Table of Contents Author Guidelines Submit a Manuscript
Journal of Sensors
Volume 2014 (2014), Article ID 650891, 11 pages
http://dx.doi.org/10.1155/2014/650891
Research Article

An Integrated Denoising Method for Sensor Mixed Noises Based on Wavelet Packet Transform and Energy-Correlation Analysis

1School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China
2Xuyi Mine Equipment and Materials R&D Center, China University of Mining & Technology, Huai’an 211700, China

Received 20 June 2014; Accepted 22 August 2014; Published 20 October 2014

Academic Editor: Tuan Guo

Copyright © 2014 Chao Tan 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. Q. Zhang, H. Liu, X. Zhou, and Y. Wang, “Wavelet soft-threshold denoising method of power quality signal,” High Voltage Engineering, vol. 32, no. 1, pp. 99–101, 2006. View at Google Scholar · View at Scopus
  2. Z.-C. Liu, X.-G. Chen, and Y.-F. Li, “Detection and identification of abrupt changes for on-line sensor output signal,” Transaction of Beijing Institute of Technology, vol. 26, no. 12, pp. 1104–1108, 2006. View at Google Scholar · View at Scopus
  3. Z. Liu, “Analysis of noise at coal face by fully-mechanized coal winning technology,” Journal of China University of Mining and Technology: English versions, vol. 13, no. 1, pp. 113–116, 2003. View at Google Scholar
  4. A. M. Rao and D. L. Jones, “A denoising approach to multisensor signal estimation,” IEEE Transactions on Signal Processing, vol. 48, no. 5, pp. 1225–1234, 2000. View at Publisher · View at Google Scholar · View at Scopus
  5. S. V. Vaseghi, Advanced Digital Signal Processing and Noise Reduction, John Wiley & Sons, New York, NY, USA, 2008.
  6. Z. Wang and S. Li, “Discrete fourier transform and discrete wavelet packet transform in speech denoising,” in Proceedings of the 5th International Congress on Image and Signal Processing (CISP '12), pp. 1588–1591, IEEE, October 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Ding and H. Zhu, “Two-Dimensional gibbs phenomenon for fractional fourier series and its resolution,” in Artificial Intelligence and Computational Intelligence, vol. 7530 of Lecture Notes in Computer Science, pp. 530–538, Springer, Berlin, Germany, 2012. View at Publisher · View at Google Scholar
  8. S. G. Mallat, “Theory for multiresolution signal decomposition: the wavelet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674–693, 1989. View at Publisher · View at Google Scholar · View at Scopus
  9. R. Coifman, Y. Meyer, and M. V. Wickerhauser, “Size properties of wavelet packets,” In20, pp. 453–470, 1992. View at Google Scholar
  10. T. Xu and Q. Wang, “Application of multiscale principal component analysis based on wavelet packet in sensor fault diagnosis,” Proceedings of the Chinese Society of Electrical Engineering, vol. 27, no. 9, pp. 28–31, 2007. View at Google Scholar · View at Scopus
  11. M. Karimi-Ghartemani and A. K. Ziarani, “A nonlinear time-frequency analysis method,” IEEE Transactions on Signal Processing, vol. 52, no. 6, pp. 1585–1595, 2004. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  12. P. R. Hansen, J. Large, and A. Lunde, “Moving average-based estimators of integrated variance,” Econometric Reviews, vol. 27, no. 1–3, pp. 79–111, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  13. W. Zhou, B. Lu, T. G. Habetler, and R. G. Harley, “Incipient bearing fault detection via motor stator current noise cancellation using wiener filter,” IEEE Transactions on Industry Applications, vol. 45, no. 4, pp. 1309–1317, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. P. Flandrin, G. Rilling, and P. Gonçalvés, “Empirical mode decomposition as a filter bank,” IEEE Signal Processing Letters, vol. 11, no. 2, pp. 112–114, 2004. