A Generalized Method of Decreasing Data Redundancy

Authors

  • Yurii Iliash

DOI:

https://doi.org/10.47839/ijc.21.4.2786

Keywords:

redundancy decreasing, recurrent code sequences, quasi-stationary flow

Abstract

In this paper, a method of decreasing the redundancy of information flow by using recurrent properties of Galois code sequences is proposed. For this purpose, the service information is compiled and the priority compression is identified. The method is based on applying one of the adaptive algorithms (prediction first-order, interpolation zero-order, interpolation first-order) by comparing the efficiency of its use when applied to the selected fragments of a signal. It is shown that the developed method is effective for the quick-change signals when the structure and behavior of a signal change drastically. The efficiency of redundancy decreasing at the different sampling rate and the number of the significant samples is evaluated. This makes it possible to establish the limits of the positive effect for redundancy of information flows for the existing and developed methods. Experimental research is carried out for various permissible deviations with obtaining the number of the significant readings. A comparison of the obtained data with results of applying the existing methods in deep pumping installations proved that the proposed method is in 1.3 times more effective than existing ones.

References

R. Kajol. T. Sanjeev, “Data compression algorithm for computer vision applications: A survey,” Proceedings of the 2017 IEEE International Conference on Computing, Communication and Automation (ICCCA), 2017, pp. 1214-1219. https://doi.org/10.1109/CCAA.2017.8229984.

D. N. Karthika, et al., “WITHDRAWN: A new lossless compression method using direction adaptive-discrete wavelet transform and modified SPIHT coding,” Materials Today: Proceedings, 2021. https://doi.org/10.1016/j.matpr.2021.03.387.

J. Uthayakumar, T. Vengattaraman, P. Dhavachelvan, “A survey on data compression techniques: From the perspective of data quality, coding schemes, data type and applications,” Journal of King Saud University – Computer and Information Sciences, vol. 33, issue 2, pp. 119-140, 2021. https://doi.org/10.1016/j.jksuci.2018.05.006.

D. Pesenti, et al., “Adaptive resampling for data compression,” Array, vol. 12, 100076, 2021. https://doi.org/10.1016/j.array.2021.100076.

J. Chandan Kumar, K. Maheshkumar, “Electrocardiogram data compression techniques for cardiac healthcare systems: A methodological review,” IRBM, vol. 43, issue 3, pp. 217-228, 2021. https://doi.org/10.1016/j.irbm.2021.06.007.

F. Pourkamali-Anaraki and W. D. Bennette, “Adaptive data compression for classification problems,” IEEE Access, vol. 9, pp. 157654-157669, 2021. https://doi.org/10.1109/ACCESS.2021.3130551.

M. Mahajan, R. C. Gangwar and S. Mahajan, “To improve transmission loss using data redundancy and data compression for critical range based application,” Proceedings of the 2016 IEEE International Conference on Inventive Computation Technologies (ICICT), 2016, pp. 1-7. https://doi.org/10.1109/INVENTIVE.2016.7823179.

K. S. Umadevi, G. Arpita, S. Shalu Achamma, “A classification algorithm to reduce data redundancy in wireless sensor networks,” Advanced Science Letters, vol. 24m issue 8, pp. 6020-6024, 2018. https://doi.org/10.1166/asl.2018.12239.

O. Tomohiro, U. Kiyoshi, “Data redundancy dynamic control method for high availability distributed clusters,” Proceedings of the Ninth International Symposium on Information and Communication Technology, 2018, pp. 185-191. https://doi.org/10.1145/3287921.3287967.

S. Gul, et al., “Data redundancy reduction for energy-efficiency in wireless sensor networks: A comprehensive review,” IEEE Access, vol. 9, pp. 157859-157888, 2021. https://doi.org/10.1109/ACCESS.2021.3128353.

S. Urvashi, S. Meenakshi, P. Emjee, “Predictor based block adaptive near-lossless coding technique for magnetic resonance image sequence,” Procedia Computer Science, vol. 167, pp. 696-705, 2020. https://doi.org/10.1016/j.procs.2020.03.335.

E. Crespo Marques, N. Maciel, L. Naviner, H. Cai and J. Yang, “A review of sparse recovery algorithms,” IEEE Access, vol. 7, pp. 1300-1322, 2019. https://doi.org/10.1109/ACCESS.2018.2886471.

