Volume 14, Issue 4 (3-2018)                   JSDP 2018, 14(4): 19-30 | Back to browse issues page

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sadeghi H, Akhavan Bitaghsir A. Signal Detection Based on GPU-Assisted Parallel Processing for Infrastructure-based Acoustical Sensor Networks. JSDP. 2018; 14 (4) :19-30
URL: http://jsdp.rcisp.ac.ir/article-1-424-en.html
Abstract:   (1031 Views)

Nowadays, several infrastructure-based low-frequency acoustical sensor networks are employed in different applications to monitor the activity of diverse natural and man-made phenomena, such as avalanches, earthquakes, volcanic eruptions, severe storms, super-sonic aircraft flights, etc. Two signal detection methods are usually implemented in these networks for the purpose of event occurrence identification, which are the progressive multi-channel correlator (PMCC) and the so-called Fisher detector. But, the Fisher method is more important and applicable in low signal-to-noise (SNR) ratio conditions, which is of a special interest in acoustical monitoring networks. Unfortunately, an important disadvantage of this algorithm is its relative high detection-time; which limits its application for real-time detection scenarios. This disadvantage is fundamentally due to a beam forming process in Fisher algorithm, which requires doing complete search in a slowness-network, constructed from possible incoming wave front directions and speeds. To address this issue, we propose a method for implementation of this beam forming on a graphics processing unit (GPU), in order to realize a fast-computing and/or near real-time signal processing technique. In addition, we also propose a parallel-processing algorithm for further enhancement of the performance of this GPU-based Fisher detector. Simulation results confirm the performance improvement of Fisher detector, in terms of required processing time for acoustical signal detection applications.

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Type of Study: Research | Subject: Paper
Received: 2015/09/21 | Accepted: 2017/12/2 | Published: 2018/03/13 | ePublished: 2018/03/13

1. [1] V. L. Zimmer, N. Sitar, "Detection and location of rock falls using seismic and infrasound sensors," Engineering Geology, vol. 193, pp. 49-60, Apr. 2015. [DOI:10.1016/j.enggeo.2015.04.007]
2. [2] R.D. Costley, W. G. Frazier, et al., "Frequency-wavenumber processing for infrasound distributed arrays," Journal of Acoustical Society of America, vol. 134, no. 4, pp.EL307-EL311, 2013. [DOI:10.1121/1.4818940] [PMID]
3. [3] S. Havens, H.-P. Marshall, et al., Real-Time Avalanche Detection for High Risk Areas, Research Report, Transportation Department, Idaho University, Dec. 2014, available online at: https://itd.idaho.gov/highways/research/archived/reports/RP219Final12312014.pdf.
4. [4] W. W. Arrasmith, E. R. Coots, J. V. Olson, and E. A. Skowbo, "Analyzing infrasound and seismic signals emanating from a waterborne system using canonical modeling and analysis methods", International Journal of Modeling and Optimization, vol. 4, no. 3, pp. 176-181, Jun. 2014. [DOI:10.7763/IJMO.2014.V4.369]
5. [5] M. Charbit, I Che, and A Le Pichon, "Asymptotic distribution of GLRT versus Fisher distribution for infrasonic detection," Geophysical Research Abstracts, vol. 15, EGU2013-3690, 2013.
6. [6] J. Park, B. W. Stump, C. Hayward, Detection of regional infrasound signals using array data: testing, tuning and physical interpretation, Journal of Acoustical Society of America, vol. 140, no. 1, pp. 240-259, Jul. 2016. [DOI:10.1121/1.4954759] [PMID]
7. [7] S. J. Arrowsmith, R. Whitaker, S. R. Taylor, "Regional monitoring of infrasound events using multiple arrays: application to Utah and Washington State", Geophysical Journal International, pp. 291–300, Jul. 2008. [DOI:10.1111/j.1365-246X.2008.03912.x]
8. [8] S. J. Arrowsmith, R. Whitaker, C. Katz, C. Hayward, "The F-detector revisited: An improved strategy for signal detection at seismic and infrasound arrays", Seismological Society of America, vol. 99, no. 1, pp. 449–453, 2009. [DOI:10.1785/0120080180]
9. [9] B. Melton, and L. Baily, "Multiple signal correlators," Geophysics, XXII (3), pp. 565-588, 1957. [DOI:10.1190/1.1438390]
10. [10] R. R. Blandford, "An automatic event detector at the Tonto Forest seismic observatory", Geophysics, vol. 39, pp. 633, 1974. [DOI:10.1190/1.1440453]
11. [11] S. Angelis et al., "Detecting hidden volcanic explosions from Mt. Cleveland Volcano, Alaska with infrasound and ground-coupled airwaves," Geophysical Research Letters, vol. 39, 2012. [PMID]
12. [12] L. G. Evers and H. W. Haak, "Tracing a meteoric trajectory with infrasound", Geophysical Research Letters, vol. 30, no. 24, Dec. 2003. [DOI:10.1029/2003GL017947]
13. [13] Y. Cansi, "An automatic seismic event processing for detection and location: the PMCC method", Geophysical Research Letters, vol. 22, pp. 1021-1024,1995. [DOI:10.1029/95GL00468]
14. [14] J. Nickolls, W.J. Dally, "The GPU computing era," IEEE Micro Magazine, vol. 30, pp. 56-69, 2010. [DOI:10.1109/MM.2010.41]
15. [15] H. Chen, S. Saïghi, L. Buhry, and S. Renaud, "Real-time simulation of biologically realistic stochastic neurons in VLSI," IEEE Transactions on Neural Networks, vol. 21, no. 9, pp. 1511–1517, Sep. 2010. [DOI:10.1109/TNN.2010.2049028] [PMID]
16. [16] S. U. Gjerald, R. Brekken, T. Hergum, J. D'hoog "Real-time ultrasound simulation using the GPU," IEEE Transactions on Ultrasonic Ferroelectrics and Frequency Control, vol. 59, pp. 885-892, 2012. [DOI:10.1109/TUFFC.2012.2273] [PMID]
17. [17] Y. Dai, et al., "Real-time visualized freehand 3D ultrasound reconstruction based on GPU," IEEE Transactions on Information Technology in Biomedicine, vol. 14, pp. 1338-1345, 2010. [DOI:10.1109/TITB.2010.2072993] [PMID]
18. [18] C. Richter, S. Schops, and M. Clemens, "GPU acceleration of finite difference schemes used in coupled electromagnetic/ thermal field simulations", IEEE Transactions on Magnetics, vol. 49, no. 5, pp. 1649-1652, May 2013. [DOI:10.1109/TMAG.2013.2238662]
19. [19] W. Rodrigues, et al., "Accelerating atomistic calculation of quantum energy eigenstates on graphic cards", Computer physics Communications, pp. 2510-2518, May 2014. [DOI:10.1016/j.cpc.2014.05.028]
20. [20] A. Artu, "Parallel wavelet-based clustering algorithm on GPUs using CUDA", Procedia Computer Science, vol. 3, pp. 396-400, 2011. [DOI:10.1016/j.procs.2010.12.066]
21. [21] L. Mussi, F. Daolio, S. Cagnoni, "Evaluation of parallel particle swarm optimization algorithms within the CUDA™ architecture", Information Sciences, pp. 4642-4657, 2011. [DOI:10.1016/j.ins.2010.08.045]
22. [22] D. B. Kirk, W.H. Wen-Mei, Programming Massively Parallel Processors: A Hands-on Approach, 2nd Edition, Morgan Kaufmann, 2011.

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