EMD performance comparison: single vs double floating points

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Laszuk, D., Cadenas, O. and Nasuto, S. J. (2016) EMD performance comparison: single vs double floating points. International journal of signal processing systems, 4 (4). pp. 349-353. ISSN 2315-4535

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Abstract/Summary

Empirical mode decomposition (EMD) is a data-driven method used to decompose data into oscillatory components. This paper examines to what extent the defined algorithm for EMD might be susceptible to data format. Two key issues with EMD are its stability and computational speed. This paper shows that for a given signal there is no significant difference between results obtained with single (binary32) and double (binary64) floating points precision. This implies that there is no benefit in increasing floating point precision when performing EMD on devices optimised for single floating point format, such as graphical processing units (GPUs).

Item Type:	Article
Refereed:	Yes
Divisions:	Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:	45597
Uncontrolled Keywords:	Terms—Empirical Mode Decomposition, Floating Point Arithmetic, Intrinsic Mode Function, Performance Test, Signal Decomposition1

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References

[1] NVIDIA, NVIDIA CUDA Toolkit Release Notes, 2015 (accessed April 1, 2015). [Online]. Available: http://docs.nvidia.com/cuda. [2] N. E. Huang et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903– 995, Mar. 1998. [3] N. E. Huang and Z. Wu, “A review on Hilbert-Huang transform: Method and its applications to geophysical studies,” Reviews of Geophysics, vol. 46, no. 2, p. RG2006, Jun. 2008. [4] G. Rilling and P. Flandrin, “On the Influence of Sampling on the Empirical Mode Decomposition”, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings. [5] R. Rato, M. Ortigueira, and A. Batista, “On the HHT, its problems, and some solutions,” Mechanical Systems and Signal Processing, vol. 22, no. 6, pp. 1374–1394, Aug. 2008. [6] IEEE, “IEEE Standard for Floating-Point Arithmetic,” pp. 1–70, 2008. [7] E. Jones, T. Oliphant, P. Peterson et al., “SciPy: Open source scientific tools for Python,” 2001–. [Online]. Available: http://www.scipy.org/ [8] D. Laszuk, “Python implemention of Empirical Mode Decomposition algorithm,” 2014–. [Online]. Available: http://www.laszukdawid.com/codes [9] K. Coughlin and K. Tung, “11-Year solar cycle in the stratosphere extracted by the empirical mode decomposition method,” Advances in Space Research, vol. 34, no. 2, pp. 323–329, Jan. 2004. [10] P. Flandrin and P. Gonc¸alv´es, “Empirical mode decompositions as data-driven wavelet-like expansions,” International Journal of Wavelets Multiresolution and Information Processing, vol. 2, no. 4, pp. 1–20, 2004. [11] N. Tsakalozos, K. Drakakis, and S. Rickard, “A formal study of the nonlinearity and consistency of the Empirical Mode Decomposition,”Signal Processing, vol. 92, no. 9, pp. 1961–1969, Sep. 2012. [12] D. Goldberg, “What every computer scientist should know about floating point arithmetic,” ACM Computing Surveys, vol. 23, no. 1, pp. 5–48, 1991.

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