S. Ljubiša, et al., “A tutorial on sparse signal reconstruction and its applications in signal processing,” Circuits, Systems, and Signal Processing, vol. 38, issue 3, pp. 1206-1263, 2019. https://doi.org/10.1007/s00034-018-0909-2.

D. Fonseca Resende, et al., “Neural signal compressive sensing,” Compressive Sensing in Healthcare, pp. 201-221, 2020. https://doi.org/10.1016/B978-0-12-821247-9.00016-0.

P. Turner, J. Liu, P. Rigollet, “A statistical perspective on coreset density estimation,” Proceedings of the International Conference on Artificial Intelligence and Statistics PMLR, 2021, pp. 2512-2520.

D. Feldman, “Core-sets: Updated survey,” In: Ros, F., Guillaume, S. (eds) Sampling Techniques for Supervised or Unsupervised Tasks. Unsupervised and Semi-Supervised Learning. Springer, Cham, 2020, pp. 23-44. https://doi.org/10.1007/978-3-030-29349-9_2.

I. Efrat, and J. Mináč, “On the descending central sequence of absolute Galois groups,” American Journal of Mathematics, vol. 133, no. 6, pp. 1503-1532, 2011. https://doi.org/10.1353/ajm.2011.0041.

J. D. Hauenstein, J. I. Rodriguez, F. Sottile, “Numerical computation of Galois groups,” Foundations of Computational Mathematics, vol. 18, issue 4, pp. 867-890, 2018. https://doi.org/10.1007/s10208-017-9356-x.

I. Rivin, “Galois groups of generic polynomials,” arXiv preprint arXiv:1511.06446, 2015. https://doi.org/10.48550/arXiv.1511.06446.

A. Feragutti, “The set of stable primes for polynomial sequences with large Galois group,” Proceedings of the American Mathematical Society, vol. 146, issue 7, pp. 2773-2784, 2018. https://doi.org/10.1090/proc/13958.

Yu. Iliash, V. Horielov, “Reduction of information redundancy based on polynomial methods of prediction,” Bulletin of Khmelnytskyi National University, no. 2, vol. 1, pp. 49-53, 2007. (in Ukrainian)

Yu. Iliash, V. Horielov, “Analysis of systems of information flow redundancy reduction,” Electronics and Control Systems, no. 3(21), pp. 49-53, 2009. (in Ukrainian)

A. Babu, P. Eswaran, S. Kumar, “Lossless compression algorithm using improved RLC for grayscale image,” Arabian Journal for Science and Engineering, vol. 41, issue 8, pp. 3061-3070, 2016. https://doi.org/10.1007/s13369-016-2082-x.

Ya. Nykolaychuk, P. Humennij, “Theoretical bases, methods, and processors for transforming information in Galois field codes on the basis of the vertical information technology,” Cybernetics and Systems Analysis, vol. 50, pp. 338-347, 2014. https://doi.org/10.1007/s10559-014-9622-8.

N. Yatskiv, “Compression of the technological data in terms of Galois basic functions,” Proceedings of the Second IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2003), 2003, pp. 404-407. https://doi.org/10.1109/IDAACS.2003.4447699.

Anatoly Beletsky, “Generalized Galois-Fibonacci matrix generators pseudo-random sequences,” International Journal of Computer Network and Information Security (IJCNIS), vol. 13, no. 6, pp. 57-69, 2021. https://doi.org/10.5815/ijcnis.2021.06.05.

Shivashankar S., Medha Kudari, Prakash S. Hiremath, “Galois field-based approach for rotation and scale invariant texture classification,” International Journal of Image, Graphics and Signal Processing (IJIGSP), vol.10, no.9, pp. 56-64, 2018. https://doi.org/10.5815/ijigsp.2018.09.07.

A. Vambol, “Improved polynomial-time plaintext-recovery attack on the matrix-based knapsack cipher,” Radioelectronics and Computer Systems, no. 3(95), pp. 67-74, 2020, https://doi.org/10.32620/reks.2020.3.07. (in Ukrainian)

Downloads

Published

2022-12-31

How to Cite

Iliash, Y. (2022). A Generalized Method of Decreasing Data Redundancy. International Journal of Computing, 21(4), 495-501. https://doi.org/10.47839/ijc.21.4.2786

Issue

Section

Articles