國立虎尾科技大學 |

A Compressed Sensing Based Analog-to-Information Converter : = Design, Implementation and Practical Experiments.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	A Compressed Sensing Based Analog-to-Information Converter :/
Reminder of title:	Design, Implementation and Practical Experiments.
Author:	D'Angelo, Robert.
Description:	1 online resource (96 pages)
Notes:	Source: Masters Abstracts International, Volume: 53-02.
Contained By:	Masters Abstracts International53-02(E).
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9781303986253

A Compressed Sensing Based Analog-to-Information Converter : = Design, Implementation and Practical Experiments.
D'Angelo, Robert.

A Compressed Sensing Based Analog-to-Information Converter :Design, Implementation and Practical Experiments. - 1 online resource (96 pages)

Source: Masters Abstracts International, Volume: 53-02.

Thesis (M.S.)

Includes bibliographical references

Wireless sensor systems are limited by the energy consumption of the sensor nodes, which are themselves limited by the transmitter. Transmitter energy consumption can be reduced by reducing the amount of transmitted data, which is usually determined by the maximum bandwidth of the signal of interest according to Nyquist theory. Compressed sensing is a mathematical framework for sampling signals at the rate of information rather than at the Nyquist rate. If the signals of interest are su ciently sparse in some domain, compressed sensing can be used to reduce the number of samples required to reconstruct the signal on the receiver side to below that dictated by Nyquist theory. Thus, compressed sensing can reduce power consumption by compressing data at the source. This thesis presents the analysis and experimental results of a compressed sensing based analog-to-information converter (AIC) in 90nm CMOS technology. This AIC utilizes a random sampling analog-to-digital converter (ADC) to acquire the data, and l1-minimization to reconstruct the data. It was found that this algorithm performs poorly as sparsity increases, and a modied algorithm that exploits group sparsity was used to demonstrate acquisition of wide band signals as well as various biomedical signals. Applicability of these methods are analyzed in depth in particular for wireless acquisition of multi-channel EEG.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781303986253Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

A Compressed Sensing Based Analog-to-Information Converter : = Design, Implementation and Practical Experiments.
LDR:02700ntm a2200361Ki 4500 001 911979
005 20180605073452.5
006 m o u
007 cr mn||||a|a||
008 190606s2014 xx obm 000 0 eng d
020 $a 9781303986253
035 $a (MiAaPQ)AAI1558530
035 $a (MiAaPQ)tuftsase:11197
035 $a AAI1558530
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a D'Angelo, Robert. $3 1184137
245 1 2 $a A Compressed Sensing Based Analog-to-Information Converter : $b Design, Implementation and Practical Experiments.
264 0 $c 2014
300 $a 1 online resource (96 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 53-02.
500 $a Adviser: Sameer Sonkusale.
502 $a Thesis (M.S.) $c Tufts University $d 2014.
504 $a Includes bibliographical references
520 $a Wireless sensor systems are limited by the energy consumption of the sensor nodes, which are themselves limited by the transmitter. Transmitter energy consumption can be reduced by reducing the amount of transmitted data, which is usually determined by the maximum bandwidth of the signal of interest according to Nyquist theory. Compressed sensing is a mathematical framework for sampling signals at the rate of information rather than at the Nyquist rate. If the signals of interest are su ciently sparse in some domain, compressed sensing can be used to reduce the number of samples required to reconstruct the signal on the receiver side to below that dictated by Nyquist theory. Thus, compressed sensing can reduce power consumption by compressing data at the source. This thesis presents the analysis and experimental results of a compressed sensing based analog-to-information converter (AIC) in 90nm CMOS technology. This AIC utilizes a random sampling analog-to-digital converter (ADC) to acquire the data, and l1-minimization to reconstruct the data. It was found that this algorithm performs poorly as sparsity increases, and a modied algorithm that exploits group sparsity was used to demonstrate acquisition of wide band signals as well as various biomedical signals. Applicability of these methods are analyzed in depth in particular for wireless acquisition of multi-channel EEG.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
650 4 $a Information technology. $3 559429
650 4 $a Biomedical engineering. $3 588770
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
690 $a 0489
690 $a 0541
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Tufts University. $b Electrical Engineering. $3 1184138
773 0 $t Masters Abstracts International $g 53-02(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1558530 $z click for full text (PQDT)