Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

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2.10

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Utkir Khamdamov  1 , Alisher Abdullayev2 , Mukhriddin Mukhiddinov1 , and Sirojiddin Xalilov3

1Department of Hardware and Software of Control Systems in Telecommunications, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent 100200, Uzbekistan
2Department for Development of Information and Communication Technologies, Ministry of Higher and Secondary Specialized Education of the Republic of Uzbekistan, Tashkent, 100095, Uzbekistan
3Department of Information Technologies, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent 100200, Uzbekistan


 

Received: July 27, 2020
Accepted: September 29, 2020
Publication Date: April 1, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202104_24(2).0003  


ABSTRACT


With the development of the digital signal processing systems, the tasks of discrete signal reconstruction have become most actual, which can be related to the function of interpolation tasks in computational mathematics. This paper presents the results of using local basis splines as a mathematical apparatus for approximation one-dimensional and two-dimensional signals used in the field of geophysics information systems. The internet service now being built would enable the geophysical information system to be accessed by organizations and people associated with the universities and scientific institutes. As a result of research, the effective methods and algorithms for calculating approximation coefficients based on windows consisting of certain numbers of signal samples are proposed. All experiments were carried out for approximation values of the linear and harmonic form analytical functions, as well as to restore the geophysical signals forms. To evaluate the reliability of the proposed methods and algorithms, the approximation results were estimated by calculating the mean square error of signal recovery


Keywords: spline, basic spline, cubic spline, interpolation, approximation, information systems, information and communication technologies, signal recovery, geophysical signal, geophysics, gravity exploration


