1258 results in Electromagnetics
2 - Electromagnetic Scattering: The Vector Model
- Natalia K. Nikolova, McMaster University, Ontario
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- Introduction to Microwave Imaging
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- 08 July 2017
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- 13 July 2017, pp 111-153
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7 - Looking Forward: Nonlinear Reconstruction
- Natalia K. Nikolova, McMaster University, Ontario
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- Introduction to Microwave Imaging
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- 08 July 2017
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- 13 July 2017, pp 290-296
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Summary
This book has introduced the reader to the principles of EM scattering theory along with the basic linear (or direct) reconstruction approaches of MW and millimeter-wave imaging. It has been emphasized that the linear reconstruction methods suffer from limitations stemming from the linearizing approximations of the forward model of scattering. The linearized models are incapable of taking into account multiple scattering and mutual-coupling effects. Such effects dominate the scattering in complex heterogeneous objects such as living tissue, luggage items, and structural components in civil engineering.
This is why the forefront of MW imaging research is focused on reconstruction approaches that can tackle nonlinear scattering. Yet, research and development in MW nonlinear reconstruction demand familiarity with the basics of scattering theory and linear reconstruction along with some understanding of the nature of the MW signals and measurements. Familiarity with linear reconstruction methods is especially important because these are often employed as modules in nonlinear inversion strategies.
It is the author's hope that this text has aided the novice on a difficult journey through a thick forest of equations in mathematical physics and an overwhelming gallery of clever inversion techniques to the highly technical subject of MW metrology. By no means is the present text exhaustive. Numerous references have been provided throughout and many more exist in the scientific and engineering literature. MW imaging is a vast and dynamic field of research, and one has to keep an eye on new developments.
But most importantly, it is the author's hope that the reader will continue this journey. The next big step is nonlinear reconstruction. The monograph of Pastorino [46] dedicates special attention to this subject and contains an extensive list of references. The applications of microwaves in medical imaging almost exclusively employ nonlinear strategies. Overviews of these applications can be found in [93, chapter 7], [257], and [134, 135].
Here is a taste of what lies ahead. The nonlinear reconstruction approaches share two common characteristics. First, they are quantitative, i.e., they are, in principle, capable of recovering the complex permittivity distribution of the object under test (OUT). This is a consequence of their second common characteristic: they do not employ linearizing approximations regarding the internal field distribution in the OUT such as the Born or Rytov approximations that we discussed in Sections 1.13 through 1.18.
6 - Performance Metrics in Imaging
- Natalia K. Nikolova, McMaster University, Ontario
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- Introduction to Microwave Imaging
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- 08 July 2017
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- 13 July 2017, pp 266-289
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Summary
There are several basic metrics used to evaluate the performance of a MWimaging system. They share similar definitions and methods of evaluation with the optical, acoustic, magnetic-resonance, or X-ray imaging systems. These metrics can be divided into two groups: (i) those that measure the quality of the raw data produced by the acquisition hardware and (ii) those that measure the quality of the final images.
The choice of metrics used to evaluate the quality of the final images is dependent on whether the images are qualitative or quantitative. The accuracy of the quantitative images is usually evaluated with the relative root-mean-square error (RRMSE), which we already used in some examples; see Eq. (4.98). This approach requires an experiment with an object, the permittivity distribution of which is known exactly. The evaluation of the accuracy of a qualitative image is somewhat subjective because it depends on how the image is displayed and interpreted. This is why, in medical imaging, various metrics have been introduced such as the signal-to-noise ratio of an ideal observer (SNRI), the image contrast resolution, the spectrum of the noise equivalent quanta (NEQ), the SNR of the decision statistics, the detective quantum efficiency, and others; see, e.g., [238, 239, 240]. They are studied in relation to the clinical specificity and sensitivity of an imaging method. These metrics are not common in MW imaging and they are beyond the scope of this text.
Here, we focus on the first group of metrics since they are independent of the reconstruction approach used to process the data. The most common metrics related to the performance of the hardware are: (i) the best achievable spatial resolution, where indicates the direction in which the resolution is assessed, (ii) the data signal-to-noise ratio SNRd, (iii) the data dynamic range Dd, and (iv) the physical contrast sensitivity.
