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Compressive sensing for wireless sensor networks

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Compressive sensing for wireless sensor networks. / Mahmudimanesh, M.; Khelil, A.; Suri, Neeraj.
Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning. CRC Press, 2012. p. 379-396.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Harvard

Mahmudimanesh, M, Khelil, A & Suri, N 2012, Compressive sensing for wireless sensor networks. in Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning. CRC Press, pp. 379-396. https://doi.org/10.1201/b14300

APA

Mahmudimanesh, M., Khelil, A., & Suri, N. (2012). Compressive sensing for wireless sensor networks. In Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning (pp. 379-396). CRC Press. https://doi.org/10.1201/b14300

Vancouver

Mahmudimanesh M, Khelil A, Suri N. Compressive sensing for wireless sensor networks. In Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning. CRC Press. 2012. p. 379-396 doi: 10.1201/b14300

Author

Mahmudimanesh, M. ; Khelil, A. ; Suri, Neeraj. / Compressive sensing for wireless sensor networks. Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning. CRC Press, 2012. pp. 379-396

Bibtex

@inbook{3fcbcdafc884402aaea66f1fbb74f2bc,
title = "Compressive sensing for wireless sensor networks",
abstract = "Compressive sensing (CS) is a new paradigm in signal processing and sampling theory. In this chapter, we introduce the mathematical foundations of this novel theory and explore its applications in wireless sensor networks (WSNs). CS is an important achievement in sampling theory and signal processing. It is increasingly being implied in many areas like multimedia, machine learning, medical imaging, etc. We focus on the aspects of CS theory that has direct applications in WSNs. We also investigate the most well-known implementations of CS theory for data collection in WSNs. {\textcopyright} 2013 by Taylor & Francis Group, LLC.",
author = "M. Mahmudimanesh and A. Khelil and Neeraj Suri",
note = "Export Date: 7 October 2019",
year = "2012",
doi = "10.1201/b14300",
language = "English",
isbn = "9780429066962",
pages = "379--396",
booktitle = "Intelligent Sensor Networks",
publisher = "CRC Press",

}

RIS

TY - CHAP

T1 - Compressive sensing for wireless sensor networks

AU - Mahmudimanesh, M.

AU - Khelil, A.

AU - Suri, Neeraj

N1 - Export Date: 7 October 2019

PY - 2012

Y1 - 2012

N2 - Compressive sensing (CS) is a new paradigm in signal processing and sampling theory. In this chapter, we introduce the mathematical foundations of this novel theory and explore its applications in wireless sensor networks (WSNs). CS is an important achievement in sampling theory and signal processing. It is increasingly being implied in many areas like multimedia, machine learning, medical imaging, etc. We focus on the aspects of CS theory that has direct applications in WSNs. We also investigate the most well-known implementations of CS theory for data collection in WSNs. © 2013 by Taylor & Francis Group, LLC.

AB - Compressive sensing (CS) is a new paradigm in signal processing and sampling theory. In this chapter, we introduce the mathematical foundations of this novel theory and explore its applications in wireless sensor networks (WSNs). CS is an important achievement in sampling theory and signal processing. It is increasingly being implied in many areas like multimedia, machine learning, medical imaging, etc. We focus on the aspects of CS theory that has direct applications in WSNs. We also investigate the most well-known implementations of CS theory for data collection in WSNs. © 2013 by Taylor & Francis Group, LLC.

U2 - 10.1201/b14300

DO - 10.1201/b14300

M3 - Chapter

SN - 9780429066962

SP - 379

EP - 396

BT - Intelligent Sensor Networks

PB - CRC Press

ER -