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Artificial Intelligence Review

, Volume 52, Issue 3, pp 1629–1653 | Cite as

A review on the cosine modulated filter bank studies using meta-heuristic optimization algorithms

  • Gokcen OzdemirEmail author
  • Nurhan Karaboga
Article

Abstract

There has been a significant increase in the number of designs based on optimization techniques as the usage areas of computers have increased day by day. In this paper, Cosine Modulated Filter Banks (CMFBs) using meta-heuristic optimization techniques are reviewed. The basic features of the meta-heuristic optimization algorithms which used in related studies and the purpose of these algorithms in CMFB design are explored. The paper begins with a definition of CMFBs and continues with meta-heuristic algorithms used in CMFB studies. Later, the meta-heuristic algorithms used to design the CMFBs are briefly described. Finally, it is reviewed where algorithms are used in the CMFB designs. This study aims to clarify the role of meta-heuristic algorithms in CMFB design. The main contribution of the study to the literature is not just describing meta-heuristic algorithms, the proposed methods in the literature to design the CMFB are fully described and compared.

Keywords

Cosine modulated filter bank FIR filter Heuristic Optimization algorithms Hybrid optimization 

References

  1. Agarwal R, Sudhakar R (1983)Multiplier-less design of FIR filters. In: ICASSP ’83. IEEE international conference on acoustics, speech, and signal processing, Apr pp 209–212.  https://doi.org/10.1109/icassp.1983.1172211
  2. Alhava J, Viholainen A (2000) Coefficient quantization in nearly perfect-reconstruction cosine-modulated filter banks. In: 2000 IEEE Proceedings international conference on acoustics, speech, and signal processing, vols. I–Vi, pp 536–539Google Scholar
  3. Argenti F, Brogelli B, DelRe E (1996) Cosine-modulated non-uniform filter banks. In: 1996 IEEE Proceedings on digital signal processing workshop, pp 129–132.  https://doi.org/10.1109/Dspws.1996.555477
  4. Baderia K, Kumar A, Singh GK (2015) Design of multi-channel filter bank using ABC optimized fractional derivative constraints. In: 2015 International conference on communications and signal processing (Iccsp). pp 490–494Google Scholar
  5. Baderia K, Kumar A, Singh GK (2016) An improved method for designing cosine modulated filter bank using polyphase components. In: 2016 3rd International conference on signal processing and integrated networks (SPIN), 11-12 Feb. pp 9–13.  https://doi.org/10.1109/spin.2016.7566653
  6. Baicher GS (2007) Optimal design of a class of M-channel uniform filter bank using genetic algorithms. In: ICSPC: 2007 IEEE international conference on signal processing and communications, vols 1–3, pp 1515–1518Google Scholar
  7. Beaulieu FD, Champagne B (2009) Design of prototype filters for perfect reconstruction DFT filter bank transceivers. Signal Process 89:87–98.  https://doi.org/10.1016/j.sigpro.2008.07.014 CrossRefzbMATHGoogle Scholar
  8. Bindiya TS, Elias E (2015) Design of multiplier-less sharp transition width non-uniform filter banks using gravitational search algorithm. Int J Electron 102:48–70.  https://doi.org/10.1080/00207217.2014.905992 CrossRefGoogle Scholar
  9. Bindiya TS, Elias E (2016a) Design of FRM-based MDFT filter banks in the canonic signed digit space using modified meta-heuristic algorithms. Int J Signal Imaging 9:20–37.  https://doi.org/10.1504/Ijsise.2016.074649
  10. Bindiya TS, Elias E (2016b) Meta-heuristic evolutionary algorithms for the design of optimal multiplier-less recombination filter banks. Inf Sci 339:31–52.  https://doi.org/10.1016/j.ins.2015.12.018 CrossRefGoogle Scholar
