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9 - Soft Computing based CAD Packages for EM Applications

Published online by Cambridge University Press:  05 July 2016

Balamati Choudhury  and
Rakesh Mohan Jha
Balamati Choudhury
Affiliation:
National Aerospace Laboratories
Rakesh Mohan Jha
Affiliation:
National Aerospace Laboratories
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Summary

This chapter covers the use of soft computing for creating various CAD packages. These packages deliver a range of features including efficiency, quick response as well as an easy-to-use user interface that allows even users not well versed with soft computing to arrive at optimized solutions for their applications. In the chapter, the development of two such CAD packages is discussed: one for the design of metamaterial split ring resonators and the other for path loss prediction in rural and urban environments. The CAD package for metamaterial structures uses PSO for design optimization whereas the path loss prediction CAD package uses neural network. Soft computing techniques based CAD package are gaining momentum in the field of electromagnetic (EM) applications because of their properties such as global optimization, quick response and accuracy [Mishra and Patnaik, 1993].

CAD Package for Metamaterial Structures

Metamaterials are artificially designed EM structures that acts as an effective medium with negative refractive index at a desired frequency of operation. Split ring resonators (SRR) are proven metamaterial structures for various applications such as metamaterial based antennas, radar absorbing materials, THz absorbers for biomedical applications, frequency selective surfaces, invisibility cloaks [Kwon et al., 2007; Jin and Samii, 2007; Goudos and Sahalos, 2006; Ivsic et al., 2010], etc. Due to the inherent narrowband operation of the split-ring resonators, it is critical to ensure that the resonant frequency of the same is equal to that of the application.

Therefore, a CAD package is developed here to obtain the optimized structural parameters for different configurations of the metamaterial SRR for a desired frequency of operation. This CAD package uses particle swarm optimization (PSO), which is based on the movement and intelligence of swarms as discussed in Chapter 2, for optimizing difficult multidimensional discontinuous problems. The equivalent circuit analysis (ECA) method is used here as an electromagnetic solution tool for SRR design optimization. Hence PSO in conjunction with ECA method provides optimal structural parameters of the different configurations of SRRs.

Equivalent circuit analysis of square SRR

The analysis of metamaterial structures is carried out using equivalent circuit analysis method where the metamaterial SRR is represented as equivalent circuit of lumped elements. Several metamaterial configurations, such as single ring, double ring, and triple ring square SRR are considered. In addition to these structures, circular SRR is also analyzed.

Type
Chapter
Information
Soft Computing in Electromagnetics
Methods and Applications
 , pp. 182 - 204
Publisher: Cambridge University Press
Print publication year: 2016