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Mellone, L. Palmerini, A. Cappello, and L. Chiari, “Hilbert-huang-based tremor removal to assess postural properties from accelerometers,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 6, pp. 1752–1761, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. S. Abbasion, A. Rafsanjani, A. Farshidianfar, and N. Irani, “Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine,” Mechanical Systems and Signal Processing, vol. 21, no. 7, pp. 2933–2945, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. R. J. Martis, U. R. Acharya, and H. Prasad, “Application of higher order statistics for atrial arrhythmia classification,” Biomedical Signal Processing and Control, vol. 8, no. 6, pp. 888–900, 2013. View at Google Scholar
  18. G. Li, P. Niu, W. Zhang, and Y. Zhang, “Control of discrete chaotic systems based on echo state network modeling with an adaptive noise canceler,” Knowledge-Based Systems, vol. 35, pp. 35–40, 2012. View at Publisher · View at Google Scholar · View at Scopus
  19. L.-D. Liao, Q.-H. He, Z.-L. Hu, and D.-Q. Zhang, “Independent component analysis of excavator noise in strong interference surrounding,” Journal of Central South University (Science and Technology), vol. 43, no. 9, pp. 3426–3430, 2012. View at Google Scholar · View at Scopus
  20. F. Moret, C. M. Poloschek, W. A. Lagrèze et al., “Visualization of fundus vessel pulsation using principal component analysis,” Investigative Ophthalmology & Visual Science, vol. 52, no. 8, pp. 5457–5464, 2011. View at Google Scholar
  21. S. K. Jha and R. D. S. Yadava, “Denoising by singular value decomposition and its application to electronic nose data processing,” IEEE Sensors Journal, vol. 11, no. 1, pp. 35–44, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Demirli and J. Saniie, “Model-based estimation pursuit for sparse decomposition of ultrasonic echoes,” IET Signal Processing, vol. 6, no. 4, pp. 313–325, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  23. E. Eweda and N. J. Bershad, “Stochastic analysis of a stable normalized least mean fourth algorithm for adaptive noise canceling with a white Gaussian reference,” IEEE Transactions on Signal Processing, vol. 60, no. 12, pp. 6235–6244, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  24. G. Wang, T. Li, G. Zhang et al., “Position estimation error reduction using recursive-least-square adaptive filter for model-based sensorless interior permanent magnet synchronous motor drives,” IEEE Transactions on Industrial Electronics, vol. 61, no. 9, pp. 5115–5125, 2014. View at Google Scholar
  25. F. Adamo, G. Andria, F. Attivissimo, A. M. L. Lanzolla, and M. Spadavecchia, “A comparative study on mother wavelet selection in ultrasound image denoising,” Measurement, vol. 46, no. 8, pp. 2447–2456, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. R. R. Coifman and M. V. Wickerhauser, “Entropy-based algorithms for best basis selection,” IEEE Transactions on Information Theory, vol. 38, no. 2, pp. 713–718, 1992. View at Publisher · View at Google Scholar · View at Scopus
  27. A. M. Hasan, K. Samsudm, A. R. Ramli, and R. S. Azmir, “Wavelet-based pre-filtering for low cost inertial sensors,” Journal of Applied Sciences, vol. 10, no. 19, pp. 2217–2230, 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. D. L. Donoho, “De-noising by soft-thresholding,” IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613–627, 1995. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  29. P. Mercorelli, “Denoising and harmonic detection using nonorthogonal wavelet packets in industrial applications,” Journal of Systems Science & Complexity, vol. 20, no. 3, pp. 325–343, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  30. Y. Li, T. Zhang, L. Deng, B. Wang, and M. Nakamura, “Denoising and rhythms extraction of EEG under +Gz acceleration based on wavelet packet transform,” in Proceedings of the 7th ICME International Conference on Complex Medical Engineering (CME '13), pp. 642–647, Beijing, China, May 2013. View at Publisher · View at Google Scholar · View at Scopus
