Projection matrix optimisation for compressive sensing based applications in embedded systems

Authors:
Yiran Shen;Wen Hu;Mingrui Yang;Bo Wei;Chun Tung Chou
Affiliations:
University of New South Wales, Sydney, Australia and CSIRO Computational Informatics, Australia;CSIRO Computational Informatics, Australia;CSIRO Computational Informatics, Australia;University of New South Wales, Sydney, Australia and CSIRO Computational Informatics, Australia;University of New South Wales, Sydney, Australia
Venue:
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
Year:
2013

Citing 2
Cited 0

Reed muller sensing matrices and the LASSO

SETA'10 Proceedings of the 6th international conference on Sequences and their applications
Efficient background subtraction for real-time tracking in embedded camera networks

Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems

Quantified Score

Hi-index	0.00

Visualization

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.