  11. Blum C, Puchinger J, Raidl GR, Roli A (2011) Hybrid metaheuristics in combinatorial optimization: a survey. Appl Soft Comput 11:4135–4151.  https://doi.org/10.1016/j.asoc.2011.02.032 CrossRefzbMATHGoogle Scholar
  12. Bregovic R, Saramaki T (2002) An efficient approach for designing nearly perfect-reconstruction low-delay cosine-modulated filter banks. In: 2002 IEEE International symposium on circuits and systems, vol I, pp. 825–828Google Scholar
  13. Cao YJ, Wu QH (1999) Optimization of control parameters in genetic algorithms: a stochastic approach. Int J Syst Sci 30:551–559.  https://doi.org/10.1080/002077299292290 CrossRefzbMATHGoogle Scholar
  14. Chan SC, Liu W, Ho KL (2001) Multiplierless perfect reconstruction modulated filter banks with sum-of-powers-of-two coefficients. IEEE Signal Proc Lett 8:163–166.  https://doi.org/10.1109/97.923040 CrossRefGoogle Scholar
  15. Chan SC, Xie XM, Yuk TI (2000) Theory and design of a class of cosine-modulated non-uniform filter banks. In: 2000 IEEE international conference on acoustics, speech, and signal processing, proceedings, vols I–Vi, pp 504–507Google Scholar
  16. Chen B, Qi X, Sun X, Shi Y-Q (2017) Quaternion pseudo-Zernike moments combining both of RGB information and depth information for color image splicing detection. J Vis Commun Image R.  https://doi.org/10.1016/j.jvcir.2017.08.011 Google Scholar
  17. Chen ZY et al. (2015) Unobtrusive sensing incremental social contexts using fuzzy class incremental learning. In: 2015 IEEE International conference on data mining (ICDM). pp 71–80.  https://doi.org/10.1109/Icdm.2015.156
  18. Chris Prema S, Dasgupta SK (2016) An iterative design with variable step prototype filter for cosine modulated filter bank. Radioengineering 25:156–160CrossRefGoogle Scholar
  19. Cruz-Roldan F, Monteagudo-Prim M (2004) Efficient implementation of nearly perfect reconstruction FIR cosine-modulated filterbanks. IEEE Trans Signal Process 52:2661–2664.  https://doi.org/10.1109/Tsp.2004.831913 CrossRefGoogle Scholar
  20. Cruz-Roldan F, Salcedo-Sanz S, Portilla-Figueras JA, Gimeno-Martinez N (2007) Evolutionary programming techniques for designing M-channel cosine modulated filter banks. In: IEEE International symposium on, pp 279–284Google Scholar
  21. Cruz-Roldan F, Santamaria I, Bravo AM (2004) Frequency sampling design of prototype filters for nearly perfect reconstruction cosine-modulated filter banks. IEEE Signal Proc Lett 11:397–400.  https://doi.org/10.1109/Lsp.2003.821663 CrossRefGoogle Scholar
  22. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New YorkzbMATHGoogle Scholar
  23. Eberhart R, Kennedy JA (1995) new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, 1995. MHS ’95., 4–6 Oct. pp 39–43.  https://doi.org/10.1109/mhs.1995.494215
  24. Fogel LJ, Owens AJ, Walsh MJ (1965) Intelligent decision-making through a simulation of evolution. IEEE Trans Hum Factors Electron HFE 6:13–23.  https://doi.org/10.1109/thfe.1965.6591252 CrossRefGoogle Scholar
  25. Gao X et al (2017) Sparse online learning of image similarity. ACM Trans Intell Syst Technol 8:1–22.  https://doi.org/10.1145/3065950 Google Scholar
  26. Gao XY, Chen ZY, Tang S, Zhang YD, Li JT (2016) Adaptive weighted imbalance learning with application to abnormal activity recognition. Neurocomputing 173:1927–1935.  https://doi.org/10.1016/j.neucom.2015.09.064 CrossRefGoogle Scholar
  27. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(72):60–68CrossRefGoogle Scholar
  28. Giron-Sierra JM (2017) Digital signal processing with matlab examples. Springer, Berlin.  https://doi.org/10.1007/978-981-10-2537-2 CrossRefGoogle Scholar
  29. Gordana J-D (ed) (2002) Multirate systems: design and applications. IGI Publishing, HersheyGoogle Scholar
