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International Journal of Corrosion
Volume 2017, Article ID 7925404, 7 pages
https://doi.org/10.1155/2017/7925404
Research Article

The Discrete Wavelet Transform and Its Application for Noise Removal in Localized Corrosion Measurements

1Engineering Institute, Autonomous University of Baja California, Boulevard Benito Juarez, Insurgentes Este, 21280 Mexicali, BC, Mexico
2National Center of Metallurgical Research (CENIM) Madrid, The Spanish State Council for Scientific Research (CSIC), Madrid, Spain

Correspondence should be addressed to Rogelio Ramos; xm.ude.cbau@isomarr

Received 30 January 2017; Accepted 24 April 2017; Published 4 June 2017

Academic Editor: Flavio Deflorian

Copyright © 2017 Rogelio Ramos et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. Akid and M. Garma, “Scanning vibrating reference electrode technique: a calibration study to evaluate the optimum operating parameters for maximum signal detection of point source activity,” Electrochimica Acta, vol. 49, no. 17-18, pp. 2871–2879, 2004. View at Publisher · View at Google Scholar · View at Scopus
  2. H. S. Isaacs and Y. Ishikawa, “Current and Potential Transients during Localized Corrosion of Stainless Steel,” Journal of The Electrochemical Society, vol. 132, no. 6, pp. 1288–1293, 1985. View at Publisher · View at Google Scholar · View at Scopus
  3. S. Fujimoto and T. Shibata, Denki Kagaku, 64, 967 (1996).
  4. R. Ramos, R. K. Zlatev, M. S. Stoytcheva, B. Valdez, S. Flores, and A. M. Herrera, “Pitting corrosion characterization by SVET applying a synchronized noise suppression technique,” ECS Transactions, vol. 29, no. 1, pp. 33–42, 2010, The lectrochemical Society. View at Publisher · View at Google Scholar · View at Scopus
  5. P. Marcus and F. Mansfeld, Analytical Methods in Corrosion Science and Engineering, 2006.
  6. R. Ramos, R. Zlatev, B. Valdez, M. Stoytcheva, M. Carrillo, and J.-F. García, “LabVIEW 2010 computer vision platform based virtual instrument and its application for pitting corrosion study,” Journal of Analytical Methods in Chemistry, vol. 2013, Article ID 193230, 8 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. H. Meng, J.-Y. Li, and Y.-H. Tang, “Virtual instrument for determining rate constant of second-order reaction by pX based on LabVIEW 8.0,” Journal of Automated Methods and Management in Chemistry, vol. 2009, Article ID 849704, 7 pages, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. W.-B. Wang, J.-Y. Li, and Q.-J. Wu, “The design of a chemical virtual instrument based on LabVIEW for determining temperatures and pressures,” Journal of Automated Methods and Management in Chemistry, vol. 2007, Article ID 68143, 7 pages, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. R. Moshrefi, M. G. Mahjani, and M. Jafarian, “Application of wavelet entropy in analysis of electrochemical noise for corrosion type identification,” Electrochemistry Communications, vol. 48, pp. 49–51, 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. H. Men, C. Wang, Y. Peng, S. Yang, and Z. Xu, “Wavelet analysis and its application in the field of microbial corrosion,” in Proceedings of the 3rd International Symposium on Intelligent Information Technology Application, IITA 2009, IEEE Computer Society, NanChang, China, November 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. J. A. Wharton, R. J. K. Wood, and B. G. Mellor, “Wavelet analysis of electrochemical noise measurements during corrosion of austenitic and superduplex stainless steels in chloride media,” Corrosion Science, vol. 45, no. 1, pp. 97–122, 2003. View at Publisher · View at Google Scholar · View at Scopus
  12. X. Wang, J. Wang, C. Fu, and Y. Gao, “Determination of corrosion type by wavelet-based fractal dimension from electrochemical noise,” International Journal of Electrochemical Science, vol. 8, pp. 7211–7222, 2013. View at Google Scholar
  13. L. A. Montejo and L. E. Suárez, “Aplicaciones de la Transformada Ondícula (“Wavelet”) en Ingeniería Estructural,” in Mecanica Computacional, vol. XXVI, pp. 2742–2753, Córdoba, Argentina, October 2007. View at Google Scholar
  14. M. Bitenc, D. S. Kieffer, and K. Khoshelham, “Evaluation of wavelet denoising methods for small-scale joint rougness estimation using terrestrial laser scanning,” in Proceedings of the ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. II-3/W5, ISPRS Geospatial Week 2015, La Grade Motte, France, 28 Sep–03 Oct 2015, 28 Sep-03 Oct 2015.
  15. S. G. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Transactions on Image Processing, vol. 9, no. 9, pp. 1532–1546, 2000. View at Publisher · View at Google Scholar · View at MathSciNet
  16. D. L. Donoho and J. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika, vol. 81, no. 3, pp. 425–455, 1994. View at Google Scholar · View at MathSciNet
  17. D. L. Donoho, “De-noising by soft-thresholding,” IEEE Transactions on Information Theory, vol. 41, no. 3, 1995. View at Publisher · View at Google Scholar · View at MathSciNet
  18. R. R. Coifman and D. L. Donoho, Translation-Invariant De-Noising, Spriger, New York, USA, 1995.
  19. B. Wu and C. Cai, “Wavlet denoising and its implementation in LabVIEW,” in Proceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP '09, IEEE, Tianjin, China, October 2009. View at Publisher · View at Google Scholar · View at Scopus