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dc.contributor.advisorJoel Dawson.en_US
dc.contributor.authorKhanna, Taniaen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2013-04-12T19:24:51Z
dc.date.available2013-04-12T19:24:51Z
dc.date.copyright2012en_US
dc.date.issued2012en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/78448
dc.descriptionThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 97-100).en_US
dc.description.abstractIn recent years, trends in the medical industry have created a growing demand for implantable medical devices. In particular, the need to provide doctors a means to continuously monitor biometrics over long time scales with increased precision is paramount to efficient healthcare. To make medical implants more attractive, there is a need to reduce their size and power consumption. Small medical implants would allow for less invasive procedures, greater comfort for patients, and increased patient compliance. Reductions in power consumption translate to longer battery life. The two primary limitations to the size of small medical implants are the batteries that provide energy to circuit and sensor components and the antennas that enable wireless communication to terminals outside of the body. The theory is applied in the context of the long term monitoring of Parkinson's tremors. This work investigates how to reduce the amount of data needing to acquire a signal by applying compressive sampling thereby alleviating the demand on the energy source. A low energy SAR ADC is designed using adiabatic charging to further reduce energy usage. This application is ideal for adiabatic techniques because of the low frequency of operation and the ease with which we can reclaim energy from discharging the capacitors. Keywords: SAR ADC, adiabatic, compressive sampling, biometric, implantsen_US
dc.description.statementofresponsibilityby Tania Khanna.en_US
dc.format.extent100 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleLow power data acquisition for microImplant biometric monitoring of tremorsen_US
dc.typeThesisen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc832432383en_US


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