  30. Gu B, Sheng VS (2017) A robust regularization path algorithm for $nu $ -support vector classification. IEEE Trans Neural Netw Learn Syst 28:1241–1248.  https://doi.org/10.1109/tnnls.2016.2527796 CrossRefGoogle Scholar
  31. Gupta L, Ingale V, Nalbalwar S (2016) R peak detection using cosine modulated filter bank for HRV analysis of Normal Sinus Rhythm and SVT. In: 2016 International conference on advanced communication control and computing technologies (ICACCCT), 25–27 May . pp 801–805.  https://doi.org/10.1109/icaccct.2016.7831749
  32. Hansen P, Mladenović N (2001) Variable neighborhood search: principles and applications. Eur J Oper Res 130:449–467.  https://doi.org/10.1016/S0377-2217(00)00100-4 MathSciNetCrossRefzbMATHGoogle Scholar
  33. Hansen P, Mladenović N, Moreno Pérez JA (2008) Variable neighbourhood search. Methods Appl 4OR 6:319–360.  https://doi.org/10.1007/s10288-008-0089-1 MathSciNetzbMATHGoogle Scholar
  34. Holland JH (1975) Adaptation in natural and artificial systems : an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Ann ArborzbMATHGoogle Scholar
  35. Ibarra-Manzano OG, Jovanovic-Dolecek G (1999) Cosine-modulated FIR filter banks satisfying perfect reconstruction: an iterative algorithm. In: Proceedings 42nd midwest symposium on circuits and systems, vol 1 and 2, pp 1061–1064Google Scholar
  36. Imran M, Hashim R, Khalid NEA (2013) An overview of particle swarm optimization variants. Proc Eng 53:491–496.  https://doi.org/10.1016/j.proeng.2013.02.063 CrossRefGoogle Scholar
  37. Jeong-Jin L, Byeong Gi L (1995) A design of nonuniform cosine modulated filter banks. IEEE Trans Circ Syst II Anal Digit Signal Process 42:732–737.  https://doi.org/10.1109/82.475253 CrossRefGoogle Scholar
  38. Jun S, Bin F, Wenbo X (2004) Particle swarm optimization with particles having quantum behavior. In: Proceedings of the 2004 congress on evolutionary computation (IEEE Cat. No.04TH8753), 19-23 June, vol 321, pp 325–331.  https://doi.org/10.1109/cec.2004.1330875
  39. Kalathil S, Elias E (2015) Non-uniform cosine modulated filter banks using meta-heuristic algorithms in CSD space. J Adv Res 6:839–849.  https://doi.org/10.1016/j.jare.2014.06.008 CrossRefGoogle Scholar
  40. Kalathil S, Elias E (2016) Design of multiplier-less sharp non-uniform cosine modulated filter banks for efficient channelizers in software defined radio. Eng Sci Technol Int J 19:147–160.  https://doi.org/10.1016/j.jestch.2015.06.003 CrossRefGoogle Scholar
  41. Kalathil S, Kumar BS, Elias E (2015) Efficient design of multiplier-less digital channelizers using recombination non-uniform filter banks. J King Saud Univ Eng Sci.  https://doi.org/10.1016/j.jksues.2015.11.002 Google Scholar
  42. Kalathil S, Elias E (2013) Design of multiplier-less cosine modulated filter banks with sharp transition using evolutionary algorithms international. J Comput Appl 68:1–9.  https://doi.org/10.5120/11748-7025 Google Scholar
  43. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes university, engineering faculty, computer engineering departmentGoogle Scholar
  44. Karaboga D (2010) Artificial bee colony algorithm. Scholarpedia 5:6915.  https://doi.org/10.4249/scholarpedia.6915 CrossRefGoogle Scholar
  45. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39:459–471.  https://doi.org/10.1007/s10898-007-9149-x MathSciNetCrossRefzbMATHGoogle Scholar
  46. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8:687–697.  https://doi.org/10.1016/j.asoc.2007.05.007 CrossRefGoogle Scholar
  47. Karaboga D, Gorkemli BA(2012) A quick artificial bee colony-qABC-algorithm for optimization problems. In: 2012 International symposium on innovations in intelligent systems and applications, 2-4 July, pp 1–5.  https://doi.org/10.1109/inista.2012.6247010
  48. Karaboga D, Gorkemli B (2014) A quick artificial bee colony (qABC) algorithm and its performance on optimization problems. Appl Soft Comput 23:227–238.  https://doi.org/10.1016/j.asoc.2014.06.035 CrossRefGoogle Scholar
