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Spider monkey optimization method to design two channel quadrature mirror filter bank with linear phase

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Abstract

Spider monkey optimization (SMO) algorithm is a newly introduced nature inspired algorithm (NIA) for solving complex optimization problems based on fission and fusion social structure (FFSS). In this work, two channel linear phase quadrature mirror filter (QMF) bank has been designed using SMO algorithm and a nearly ideal system response is achieved by optimizing the filter tap weights of prototype filter. The overall objective function of the two channel QMF bank which is to be minimized is a linear combination of three main parameters: pass-band error, stop-band residual energy of the prototype filter and square error of the transfer function at quadrature frequency. Simulations have been performed for low and high order QMF banks and the obtained results show that the performance of the suggested novel approach is superior than already existing nature inspired and gradient based algorithms.

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References

  1. Croisier DE and Galand C (1976) Perfect channel splitting by use of interpolation/decimation/tree decomposition techniques. In: Proceedings of the International Symposium on Information Circuits and Systems, Patras, Greece

  2. Wang H et al (2020) Iterative technique for optimizing two-channel Quadrature mirror, bi-orthogonal and graph filter bank. Signal Process 169:107405. https://doi.org/10.1016/j.sigpro.2019.107405

    Article  Google Scholar 

  3. Vetterli M (1984) Multi-dimensional sub-band coding: some theory and algorithms. Signal Process 6(2):97–112

    Article  MathSciNet  Google Scholar 

  4. Sablatash M (2008) Designs and architectures of filter bank trees for spectrally efficient multi-user communications: review, modifications and extensions of wavelet packet filter bank trees. Signal Image Video Process 5(1):09–37

    Article  MATH  Google Scholar 

  5. Agrawal SK, Sahu OP (2013) Two-channel quadrature mirror filter bank: an overview. ISRN Signal Process Hindawi. https://doi.org/10.1155/2013/815619 (Article ID 815619)

    Article  Google Scholar 

  6. Kim YG et al (2022) An Efficient Compression Method of Underwater Acoustic Sensor Signals for Underwater Surveillance. Sensors 22(9):3415. https://doi.org/10.3390/s22093415

    Article  Google Scholar 

  7. Jou Y-D, Lin Z-P, Chen F-K (2018) Neural network based design of 2-channel quadrature mirror filter banks. Int J Circ Theory Appl 46(12):2349–2363

    Article  Google Scholar 

  8. Upendar J, Gupta CP, Singh GK (2010) Designing of two channel quadrature mirror filter bank using particle swarm optimization. Digit Signal Process 20(10):304–313

    Article  Google Scholar 

  9. Rafi SM, Kumar A, Singh GK (2013) An improved particle swarm optimization method for multi-rate filter bank design. J Frankl Inst 350(4):757–769

    Article  MATH  Google Scholar 

  10. Agrawal SK, Sahu OP (2014) Artificial bee colony algorithm to design two-channel quadrature mirror filter banks. Swarm Evolut Comput. https://doi.org/10.1016/j.swevo.2014.12.001

    Article  Google Scholar 

  11. Ghosh P, Das S, Zafar H (2012) Adaptive-differential-evolution-based design of two-channel quadrature mirror filter banks for sub-band coding and data transmission. IEEE Trans Syst Man Cybern Part C 42(6):1613–1623

    Article  Google Scholar 

  12. Dhabal S, Venkateswaran P (2017) An efficient gbest-guided Cuckoo Search algorithm for higher order two channel filter bank design. Swarm Evolut Comput 33:68–84

    Article  Google Scholar 

  13. Kuldeep B, Singh VK, Kumar A, Singh GK (2015) Design of two-channel filter bank using nature inspired optimization based fractional derivative constraints. ISA Transect 54(1):101–116

    Article  Google Scholar 

  14. Sahu OP, Soni MK, Talwar IM (2006) Marquardt optimization method to design two channel quadrature mirror filter banks. Digit Signal Process 16(6):870–879

    Article  Google Scholar 

  15. Kuldeep B, Kumar A, Singh GK (2015) Design of Quadrature mirror filter bank using Lagrange multiplier method based on fractional derivative constraints. Eng Sci Technol Int J 18:235–243

    Google Scholar 

  16. Ho CYK et al (2010) Two-channel linear phase FIR QMF bank minimax design via global nonconvex optimization programming. IEEE Trans Signal Process 58(8):4436–4441

    Article  MathSciNet  MATH  Google Scholar 

  17. Kumar A, Singh GK, Anand RS (2011) An improved method for the design of Quadrature mirror filter banks using the Levenberg-Marquardt optimization. Signal image Video Process 7(2):209–220

    Article  Google Scholar 

  18. Zhao R, Lai X and Lin Z (2018) An iterative reweighted minimax algorithm for the design of FRM-based two-channel QMF Banks. IEEE International Symposium on Circuits and Systems (ISCAS), Florence, pp 1–5

