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B-Spline Approximation for Polynomial Splines

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Signal Processing Applications Using Multidimensional Polynomial Splines

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSAPPLSCIENCES))

Abstract

This chapter has discussed specialised computing structure for running B-spline approximation. The spline functions and generalised spectral methods are widely used for the analysis and recovery of signals. The broken spline function is the simplest and historical example of splines. Spline functions are a developing field of the function approximation and digital analysis theory.

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References

  1. D. Amadori, L. Gosse, Error estimates for well-balanced schemes on simple balance laws: one-dimensional position-dependent models. BCAM Springer Briefs in Mathematics (2015)

    Google Scholar 

  2. A. Jagannathan, R. Orbach, O. Entin-Wohlman, Chapter 4 Thermal Conduction due to Hopping Processes in Amorphous Solids, ed. by M. Pollak, B. Shklovskii, Modern Problems in Condensed Matter Sciences, vol 28 (Elsevier, 1991), pp. 125–141. ISSN 0167-7837, ISBN 9780444880376. https://doi.org/10.1016/B978-0-444-88037-6.50010-5

    Google Scholar 

  3. B. Hofner, A. Mayr, N. Robinzonov et al., Comput Statistics 29(1–2), 3–35 (2014). https://doi.org/10.1007/s00180-012-0382-5

    Article  MathSciNet  Google Scholar 

  4. X. Jia, P. Ziegenhein, S.B. Jiang, GPU-based high-performance computing for radiation therapy. Phys. Med. Biol. 59(4), R151–R182 (2014). https://doi.org/10.1088/0031-9155/59/4/r151

    Article  Google Scholar 

  5. N. Brisebarre, S. Chevillard, M. Ercegovac, J.-M. Muller, S. Torres, An Efficient Method for Evaluating Polynomial and Rational Function Approximations, (2008) pp. 233–238. https://doi.org/10.1109/asap.2008.4580185

  6. X. Liu, S. Guillas, M.J. Lai, Efficient spatial modeling using the SPDE approach with bivariate splines. J. Comput. Graphical Stat. 25(4), 1176–1194 (2016). https://doi.org/10.1080/10618600.2015.1081597

    Article  MathSciNet  Google Scholar 

  7. D. Inman, R. Elmore, B. Bush, A case study to examine the imputation of missing data to improve clustering analysis of building electrical demand. Building Serv. Eng. Res. Technol. 36(5), 628–637 (2015). https://doi.org/10.1177/0143624415573215

    Article  Google Scholar 

  8. C. Beccari, G. Casciola, M.-L. Mazure, Design or not design? (Numerical Algorithms, A numerical characterisation for piecewise Chebyshevian splines, 2018). https://doi.org/10.1007/s11075-018-0533-z

    Book  Google Scholar 

  9. A. Grigorenko, S. Yaremchenko, Investigation of static and dynamic behavior of anisotropic inhomogeneous shallow shells by Spline approximation method. J. Civil Eng. Manage. 15(1), 87–93 (2009). https://doi.org/10.3846/1392-3730.2009.15.87-93

    Article  Google Scholar 

  10. B. Despres, Ch. Buet, The structure of well-balanced schemes for Friedrichs systems with linear relaxation. Appl. Math. Comput. 272, 440–459 (2016)

    MathSciNet  Google Scholar 

  11. C.G. Zhu, X.Y. Zhao, Self-intersections of rational Bézier curves. Graph. Models 76(5), 312–320 (2014). https://doi.org/10.1016/j.gmod.2014.04.001

    Article  Google Scholar 

  12. Laurent Gosse, ℒ-splines and viscosity limits for well-balanced schemes acting on linear parabolic equations. Acta Applicandae Mathematicae, Springer 153(1), 101–124 (2018). https://doi.org/10.1007/s10440-017-0122-5

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics Engineering, Hankuk University of Foreign Studies (Global Campus), Yongin, Korea (Republic of)

    Dhananjay Singh

  2. School of Technology Studies, Endicott College of International Studies, Woosong University, Daejeon, Korea (Republic of)

    Madhusudan Singh

  3. Head of Department of Information Technologies, Tashkent University of Information Technologies, Tashkent, Uzbekistan

    Zaynidinov Hakimjon

Authors
  1. Dhananjay Singh
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  2. Madhusudan Singh
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  3. Zaynidinov Hakimjon
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Correspondence to Dhananjay Singh .

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Singh, D., Singh, M., Hakimjon, Z. (2019). B-Spline Approximation for Polynomial Splines. In: Signal Processing Applications Using Multidimensional Polynomial Splines. SpringerBriefs in Applied Sciences and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-13-2239-6_2

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  • DOI: https://doi.org/10.1007/978-981-13-2239-6_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2238-9

  • Online ISBN: 978-981-13-2239-6

  • eBook Packages: EngineeringEngineering (R0)

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