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Reduced Cycle Spinning Method for the Undecimated Wavelet Transform

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Reduced Cycle Spinning Method for the Undecimated Wavelet Transform

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Rodríguez-Hernández, MA. (2019). Reduced Cycle Spinning Method for the Undecimated Wavelet Transform. Sensors. 19(12):1-16. https://doi.org/10.3390/s19122777

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/160606

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Title: Reduced Cycle Spinning Method for the Undecimated Wavelet Transform
Author: Rodríguez-Hernández, Miguel A.
UPV Unit: Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Issued date:
Abstract:
[EN] The Undecimated Wavelet Transform is commonly used for signal processing due to its advantages over other wavelet techniques, but it is limited for some applications because of its computational cost. One of the methods ...[+]
Subjects: Cycle spinning , Undecimated wavelet transform , Computational cost , Denoising , Ultrasonic
Copyrigths: Reconocimiento (by)
Source:
Sensors. (eissn: 1424-8220 )
DOI: 10.3390/s19122777
Publisher:
MDPI AG
Publisher version: https://doi.org/10.3390/s19122777
Project ID:
info:eu-repo/grantAgreement/MINECO//TEC2015-71932-REDT/ES/ELASTIC NETWORKS: NUEVOS PARADIGMAS DE REDES ELASTICAS PARA UN MUNDO RADICALMENTE BASADO EN CLOUD Y FOG COMPUTING/
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-094151-B-I00/ES/SLICING DINAMICO EN REDES DE ACCESO RADIO 5G/
info:eu-repo/grantAgreement/MINECO//TIN2013-47272-C2-1-R/ES/PLATAFORMA DE SERVICIOS PARA CIUDADES INTELIGENTES CON REDES M2M DENSAS/
Thanks:
This research was funded by grants number PGC2018-09415-B-I00 (MCIU/AEI/FEDER, UE) and TEC2015-71932-REDT.
Type: Artículo

References

Signal Processing Fourier and Wavelet Representationshttp://www.fourierandwavelets.org/SPFWR_a3.1_2012.pdf

Zhao, H., Zuo, S., Hou, M., Liu, W., Yu, L., Yang, X., & Deng, W. (2018). A Novel Adaptive Signal Processing Method Based on Enhanced Empirical Wavelet Transform Technology. Sensors, 18(10), 3323. doi:10.3390/s18103323

Gradolewski, D., Magenes, G., Johansson, S., & Kulesza, W. (2019). A Wavelet Transform-Based Neural Network Denoising Algorithm for Mobile Phonocardiography. Sensors, 19(4), 957. doi:10.3390/s19040957 [+]
Signal Processing Fourier and Wavelet Representationshttp://www.fourierandwavelets.org/SPFWR_a3.1_2012.pdf

Zhao, H., Zuo, S., Hou, M., Liu, W., Yu, L., Yang, X., & Deng, W. (2018). A Novel Adaptive Signal Processing Method Based on Enhanced Empirical Wavelet Transform Technology. Sensors, 18(10), 3323. doi:10.3390/s18103323

Gradolewski, D., Magenes, G., Johansson, S., & Kulesza, W. (2019). A Wavelet Transform-Based Neural Network Denoising Algorithm for Mobile Phonocardiography. Sensors, 19(4), 957. doi:10.3390/s19040957

Shikhsarmast, F., Lyu, T., Liang, X., Zhang, H., & Gulliver, T. (2018). Random-Noise Denoising and Clutter Elimination of Human Respiration Movements Based on an Improved Time Window Selection Algorithm Using Wavelet Transform. Sensors, 19(1), 95. doi:10.3390/s19010095

Shensa, M. J. (1992). The discrete wavelet transform: wedding the a trous and Mallat algorithms. IEEE Transactions on Signal Processing, 40(10), 2464-2482. doi:10.1109/78.157290

Li, M., & Ghosal, S. (2015). Fast Translation Invariant Multiscale Image Denoising. IEEE Transactions on Image Processing, 24(12), 4876-4887. doi:10.1109/tip.2015.2470601

Hazarika, D., Nath, V. K., & Bhuyan, M. (2016). SAR Image Despeckling Based on a Mixture of Gaussian Distributions with Local Parameters and Multiscale Edge Detection in Lapped Transform Domain. Sensing and Imaging, 17(1). doi:10.1007/s11220-016-0141-8

