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Wavelet Families and Variants

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Pathological Brain Detection

Part of the book series: Brain Informatics and Health ((BIH))

Abstract

In this chapter, four important wavelet families are discussed: the Daubechies wavelet family, the Coiflet wavelet family, the Morlet wavelet family, and the biorthogonal wavelet family. The wavelet display function and the “waveinfo” command are introduced so that the detailed curve shape of scaling and wavelet functions, for both decomposition and reconstruction, can be viewed in arbitrary accuracy. Several popular wavelet transform variants are presented. The ε-decimated wavelet transform chooses either odd or even index randomly.

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References

  1. Bajaj N, Kashyap R (2012) Extension of wavelet family in fractional fourier domain. In: 1st international conference on emerging technology trends in electronics, communication and networking, Surat, India. IEEE, pp 1–4

    Google Scholar 

  2. Li LY, Shi KL (2016) Research and realization of transient disturbance detection algorithm based coiflet wavelets and FPGA. Int J Future Gener Commun Network 9(2):133–142

    Article  Google Scholar 

  3. Narkhedkar SG, Patel PK (2014) Recipe of speech compression using coiflet wavelet. In: International conference on contemporary computing and informatics (IC3I), Mysuru, India. IEEE, pp 1135–1139

    Google Scholar 

  4. Le TH, Caracoglia L (2015) Rectangular prism pressure coherence by modified Morlet continuous wavelet transform. Wind Struct 20(5):661–682

    Article  Google Scholar 

  5. Saatlo AN, Ozoguz S (2015) CMOS implementation of scalable Morlet wavelet for application in signal processing. In: 38th international conference on telecommunications and signal processing (TSP), Prague, Czech Republic. IEEE, pp 4–9

    Google Scholar 

  6. Mousavi SA, Hanifeloo Z, Sumari P, Arshad MRM (2016) Enhancing the diagnosis of corn pests using Gabor wavelet features and SVM classification. J Sci Ind Res 75(6):349–354

    Google Scholar 

  7. Lu HM (2016) Facial emotion recognition based on biorthogonal wavelet entropy, fuzzy support vector machine, and stratified cross validation. IEEE Access 4:8375–8385. https://doi.org/10.1109/ACCESS.2016.2628407

    Article  Google Scholar 

  8. Zhan TM, Chen Y (2016) Multiple sclerosis detection based on biorthogonal wavelet transform, RBF kernel principal component analysis, and logistic regression. IEEE Access 4:7567–7576. https://doi.org/10.1109/ACCESS.2016.2620996

    Article  MathSciNet  Google Scholar 

  9. Postnikov EB, Singh VK (2015) Continuous wavelet transform with the Shannon wavelet from the point of view of hyperbolic partial differential equations. Anal Math 41(3):199–206. https://doi.org/10.1007/s10476-015-0206-2

    Article  MathSciNet  MATH  Google Scholar 

  10. Abidin ZZ, Manaf M, Shibhgatullah AS (2013) Experimental approach on thresholding using reverse biorthogonal wavelet decomposition for eye image. In: International conference on signal and image processing applications, Melaka, Malaysia. IEEE, pp 349–353

    Google Scholar 

  11. Boufares O, Aloui N, Cherif A (2016) Adaptive threshold for background subtraction in moving object detection using stationary wavelet transforms 2D. Int J Adv Comput Sci Appl 7(8):29–36

    Google Scholar 

  12. Juneau PM, Garnier A, Duchesne C (2015) The undecimated wavelet transform-multivariate image analysis (UWT-MIA) for simultaneous extraction of spectral and spatial information. Chemometr Intell Lab Syst 142:304–318. https://doi.org/10.1016/j.chemolab.2014.09.007

    Article  Google Scholar 

  13. Kumar A, Sunkaria RK (2016) Two-channel perfect reconstruction (PR) quadrature mirror filter (QMF) bank design using logarithmic window function and spline function. SIViP 10(8):1473–1480. https://doi.org/10.1007/s11760-016-0958-6

    Article  Google Scholar 

  14. Li Y (2016) Detection of dendritic spines using wavelet packet entropy and fuzzy support vector machine. CNS Neurol Disord: Drug Targets 15:116–121. https://doi.org/10.2174/1871527315666161111123638

  15. Yang J (2015) Preclinical diagnosis of magnetic resonance (MR) brain images via discrete wavelet packet transform with Tsallis entropy and generalized eigenvalue proximal support vector machine (GEPSVM). Entropy 17(4):1795–1813. https://doi.org/10.3390/e17041795

    Article  Google Scholar 

  16. Yang M (2016) Dual-tree complex wavelet transform and twin support vector machine for pathological brain detection. Appl Sci 6(6), Article ID: 169

    Google Scholar 

  17. Lama RK, Choi MR, Kwon GR (2016) Image interpolation for high-resolution display based on the complex dual-tree wavelet transform and hidden Markov model. Multimedia Tools Appl 75(23):16487–16498. https://doi.org/10.1007/s11042-016-3245-1

    Article  Google Scholar 

  18. Fahmy MF, Fahmy OM (2016) An enhanced denoising technique using dual tree complex wavelet transform. In: El Khamy S, El Badawy H, El Diasty S (eds), 33rd national radio science conference NRSC, Aswan, Egypt. IEEE, pp 205–211

    Google Scholar 

  19. Canbay F, Levent VE, Serbes G, Fatih Ugurdag H, Goren S, Aydin N (2016) A multi-channel real time implementation of dual tree complex wavelet transform in field programmable gate arrays. In: Kyriacou E, Christofides S, Pattichis C (eds) XIV mediterranean conference on medical and biological engineering and computing 2016. IFMBE proceedings, vol 57. Springer, Cham, pp 114–118. https://doi.org/10.1007/978-3-319-32703-7_24

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Wang, SH., Zhang, YD., Dong, Z., Phillips, P. (2018). Wavelet Families and Variants. In: Pathological Brain Detection. Brain Informatics and Health. Springer, Singapore. https://doi.org/10.1007/978-981-10-4026-9_6

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  • DOI: https://doi.org/10.1007/978-981-10-4026-9_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4025-2

  • Online ISBN: 978-981-10-4026-9

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