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References

Baena, J. D., J., Bonache, F., Martin, R. M., Silero, F., Falcone, T., Lopetagi, M. A. G., Laso, J., Garcia-Garcia, I., Gil, M. F., Portilo, and M., Sorolla, “Equivalent-circuit models for split ring resonators and complementary split ring resonators coupled to planar transmission lines,” IEEE Transactions on Microwave Theory and Techniques, vol. 53, pp. 1451–1461, Apr. 2005.Google Scholar
Bilotti, F., A., Toscano, and L., Vegni, “Design of spiral and multiple split-ring resonators for the realization of miniaturized metamaterial samples,” IEEE Transactions on Antennas and Propagation, vol. 55, pp. 2258–2267, 2007a.Google Scholar
Bilotti, F., A., Toscano, L., Vegni, K., Aydin, K. B., Alici, and E., Ozbay “Equivalent-circuit models for the design of metamaterials based on artificial magnetic inclusions,” IEEE Transactions on Microwave Theory and Techniques, vol. 55, pp. 2865–2873, Dec. 2007b.Google Scholar
Choudhury, B., Sangeetha, M., and R.M., Jha, “Particle swarm optimization for multiband metamaterial fractal antenna,” Journal of Optimization, vol. 2013, DOI: 10.1155/2013/989135, pp. 1–8, 2013a.Google Scholar
Choudhury, B., Thiruveni, B., P. V., Reddy, and R.M., Jha “Soft computing techniques for terahertz metamaterial RAM design for biomedical applications,” Computers, Materials & Continua, vol. 37, no. 3, pp. 135–146, 2013b.Google Scholar
Devabhaktuni, V. K., M. C. E., Yagoub, Y., Fang, J., Xu, and Q., Zhang, “Neural networks for microwave modeling: Model development issues and nonlinear modeling techniques,” International Journal of RF and Microwave Computer Aided Engineering, vol. 11, pp. 4–21, 2001.Google Scholar
Fan, J. W., C. H., Liang, and X. W., Dai, “Design of cross-coupled dual–band filter with equallength split-ring resonators,” Proceedings of Progress in Electromagnetics Research, PIER 75, pp. 285–293, 2007.Google Scholar
Goudos, S. K., and J. N., Sahalos, “Microwave absorber optimal design using multi-objective particle swarm optimization,” Microwave and Optical Technology Letters, vol. 48, pp. 1553–1558, Aug. 2006.Google Scholar
Hata, M. “Empirical formula for propagation loss in land mobile radio services,” IEEE Transactions on Vehicular Technology, vol. 29, pp. 317–325, 1980.Google Scholar
Haykins, S., Neural Networks: A Comprehensive Foundation, Prentice Hall International, NJ, ISBN: 9780139083853, 842p., 1999.
Ivsic, B., T., Komljenovic, and Z., Sipus, “Time and frequency domain analysis of uniaxial multilayer cylinders used for invisible cloak realization,” Proceedings of conference on ICECom, pp. 1–5, Sep. 2010.Google Scholar
Jin, N. and Y. R., Samii, “Particle swarm optimization of miniaturized quadrature reflection phase structure for low-profile antenna applications,” Proceedings of IEEE Antennas and Propagation Society International Symposium, vol. 2, pp. 255–258, Jul. 2005.Google Scholar
Kennedy, J. and R., Eberhart, “Particle swarm optimization,” Proceedings of IEEE International Conference on Neural Networks (ICNN–95), vol. IV, pp.1942–1948, 1995.Google Scholar
Kwon, H., Z., Bayraktar, D. H., Werner, U. K., Chettiar, A. V., Kildishev, and V. M., Shalaev, “Nature-based optimization of 2D negative-index metamaterials,” Proceedings of IEEE Antennas and Propagation Society International Symposium, pp. 1589–1592, Jun. 2007.Google Scholar
Mishra, R. K. and A., Patnaik, “Neural network-based CAD model for the design of square patch antenna,” IEEE Transactions on Antennas and Propagation, vol. 46, pp. 1890–1891, Dec. 1998.Google Scholar
Mukherjee, S., R., Chaudhuri, and C., Saha, “Square split ring resonator: A new approach in estimation of resonant frequency,” Proceedings of 12th Symposium on Antennas and Propagation, APSYM 2010, pp. 146–150, Dec., 2010.Google Scholar
Oishi, J., K., Asakura, and T., Watanabe, “A communication model for inter-vehicle communication simulation systems based on properties of urban areas,” International Journal of Computer Science and Network Security, vol. 6, no.10, pp. 213–219, Oct. 2006.Google Scholar
Pradeep, A., S., Mridula, and P., Mohanan, “Design of an edge-coupled dual-ring split ring resonator,” IEEE Antennas and Propagation Magazine, vol. 53, no. 4, pp. 45–54, Aug. 2011.Google Scholar
Raida, Z., “Modeling EM structures in neural network toolbox of Matlab,” IEEE Antennas and Propagation Magazine, vol. 44, no. 6, pp. 46–67, 2002.Google Scholar
Rappaport, T., Wireless Communications: Principles and Practice, Prentice-Hall, NJ, ISBN: 9788131728826, 707p., 1996.
Robinson, J. and Y. R., Samii, “Particle swarm optimization in electromagnetics,” IEEE Transactions on Antennas and Propagation., vol. 52, no. 12, pp. 397–407, 2004.Google Scholar
Sotiroudis, S. P., K., Siakavara, and J. N., Sahalos, “A neural network approach to the prediction of the propagation path-loss for mobile communications systems in urban environments” Proceeding of Progress in Electromagnetic Research Symposiym, pp. 162–166, Aug. 2007.Google Scholar

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