasound) microphone arrays, and radio antenna networks. We propose a non-iterative algorithm based on recent stratified approaches: (i) rank constraints on modified measurement matrix, (ii) factorization techniques that determine transmitters and receivers up to unknown affine transformation and (iii) determining the affine stratification using remaining non-linear constraints. This results in a unified approach to solve almost all minimal problems. Such algorithms are important components for systems for self-localization. Experiments are shown both for simulated and real data with promising results.
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| 9. |
- Aström, Kalle, et al.
(författare)
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Extension of Time-Difference-of-Arrival Self Calibration Solutions Using Robust Multilateration
- 2021
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Ingår i: 29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings. - 2076-1465 .- 2219-5491. - 9789082797060 ; , s. 870-874
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Konferensbidrag (refereegranskat)abstract
- Recent advances in robust self-calibration have made it possible to estimate microphone positions and at least partial sound source positions using ambient sound. However, there are limits on how well sound source paths can be recovered using state-of-the-art techniques. In this paper we develop and evaluate several techniques to extend partial and incomplete solutions. We present minimal solvers for sound source positioning using non-overlapping pairs of microphone positions and their respective time-difference measurements, and show how these new solvers can be used in a hypothesis and test setting. We also investigate techniques that exploit temporal smoothness of the sound source paths. The different techniques are evaluated on both real and synthetic data, and compared to several state-of-the-art techniques for time-difference-of-arrival multilateration.
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| 10. |
- 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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