  49. Kennedy J (1997) The particle swarm: social adaptation of knowledge. In: IEEE International conference on evolutionary computation 1997, 13–16 Apr. pp 303–308.  https://doi.org/10.1109/icec.1997.592326
  50. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Neural networks. In: IEEE International Conference on 1995. Nov/Dec, vol. 1944, pp 1942–1948.  https://doi.org/10.1109/icnn.1995.488968
  51. Koilpillai RD, Vaidyanathan PP (1991) New results on cosine-modulated fir filter banks satisfying perfect reconstruction Icassp 91, vols 1-5, pp. 1793–1796Google Scholar
  52. Koilpillai RD, Vaidyanathan PP (1992) Cosine-modulated fir filter banks satisfying perfect reconstruction. IEEE Trans Signal Process 40:770–783.  https://doi.org/10.1109/78.127951 CrossRefGoogle Scholar
  53. Kuldeep B, Kumar A, Singh GK (2015a) Design of multi-channel cosine-modulated filter bank based on fractional derivative constraints using cuckoo search algorithm. Circ Syst Signal Process 34:3325–3351.  https://doi.org/10.1007/s00034-015-0008-6 CrossRefGoogle Scholar
  54. Kuldeep B, Kumar A, Singh GK (2015b) PSO based optimized fractional derivative constraints for designing M-channel filter bank. In: 2015 International conference on signal processing, computing and control (ISPCC), 24-26 Sept. 2015 pp 140–144.  https://doi.org/10.1109/ispcc.2015.7375013
  55. Kumar A, Singh GK, Anurag S (2015) An optimized cosine-modulated nonuniform filter bank design for subband coding of ECG signal. J King Saud Univ Eng Sci 27:158–169.  https://doi.org/10.1016/j.jksues.2013.10.001 Google Scholar
  56. Lee JJ, Lee BG (1995) A design of nonuniform cosine-modulated filter banks. IEEE Trans Circ II 42:732–737.  https://doi.org/10.1109/82.475253 Google Scholar
  57. Li JL, Nguyen TQ, Tantaratana S (1997) A simple design method for near-perfect-reconstruction nonuniform filter banks. IEEE Trans Signal Proces 45:2105–2109.  https://doi.org/10.1109/78.611222 CrossRefGoogle Scholar
  58. Lin YP, Vaidyanathan PP (1995) Linear-phase cosine-modulated maximally decimated filter banks with perfect reconstruction. IEEE Trans Signal Process 43:2525–2539.  https://doi.org/10.1109/78.482104 CrossRefGoogle Scholar
  59. Liu H, Yang Z, Cao Z (2012) Design perfect reconstruction cosine-modulated filter bank by variable neighbourhood search-least-mean-square error. IET Signal Process 6:273–280.  https://doi.org/10.1049/iet-spr.2011.0077 MathSciNetCrossRefGoogle Scholar
  60. Liu Q, Cai W, Shen J, Fu Z, Liu X, Linge N (2016) A speculative approach to spatial-temporal efficiency with multi-objective optimization in a heterogeneous cloud environment. Secur Commun Netw 9:4002–4012.  https://doi.org/10.1002/sec.1582 CrossRefGoogle Scholar
  61. Malvar HS (1990) Modulated QMF filter banks with perfect reconstruction. Electron Lett 26:906–907.  https://doi.org/10.1049/El:19900592 CrossRefGoogle Scholar
  62. Malvar HS (1992) Extended lapped transforms: properties, applications, and fast algorithms. IEEE Trans Signal Process 40:2703–2714.  https://doi.org/10.1109/78.165657 CrossRefzbMATHGoogle Scholar
  63. Manoj VJ, Elias E (2008) Design of cosine modulated filterbank transmultiplexer for unknown channels using genetic algorithm. Tencon IEEE Region 385–389Google Scholar
  64. Manoj VJ, Elias E (2009a) Design of multiplier-less nonuniform filter bank transmultiplexer using genetic algorithm. Signal Process 89:2274–2285.  https://doi.org/10.1016/j.sigpro.2009.05.006
  65. Manoj VJ, Elias E (2009b) On the design of multiplier-less nonuniform filterbank transmultiplexer using particle swarm optimization. World Cong Nat Biol.  https://doi.org/10.1109/Nabic.2009.5393600
