Research PaperNo Access

Design of Optimal Two-Dimensional FIR Filters with Quadrantally Symmetric Properties Using Vortex Search Algorithm

Department of Electronics and Communication Engineering, Bharati Vidyapeeth College of Engineering, New Delhi-110063, India

Department of Electronics and Communication Engineering, Indira Gandhi Delhi Technical University for Women (IGDTUW), New Delhi-110006, India

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Richa Yadav

Department of Electronics and Communication Engineering, Indira Gandhi Delhi Technical University for Women (IGDTUW), New Delhi-110006, India

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Ashwni Kumar

Department of Electronics and Communication Engineering, Indira Gandhi Delhi Technical University for Women (IGDTUW), New Delhi-110006, India

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, and

Manjeet Kumar

http://orcid.org/0000-0001-6578-9741

Department of Electronics and Communication Engineering, Bennett University, Greater Noida, Uttar Pradesh-201310, India

E-mail Address: manjeetchhillar@gmail.com

Corresponding author.

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https://doi.org/10.1142/S0218126620501558Cited by:16 (Source: Crossref)

Abstract

This research paper presents a new evolutionary technique named vortex search optimization (VSO) to design digital 2D finite impulse response (FIR) filter for improved performance both in pass-band and stop-band regions. Optimum filter coefficients are calculated by minimizing the deviation of actual frequency response from specified or desired response. Efficiency of the designed filter is measured by several parameters, such as maximum pass-band ripple, maximum stop-band ripple, mean attenuation in stop band and time taken, to execute the code. Analysis of the performance of designed filter is correlated with various different algorithms like real coded genetic algorithm, particle swarm optimization, genetic search algorithm and hybrid particle swarm optimization gravitational algorithm. Comparative study shows significant reduction in pass-band error, stop-band error and execution time.

This paper was recommended by Regional Editor Piero Malcovati.

Keywords:

