| 1. |
- Aström, Kalle, et al.
(författare)
-
Extension of Time-Difference-of-Arrival Self Calibration Solutions Using Robust Multilateration
- 2021
-
Ingår i: 29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings. - 2219-5491 .- 2076-1465. - 9789082797060 ; , s. 870-874
-
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.
|
|
| 2. |
- Ferranti, Luca, et al.
(författare)
-
Homotopy Continuation for Sensor Networks Self-Calibration
- 2021
-
Ingår i: 29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings. - 2219-5491. - 9789082797060 ; 2021-August, s. 1725-1729
-
Konferensbidrag (refereegranskat)abstract
- Given a sensor network, TDOA self-calibration aims at simultaneously estimating the positions of receivers and transmitters, and transmitters time offsets. This can be formulated as a system of polynomial equations. Due to the elevated number of unknowns and the nonlinearity of the problem, obtaining an accurate solution efficiently is nontrivial. Previous work has shown that iterative algorithms are sensitive to initialization and little noise can lead to failure in convergence. Hence, research has focused on algebraic techniques. Stable and efficient algebraic solvers have been proposed for some network configurations, but they do not work for smaller networks. In this paper, we use homotopy continuation to solve four previously unsolved configurations in 2D TDOA self-calibration, including a minimal one. As a theoretical contribution, we investigate the number of solutions of the new minimal configuration, showing this is much lower than previous estimates. As a more practical contribution, we also present new subminimal solvers, which can be used to achieve unique accurate solutions in previously unsolvable configurations. We demonstrate our solvers are stable both with clean and noisy data, even without nonlinear refinement afterwards. Moreover, we demonstrate the suitability of homotopy continuation for sensor network calibration problems, opening prospects to new applications.
|
|
| 3. |
- Juhlin, Maria, et al.
(författare)
-
Localization Of Multiple Jammers In Wireless Sensor Networks
- 2021
-
Ingår i: 29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings. - 2219-5491. - 9789082797060 ; 2021-August, s. 1596-1600
-
Konferensbidrag (refereegranskat)abstract
- Wireless sensor networks are susceptible to jamming attacks that can result in communication breakdowns. Preemptive measures to prevent jamming attacks is an active research field, but to stop an ongoing attack often requires that one is able to locate jammers in order to neutralize them. Several methods exist for the case when the network is corrupted by a single jammer, although these generally do not allow for cases when more than one jammer is present. In this work, we introduce an iterative procedure that determines the number of jammers corrupting the network as part of the localization of the jammers. The performance of the method is illustrated using numerical examples.
|
|
| 4. |
- Reinhold, Isabella, et al.
(författare)
-
Non-parametric Envelope Estimation for the Matched Window Reassignment
- 2021
-
Ingår i: 29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings. - 2219-5491. - 9789082797060 ; 2021-August, s. 1920-1924
-
Konferensbidrag (refereegranskat)abstract
- The reassigned spectrogram is a powerful tool for analysing non-stationary signals, and in an ideal setting it gives perfect time and frequency localisation. A method very well suited for oscillating transient signals is the matched window reassignment, which requires a matching window, i.e. the envelope of the transient, to be known or estimated beforehand. This paper proposes a novel method for estimating the envelope of noisy transients, using a non-parametric and computationally efficient approach. The estimated envelopes are used to calculate the matched window reassignment, obtaining estimates of the time-frequency centre of the transients. The reassignment using the estimated envelopes is shown to give good estimates of the time-frequency centres, and good localisation in time and frequency. The novel envelope estimation approach is illustrated on measured marine biosonar data.
|
|
| 5. |
- Sandsten, Maria, et al.
(författare)
-
A Novel Multitaper Reassignment Method for Estimation of Phase Synchrony
- 2021
-
Ingår i: 29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings. - 2219-5491. - 9789082797060 ; 2021-August, s. 2164-2168
-
Konferensbidrag (refereegranskat)abstract
- The matched phase reassignment, developed to estimate phase synchrony of transient oscillatory signals, is extended into a multitaper phase reassignment (MTPR) method. The method gives perfect time-frequency localization for two transients with zero phase difference and estimates of time locations and oscillatory frequencies in low signal-to-noise ratios. For different signal-to-noise ratios between channels a suggestion of corrected reassignment vector expressions is given, resulting in minimized variance. The MTPR outperforms the matched phase reassignment as well as state-of-the-art methods, such as Pearson's linear correlation, time-frequency cross-spectrogram phase estimation and the Phase Lag Index method. An example of estimated phase differences, time locations and oscillatory frequencies of electrical signals measured from the brain is also shown.
|
|
| 6. |
- Sandsten, Maria, et al.
(författare)
-
Parameter Estimation from the Cross-Spectrogram Reassignment Vectors
- 2021
-
Ingår i: 29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings. - 2219-5491. - 9789082797060 ; 2021-August, s. 1915-1919
-
Konferensbidrag (refereegranskat)abstract
- In this paper we propose a novel technique to estimate the parameters of two Gaussian envelope oscillatory signals, with the same time-locations and oscillatory frequencies but possibly different phases. The phase difference and the length of the Gaussian envelope are estimated directly from the slopes of the corresponding cross-spectrogram reassignment vectors. Including the phase difference and the envelope length in the scaled reassignment of the cross-spectrogram will give a perfectly concentrated time-frequency spectrum where the location of the maximum gives estimates of the time and oscillatory frequency parameters. The proposed method is evaluated for different SNRs and is also compared to state-of-the-art techniques for phase estimation of oscillatory electrical activity measured from the brain.
|
|
| 7. |
- Akesson, Maria, et al.
(författare)
-
Highly Accurate and Noise-Robust Phase Delay Estimation using Multitaper Reassignment
- 2023
-
Ingår i: 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings. - 2219-5491. - 9789464593600 ; , s. 1763-1767
-
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.
|
|
| 8. |
- Akesson, Maria, et al.
(författare)
-
Phase Reassignment with Efficient Estimation of Phase Difference
- 2022
-
Ingår i: 30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings. - 2219-5491. - 9789082797091 ; 2022-August, s. 2126-2130
-
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.
|
|
| 9. |
- Andersson, Matilda, et al.
(författare)
-
Augmentation Strategies for Self-Supervised Representation Learning from Electrocardiograms
- 2023
-
Ingår i: 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings. - 2219-5491. - 9789464593600 ; , s. 1075-1079
-
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.
|
|
| 10. |
- Diaw, Moustapha, et al.
(författare)
-
Fast process for classifying structural image pairs using Weibull parameters extracted from undersampled Local Dissimilarity Maps
- 2021
-
Ingår i: 2021 29th European Signal Processing Conference (EUSIPCO). - : European Signal Processing Conference, EUSIPCO. - 9789082797060 - 9781665409001 ; , s. 631-635
-
Konferensbidrag (refereegranskat)abstract
- In previous works, the Local Dissimilarity Map (LDM) was proposed to compare two binary and grayscale images. This measure is based on a Hausdorff distance, which allows to quantify locally the dissimilarities between images. In this paper, we proposed the two-parameter Weibull distribution to model the LDM and the undersampled LDMs for two structural images. To classify structural image pairs, we used the two parameters of Weibull distribution for each LDM as descriptors. They are relevant to discriminate image pairs into similar and dissimilar classes. Experiments were made on the MNIST image dataset and in our own old print image dataset. The results shown our approach is more accurate than the other measures in the literature.
|
|