  66. Manoj VJ, Elias E (2012) Artificial bee colony algorithm for the design of multiplier-less nonuniform filter bank transmultiplexer. Inf Sci 192:193–203.  https://doi.org/10.1016/j.ins.2011.02.023 CrossRefGoogle Scholar
  67. Martin P, Cruz-Roldan F, Saramaki T (2003) A windowing approach for designing critically sampled nearly perfect-reconstruction cosine-modulated transmultiplexers and filter banks ISPA. In: 2003 Proceedings of the 3rd international symposium on image and signal processing and analysis, Pts 1 and 2, pp 755–760Google Scholar
  68. Mertins A (1999) Signal analysis: wavelets, filter banks, time-frequency transforms, and applications. Wiley, England.  https://doi.org/10.1002/0470841834 CrossRefzbMATHGoogle Scholar
  69. Milic L (2009) Multirate filtering for digital signal processing: MATLAB applications. Information Science Reference - Imprint of: IGI Publishing.  https://doi.org/10.4018/978-1-60566-178-0
  70. Mladenović N, Hansen P (1997) Variable neighborhood search. Comput Oper Res 24:1097–1100.  https://doi.org/10.1016/S0305-0548(97)00031-2 MathSciNetCrossRefzbMATHGoogle Scholar
  71. Nasir M, Mondal AK, Sengupta S, Das S, Abraham A (2011) An improved multiobjective evolutionary algorithm based on decomposition with fuzzy dominance. In: 2011 IEEE congress on evolutionary computation (Cec) pp 765–772Google Scholar
  72. Nasir M, Sengupta S, Das S (2012) Efficient design of cosine-modulated filter banks using evolutionary multi-objective optimization swarm, evolutionary, and memetic computing. SEMCCO 7677:785–792Google Scholar
  73. Nguyen TQ, Laakso TI, Tuncer TE (1994) On perfect-reconstruction allpass-based cosine-modulated IIR filter banks. IEEE Int Symp Circ Syst 2:B33–B36CrossRefGoogle Scholar
  74. Ozdemir G, Karaboga N (2017a) Analysis of the effects of control parameters’ variation of ABC algorithm used in filter bank design on the performance. Akıllı Sistemlerde Yenilikler ve Uygulamaları Konferansı, Asyu2017, Antalya, 5-7 OctoberGoogle Scholar
  75. Ozdemir G, Karaboga N(2017b) Design of M-Channel uniform cosine modulated filter bank using qABC Algorithm. In: International workshop on mathematical methods in engineering, MME2017, Ankara, Turkey, 27-29 April. p 111Google Scholar
  76. Ozdemir G, Karaboga N, Koza (2017)T Performance comparison of two channel CMFB and QMF bank designed via ABC algorithm. In: 2017 25th Signal processing and communications applications conference (SIU), 15-18 May . pp 1–4.  https://doi.org/10.1109/siu.2017.7960422
  77. Pan Z, Zhang Y, Kwong S (2015) Efficient motion and disparity estimation optimization for low complexity multiview video coding. IEEE Trans Broadcast 61:166–176.  https://doi.org/10.1109/tbc.2015.2419824 CrossRefGoogle Scholar
  78. Raghu I, Chowdary SS, Elias E (2016) Efficient spectrum sensing for cognitive radio using cosine modulated filter banks. In: 2016 IEEE region 10 conference (TENCON), 22-25 Nov. pp 2086–2089.  https://doi.org/10.1109/tencon.2016.7848393
  79. Ramstad TA, Tanem JP (1991) Cosine-modulated analysis-synthesis filterbank with critical sampling and perfect reconstruction. ICASSP 91, vols 1–5, pp 1789–1792Google Scholar
  80. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248.  https://doi.org/10.1016/j.ins.2009.03.004 CrossRefzbMATHGoogle Scholar
  81. Rothweiler J (1983) Polyphase quadrature filters–a new subband coding technique. In: IEEE International conference on ICASSP ’83 acoustics, speech, and signal processing, Apr . pp 1280–1283.  https://doi.org/10.1109/icassp.1983.1172005
  82. Sakthivel V, Elias E (2015) Design of low complexity sharp MDFT filter banks with perfect reconstruction using hybrid harmony-gravitational search algorithm. Eng Sci Technol Int J 18:648–657.  https://doi.org/10.1016/j.jestch.2015.03.012 CrossRefGoogle Scholar
