A hardware platform to test analog-to-information conversion and non-uniform sampling
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
| Published: |
Massachusetts Institute of Technology
2014
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/85482 |
| _version_ | 1811092013201752064 |
|---|---|
| author | Perez, Miguel E., M. Eng. Massachusetts Institute of Technology |
| author2 | Hae-Seung Lee. |
| author_facet | Hae-Seung Lee. Perez, Miguel E., M. Eng. Massachusetts Institute of Technology |
| author_sort | Perez, Miguel E., M. Eng. Massachusetts Institute of Technology |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013. |
| first_indexed | 2024-09-23T15:11:31Z |
| format | Thesis |
| id | mit-1721.1/85482 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:11:31Z |
| publishDate | 2014 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/854822019-04-12T15:04:02Z A hardware platform to test analog-to-information conversion and non-uniform sampling Compressed sensing front end for medical applications Perez, Miguel E., M. Eng. Massachusetts Institute of Technology Hae-Seung Lee. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013. Cataloged from PDF version of thesis. Includes bibliographical references (pages 121-123). The Nyquist-Shannon sampling theorem tells us that in order to fully recover a band-limited signal previously converted to discrete data points, said signal must have been sampled at a frequency greater than twice its bandwidth. This theorem puts a burden on circuits like ADCs, in the sense that the higher the bandwidth of a signal, the faster the ADC must be by a factor of at least 2. This in turn translates into higher power consumption. The problem can be mitigated to a certain extent by the use of zero-crossing based ADCs which consume much less power than conventional op-amp based ones, while maintaining the same performance levels. However, the burden still remains, and with the increase in the use of biologically implantable devices, the need for the utmost power efficiency is essential. This is where the theory of compressed sensing seems to offer an alternate solution. Instead of solving the problem with the brute force approach of increasing power consumption to meet performance, compressed sensing promises to increase the effective figure of merit (FOM) by exploiting certain characteristics in the signal's structure. Compressed sensing tells us, that a signal that meets certain criteria, does not need to be sampled at twice its bandwidth in order to be fully recoverable. This means that an ADC no longer has to operate at the Nyquist rate to guarantee that the signal will not be distorted and as a result its power consumption can be reduced considerably. This allows for more robust and energy efficient data acquisition circuits. This means more efficient and longer lasting implantable monitoring devices along with the ability to perform on-site data processing. by Miguel E. Perez. M. Eng. 2014-03-06T15:45:12Z 2014-03-06T15:45:12Z 2012 2013 Thesis http://hdl.handle.net/1721.1/85482 870996641 eng 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 123 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Perez, Miguel E., M. Eng. Massachusetts Institute of Technology A hardware platform to test analog-to-information conversion and non-uniform sampling |
| title | A hardware platform to test analog-to-information conversion and non-uniform sampling |
| title_full | A hardware platform to test analog-to-information conversion and non-uniform sampling |
| title_fullStr | A hardware platform to test analog-to-information conversion and non-uniform sampling |
| title_full_unstemmed | A hardware platform to test analog-to-information conversion and non-uniform sampling |
| title_short | A hardware platform to test analog-to-information conversion and non-uniform sampling |
| title_sort | hardware platform to test analog to information conversion and non uniform sampling |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/85482 |
| work_keys_str_mv | AT perezmiguelemengmassachusettsinstituteoftechnology ahardwareplatformtotestanalogtoinformationconversionandnonuniformsampling AT perezmiguelemengmassachusettsinstituteoftechnology compressedsensingfrontendformedicalapplications AT perezmiguelemengmassachusettsinstituteoftechnology hardwareplatformtotestanalogtoinformationconversionandnonuniformsampling |