We start with the spatial resolution, which dictates the choice of the frequency and the bandwidth of the system. Moreover, it plays a role in the design of the acquisition surfaces and the mutual placement of the transmitting (Tx) and the receiving (Rx) antennas. It should be noted that the spatial resolution of an image is also dependent on the specifics of the reconstruction algorithm and whether evanescent-field information is available in the acquired data.
3 - Scattering Parameters in Microwave Imaging
- Natalia K. Nikolova, McMaster University, Ontario
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- Introduction to Microwave Imaging
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- 13 July 2017, pp 154-181
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Summary
The scattering parameters, also known as S-parameters, are the most common network parameters used to describe the performance of the microwave (MW) devices, circuits, and networks in the frequency domain. They are also the measured quantities in frequency-sweep measurements. The most widely used MW test instrument is the vector network analyzer (VNA), which measures accurately and efficiently the Sparameters of N-port networks. The most common VNAs have 2 ports, i.e., N = 2, but VNAs with 3, 4, 8, and even 24 ports are commercially available. Moreover, radiofrequency (RF) switches are available that can increase the number of ports that a VNA can handle.
In MW imaging, the illuminating and receiving antennas together with the measurement setup and the imaged object form a MW network. This is illustrated in Fig. 3.1. If the setup consists of N antennas, the VNA (possibly with an additional RF switch) must have N ports to which the antennas are connected with precision coaxial cables and connectors. The cables and the connectors come in many varieties and have widely varying performance characteristics. Note that using high-quality cables and connectors is critical for a good (repeatable) measurement. It is this network's S-parameters that comprise the data. Since the VNA measures one frequency at a time, we have a subset of S-parameter data at each frequency. At each frequency, one port (one antenna) at a time is excited while all other antennas (marked as Rx in Fig. 3.1) receive. This allows for acquiring the respective transmission S-parameters. The reflection S-parameter at the terminals of the transmitting (Tx) antenna is also acquired. This is repeated for each antenna (each port) in the setup. Thus, at each frequency we have a set of N ×N S-parameters, which form the so-called scattering matrix or S-matrix of the network.
Basics ofS-Parameters
Electrical engineers are familiar with the various parameters used to characterize an N-port (2N-terminal) network such as the Z-parameters (the impedance matrix), the Y -parameters (the admittance matrix), the H-parameters (the hybrid matrix), and the ABCD parameters of 2-ports (the transmission matrix). All of these parameter sets relate the voltages and the currents at the network's ports.
Introduction to Microwave Imaging
- Natalia K. Nikolova
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- 08 July 2017
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With this self-contained, introductory text, readers will easily understand the fundamentals of microwave and radar image generation. Written with the complete novice in mind, and including an easy-to-follow introduction to electromagnetic scattering theory, it covers key topics such as forward models of scattering for interpreting S-parameter and time-dependent voltage data, S-parameters and their analytical sensitivity formulae, basic methods for real-time image reconstruction using frequency-sweep and pulsed-radar signals, and metrics for evaluating system performance. Numerous application examples and practical tutorial exercises provided throughout allow quick understanding of key concepts, and sample MATLAB codes implementing key reconstruction algorithms accompany the book online. This one-stop resource is ideal for graduate students taking introductory courses in microwave imaging, as well as researchers and industry professionals wanting to learn the fundamentals of the field.
List of Tables
- Balamati Choudhury, Rakesh Mohan Jha
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- Soft Computing in Electromagnetics
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- 04 January 2016, pp xvii-xviii
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5 - PSO for Radar Absorbers
- Balamati Choudhury, Rakesh Mohan Jha
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- Soft Computing in Electromagnetics
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Summary
As the name suggests, absorbers are devices that absorb electromagnetic radiation incident on them. Absorbers are hence used in applications where minimum reflection is desired such as construction of anechoic chambers, stealth aircraft, etc. Absorbers are also used to enhance the performance of detectors in various imaging systems like terahertz spectroscopy. Absorbers generally comprise of layers of different material placed one behind the other. Due to the nature of its construction, absorbers are extremely band specific. The selection of the material parameters and thickness of these layers determines the frequency and bandwidth of operation. This selection process is complex and time consuming as the designer must focus on the combination of material as well as its thickness simultaneously.