  83. Saramaki T (1992) Designing prototype filters for perfect-reconstruction cosine-modulated filter banks. In: 1992 IEEE International symposium on circuits and systems, vol 1-6, pp 1605–1608Google Scholar
  84. Shaeen K, Elias E (2015) Prototype filter design approaches for near perfect reconstruction cosine modulated filter banks: a review. J Signal Process Syst 81:183–195.  https://doi.org/10.1007/s11265-014-0929-5 CrossRefGoogle Scholar
  85. Sharma I, Kumar A, Singh GK (2016a) Adjustable window based design of multiplier-less cosine modulated filter bank using swarm optimization algorithms. AEU-Int J Electron C 70:85–94.  https://doi.org/10.1016/j.aeue.2015.10.008 CrossRefGoogle Scholar
  86. Sharma I, Kumar A, Singh GK, Lee HN (2016b)Design of multiplierless cosine modulated filterbank using hybrid technique in sub-expression space. In: 2016 IEEE International conference on digital signal processing (DSP), 16-18 Oct. 2016 . pp 360–364.  https://doi.org/10.1109/icdsp.2016.7868579
  87. Sharma K, Sharma A (2016) Design of Cosine Modulated Filter Banks exploiting spline function for spectrum sensing in Cognitive Radio applications. In: 2016 IEEE 1st international conference on power electronics, intelligent control and energy systems (ICPEICES), 4-6 July . pp 1–5.  https://doi.org/10.1109/icpeices.2016.7853205
  88. Shi Y, Eberhart RA(1998) modified particle swarm optimizer. In: 1998 IEEE International conference on evolutionary computation proceedings. IEEE World congress on computational intelligence (Cat. No.98TH8360), 4-9 May 1998. pp 69–73.  https://doi.org/10.1109/icec.1998.699146
  89. Srinivas M, Patnaik LM (1994) Genetic algorithms: a survey. Computer 27:17–26.  https://doi.org/10.1109/2.294849 CrossRefGoogle Scholar
  90. Su DB, Xu WB, Sun J (2009) Quantum-behaved particle swarm optimization with crossover operator. In: Proceedings of the 2009 international conference on wireless networks and information systems pp 399–402.  https://doi.org/10.1109/Wnis.2009.74
  91. Sun J, Xu WB, Feng B (2005a) Adaptive parameter control for quantum-behaved particle swarm optimization on individual level. In: International conference on systems, man and cybernetics, vol 1–4, pp 3049–3054Google Scholar
  92. Sun J, Xu WB, Liu J (2005b) Parameter selection of quantum-behaved particle swarm optimization. Proc Adv Nat Comput 3612:543–552Google Scholar
  93. Tan F, Zhang T, Gao C, Huang L(2011) Optimal design of Cosine Modulated Filter Banks using quantum-behaved particle swarm optimization algorithm. In: 2011 4th International congress on image and signal processing (CISP), 15-17 Oct. 2011. pp 2280–2284.  https://doi.org/10.1109/cisp.2011.6100708
  94. Vaidyanathan PP (1993) Multirate systems and filter banks. Prentice-Hall, Upper Saddle RiverzbMATHGoogle Scholar
  95. Vasant P (2012) Meta-heuristics optimization algorithms in engineering, business, economics, and finance. IGI GlobalGoogle Scholar
  96. Vishwakarma A, Kumar A, Singh GK (2017) Design of near-perfect-reconstructed transmultiplexer using different modulation techniques: a comparative study. J King Saud Univ Eng Sci 29:257–263.  https://doi.org/10.1016/j.jksues.2015.10.007 Google Scholar
  97. Walton S, Hassan O, Morgan K, Brown MR (2011) Modified cuckoo search: a new gradient free optimisation algorithm. Chaos Solitons Fractals 44:710–718.  https://doi.org/10.1016/j.chaos.2011.06.004 CrossRefGoogle Scholar
  98. Wen X, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295:395–406.  https://doi.org/10.1016/j.ins.2014.10.040 CrossRefGoogle Scholar
  99. Xi ML, Sun J, Xu WB (2007) Quantum-behaved particle swarm optimization with elitist mean best position. Complex Syst Appl Modeling Control Simul 14:1643–1647Google Scholar
