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dc.contributor.authorFeizi-Khankandi, Soheil
dc.contributor.authorGoyal, Vivek K.
dc.contributor.authorMedard, Muriel
dc.date.accessioned2012-08-17T15:17:22Z
dc.date.available2012-08-17T15:17:22Z
dc.date.issued2011-02
dc.date.submitted2010-09
dc.identifier.isbn978-1-4244-8215-3
dc.identifier.urihttp://hdl.handle.net/1721.1/72184
dc.description.abstractIn this paper, we introduce a class of Locally Adaptive Sampling schemes. In this sampling family, time intervals between samples can be computed by using a function of previously taken samples, called a sampling function. Hence, though it is a non-uniform sampling scheme, we do not need to keep sampling times. The aim of LAS is to have the average sampling rate and the reconstruction error satisfy some requirements. We propose four different schemes of LAS. The first two are designed for deterministic signals. First, we derive a Taylor Series Expansion (TSE) sampling function, which only assumes the third derivative of the signal is bounded, but requires no other specific knowledge of the signal. Then, a Discrete Time-Valued (DTV) sampling function is proposed, where the sampling time intervals are chosen from a lattice. Next, we consider stochastic signals. We propose two sampling methods based on linear prediction filters: a Generalized Linear Prediction (GLP) sampling function, and a Linear Prediction sampling function with Side Information (LPSI). In GLP method, we only assume the signal is locally stationary. However, LPSI is specifically designed for a known signal model.en_US
dc.description.sponsorshipUnited States. Air Force Office of Scientific Research (Award Number 016974-002)en_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ALLERTON.2010.5706901en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceIEEEen_US
dc.titleLocally adaptive samplingen_US
dc.typeArticleen_US
dc.identifier.citationFeizi, Soheil, Vivek K Goyal, and Muriel Medard. “Locally Adaptive Sampling.” IEEE, 2010. 152–159. © Copyright 2010 IEEEen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Research Laboratory of Electronicsen_US
dc.contributor.approverGoyal, Vivek K.
dc.contributor.mitauthorFeizi-Khankandi, Soheil
dc.contributor.mitauthorGoyal, Vivek K.
dc.contributor.mitauthorMedard, Muriel
dc.relation.journal2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton)en_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
dspace.orderedauthorsFeizi, Soheil; Goyal, Vivek K; Medard, Murielen
dc.identifier.orcidhttps://orcid.org/0000-0002-0964-0616
dc.identifier.orcidhttps://orcid.org/0000-0003-4059-407X
dspace.mitauthor.errortrue
mit.licensePUBLISHER_POLICYen_US


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