EMD performance comparison: single vs double floating points

Journal article

Laszuk, D, Cadenas, O. and Nasuto, J (2016). EMD performance comparison: single vs double floating points. International journal of signal processing systems. 4 (4), pp. 349-353. https://doi.org/10.18178/ijsps.4.4.349-353
AuthorsLaszuk, D, Cadenas, O. and Nasuto, J
Abstract

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).

KeywordsEmpirical Mode Decomposition; Signal Decompoisition; Floating point comparison
Year2016
JournalInternational journal of signal processing systems
Journal citation4 (4), pp. 349-353
PublisherEngineering and Technology Publishing
ISSN2315-4535
Digital Object Identifier (DOI)https://doi.org/10.18178/ijsps.4.4.349-353
Publication dates
Print01 Aug 2016
Publication process dates
Deposited15 May 2017
Accepted18 May 2016
Publisher's version
License
File Access Level
Open
Permalink -

https://openresearch.lsbu.ac.uk/item/872z5

Download files

Publisher's version
20160831082610730.pdf
License: CC BY 4.0
File access level: Open

  • 87
    total views
  • 168
    total downloads
  • 1
    views this month
  • 2
    downloads this month

Export as

Related outputs

Preprocessing 2D data for fast convex hull computations
Cadenas, O and Megson, GM (2019). Preprocessing 2D data for fast convex hull computations. PLoS ONE. 14 (2), p. e0212189. https://doi.org/10.1371/journal.pone.0212189
KurSL: Model of anharmonic coupled oscillations based on Kuramoto coupling and Sturm-Liouville problem
Cadenas, O, Laszuk, D and Slawomir, N (2018). KurSL: Model of anharmonic coupled oscillations based on Kuramoto coupling and Sturm-Liouville problem. Advances in Data Science and Adaptive Analysis. 10 (02). https://doi.org/10.1142/S2424922X18400028
Running Median Algorithm and Implementation for Integer Streaming Applications
Cadenas, O and Megson, GM (2018). Running Median Algorithm and Implementation for Integer Streaming Applications. IEEE Embedded Systems Letters. 11 (2), pp. 58-61. https://doi.org/10.1109/LES.2018.2868409
Rapid preconditioning of data for accelerating convex hull algorithms
Cadenas, O and Megson, G (2014). Rapid preconditioning of data for accelerating convex hull algorithms. Electronics Letters. 50 (4), pp. 270-272. https://doi.org/10.1049/el.2013.3507
Virtualization for cost-effective teaching of assembly language
Cadenas, O, Sherratt, S, Howlett, D, Guy, C and Lundqvist, K (2015). Virtualization for cost-effective teaching of assembly language. IEEE Transactions on Education. 58 (4), pp. 282-288. https://doi.org/10.1109/TE.2015.2405895
Median architecture by accumulative parallel counters
Cadenas, O, Megson, G and Sherratt, S (2015). Median architecture by accumulative parallel counters. IEEE Transactions on Circuits and Systems II: Express Briefs. 62 (7), pp. 661-665. https://doi.org/10.1109/TCSII.2015.2415655
Pipelined median architecture
Cadenas, O (2015). Pipelined median architecture. Electronics Letters. 51 (24), pp. 1999-2001. https://doi.org/10.1049/el.2015.1898
Preconditioning 2D integer data for fast convex hull computations
Cadenas, O, Megson, G.M. and Luengo Hendriks, C.L. (2016). Preconditioning 2D integer data for fast convex hull computations. PLoS ONE. 11 (3). https://doi.org/10.1371/journal.pone.0149860
On the Phase Coupling of Two Components Mixing in Empirical Mode Decomposition
Laszuk, D, Cadenas, O. and Nasuto, Slawomir J. (2016). On the Phase Coupling of Two Components Mixing in Empirical Mode Decomposition. Advances in Data Science and Adaptive Analysis. 8 (1). https://doi.org/10.1142/S2424922X16500042