1. |
- Agrell, Erik, 1965, et al.
(author)
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Multidimensional sampling of isotropically bandlimited signals
- 2018
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In: IEEE Signal Processing Letters. - 1070-9908 .- 1558-2361. ; 25:3, s. 383-387
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Journal article (peer-reviewed)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.
(author)
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Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction
- 2005
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In: IEEE Signal Processing Letters. - : Institutionen för medicinsk teknik. - 1070-9908 .- 1558-2361. ; 12:12, s. 812-815, s. 812-815
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Journal article (peer-reviewed)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.
(author)
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Joint Transmit and Reflective Beamformer Design for Secure Estimation in IRS-Aided WSNs
- 2022
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In: 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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Journal article (peer-reviewed)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.
(author)
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Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances
- 2015
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In: IEEE Signal Processing Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 1070-9908 .- 1558-2361. ; 22:12, s. 2450-2454
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Journal article (peer-reviewed)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.
(author)
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Greedy Reduction Algorithms for Mixtures of Exponential Family
- 2015
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In: IEEE Signal Processing Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 1070-9908 .- 1558-2361. ; 22:6, s. 676-680
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Journal article (peer-reviewed)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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8. |
- Babu, Prabhu, et al.
(author)
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Multiple Hypothesis Testing-Based Cepstrum Thresholding for Nonparametric Spectral Estimation
- 2022
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In: IEEE Signal Processing Letters. - : IEEE. - 1070-9908 .- 1558-2361. ; 29, s. 2367-2371
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Journal article (peer-reviewed)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.
(author)
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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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In: IEEE Signal Processing Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 1070-9908 .- 1558-2361. ; 28, s. 1180-1184
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Journal article (peer-reviewed)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.
(author)
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Vision-Based Target Tracking and Surveillance With Robust Set-Valued State Estimation
- 2010
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In: IEEE Signal Processing Letters. - 1070-9908 .- 1558-2361. ; 17:3, s. 289-292
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Journal article (peer-reviewed)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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11. |
- Blomberg, Niclas, et al.
(author)
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Regularization Paths for Re-Weighted Nuclear Norm Minimization
- 2015
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In: IEEE Signal Processing Letters. - 1070-9908 .- 1558-2361. ; 22:11, s. 1980-1984
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Journal article (peer-reviewed)abstract
- We consider a class of weighted nuclear norm optimization problems with important applications in signal processing, system identification, and model order reduction. The nuclear norm is commonly used as a convex heuristic for matrix rank constraints. Our objective is to minimize a quadratic cost subject to a nuclear norm constraint on a linear function of the decision variables, where the trade-off between the fit and the constraint is governed by a regularization parameter. The main contribution is an algorithm to determine the so-called approximate regularization path, which is the optimal solution up to a given error tolerance as a function of the regularization parameter. The advantage is that we only have to solve the optimization problem for a fixed number of values of the regularization parameter, with guaranteed error tolerance. The algorithm is exemplified on a weighted Hankel matrix model order reduction problem.
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12. |
- Brandt, Rasmus, 1985-, et al.
(author)
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Globally Optimal Base Station Clustering in Interference Alignment-Based Multicell Networks
- 2016
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In: IEEE Signal Processing Letters. - : IEEE. - 1070-9908 .- 1558-2361. ; 23:4, s. 512-516
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Journal article (peer-reviewed)abstract
- Coordinated precoding based on interference alignment is a promising technique for improving the throughputs in future wireless multicell networks. In small networks, all base stations can typically jointly coordinate their precoding. In large networks however, base station clustering is necessary due to the otherwise overwhelmingly high channel state information (CSI) acquisition overhead. In this work, we provide a branch and bound algorithm for finding the globally optimal base station clustering. The algorithm is mainly intended for benchmarking existing suboptimal clustering schemes. We propose a general model for the user throughputs, which only depends on the long-term CSI statistics. The model assumes intracluster interference alignment and is able to account for the CSI acquisition overhead. By enumerating a search tree using a best-first search and pruning sub-trees in which the optimal solution provably cannot be, the proposed method converges to the optimal solution. The pruning is done using specifically derived bounds, which exploit some assumed structure in the throughput model. It is empirically shown that the proposed method has an average complexity which is orders of magnitude lower than that of exhaustive search.
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15. |
- Chatterjee, Saikat, et al.
