Title:
Energy-efficient digital design of reliable, low-throughput wireless biomedical systems

Simple item page
dc.contributor.advisor Mukhopadhyay, Saibal
dc.contributor.author Tolbert, Jeremy Reynard en_US
dc.contributor.committeeMember Ghovanloo, Maysam
dc.contributor.committeeMember Kim, Hyesoon
dc.contributor.committeeMember Lee, Hsien-Hsin
dc.contributor.committeeMember Lim, Sung Kyu
dc.contributor.department Electrical and Computer Engineering en_US
dc.date.accessioned 2013-01-17T21:04:46Z
dc.date.available 2013-01-17T21:04:46Z
dc.date.issued 2012-08-24 en_US
dc.description.abstract The main objective of this research is to improve the energy efficiency of low throughput wireless biomedical systems by employing digital design techniques. The power consumed in conventional wireless EEG (biomedical) systems is dominated by digital microcontroller and the radio frequency (RF) transceiver. To reduce the power associated with the digital processor, data compression can reduce the volume of data transmitted. An adaptive data compression algorithm has been proposed to ensure accurate representations of critical epileptic signals, while also preserving the overall power. Further advances in power reduction are also presented by designing a custom baseband processor for data compression. A functional system has been hardware verified and ASIC optimized to reduce the power by over 9X compared to existing methods. The optimized processor can operate at 32MHz with a near threshold supply of 0.5V in a conventional 45nm technology. While attempting to reach high frequencies in the near threshold regime, the probability of timing violations can reduce the robustness of the system. To further optimize the implementation, a low voltage clock tree design has been investigated to improve the reliability of the digital processor. By implementing the proposed clock tree design methodology, the digital processor can improve its robustness (by reducing the probability of timing violations) while reducing the overall power by more than 5 percent. Future work suggests examining new architectures for low-throughput processing and investigating the proposed systems' potential for a multi-channel EEG implementation. en_US
dc.description.degree PhD en_US
dc.identifier.uri http://hdl.handle.net/1853/45787
dc.publisher Georgia Institute of Technology en_US
dc.subject EEG en_US
dc.subject Digital design en_US
dc.subject Subthreshold en_US
dc.subject Low power en_US
dc.subject Data compression en_US
dc.subject Energy-efficiency en_US
dc.subject Wireless systems en_US
dc.subject.lcsh Medical electronics
dc.subject.lcsh Energy consumption
dc.subject.lcsh Data compression (Computer science)
dc.subject.lcsh Electroencephalography
dc.title Energy-efficient digital design of reliable, low-throughput wireless biomedical systems en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Mukhopadhyay, Saibal
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication 62df0580-589a-4599-98af-88783123945a
relation.isOrgUnitOfPublication 5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
tolbert_jeremy_r_201212_phd.pdf
Size:
4.21 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Theses and Dissertations