References

1. P. Golik, B. Harb, A. Misra, M. Riley, A. Rudnick and E. Weinstein , Mobile music modeling, analysis and recognition, Int. Conf. Acoustics, Speech and Signal Processing (Japan, 2012), pp. 2353–2356. Google Scholar
2. M. Rakshit and S. Das , An efficient ECG denoising methodology using empirical mode decomposition and adaptive switching mean filter, Biomed. Signal Process. Control 40 (2018) 140–148. Web of Science, Google Scholar
3. F. Latifoglu , A novel approach to speckle noise filtering based on Artificial Bee Colony algorithm: An ultrasound image application, Comput. Methods Programs in Biomed. 111(3) (2013) 561–569. Web of Science, Google Scholar
4. A. Adam, Z. Ibrahim and M. I. Shapiai , Feature selection and classifier parameter estimation for EEG signal peak detection using gravitational search algorithm, 4th Int.Conf. Artificial Intelligence with Applications in Engineering and Technology (Malaysia, 2014), pp. 103–108. Google Scholar
5. Y. W. Bai and C. L. Lu , The embedded digital stethoscope uses the adaptive noise cancellation filter and the type I Chebyshev IIR bandapass filter to reduce the noise of the heart sound, Proc. 7th Int. Workshop on Enterprise Networking and Computing in Healthcare Industry, 2015, pp. 278–281. Google Scholar
6. D. Burnshtein and S. Gannot , Speech enhancement using a mixture OO/maximum model, IEEE Trans. Speech Audio Process. 6 (2002) 445–455. Google Scholar
7. R. Indrakantia, N. Haridasb and E. Eliasa , High performance continuous variable bandwidth digital filter design for hearing aid application, Int. J. Electron. Commun. (AEÜ) 92 (2018) 36–53. Web of Science, Google Scholar
8. S.-C. Pei and J.-J. Shyu , Design of two-dimensional FIR digital filters by McClellan transformation and least-squares contour mapping, Elsevier Signal Process. 44 (1999) 19–26. Web of Science, Google Scholar
9. H. C. Lu and K. H. Yeh , 2-D FIR filters design using least square error with scaling-free McClellan transformation, IEEE Trans. Circuits Syst. 47 (2000) 1104–1107. Google Scholar
10. H. C. Lu and K. H. Yeh , Optimal design of 2-D FIR digital filters by scaling-free McClellan transformation using least-squares estimation, Elsevier Signal Process 58 (1997) 303–308. Web of Science, Google Scholar
11. R. Rabiner Lawrence and W. Schafer Ronald , Recursive and nonrecursive realizations of digital filters designed by frequency sampling techniques, IEEE Trans. Audio Electroaccoustics 3 (1971) 200–207. Google Scholar
12. T. C. Speake and R. M. Mersereau , A comparison of different window formulations for two dimensional FIR filter design, IEEE Acoustics Speech Signal Processing Conf. Record (Washington USA, April 1979), pp. 5–8. Google Scholar
13. T. C. Speake and R. M. Mersereau , A note on the use of windows for two-dimensional FIR filter design, IEEE Trans. Acoust. Speech Signal Process 1 (1981) 125–127. Google Scholar
14. S. T. Tzeng , Design of 2-D FIR digital filters with specified magnitude and group delay responses by GA approach, Signal Process 87 (2007) 2036–2044. Web of Science, Google Scholar
15. K. Ros and F. Chikouche , An advanced genetic algorithm for designing 2-D FIR filters, IEEE Pacific Rim Conf. Communications, Computers and Signal Processing, pp. 23–26, Victoria BC, 2011, pp. 60–65. Google Scholar
16. A. Alizadegan, B. Asady and M. Ahmadpour , Two modified versions of artificial bee colony algorithm, Appl. Math. Comput. 225 (2013) 601–609. Web of Science, Google Scholar
17. W. F. Gao, S. Y. Liu and L. L. Huang , A novel artificial bee colony algorithm based on modified search equation and orthogonal learning, IEEE Trans. Cybernet. 43 (2013) 1011–1024. Web of Science, Google Scholar
18. S. Dhabal and P. Venkateswaran , Two-dimensional IIR filter design using simulated annealing based particle swarm optimization, J. Optim. 10 (2014). Google Scholar
19. S. Dhabal and P. Venkateswaran , An efficient g-best-guided Cuckoo Search algorithm for higher order two channel filter bank design, Swarm Evolut. Comput. 33 (2017) 68–84. Web of Science, Google Scholar
20. A. Aggarwal, M. Kumar, T. K. Rawat and D. K. Upadhyay , Optimal dsign of 2D FIR filters with quadrantally symmetric properties using fractional derivative constraints, Circuits Syst. Signal Process., https://doi.org/10.1007/s00034-016-0283. Google Scholar
21. E. Rashedi, H. Nezamabadi-pour and S. Saryazdi , GSA: A gravitational search algorithm, Inf. Sci. 179 (2009) 2232–2248. Web of Science, Google Scholar
22. M. Manuel and E. Elias , Design of sharp 2-d multiplier-less circularly symmetric fir filter using harmony search algorithm and frequency transformation, J. Signal Inf. Process. 3 344–351, https://doi.org/10.4236/jsip. 2012.33044. Google Scholar
23. L. H. Wu, Y. N. Wang and X. F. Yuan , Design of 2-d recursive filters using self-adaptive mutation differential evolution algorithm, Int. J. Comput. Intell. Syst. 4 (2011) 644–654. Web of Science, Google Scholar
