MIT Libraries homeMIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Theses - Dept. of Electrical Engineering and Computer Sciences
  • Electrical Engineering and Computer Sciences - Master's degree
  • View Item
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Theses - Dept. of Electrical Engineering and Computer Sciences
  • Electrical Engineering and Computer Sciences - Master's degree
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Building compressed sensing systems : sensors and analog-to-information converters

Author(s)
Salehi-Abari, Omid
Thumbnail
DownloadFull printable version (12.42Mb)
Alternative title
Sensors and analog-to-information converters
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Vladimir Stojanović.
Terms of use
M.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. http://dspace.mit.edu/handle/1721.1/7582
Metadata
Show full item record
Abstract
Compressed sensing (CS) is a promising method for recovering sparse signals from fewer measurements than ordinarily used in the Shannon's sampling theorem [14]. Introducing the CS theory has sparked interest in designing new hardware architectures which can be potential substitutions for traditional architectures in communication systems. CS-based wireless sensors and analog-to-information converters (AIC) are two examples of CS-based systems. It has been claimed that such systems can potentially provide higher performance and lower power consumption compared to traditional systems. However, since there is no end-to-end hardware implementation of these systems, it is difficult to make a fair hardware-to-hardware comparison with other implemented systems. This project aims to fill this gap by examining the energy-performance design space for CS in the context of both practical wireless sensors and AICs. One of the limitations of CS-based systems is that they employ iterative algorithms to recover the signal. Since these algorithms are slow, the hardware solution has become crucial for higher performance and speed. In this work, we also implement a suitable CS reconstruction algorithm in hardware.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
 
Cataloged from PDF version of thesis.
 
Includes bibliographical references (p. 93-96).
 
Date issued
2012
URI
http://hdl.handle.net/1721.1/78472
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

Collections
  • Electrical Engineering and Computer Sciences - Master's degree
  • Electrical Engineering and Computer Sciences - Master's degree

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries homeMIT Libraries logo

Find us on

Twitter Facebook Instagram YouTube RSS

MIT Libraries navigation

SearchHours & locationsBorrow & requestResearch supportAbout us
PrivacyPermissionsAccessibility
MIT
Massachusetts Institute of Technology
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.