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Projection matrix optimisation for compressive sensing based applications in embedded systems

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Projection matrix optimisation for compressive sensing based applications in embedded systems. / Shen, Yiran; Hu, Wen; Yang, Mingrui et al.
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems. New York: Association for Computing Machinery (ACM), 2013. p. 2 22.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Shen, Y, Hu, W, Yang, M, Wei, B & Chou, CT 2013, Projection matrix optimisation for compressive sensing based applications in embedded systems. in Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems., 22, Association for Computing Machinery (ACM), New York, pp. 2, SenSys '13: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, Rome, Italy, 11/11/13. https://doi.org/10.1145/2517351.2517374

APA

Shen, Y., Hu, W., Yang, M., Wei, B., & Chou, C. T. (2013). Projection matrix optimisation for compressive sensing based applications in embedded systems. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (pp. 2). Article 22 Association for Computing Machinery (ACM). https://doi.org/10.1145/2517351.2517374

Vancouver

Shen Y, Hu W, Yang M, Wei B, Chou CT. Projection matrix optimisation for compressive sensing based applications in embedded systems. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems. New York: Association for Computing Machinery (ACM). 2013. p. 2. 22 doi: 10.1145/2517351.2517374

Author

Shen, Yiran ; Hu, Wen ; Yang, Mingrui et al. / Projection matrix optimisation for compressive sensing based applications in embedded systems. Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems. New York : Association for Computing Machinery (ACM), 2013. pp. 2

Bibtex

@inproceedings{ab1e19578f294e5f8d2c2abc0acc59dc,
title = "Projection matrix optimisation for compressive sensing based applications in embedded systems",
abstract = "The information-preserving sampling properties of compressive sensing have found a number of successful applications, such as sensor scheduling, localisation and tracking to deal with the resource constraints of the embedded systems. In this paper, we investigate an approach to improve the performance of compressive sensing applications through a novel strategy for optimising the projection matrix. We formulate the projection matrix optimisation problem and apply greedy algorithm to solve the optimisation problem efficiently. We evaluate the proposed approach by an emerging background subtraction method designed specifically for the embedded systems and show the proposed approach outperforms existing approaches significantly with little overhead.",
author = "Yiran Shen and Wen Hu and Mingrui Yang and Bo Wei and Chou, {Chun Tung}",
year = "2013",
month = nov,
day = "11",
doi = "10.1145/2517351.2517374",
language = "Undefined/Unknown",
isbn = "9781450320276",
pages = "2",
booktitle = "Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems",
publisher = "Association for Computing Machinery (ACM)",
note = "SenSys '13: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems ; Conference date: 11-11-2013 Through 13-11-2013",
url = "http://sensys.acm.org/2013/",

}

RIS

TY - GEN

T1 - Projection matrix optimisation for compressive sensing based applications in embedded systems

AU - Shen, Yiran

AU - Hu, Wen

AU - Yang, Mingrui

AU - Wei, Bo

AU - Chou, Chun Tung

PY - 2013/11/11

Y1 - 2013/11/11

N2 - The information-preserving sampling properties of compressive sensing have found a number of successful applications, such as sensor scheduling, localisation and tracking to deal with the resource constraints of the embedded systems. In this paper, we investigate an approach to improve the performance of compressive sensing applications through a novel strategy for optimising the projection matrix. We formulate the projection matrix optimisation problem and apply greedy algorithm to solve the optimisation problem efficiently. We evaluate the proposed approach by an emerging background subtraction method designed specifically for the embedded systems and show the proposed approach outperforms existing approaches significantly with little overhead.

AB - The information-preserving sampling properties of compressive sensing have found a number of successful applications, such as sensor scheduling, localisation and tracking to deal with the resource constraints of the embedded systems. In this paper, we investigate an approach to improve the performance of compressive sensing applications through a novel strategy for optimising the projection matrix. We formulate the projection matrix optimisation problem and apply greedy algorithm to solve the optimisation problem efficiently. We evaluate the proposed approach by an emerging background subtraction method designed specifically for the embedded systems and show the proposed approach outperforms existing approaches significantly with little overhead.

U2 - 10.1145/2517351.2517374

DO - 10.1145/2517351.2517374

M3 - Conference contribution/Paper

SN - 9781450320276

SP - 2

BT - Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems

PB - Association for Computing Machinery (ACM)

CY - New York

T2 - SenSys '13: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems

Y2 - 11 November 2013 through 13 November 2013

ER -