(author)
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Optimum transform domain split VQ
- 2008
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In: IEEE Signal Processing Letters. - 1070-9908 .- 1558-2361. ; 15, s. 285-288
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Journal article (peer-reviewed)
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16. |
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17. |
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18. |
- Chen, Zheng, et al.
(author)
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Consensus-Based Distributed Computation of Link-Based Network Metrics
- 2021
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In: IEEE Signal Processing Letters. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1070-9908 .- 1558-2361. ; 28, s. 249-253
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Journal article (peer-reviewed)abstract
- Average consensus algorithms have wide applications in distributed computing systems where all the nodes agree on the average value of their initial states by only exchanging information with their local neighbors. In this letter, we look into link-based network metrics which are polynomial functions of pair-wise node attributes defined over the links in a network. Different from node-based average consensus, such link-based metrics depend on both the distribution of node attributes and the underlying network topology. We propose a general algorithm using the weighted average consensus protocol for the distributed computation of link-based network metrics and provide the convergence conditions and convergence rate analysis.
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19. |
- Chopra, Ribhu, et al.
(author)
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Analysis of Nonorthogonal Training in Massive MIMO Under Channel Aging With SIC Receivers
- 2019
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In: IEEE Signal Processing Letters. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1070-9908 .- 1558-2361. ; 26:2, s. 282-286
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Journal article (peer-reviewed)abstract
- We analyze the effect of channel aging on the achievable rate of time division duplexed massive multiple input multiple output systems serving a number of users under aging channels, using nonorthogonal multiple access (NOMA) and orthogonal multiple access (OMA). Using the recently proposed shared uplink pilot based channel estimation for NOMA, we derive bounds on the channel estimation error variance for the two schemes. We then derive the achievable spectral efficiencies of the two schemes. Using numerical results, we show that, in slowly varying channels, using NOMA with shared pilots is preferable over OMA, while the reverse is true under fast varying channels.
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20. |
- Dai, Liang, et al.
(author)
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On the exponential convergence of the Kaczmarz algorithm
- 2015
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In: IEEE Signal Processing Letters. - 1070-9908 .- 1558-2361. ; 22:10, s. 1571-1574
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Journal article (peer-reviewed)abstract
- The Kaczmarz algorithm (KA) is a popular method for solving a system of linear equations. In this note we derive a new exponential convergence result for the KA. The key allowing us to establish the new result is to rewrite the KA in such a way that its solution path can be interpreted as the output from a particular dynamical system. The asymptotic stability results of the corresponding dynamical system can then be leveraged to prove exponential convergence of the KA. The new bound is also compared to existing bounds.
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21. |
- Dai, Liang, et al.
(author)
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On the randomized Kaczmarz algorithm
- 2014
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In: IEEE Signal Processing Letters. - 1070-9908 .- 1558-2361. ; 21:3, s. 330-333
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Journal article (peer-reviewed)
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22. |
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23. |
- Daniel, Ondrej, et al.
(author)
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Delay-Accuracy Tradeoff in Opportunistic Time-of-Arrival Localization
- 2018
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In: IEEE Signal Processing Letters. - 1070-9908 .- 1558-2361. ; 25:6, s. 763-767
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Journal article (peer-reviewed)abstract
- While designing a positioning network, the localization performance is traditionally the main concern. However, collection of measurements together with channel access methods require a nonzero time, causing a delay experienced by network nodes. This fact is usually neglected in the positioning-related literature. In terms of the delay-accuracy tradeoff, broadcast schemes have an advantage over unicast, provided nodes can be properly synchronized. In this letter, we analyze the delay-accuracy tradeoff for localization schemes in which the position estimates are obtained based on broadcasted ranging signals. We find that for dense networks, the tradeoff is the same for cooperative and noncooperative networks, and cannot exceed a certain threshold value.
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24. |
- del Aguila Pla, Pol, et al.
(author)
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Convex Quantization Preserves Logconcavity
- 2022
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In: IEEE Signal Processing Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 1070-9908 .- 1558-2361. ; 29, s. 2697-2701
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Journal article (peer-reviewed)abstract
- A logconcave likelihood is as important to proper statistical inference as a convex cost function is important to variational optimization. Quantization is often disregarded when writing likelihood models, ignoring the limitations of the physical detectors used to collect the data. These two facts call for the question: would including quantization in likelihood models preclude logconcavity? are the true data likelihoods logconcave? We provide a general proof that the same simple assumption that leads to logconcave continuous-data likelihoods also leads to logconcave quantized-data likelihoods, provided that convex quantization regions are used.
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