  19. Jou Y-D, Lin Z-P, Chen F-K (2018) Improved design of two-channel quadrature mirror filter using explicit toeplitz expressions. In: IEEE 7th Global Conference On Consumer Electronics (GCCE), pp 369–370

  20. Jou Y-D, Lin Z-P, Chen F-K (2017) Design of all-pass-based quadrature mirror filter banks using a lyapunov error criterion, signal processing, computer networks and telecommunication, Okinawa, Japan, ICIIBMS (IEEE), pp 39–42

  21. Singh H, Dwivedi AK, Nagaria D (2021) Quadrature mirror filter bank design based on hybrid bee colony technique. Automatika 62(2):264–274. https://doi.org/10.1080/00051144.2021.1933362

    Article  Google Scholar 

  22. Andrew L, Franques V, Jain V (1997) Eigen design of quadrature mirror filters. IEEE Trans Circ Syst II Analog Digital Signal Process 44(9):754–757

    Article  Google Scholar 

  23. Lu WS, Xu H, Antoniou A (1998) A new method for the design of FIR quadrature mirror-image filter banks. IEEE Trans Circ Syst II Analog Digital Signal Process 45(7):922–926

    Article  Google Scholar 

  24. Engelbrecht AP (2006) Fundamentals of computational swarm intelligence. Wiley, New York

    Google Scholar 

  25. Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems (no. 1). Oxford University Press, Berlin

    Book  MATH  Google Scholar 

  26. Bansal JC, Sharma H, Jadon SS, Clerc M (2014) Spider monkey optimization algorithm for numerical optimization. Memet Comput 6(1):31–47

    Article  Google Scholar 

  27. Vasundhara Devi R, Siva Sathya S (2017) Monkey behavior based algorithms—a survey. IJ Intell Syst Appl 12:67–86

    Google Scholar 

  28. Swami V, Kumar S, Jain S, Algorithm AISMO, Computing S (2017) Theories and applications. Adv Intell Syst Comput 583:73–81

    Google Scholar 

  29. Kaur A, Sharma H and Sharma N (2017) Disruption Operator-Based spider monkey optimization algorithm, International Conference on Computing, Communication and Automation (ICCCA), pp 216–221

  30. Kumar A, Singh GK, Anand RS (2010) An improved method for the designing quadrature mirror filter banks via unconstrained optimization. J Math Model Algorithm 9(1):99–111

    Article  MathSciNet  MATH  Google Scholar 

  31. Kumar A, Rafi SM, Singh GK (2012) A hybrid method for designing linear-phase quadrature mirror filter bank. Digital Signal Process 22(3):453–462

    Article  MathSciNet  Google Scholar 

  32. Himani, Agrawal SK (2020) Modified Gbest-Guided ABC algorithm approach applied to various nonlinear problems, optical and wireless technologies. Lecture notes in electrical engineering, vol 648. Springer, Singapore. https://doi.org/10.1007/978-981-15-2926-9_67

    Book  Google Scholar 

  33. Baderia K, Kumar A, Singh GK (2015) Design of quadrature mirror filter bank using polyphase components based on optimal fractional derivative constraints. Int J Electron Commun (AEÜ) 69(9):1254–1264

    Article  Google Scholar 

  34. Bansal S, Rattan M (2022) Design of cognitive radio system and comparison of modified whale optimization algorithm with whale optimization algorithm. Int J Inf Technol 14:999–1010. https://doi.org/10.1007/s41870-019-00346-2

    Article  Google Scholar 

  35. Usha-Kirana SP, D’Mello DA (2021) Energy-efficient enhanced Particle Swarm Optimization for virtual machine consolidation in cloud environment. Int J Inf Technol 13:2153–2161. https://doi.org/10.1007/s41870-021-00745-4

    Article  Google Scholar 

  36. Kaviarasan R, Arulmurgan A (2021) Salp swarm bio inspired algorithm for detecting non line of sight vehicles in VANETs. Int J Inf Technol 13:1601–1613. https://doi.org/10.1007/s41870-021-00697-9

    Article  Google Scholar 

  37. Nagpal R, Singh P, Garg BP (2019) Concurrent bacterial foraging with emotional intelligence for global optimization. Int J Inf Technol 11:313–320. https://doi.org/10.1007/s41870-018-0215-z

    Article  Google Scholar 

  38. Preeti DK (2017) Feature selection for face recognition using DCT-PCA and Bat algorithm. Int J Inf Technol 9:411–423. https://doi.org/10.1007/s41870-017-0051-6

    Article  Google Scholar 

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Authors and Affiliations

  1. Electronics and Communication Engineering Department, Government Women Engineering College, Ajmer, Rajasthan, 305002, India

    Surendra Kumar Agrawal &  Himani

  2. MNIT, Jaipur, India

    Himani

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  1. Surendra Kumar Agrawal
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Correspondence to Surendra Kumar Agrawal.

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Agrawal, S.K., Himani Spider monkey optimization method to design two channel quadrature mirror filter bank with linear phase. Int. j. inf. tecnol. 15, 499–510 (2023). https://doi.org/10.1007/s41870-022-01132-3

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