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Minimal Non-Uniform Sampling For Multi-Dimensional Period Identification

Tenneti, Srikanth V. and Vaidyanathan, P. P. (2018) Minimal Non-Uniform Sampling For Multi-Dimensional Period Identification. In: 52nd Asilomar Conference on Signals, Systems, and Computers. IEEE , Piscataway, NJ, pp. 2104-2108. ISBN 978-1-5386-9218-9.

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This paper addresses a fundamental question in the context of multi-dimensional periodicity. Namely, to distinguish between two N-dimensional periodic patterns, what is the least number of (possibly non-contiguous) samples that need to be observed? This question was only recently addressed for one-dimensional signals. This paper generalizes those results to N-dimensional signals. It will be shown that the optimal sampling pattern takes the form of sparse and uniformly separated bunches. Apart from new theoretical insights, this paper’s results may provide the foundation for fast N-dimensional period recognition algorithms that use minimal number of samples.

Item Type:Book Section
Related URLs:
URLURL TypeDescription
Tenneti, Srikanth V.0000-0002-5415-3681
Vaidyanathan, P. P.0000-0003-3003-7042
Additional Information:© 2018 IEEE. This work was supported in parts by the ONR grants N00014-17-1-2732 and N00014-18-1-2390, the NSF grant CCF-1712633, and an Amazon post doctoral fellowship facilitated through the Information Science and Technology (IST) initiative at Caltech.
Funding AgencyGrant Number
Office of Naval Research (ONR)N00014-17-1-2732
Office of Naval Research (ONR)N00014-18-1-2390
Subject Keywords:Multidimensional periodicity, period estimation, sparse sampling, non-uniform sampling
Record Number:CaltechAUTHORS:20190301-155512046
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Official Citation:S. V. Tenneti and P. P. Vaidyanathan, "Minimal Non-Uniform Sampling For Multi-Dimensional Period Identification," 2018 52nd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2018, pp. 2104-2108. doi: 10.1109/ACSSC.2018.8645347
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:93406
Deposited By: Tony Diaz
Deposited On:02 Mar 2019 00:08
Last Modified:16 Nov 2021 16:57

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