Works (7)
Updated: July 5th, 2023 15:41
2018 journal article
Performance Limits With Additive Error Metrics in Noisy Multimeasurement Vector Problems
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 66(20), 5338–5348.
author keywords: Active user detection; error metric; message passing; multi-measurement vector problem
TL;DR:
This work proposes a novel setup for active user detection in multiuser communication and demonstrates the promise of the proposed setup, and proposes a message passing algorithmic framework that achieves the optimal performance of the algorithm.
(via Semantic Scholar)
open access via arxiv.org (repository)
Sources: Web Of Science, NC State University Libraries
Added: October 16, 2018
2016 conference paper
Multi-processor approximate message passing using lossy compression
International conference on acoustics speech and signal processing, 6240–6244.
TL;DR:
A communication-efficient multi-processor compressed sensing framework based on the approximate message passing algorithm is proposed, which performs lossy compression on the data being communicated between processors, resulting in a reduction in communication costs with a minor degradation in recovery quality.
(via Semantic Scholar)
Sources: NC State University Libraries, NC State University Libraries
Added: August 6, 2018
2016 journal article
Performance Limits for Noisy Multimeasurement Vector Problems
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 65(9), 2444–2454.
author keywords: Approximate message passing; multimeasurement vector problem; replica analysis
TL;DR:
Numerical results illustrate that more signal vectors in the jointly sparse signal ensemble lead to a better phase transition, and the MMSE's of complex CS problems with both real and complex measurement matrices are also analyzed.
(via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018
2016 conference paper
Performance trade-offs in multi-processor approximate message passing
2016 ieee international symposium on information theory, 680–684.
TL;DR:
It is proved that the achievable region of this MOP is convex, and conjecture how the combined cost of computation and communication scales with the desired mean squared error.
(via Semantic Scholar)
Sources: NC State University Libraries, NC State University Libraries
Added: August 6, 2018
2015 journal article
Recovery From Linear Measurements With Complexity-Matching Universal Signal Estimation
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 63(6), 1512–1527.
author keywords: Compressed sensing; MAP estimation; Markov chain Monte Carlo; universal algorithms
TL;DR:
This paper considers universal CS recovery, where the statistics of a stationary ergodic signal source are estimated simultaneously with the signal itself, and focuses on a maximum a posteriori (MAP) estimation framework that leverages universal priors to match the complexity of the source.
(via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018
2014 conference paper
Complexity-adaptive universal signal estimation for compressed sensing
2014 IEEE Workshop on Statistical Signal Processing (SSP), 388–391.
TL;DR:
This paper significantly improves on previous work, especially for continuous-valued signals, by offering a four-stage algorithm called Complexity-Adaptive Universal Signal Estimation (CAUSE), where the alphabet size of the estimate adaptively matches the coding complexity of the signal.
(via Semantic Scholar)
Sources: NC State University Libraries, NC State University Libraries
Added: August 6, 2018
2013 conference paper
Performance regions in compressed sensing from noisy measurements
2013 47th Annual Conference on Information Sciences and Systems (CISS).
TL;DR:
It is shown that in several regions, which have different measurement rates and noise levels, the reconstruction error behaves differently, and it may be possible to develop reconstruction algorithms with lower error in that region.
(via Semantic Scholar)
Sources: NC State University Libraries, NC State University Libraries
Added: August 6, 2018
Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.
Certain data included herein are derived from the Web of Science© and InCites© (2024) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.