Sakhaee, E., & Entezari, A. (2017). Joint Inverse Problems for Signal Reconstruction via Dictionary Splitting. IEEE Signal Processing Letters, 24(8), 1203-1207. doi:10.1109/lsp.2017.2701815

Ong, F., Uecker, M., Tariq, U., Hsiao, A., Alley, M. T., Vasanawala, S. S., & Lustig, M. (2014). Robust 4D flow denoising using divergence-free wavelet transform. Magnetic Resonance in Medicine, 73(2), 828-842. doi:10.1002/mrm.25176

Rehman, N. ur, Abbas, S. Z., Asif, A., Javed, A., Naveed, K., & Mandic, D. P. (2017). Translation invariant multi-scale signal denoising based on goodness-of-fit tests. Signal Processing, 131, 220-234. doi:10.1016/j.sigpro.2016.08.019

Mota, H. de O., Vasconcelos, F. H., & de Castro, C. L. (2016). A comparison of cycle spinning versus stationary wavelet transform for the extraction of features of partial discharge signals. IEEE Transactions on Dielectrics and Electrical Insulation, 23(2), 1106-1118. doi:10.1109/tdei.2015.005300

Li, D., Wang, Y., Lin, J., Yu, S., & Ji, Y. (2016). Electromagnetic noise reduction in grounded electrical‐source airborne transient electromagnetic signal using a stationarywavelet‐based denoising algorithm. Near Surface Geophysics, 15(2), 163-173. doi:10.3997/1873-0604.2017003

San Emeterio, J. L., & Rodriguez-Hernandez, M. A. (2014). Wavelet Cycle Spinning Denoising of NDE Ultrasonic Signals Using a Random Selection of Shifts. Journal of Nondestructive Evaluation, 34(1). doi:10.1007/s10921-014-0270-8

Rodriguez-Hernandez, M. A., & Emeterio, J. L. S. (2015). Noise reduction using wavelet cycle spinning: analysis of useful periodicities in the z-transform domain. Signal, Image and Video Processing, 10(3), 519-526. doi:10.1007/s11760-015-0762-8

Rodriguez-Hernandez, M. A. (2016). Shift selection influence in partial cycle spinning denoising of biomedical signals. Biomedical Signal Processing and Control, 26, 64-68. doi:10.1016/j.bspc.2015.12.002

Beylkin, G., Coifman, R., & Rokhlin, V. (1991). Fast wavelet transforms and numerical algorithms I. Communications on Pure and Applied Mathematics, 44(2), 141-183. doi:10.1002/cpa.3160440202

Beylkin, G. (1992). On the Representation of Operators in Bases of Compactly Supported Wavelets. SIAM Journal on Numerical Analysis, 29(6), 1716-1740. doi:10.1137/0729097

Donoho, D. L., & Johnstone, I. M. (1994). Ideal spatial adaptation by wavelet shrinkage. Biometrika, 81(3), 425-455. doi:10.1093/biomet/81.3.425

Donoho, D. L., & Johnstone, I. M. (1995). Adapting to Unknown Smoothness via Wavelet Shrinkage. Journal of the American Statistical Association, 90(432), 1200-1224. doi:10.1080/01621459.1995.10476626

Johnstone, I. M., & Silverman, B. W. (1997). Wavelet Threshold Estimators for Data with Correlated Noise. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 59(2), 319-351. doi:10.1111/1467-9868.00071

Pardo, E., San Emeterio, J. L., Rodriguez, M. A., & Ramos, A. (2006). Noise reduction in ultrasonic NDT using undecimated wavelet transforms. Ultrasonics, 44, e1063-e1067. doi:10.1016/j.ultras.2006.05.101

Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41(3), 613-627. doi:10.1109/18.382009

Lázaro, J. C., San Emeterio, J. L., Ramos, A., & Fernández-Marrón, J. L. (2002). Influence of thresholding procedures in ultrasonic grain noise reduction using wavelets. Ultrasonics, 40(1-8), 263-267. doi:10.1016/s0041-624x(02)00149-x

Karpur, P., Shankar, P. M., Rose, J. L., & Newhouse, V. L. (1987). Split spectrum processing: optimizing the processing parameters using minimization. Ultrasonics, 25(4), 204-208. doi:10.1016/0041-624x(87)90034-5

Pardo, E., Emeterio, S. J. L., Rodriguez, M. A., & Ramos, A. (2008). Shift Invariant Wavelet Denoising of Ultrasonic Traces. Acta Acustica united with Acustica, 94(5), 685-693. doi:10.3813/aaa.918082

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