24. C.-C. Tseng and S.-L. Lee , Designs of two-dimensional linear phase FIR filters using fractional derivative constraints, Signal Process. 93 (2013) 1141–1151. Web of Science, Google Scholar
25. S. Dhabal and P. Venkateswaran , A novel accelerated artificial bee colony algorithm for optimal design of two dimensional FIR filter, in Multidimensional Systems and Signal Processing, Vol. 28 (Springer, 2017), pp. 471–493. Google Scholar
26. C. Lv, S. Yan, G. Cheng, L. Xu and X. Tian , Design of two-dimensional IIR digital filters by using a novel hybrid optimization algorithm, Multidim. Syst. Sign. Process, https://doi.org/10.1007/s11045-016-0397-0. Web of Science, Google Scholar
27. S. Dhabal and P. Venkateswaran , An improved global-best-driven flower pollination algorithm for optimal design of two-dimensional FIR filter, Soft Comput. 23(18) (2018) 1–18. Web of Science, Google Scholar
28. M. Elhoseny, D. Oliva, V. O. -Enciso, A. E. Hassanien and M. Gunasekaran , Parameter identification of two dimensional digital filters using electro-magnetism optimization, Multimed. Tools Appl., https://doi.org/10.1007/s11042-018-6095-1. Web of Science, Google Scholar
29. B. Doğan, A modified vortex search algorithm for numerical function optimization, arXiv preprint arXiv:1606.02710 (2016). Google Scholar
30. S. Kockanat and N. Karaboga , The design approach of two-dimensional digital filters based on metaheuristic optimization algorithms: A review of the literature, Artif. Intell. Rev. (2015), https://doi.org/10.1007/s10462-014-9427-1. Web of Science, Google Scholar
31. A. Aggarwal, T. K. Rawat and D. K. Upadhyay , Optimal design of FIR high pass filter based on L1 error approximation using real coded genetic algorithm, Eng. Sci. Technol.Int. J. 18 (2015) 594–602. Google Scholar
32. A. Aggarwal, T. K. Rawat, M. Kumar and D. K. Upadhyay , Optimal design of FIR band stop filter using L1-norm based RCGA, Elsevier, Ain Shams Eng. J. 9(2) (2016) 277–289. Web of Science, Google Scholar
33. A. Aggarwal, T. K. Rawat and D. K. Upadhyay , Design of optimal digital FIR filters using evolutionary and swarm optimization techniques, Elsevier Int. J. Electron. Commun. 70 (2016) 373–385. Web of Science, Google Scholar
34. M. Kumar and T. K. Rawat , Optimal fractional delay-IIR filter design using cuckoo search algorithm, Elsevier ISA Trans. 59 (2015) 39–54. Web of Science, Google Scholar
35. M. Kumar and T. K. Rawat , Optimal design FIR fractional order differentiator using cuckoo search algorithm, Expert Syst. Appl. 42 (2015) 3433–3449. Web of Science, Google Scholar
36. A. Aggarwal, M. Kumar and T. K. Rawat , L1 error criterion based optimal FIR filter, India Conf. (INDICON), Annual IEEE (Delhi, India, 2014), pp. 1–6. Google Scholar
37. A. Aggarwal, T. K. Rawat, M. Kumar and D. K. Upadhyay , An L1-method: Application to digital symmetric type-II FIR filter design, in Intelligent Systems Technologies and Applications (Springer, Berlin, 2016), pp. 335–343. Google Scholar
38. A. Aggarwal, M. Kumar, T. K. Rawat and D. K. Upadhyay , Design of optimal 2-D FIR differentiators with quadrantally symmetric properties using the L1-method, Int. Conf. Signal Processing and Communication Systems (ICSPCS) (Australia, 2016), https://doi.org/10.1109/ICSPCS.2016.7843369. Google Scholar
39. A. Aggarwal, M. Kumar, T. K. Rawat and D. K. Upadhyay , Optimal design of 2-D FIR digital differentiator using L1-norm based cuckoo-search algorithm, Multidimens. Syst. Signal Process 28 (2017) 1569–1587. Web of Science, Google Scholar
40. A. Aggarwal, T. K. Rawat and D. K. Upadhyay , Optimal design of L1-norm based IIR digital differentiators and integrators using the bat algorithm, IET Signal Process. 11 (2017) 26–35. Web of Science, Google Scholar
41. M. Kumar, A. Aggarwal and T. K. Rawat , Bat algorithm: Application to adaptive infinite impulse response system identification, Arab. J. Sci. Eng. 41 (2016) 3587–3604. Web of Science, Google Scholar
42. M. Kumar and T. K. Rawat , Fractional order digital differentiator design based on power function and least-squares, Int. J. Electron 103 (2016) 1639–1653. Web of Science, Google Scholar
43. H. C. Lu and S. T. Tzeng , Design of two-dimensional FIR digital filters for sampling structure conversion by genetic algorithm approach, Signal Process. 80 (2000) 1445–1458. Web of Science, Google Scholar
44. K. Boudjelaba, F. Ros and D. Chikouche , An advanced genetic algorithm for designing 2-D FIR filters, IEEE Pacific Rim Conf. Communications, Computers and Signal Processing (Victoria, BC, Canada, 2011), pp. 60–65. Google Scholar
45. K. Boudjelaba, F. Ros and D. Chikouche , Adaptive genetic algorithm-based approach to improve the synthesis of two-dimensional finite impulse response filters, IET Signal Process. 8 (2014) 429–446. Web of Science, Google Scholar