[
1. Proakis, J. G., D. K. Manolakis. Digital Signal Processing: Principles, Algorithms and Applications. Pearson, India, 2007.
]Search in Google Scholar
[
2. Antoniou, A. Digital Signal Processing. Tata McGraw-Hill, 2006.
]Search in Google Scholar
[
3. Gotmare, A., S. S. Bhattacharjee, R. Patidar, N. V. George. Swarm and Evolutionary Computing Algorithms for System Identification and Filter Design: A Comprehensive Review. – Swarm Evol. Comput., Vol. 32, 2017, pp. 68-84. https://doi.org/10.1016/j.swevo.2016.06.00710.1016/j.swevo.2016.06.007
]Search in Google Scholar
[
4. Wang, X. H., Y. L. Jiao, Y. C. Niu, Y. Jie. Optimizing Signal De-Noising Algorithm for Acoustic Emission Leakage of Wavelet. – Cybernetics and Information Technologies, Vol. 16, 2016, No 1, pp. 116-125.10.1515/cait-2016-0009
]Search in Google Scholar
[
5. Miller, T. R., R. J. Hagge, J. W. Wallis, K. S. Sampathkumaran. Interactive Digital Filtering of Gated Cardiac Studies During Cine Display. – IEEE Trans. Med. Imaging, Vol. 7, 1988, pp. 188-192. https://doi.org/10.1109/42.778010.1109/42.778018230467
]Search in Google Scholar
[
6. Shajun Nisha, S., S. P. Raja. Multiscale Transform and Shrinkage Thresholding Techniques for Medical Image Denoising – Performance Evaluation. – Cybernetics and Information Technologies, Vol. 20, 2020, No 3, pp. 130-146.10.2478/cait-2020-0033
]Search in Google Scholar
[
7. Widmark, S. Causal IIR Audio Precompensator Filters Subject to Quadratic Constraints. – IEEE/ACM Trans, Audio Speech Lang. Process., Vol. 26, 2018, pp. 1897-1912. https://doi.org/10.1109/TASLP.2018.283935510.1109/TASLP.2018.2839355
]Search in Google Scholar
[
8. Boudjelaba, K., F. Ros, D. Chikouche. Potential of Particle Swarm Optimization and Genetic Algorithms for FIR Filter Design. – Circuits, Syst. Signal Process, Vol. 33, 2014, pp. 3195-3222. https://doi.org/10.1007/s00034-014-9800-y10.1007/s00034-014-9800-y
]Search in Google Scholar
[
9. Kumar, J. T., B. M. S. Rani, M. S. Kumar, M. V. Raju, K. M. Das. Performance Evaluation of Change Detection in SAR Images Based on Hybrid Antlion DWT Fuzzy c-Means Clustering. – Cybernetics and Information Technologies, Vol. 21, 2021, No 2, pp. 45-57.10.2478/cait-2021-0018
]Search in Google Scholar
[
10. Shao, P., Z. Wu, X. Zhou, D. C. Tran. FIR Digital Filter Design Using Improved Particle Swarm Optimization Based on Refraction Principle. – Soft Comput., Vol. 21, 2017, pp. 2631-2642. https://doi.org/10.1007/s00500-015-1963-310.1007/s00500-015-1963-3
]Search in Google Scholar
[
11. Ji, D. The Application of Artificial Bee Colony (ABC) Algorithm in FIR Filter Design. – In: Proc. of 12th IEEE International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD’16). IEEE, 2016, pp. 663-667. DOI: 10.1109/FSKD.2016.7603253.