  31. J. Yang, W. Xu, Y. Wang, and Q. Dai, “2-D anisotropic dual-tree complex wavelet packets and its application to image denoising,” in Proceedings of the 15th IEEE International Conference on Image Processing (ICIP '08), pp. 2328–2331, October 2008. View at Publisher · View at Google Scholar · View at Scopus
  32. T. Ke and Z. Jianjun, “A comparative study on validity assessment of wavelet de-noising,” Journal of Geodesy and Geodynamics, vol. 32, no. 2, pp. 128–133, 2012. View at Google Scholar
  33. G. G. Yen and K. C. Lin, “Wavelet packet feature extraction for vibration monitoring,” IEEE Transactions on Industrial Electronics, vol. 47, no. 3, pp. 650–667, 2000. View at Publisher · View at Google Scholar · View at Scopus
  34. N. C. Karmakar and A. K. M. Baki, “Detection of UHF band impulse radio signal through wavelet packet transform,” in Proceedings of the 7th International Conference on Electrical and Computer Engineering (ICECE '12), pp. 867–871, December 2012. View at Publisher · View at Google Scholar · View at Scopus
  35. J. J. Galiana-Merino, D. Ruiz-Fernandez, and J. J. Martinez-Espla, “Power line interference filtering on surface electromyography based on the stationary wavelet packet transform,” Computer Methods and Programs in Biomedicine, vol. 111, no. 2, pp. 338–346, 2013. View at Publisher · View at Google Scholar · View at Scopus
  36. H. Li, Q.-Z. Lin, Q.-J. Wang, Q.-J. Liu, and Y.-Z. Wu, “Research on spectrum denoising methods based on the combination of wavelet package transformation and mathematical morphology,” Spectroscopy and Spectral Analysis, vol. 30, no. 3, pp. 644–648, 2010. View at Publisher · View at Google Scholar · View at Scopus
  37. C. Yang, B. Olson, and J. Si, “A multiscale correlation of wavelet coefficients approach to spike detection,” Neural Computation, vol. 23, no. 1, pp. 215–250, 2011. View at Publisher · View at Google Scholar · View at Scopus
  38. D. Cao, L. Liping, and P. Wang, “Application of wavelet packet energy spectrum to extract the feature of the pulse signal,” in Proceedings of the 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE '10), pp. 1–4, Chengdu, China, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  39. J. Gubbi, A. Khandoker, and M. Palaniswami, “Classification of sleep apnea types using wavelet packet analysis of short-term ECG signals,” Journal of Clinical Monitoring and Computing, vol. 26, no. 1, pp. 1–11, 2012. View at Publisher · View at Google Scholar · View at Scopus
  40. J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Transactions on Image Processing, vol. 12, no. 11, pp. 1338–1351, 2003. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  41. K. Zhang, B.-J. Pang, and M. Lin, “Wavelet packet analysis for acoustic emission signals caused by debris cloud impact,” Journal of Vibration and Shock, vol. 31, no. 12, pp. 125–128, 2012. View at Google Scholar · View at Scopus
  42. S. Huang and X. Wu, “Feature extraction and classification of EEG based on energy characteristics,” Chinese Journal of Sensors and Actuators, vol. 23, no. 6, pp. 782–785, 2010. View at Google Scholar
  43. X.-H. Gu, G.-X. Zhang, D.-B. Hou, and Z.-K. Zhou, “Detection of water pipe leak location using wavelet packet decomposition and power feature extraction,” Journal of Sichuan University, vol. 37, no. 6, pp. 145–149, 2005. View at Google Scholar · View at Scopus
  44. J. Li, X. Ke, H. Guo et al., “The application of wavelet variance and wavelet entropy in signal feature ex-traction,” Journal of Xi'an University of Technology, vol. 23, no. 4, pp. 365–369, 2007. View at Google Scholar
  45. X. Zhou, C. Zhou, and B. C. Stewart, “Comparisons of discrete wavelet transform, wavelet packet transform and stationary wavelet transform in denoising PD measurement data,” in Proceedings of the IEEE International Symposium on Electrical Insulation (ISEI '06), pp. 237–240, IEEE, June 2006. View at Scopus