Parameter identification of two dimensional digital filters using electro-magnetism optimization


The design of Two-Dimensional Infinite Input Response Filters (2D IIR) is an important task in the field of signal processing. These filters are widely used in several areas of engineering as an important tool to eliminate undesired frequencies in high-noised signals. However, 2D IIR filters have parameters that need to be calibrated in order to obtain the best output, and finding these optimal values is not an easy task. On the other hand, Electro-magnetism Optimization (EMO) is a population-based technique which possess interesting convergence properties, it works following the electro-magnetism principles for solving complex optimization problems. This paper introduces an algorithm for the automatic parameter identification of 2D IIR filters using EMO, a process that is regarded as a multidimensional optimization problem. Experimental results are included to validate the efficiency of the proposed technique regarding accuracy, speed, and robustness.

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  1. 1.

    Abd Elaziz M, Oliva D, Xiong S (2017) An improved opposition-based sine cosine algorithm for global optimization. Expert Syst Appl.

    Article  Google Scholar 

  2. 2.

    Ahmed Abdelaziza, Mohamed Elhoseny, Ahmed S. Salama, A.M. Riad, A machine learning model for improving healthcare services on cloud computing environment, Measurement, Volume 119, 2018, Pages 117–128, 2018 doi:

    Article  Google Scholar 

  3. 3.

    Abo-Taleb A, Fahmy MM (1984) Design of FIR two-dimensional digital filters by successive projections. IEEE Trans Circ Syst 31:801–805.

    Article  Google Scholar 

  4. 4.

    Aggarwal A, Kumar M, Rawat TK, Upadhyay DK (2016) Optimal design of 2D FIR filters with Quadrantally symmetric properties using fractional derivative constraints. Circuits, Syst Signal Process 35:2213–2257.

    Article  MATH  Google Scholar 

  5. 5.

    Birbil ŞI, Fang SC (2003) An electromagnetism-like mechanism for global optimization. J Glob Optim 25:263–282.

    MathSciNet  Article  MATH  Google Scholar 

  6. 6.

    Birbil ŞI, Fang SC, Sheu RL (2004) On the convergence of a population-based global optimization algorithm. J Glob Optim 30:301–318

    MathSciNet  Article  Google Scholar 

  7. 7.

    Cowan EW (1968) Basic electromagnetisme. Academic Press

  8. 8.

    Cuevas-Jiménez E, Oliva-Navarro DA (2013) Modelado de filtros {IIR} usando un algoritmo inspirado en el electromagnetismo. Ing Investig y Tecnol 14:125–138.

    Google Scholar 

  9. 9.

    Darwish A, Hassanien AE, Elhoseny M, Sangaiah AK, Muhammad K (2017) The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems. J Ambient Intell Humaniz Comput.

    Article  Google Scholar 

  10. 10.

    Das S, Konar A (2007) A swarm intelligence approach to the synthesis of two-dimensional IIR filters. Eng Appl Artif Intell 20:1086–1096.

    Article  Google Scholar 

  11. 11.

    Elhoseny M, Tharwat A, Yuan X, Hassanien A (2018) Optimizing K-coverage of mobile WSNs. Expert Syst Appl 92:142–153.

    Article  Google Scholar 

  12. 12.

    Mohamed Elhoseny, Ahmed Abdelaziz, Ahmed Salama, AM Riad, Arun Kumar Sangaiah, Khan Muhammad. A hybrid model of internet of things and cloud computing to manage big data in health services applications, future generation computer systems. Elsevier, available online 15 2018. doi:

    Article  Google Scholar 

  13. 13.

    Elhoseny Mohamed, Ramírez-González Gustavo, Abu-Elnasr Osama M, Shawkat Shihab A, Arunkumar N, Farouk Ahmed Secure medical data transmission model for IoT-based healthcare systems. IEEE Access (Volume: PP, Issue: 99). doi:

    Article  Google Scholar 

  14. 14.

    Getin a E, Gerek ON, Yardimci Y (1997) FFT Algorithm IEEE Signal Process Mag 60–64

  15. 15.

    Gonzalez RC, Woods RE (1992) Digital image processing. Pearson, Prentice-Hall, New Jersey

    Google Scholar 

  16. 16.

    Kawamata M, Imakubo J, Higuchi T (1994) Optimal design method of 2-D IIR digital filters based on a simple genetic algorithm. Int Conf. Image Process:780–784

  17. 17.