]Open DOISearch in Google Scholar
[
12. Ababneh, J. I., M. H. Bataineh. Linear Phase FIR Filter Design Using Particle Swarm Optimization and Genetic Algorithms. – Digit. Signal Process. A Rev. J., Vol. 18, 2008, pp. 657-668. https://doi.org/10.1016/j.dsp.2007.05.01110.1016/j.dsp.2007.05.011
]Search in Google Scholar
[
13. Kennedy, J., R. C. Eberhart. Particle Swarm Optimization. – In: Proc. of IEEE Int. Conf. Neural Networks 1995, Vol. 4, 1995, pp. 1942-1948
]Search in Google Scholar
[
14. Aggarwal, A., T. K. Rawat, D. K. Upadhyay. Design of Optimal Digital FIR Filters Using Evolutionary and Swarm Optimization Techniques AEU. – Int. J. Electron. Commun., Vol. 70, 2016, pp. 373-385. https://doi.org/10.1016/j.aeue.2015.12.01210.1016/j.aeue.2015.12.012
]Search in Google Scholar
[
15. Mandal, S., S. P. Ghoshal, R. Kar, D. Mandal. Design of Optimal Linear Phase FIR High Pass Filter Using Craziness Based Particle Swarm Optimization Technique J. King Saud Univ. – Comput. Inf. Sci., Vol. 24, 2012, pp. 83-92. https://doi.org/10.1016/j.jksuci.2011.10.00710.1016/j.jksuci.2011.10.007
]Search in Google Scholar
[
16. Vasundhara, D. Mandal, R. Kar, S. P. Ghoshal. Digital FIR Filter Design Using Fitness Based Hybrid Adaptive Differential Evolution with Particle Swarm Optimization. – Nat. Comput., Vol. 13, 2014, pp. 55-64. https://doi.org/10.1007/s11047-013-9381-x10.1007/s11047-013-9381-x
]Search in Google Scholar
[
17. Thangaraj, R., M. Pant, A. Abraham, P. Bouvry. Particle Swarm Optimization: Hybridization Perspectives and Experimental Illustrations. – Appl. Math. Comput., Vol. 217, 2011, pp. 5208-5226. https://doi.org/10.1016/j.amc.2010.12.05310.1016/j.amc.2010.12.053
]Search in Google Scholar
[
18. Londhe, N. D., A. K. Dwivedi, S. Ghosh. Low Power 2D Finite Impulse Response Filter Design Using Modified Artificial Bee Colony Algorithm with Experimental Validation Using Field-Programmable Gate Array. – IET Sci. Meas. Technol., Vol. 10, 2016, pp. 671-678. https://doi.org/10.1049/iet-smt.2016.006910.1049/iet-smt.2016.0069
]Search in Google Scholar
[
19. Mittal, T. Design of Optimal FIR Filters Using Integrated Optimization Technique. – Circuits, Syst. Signal Process., Vol. 40, 2021, pp. 2895-2925. https://doi.org/10.1007/s00034-020-01602-810.1007/s00034-020-01602-8
]Search in Google Scholar
[
20. Grossmann, L. D., Y. C. Eldar. An L1-Method for the Design of Linear-Phase FIR Digital Filters. – Signal Process. IEEE Trans., Vol. 55, 2007, pp. 5253-5266.10.1109/TSP.2007.896088
]Search in Google Scholar
[
21. Yang, X.-S., S. Deb. Cuckoo Search via Levy Flights. – In: Proc. of 2009 World Congress on Nature Biologically Inspired Computing (NaBIC’09), IEEE, Coimbatore, India, 2009, pp. 210-214.10.1109/NABIC.2009.5393690
]Search in Google Scholar
[
22. Mareli, M., B. Twala. An Adaptive Cuckoo Search Algorithm for Optimisation. – Appl. Comput. Informatics, 2017, pp. 1-9. https://doi.org/10.1016/j.aci.2017.09.00110.1016/j.aci.2017.09.001
]Search in Google Scholar
[
23. Barthelemy, P., J. Bertolotti, D. S. Wiersma. A Lévy Flight for Light. – Nature, Vol. 453, 2008, pp. 495-498. https://doi.org/10.1038/nature0694810.1038/nature0694818497819
]Search in Google Scholar