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dc.contributor.authorPendar, Hodjaten
dc.contributor.authorSocha, John J.en
dc.contributor.authorChung, Julianneen
dc.date.accessioned2017-03-01T17:41:30Zen
dc.date.available2017-03-01T17:41:30Zen
dc.date.issued2016-08-15en
dc.identifier.issn2046-6390en
dc.identifier.urihttp://hdl.handle.net/10919/75207en
dc.description.abstractIn many physiological studies, variables of interest are not directly accessible, requiring that they be estimated indirectly from noisy measured signals. Here, we introduce two empirical methods to estimate the true physiological signals from indirectly measured, noisy data. The first method is an extension of Tikhonov regularization to large-scale problems, using a sequential update approach. In the second method, we improve the conditioning of the problem by assuming that the input is uniform over a known time interval, and then use a least-squares method to estimate the input. These methods were validated computationally and experimentally by applying them to flow-through respirometry data. Specifically, we infused CO2 in a flow-through respirometry chamber in a known pattern, and used the methods to recover the known input from the recorded data. The results from these experiments indicate that these methods are capable of subsecond accuracy. We also applied the methods on respiratory data from a grasshopper to investigate the exact timing of abdominal pumping, spiracular opening, and CO2 emission. The methods can be used more generally for input estimation of any linear system.en
dc.format.extent1163 - 1174 (12) page(s)en
dc.format.mimetypeapplication/pdfen
dc.languageEnglishen
dc.publisherCompany Of Biologists Ltden
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000382304400018&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsCreative Commons Attribution 3.0 Unporteden
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectBiologyen
dc.subjectLife Sciences & Biomedicine - Other Topicsen
dc.subjectInput estimationen
dc.subjectDeconvolutionen
dc.subjectIll-conditioned inverse problemsen
dc.subjectFlow-through respirometryen
dc.subjectDISCONTINUOUS GAS-EXCHANGEen
dc.subjectTRACHEAL SYSTEMen
dc.subjectDESERT LOCUSTen
dc.subjectDISCREPANCY PRINCIPLEen
dc.subjectAYTHYA-FULIGULAen
dc.subjectAIR-FLOWen
dc.subjectINSECTSen
dc.subjectDECONVOLUTIONen
dc.subjectRESPIRATIONen
dc.subjectVENTILATIONen
dc.titleRecovering signals in physiological systems with large datasetsen
dc.typeArticle - Refereeden
dc.description.versionPublished (Publication status)en
dc.rights.holderThe Author(s)en
dc.contributor.departmentBiomedical Engineering and Mechanicsen
dc.title.serialBIOLOGY OPENen
dc.identifier.doihttps://doi.org/10.1242/bio.019133en
dc.identifier.volume5en
dc.identifier.issue8en
dc.identifier.orcidSocha, JJ [0000-0002-4465-1097]en
dc.type.dcmitypeTexten
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Biomedical Engineering and Mechanicsen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.organisational-group/Virginia Tech/Scienceen
pubs.organisational-group/Virginia Tech/Science/COS T&R Facultyen
pubs.organisational-group/Virginia Tech/Science/Mathematicsen


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Creative Commons Attribution 3.0 Unported
License: Creative Commons Attribution 3.0 Unported