  100. Xia ZH, Wang XH, Zhang LG, Qin Z, Sun XM, Ren K (2016) A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans Inf Foren Secur 11:2594–2608.  https://doi.org/10.1109/Tifs.2016.2590944 CrossRefGoogle Scholar
  101. Xie XM (2007) Design of nonuniform cosine-modulated filter-banks with the perfect-reconstruction property and arbitrary filter lengths. Prog Nat Sci 17:340–345CrossRefzbMATHGoogle Scholar
  102. Xu M, Xu WB (2008) Parameter estimation of complex functions based on quantum-behaved particle swarm optimization algorithm. Dcabes 2008 Proceedings, Vols I and Ii:591-596Google Scholar
  103. Xu WB, Sun J (2005) Adaptive parameter selection of quantum-behaved particle swarm optimization on global level. Proc Adv Intell Comput Pt 1 3644:420–428Google Scholar
  104. Xue Y, Jiang J, Zhao B, Ma T (2017) A self-adaptive artificial bee colony algorithm based on global best for global optimization. Soft Comput.  https://doi.org/10.1007/s00500-017-2547-1 Google Scholar
  105. Yang KQ, Nomura H (2008) Quantum-behaved particle swarm optimization with chaotic search. IEICE Trans Inf Syst 91:1963–1970.  https://doi.org/10.1093/ietisy/e91-d.7.1963 CrossRefGoogle Scholar
  106. Yang X-S (2009) Harmony search as a metaheuristic algorithm. In: Geem ZW (ed) Music-inspired harmony search algorithm: theory and applications. Springer, Berlin, pp 1–14.  https://doi.org/10.1007/978-3-642-00185-7_1 Google Scholar
  107. Yang X-S (2010) Nature-inspired metaheuristic algorithms: second Edition. Luniver PressGoogle Scholar
  108. Yang XS, Suash D (2009) Cuckoo search via levy flights. In: 2009 World congress on nature & biologically inspired computing (NaBIC), 9-11 Dec. 2009. pp 210–214.  https://doi.org/10.1109/nabic.2009.5393690
  109. Yanhua Z, Sun X, Baowei W (2016) Efficient algorithm for k-barrier coverage based on integer linear programming. China Commun 13:16–23.  https://doi.org/10.1109/cc.2016.7559071 CrossRefGoogle Scholar
  110. Yao X, Liu Y, Lin GM (1999) Evolutionary programming made faster. IEEE Trans Evolut Comput 3:82–102.  https://doi.org/10.1109/4235.771163 CrossRefGoogle Scholar
  111. Yu Y, Zhao H (2017) Proportionate NSAF algorithms with sparseness-measured for acoustic echo cancellation. AEU Int J Electron Commun 75:53–62.  https://doi.org/10.1016/j.aeue.2017.03.009 CrossRefGoogle Scholar
  112. Zhang QF, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evolut Comput 11:712–731.  https://doi.org/10.1109/Tevc.2007.892759 CrossRefGoogle Scholar
  113. Zhang ZJ (2007) Efficient design of cosine modulated filter banks based on gradient information. IEEE Signal Proc Lett 14:940–943.  https://doi.org/10.1109/Lsp.2007.906624 CrossRefGoogle Scholar
  114. Zhang ZJ, Shui PL, Su T (2008) Efficient design of high-complexity cosine modulated filter banks using 2Mth band conditions. IEEE Trans Signal Process 56:5414–5426.  https://doi.org/10.1109/Tsp.2008.929672 MathSciNetCrossRefzbMATHGoogle Scholar
  115. Zhang ZJ, Yang Y (2008) Efficient design of low delay nonuniform cosine modulated filter banks based on gradient information. Chin J Electron 17:567–570Google Scholar
  116. Zhou ZL, Wang YL, Wu QMJ, Yang CN, Sun XM (2017) Effective and efficient global context verification for image copy detection. IEEE Trans Inf Foren Secur 12:48–63.  https://doi.org/10.1109/Tifs.2016.2601065 CrossRefGoogle Scholar
  117. Zhu Y, Huang C, Tao W (2010) Frequency domain optimization design of linear phase cosine modulated filter banks. In: 2010 International conference on measuring technology and mechatronics automation, 13-14 March . pp 313–316.  https://doi.org/10.1109/icmtma.2010.363

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© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.The Department of Electrical and Electronic EngineeringErciyes UniversityMelikgazi, KayseriTurkey

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