| 1. |
- Agrell, Erik, 1965, et al.
(författare)
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Multidimensional sampling of isotropically bandlimited signals
- 2018
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Ingår i: IEEE Signal Processing Letters. - 1070-9908 .- 1558-2361. ; 25:3, s. 383-387
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Tidskriftsartikel (refereegranskat)abstract
- A new lower bound on the average reconstruction error variance of multidimensional sampling and reconstruction is presented. It applies to sampling on arbitrary lattices in arbitrary dimensions, assuming a stochastic process with constant, isotropically bandlimited spectrum and reconstruction by the best linear interpolator. The lower bound is exact for any lattice at sufficiently high and low sampling rates. The two threshold rates where the error variance deviates from the lower bound gives two optimality criteria for sampling lattices. It is proved that at low rates, near the first threshold, the optimal lattice is the dual of the best sphere-covering lattice, which for the first time establishes a rigorous relation between optimal sampling and optimal sphere covering. A previously known result is confirmed at high rates, near the second threshold, namely, that the optimal lattice is the dual of the best sphere-packing lattice. Numerical results quantify the performance of various lattices for sampling and support the theoretical optimality criteria.
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| 2. |
- Ahlström, Christer, et al.
(författare)
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Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction
- 2005
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Ingår i: IEEE Signal Processing Letters. - : Institutionen för medicinsk teknik. - 1070-9908 .- 1558-2361. ; 12:12, s. 812-815, s. 812-815
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Ahmed, Mohammad Faisal, et al.
(författare)
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Joint Transmit and Reflective Beamformer Design for Secure Estimation in IRS-Aided WSNs
- 2022
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Ingår i: IEEE Signal Processing Letters. - Piscataway, NJ, United States : Institute of Electrical and Electronics Engineers (IEEE). - 1070-9908 .- 1558-2361. ; 29, s. 692-696
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Tidskriftsartikel (refereegranskat)abstract
- Wireless sensor networks (WSNs) are vulnerable to eavesdropping as the sensor nodes (SNs) communicate over an open radio channel. Intelligent reflecting surface (IRS) technology can be leveraged for physical layer security in WSNs. In this paper, we propose a joint transmit and reflective beamformer (JTRB) design for secure parameter estimation at the fusion center (FC) in the presence of an eavesdropper (ED) in a WSN. We develop a semidefinite relaxation (SDR)-based iterative algorithm, which alternately yields the transmit beamformer at each SN and the corresponding reflection phases at the IRS, to achieve the minimum mean-squared error (MSE) parameter estimate at the FC, subject to transmit power and ED signal-to-noise ratio constraints. Our simulation results demonstrate robust MSE and security performance of the proposed IRS-based JTRB technique.
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| 4. |
- Ardeshiri, Tohid, et al.
(författare)
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Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances
- 2015
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Ingår i: IEEE Signal Processing Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 1070-9908 .- 1558-2361. ; 22:12, s. 2450-2454
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 5. |
- Ardeshiri, Tohid, et al.
(författare)
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Greedy Reduction Algorithms for Mixtures of Exponential Family
- 2015
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Ingår i: IEEE Signal Processing Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 1070-9908 .- 1558-2361. ; 22:6, s. 676-680
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
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| 7. |
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| 8. |
- 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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| 9. |
- Berglund, Erik, et al.
(författare)
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Distributed Newton Method Over Graphs : Can Sharing of Second-Order Information Eliminate the Condition Number Dependence?
- 2021
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Ingår i: IEEE Signal Processing Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 1070-9908 .- 1558-2361. ; 28, s. 1180-1184
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Tidskriftsartikel (refereegranskat)abstract
- One of the main advantages of second-order methods in a centralized setting is that they are insensitive to the condition number of the objective function's Hessian. For applications such as regression analysis, this means that less pre-processing of the data is required for the algorithm to work well, as the ill-conditioning caused by highly correlated variables will not be as problematic. Similar condition number independence has not yet been established for distributed methods. In this paper, we analyze the performance of a simple distributed second-order algorithm on quadratic problems and show that its convergence depends only logarithmically on the condition number. Our empirical results indicate that the use of second-order information can yield large efficiency improvements over first-order methods, both in terms of iterations and communications, when the condition number is of the same order of magnitude as the problem dimension.
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| 10. |
- Bishop, Adrian N., et al.
(författare)
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Vision-Based Target Tracking and Surveillance With Robust Set-Valued State Estimation
- 2010
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Ingår i: IEEE Signal Processing Letters. - 1070-9908 .- 1558-2361. ; 17:3, s. 289-292
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Tidskriftsartikel (refereegranskat)abstract
- Tracking a target from a video stream (or a sequence of image frames) involves nonlinear measurements in Cartesian coordinates. However, the target dynamics, modeled in Cartesian coordinates, result in a linear system. We present a robust linear filter based on an analytical nonlinear to linear measurement conversion algorithm. Using ideas from robust control theory, a rigorous theoretical analysis is given which guarantees that the state estimation error for the filter is bounded, i.e., a measure against filter divergence is obtained. In fact, an ellipsoidal set-valued estimate is obtained which is guaranteed to contain the true target location with an arbitrarily high probability. The algorithm is particularly suited to visual surveillance and tracking applications involving targets moving on a plane.
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