    Kennedy J, Eberhart RC (1995) Particle swarm optimization. Neural Netw, 1995 Proc, IEEE Int Conf 4:1942–1948.

    Article  Google Scholar 

  18. 18.

    Kockanat S, Karaboga N (2015) The design approaches of two-dimensional digital filters based on metaheuristic optimization algorithms: a review of the literature. Artif Intell Rev 44:265–287.

    Article  Google Scholar 

  19. 19.

    Kumar M, Aggarwal A, Rawat TK (2016) Bat algorithm: application to adaptive infinite impulse response system identification. Arab J Sci Eng 41:3587–3604.

    Article  Google Scholar 

  20. 20.

    Lu HC, Tzeng ST (2000) Design of two-dimensional FIR digital filters for sampling structure conversion by genetic algorithm approach. Signal Process 80:1445–1458.

    Article  Google Scholar 

  21. 21.

    Lv C, Yan S, Cheng G et al (2016) Design of two-dimensional IIR digital filters by using a novel hybrid optimization algorithm. 1267–1281. doi:

    Article  Google Scholar 

  22. 22.

    Mladenov VM, Mastorakis N (1994) Design of two-Dimensional Recursive Filters by using neural networks. IEEE Trans Neural Netw 5:2–6

    Google Scholar 

  23. 23.

    Mostajabi T, Poshtan J, Mostajabi Z (2013) IIR model identification via evolutionary algorithms. Artif Intell Rev 44:87–101.

    Article  Google Scholar 

  24. 24.

    Nair SS, Rana KPS, Kumar V, Chawla A (2017) Efficient modeling of linear discrete filters using ant lion optimizer. Circuits, Syst Signal Process 36:1535–1568.

    Article  Google Scholar 

  25. 25.

    Oppenheim AV, Schafer RW, Buck JR (1999) Discrete-time Signal Processing, 2nd edn. Prentice-Hall, Englewood Cliffs, New Jersey

    Google Scholar 

  26. 26.

    Pham DT, Koç E (2010) Design of a two-dimensional Recursive Filter Using the bees algorithm. Int J Autom Comput 7:399–402.

    Article  Google Scholar 

  27. 27.

    Proakis JG, Monolakis DG (1996) Digital signal processing: principles, algorithms, and applications. Prentice-Hall, New Jersey

    Google Scholar 

  28. 28.

    Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci (Ny) 179:2232–2248.

    Article  MATH  Google Scholar 

  29. 29.

    Sajjad M, Nasir M, Muhammad K, Khan S, Jan Z, Sangaiah AK, Elhoseny M, Baik SW (2018) Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities. Future Gen Comput Syst, Elsevier.

  30. 30.

    Shehab A, Elhoseny M, Muhammad K, Sangaiah AK, Yang P, Huang H, Hou G Secure and robust fragile watermarking scheme for medical images. IEEE Access 6(1):10269–10278.

    Article  Google Scholar 

  31. 31.

    Stewart J (2001) Intermediate electromagnetic theory. World Scientific, Singapore

    Google Scholar 

  32. 32.

    Tsai J-T, Ho W-H, Chou J-H (2009) Design of two-dimensional IIR digital structure-specified filters by using an improved genetic algorithm. Expert Syst Appl 36:6928–6934.

    Article  Google Scholar 

  33. 33.

    Tzafestas SG (1986) Multidimensional systems: Techniques and applications. Dekker

  34. 34.

    Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82.

    Article  Google Scholar 

  35. 35.

    Yang Y, Yang B, Niu M (2017) Adaptive infinite impulse response system identification using opposition based hybrid coral reefs optimization algorithm. Appl Intell 1–18. doi:

    Article  Google Scholar 

  36. 36.

    Yuan X, Elhoseny M, El-Minir H, Riad A (2017) A genetic algorithm-based, dynamic clustering method towards improved wsn longevity. J Netw Syst Manag 25(1):21–46.

    Article  Google Scholar 

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Elhoseny, M., Oliva, D., Osuna-Enciso, V. et al. Parameter identification of two dimensional digital filters using electro-magnetism optimization. Multimed Tools Appl 79, 5005–5022 (2020).

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  • Two dimensional digital filters
  • Signal processing
  • Electro-magnetism optimization
  • Global optimization