REFERENCES


  1. [1] Karakoc, Seydi Battal Gazi, and Samir Kumar Bhowmik. Galerkin finite element solution for Benjamin–Bona–Mahony–Burgers equation with cubic Bsplines. Computers and Mathematics with Applications, 77(7):1917–1932, 2019.
  2. [2] Talaat S EL-Danaf, K R Raslan, and Khalid K Ali. Collocation method with cubic B-splines for solving the generalized regularized long wave equation. Int. J. of Num. Meth. and Appl, 15(1):39–59, 2016.
  3. [3] Tayyaba Akram, Muhammad Abbas, Ahmad Izani Ismail, Norhashidah Hj M Ali, and Dumitru Baleanu. Extended cubic B-splines in the numerical solution of time fractional telegraph equation. Advances in Difference Equations, 2019(1):365, 2019.
  4. [4] Akmalbek Abdusalomov, Mukhriddin Mukhiddinov, Oybek Djuraev, Utkir Khamdamov, and Taeg Keun Whangbo. Automatic Salient Object Extraction Based on Locally Adaptive Thresholding to Generate Tactile Graphics. Applied Sciences (Switzerland), 10(10):3350, 2020.
  5. [5] Dhananjay Singh, Hakimjon Zaynidinov, and HoonJae Lee. Piecewise-quadratic Harmut basis functions and their application to problems in digital signal processing. International Journal of Communication Systems, 23(6-7):751–762, 2010.
  6. [6] Toshiko Terakawa, Wooseok Seo, Kwang-Hee Kim, and Jin-Han Ree. Three-Dimensional Pore Fluid Pressures in Source Region of 2017 Pohang Earthquake Inferred From Earthquake Focal Mechanisms. Geophysical Research Letters, 47(9):e2019GL085964, 2020.
  7. [7] H N Zayniddinov and O U Mallayev. Paralleling of calculations and vectorization of processes in digital treatment of seismic signals by cubic spline. In IOP Conference Series: Materials Science and Engineering, volume 537, page 32002. IOP Publishing, 2019.
  8. [8] Madhusudan Singh, Hakimjon Zaynidinov, Mastura Zaynutdinova, and Dhananjay Singh. Bi-cubic Spline Based Temperature Measurement in the Thermal Field for Navigation and Time System. Journal of Applied Science and Engineering, 22(3):579–586, 2019.
  9. [9] Cesare Bracco, Carlotta Giannelli, David Grossmann, Sofia Imperatore, Dominik Mokris, and Alessandra Sestini. THB-spline approximations for turbine blade design with local B-spline approximations. arXiv preprint arXiv:2003.08706, 2020.
  10. [10] Olim Hidayov, Ikechi Augustine Ukaegbu, Hakimjon Zaynidinov, and Sang-Gug Lee. Comparative analysis of piecewise-polynomial of local bases. In International Conference on Advanced Communication Technology, ICACT (2010), volume 2, pages 947–950. IEEE, 2010.
  11. [11] T Zhanlav and R Mijiddorj. A comparative analysis of local cubic splines. Computational and Applied Mathematics, 37(5):5576–5586, 2018.
  12. [12] Olim Hidayov, Dhananjay Singh, Bakul Gohel, GKiUk Gwak, and Sang Gug Lee. A Simulink-model of specialized processor on the piecewise-polynomial bases. In International Conference on Advanced Communication Technology, ICACT (2011), pages 616–619. IEEE, 2011.
  13. [13] Johan De Villiers. Mathematics of approximation, volume 1. Springer Science & Business Media, 2012.
  14. [14] Dhananjay Singh, Madhusudan Singh, and Zaynidinov Hakimjon. B-Spline Approximation for Polynomial Splines. In Springer Verlag, pages 13–19. Springer, 2019.
  15. [15] Michael James David Powell. Approximation theory and methods. Cambridge university press, 1981.
  16. [16] Yanshu Zhu, Feng Sun, Yi-King Choi, Bert Jüttler, and Wenping Wang. Computing a compact spline representation of the medial axis transform of a 2D shape. Graphical Models, 76(5):252–262, 2014.
  17. [17] David Salomon. The computer graphics manual. Springer Science & Business Media, 2011.
  18. [18] Frank Neitzel, Nikolaj Ezhov, and Svetozar Petrovic. Total Least Squares Spline Approximation. Mathematics, 7(5):462, 2019.
  19. [19] Lukáš Mino, Imrich Szabó, and Csaba Török. Bicubic splines and biquartic polynomials. Open Computer Science, 1(open-issue), 2016.
  20. [20] Viliam Kaˇcala and Csaba Török. Speedup of Bicubic Spline Interpolation. In International Conference on Computational Science, pages 806–818. Springer, 2018.
  21. [21] Sayfiddin Bahramov and Sanjar Jovliev. Bicubic Splines in Problems of Modeling of Multidimensional Signals. Journal of information and communication convergence engineering, 9(4):420–423, 2011.
  22. [22] Chao Zeng, Fang Deng, Xin Li, and Jiansong Deng. Dimensions of biquadratic and bicubic spline spaces over hierarchical T-meshes. Journal of Computational and Applied Mathematics, 287:162–178, 2015.
  23. [23] Xilian Fu. A rational bicubic spline for visualization of shaped data. Journal of Computational Analysis and Applications, page 1532, 2018.
  24. [24] R Akhrif, A Kouibia, and M Pasadas. Approximation of generalized offset surfaces by bicubic splines. Journal of Mathematical Chemistry, 58(3):647–662, 2020.
  25. [25] A Kouibia and Miguel Pasadas. Approximation of surfaces by fairness bicubic splines. Advances in Computational Mathematics, 20(1-3):87–103, 2004.
  26. [26] Aleksandr Vasil’evich Mironenko. Uniform approximation by perfect splines. Trudy Instituta Matematiki i Mekhaniki UrO RAN, 23(3):206–213, 2017.
  27. [27] A K B Chand and M A Navascués. Natural bicubic spline fractal interpolation. Nonlinear Analysis: Theory, Methods & Applications, 69(11):3679–3691, 2008.
  28. [28] Kh E Khujamatov, D T Khasanov, and E N Reypnazarov. Research and Modelling Adaptive Management of Hybrid Power Supply Systems for Object Telecommunications based on IoT. In 2019 International Conference on Information Science and Communications Technologies (ICISCT), pages 1–5. IEEE, 2019.
  29. [29] R K Ahmadulin and L N Bakanovskaya. Objectoriented programming when developing software in geology and geophysics. In IOP Conference Series: Earth and Environmental Science, volume 50, page 12049. IOP Publishing, 2017.
  30. [30] Fenglin Peng, Le Peng, Jian Zhang, Guoqiang Xue, Maining Ma, and Yunfei Zhang. From Geophysical Data to Geophysical Informatics. Data Science Journal, 14, 2015.
  31. [31] H. N. Zaynidinov and U. Hamdamov. Parallelizing of evaluations at restoring signals by systems of cubic splines. In Proceedings of International Conference on Information Technology Promotion in Asia,, pages 24–28, 2007.
  32. [32] H.N. Zaynidinov, and U.R. Hamdamov. Roundoff Errors at a Parallelizing of Evaluations by Systems of Basic Splines. In Proceedings the Second International Conference on Mobile Computing, Communications and Applications (ICMOCCA2007), Tashkent,, pages 95–98, 2007.
  33. [33] U.R. Khamdamov. Simulation of processes of manydimensional signals processing on the basis of methods a spline – functions and software implementation. PhD thesis, 2009.