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Acoustic laptops as a research enabler
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Workshop on Embedded Networked Sensors archive
Proceedings of the 4th workshop on Embedded networked sensors table of contents
Cork, Ireland
SESSION: Sensing table of contents
Pages: 63 - 67  
Year of Publication: 2007
ISBN:978-1-59593-694-3
Authors
Michael Allen  Coventry University, UK
Lewis Girod  Mass. Inst. of Technology, Cambridge, MA
Deborah Estrin  UC, Los Angeles, CA
Sponsors
SIGBED: ACM Special Interest Group on Embedded Systems
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
Publisher
ACM  New York, NY, USA
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ABSTRACT

The Acoustic ENSBox [1] is an embedded platform which enables practical distributed acoustic sensing by providing integrated hardware and software support in a single platform. It provides a highly accurate acoustic self-calibration system which eliminates the need for manual surveying of node reference positions. In this paper, we present an Acoustic Laptop, that enables distributed acoustic research through the use of a less resource-constrained and more readily available platform. It runs exactly the same software and uses the same sensor hardware as the Acoustic ENSBox, but replaces the embedded computing platform with a standard laptop.

We describe the advantages of using the Acoustic Laptop as a rich prototyping platform for acoustic source localization and mote-class node localization applications. The Acoustic Laptop is not intended to replace the Acoustic ENSBox, but to compliment it, by providing an easily replicated prototyping platform that is extensible and resource-rich, and suitable for attended, pilot deployments. We show that the benefits gained by a laptop's extra resources enable intensive signal processing in real-time, without optimization. This enables on-line, interactive experimentation with algorithms such as Approximated Maximum Likelihood. Applications developed using the Acoustic Laptop can subsequently be run on the more deployable Acoustic ENSBox platform, unmodified apart from performance optimizations.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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J. Chen, L. Yip, J. Elson, H. Wang, D. Maniezzo, R. Hudson, K. Yao, and D. Estrin, "Coherent acoustic array processing and localization on wireless sensor networks," Proceedings of the IEEE, vol. 91, no. 8, pp. 1154--1162, 2003.
 
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L. Yip, K. Comanor, J. C. Chen, R. E. Hudson, K. Yao, and L. Vandenberghe, "Array processing for target doa, localization, and classification based on aml and svm algorithms in sensor networks.," in IPSN, pp. 269--284, 2003.
 
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H. Wang, J. Chen, A. Ali, S. Asgari, R. Hudson, K. Yao, D. Estrin, and C. Taylor, "Acoustic sensor networks for woodpecker localization," in SPIE Conference on Advanced Signal Processing Algorithms, Architectures and Implementation, 2005.
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J. Sallai, G. Balogh, M. Maróti, Á. Lédeczi, and B. Kusy, "Acoustic ranging in resource-constrained sensor networks.," in International Conference on Wireless Networks, pp. 467--, 2004.
 
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Collaborative Colleagues:
Michael Allen: colleagues
Lewis Girod: colleagues
Deborah Estrin: colleagues