In this chapter, particle swarm optimization (PSO) is used to optimize the absorbers in a time efficient manner. First, the implementation of PSO for optimising conventional microwave absorbers is discussed. Following this, PSO based optimization of a metamaterial terahertz absorber for biomedical applications is presented.
Introduction
An electromagnetic absorber is a structure that ideally absorbs all the incident electromagnetic radiation without any transmission or reflection. This is achieved by selecting materials of specific dimensions. Often, designs employ the arrangement of multiple layers of varying dimensions in order to achieve maximum absorption. At the same time, applications in stealth technology imposes another constraint on the design namely that of thickness. These two design parameters conflict each other and the designer is forced to arrive at a trade-off between the two. As mentioned previously, this task is time-consuming. As a result, researchers have turned towards soft-computing in order to design optimized RAM structures.
Abundant literature is available for implementation of genetic algorithm and micro-genetic algorithm for RAM optimization. Chakravarty et al. [Chakravarty et al., 2001] used the same in order to design an FSS based broadband microwave absorber. The work also shows that implementation of micro-genetic algorithm over genetic algorithm considerably speeds up the computation time. The algorithm was designed to simultaneously select the best materials and their thicknesses as well as vary the structural parameters of the FSS for optimized performance.
A novel idea for the fabrication of ultrathin absorbers using electromagnetic band gap materials was presented by Kern et al. [Kern et al., 2003]. The technique involved replacing previously known FSS-resistive sheet designs with a lossy, high impedance FSS layer.
6 - Characterization of Planar Transmission Lines Using ANN
- Balamati Choudhury, Rakesh Mohan Jha
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- Soft Computing in Electromagnetics
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- 04 January 2016, pp 111-123
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Summary
Advancements in microwave integrated circuits (MIC) are occurring at a rapid pace. Features of MIC such as small size and the possibility of realization of entire systems on a single chip have led to the increase in the usage of these devices in communication systems. One of the integral elements of these MICs is transmission lines. Transmission lines are not just used to connect individual modules inside the MIC but they are also essential in the realization of components like filters, mixers, couplers, and power dividers. Popular types of transmission lines used include microstrip lines, strip lines, grounded waveguides, slotlines, etc. As a result, these transmission lines must be designed carefully and analyzed; deviations in the design will lead to errors in the operation of the MIC. Traditional methods of analysis of transmission lines include conformal transformation, variational method, spectral domain method, and numerical methods like finite element method (FEM), finite difference method, and finite difference time domain method. These methods are extremely accurate and have been documented to produce results that concur with experimental observations. However, complex mathematical formulation and iterative nature imply high computational time requirement [Bhatt and Koul, 1990; Schellenberg, 1995]. This quality limits the application of these techniques in situations where quick solutions are required such as CAD packages for transmission line design. The algorithms based on artificial intelligence could be used in order to create such CAD packages. Yildiz et al. [Yildiz et al., 2004] used neural network for analysis of co-planar waveguide. The same algorithm was further used for the analysis of inverted microstrip lines [Yildiz and Saracogulu, 2003]. Although the analysis of planar transmission lines using neural network has been explored, the design of various configurations of transmission lines using NN is yet to be done.
In this chapter, the analysis and the design of various configurations of transmission lines, namely the microstrip line and the slotline, are conducted using artificial neural networks. The corresponding formulation and ANN implementation are discussed in detail.
Planar Transmission Lines
As mentioned earlier, different types of transmission lines are used in the fabrication of MIC depending upon the requirement. In this chapter, the focus is limited to two types of planar transmission lines viz. microstrip transmission lines and slotline transmission line.
3 - Soft Computing in Electromagnetics: A Review
- Balamati Choudhury, Rakesh Mohan Jha
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- Soft Computing in Electromagnetics
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Summary
Soft computing finds application in a wide range of problems in both engineering and nonengineering fields. Chapter 1 of this book discusses the potential applications of soft computing in fields ranging from engineering to finance, and architecture among others, etc. As the focus of this book is on design optimization of electromagnetic applications, it is necessary to understand the common optimization problems, and the advances and solutions to overcome them. Hence, a comprehensive review of soft computing techniques with a focus on electromagnetic applications is reported in this chapter.
Overview
An important aspect of electromagnetic applications is design and optimization towards actual hardware realization. In this chapter, an extensive literature survey of the soft computing techniques for electromagnetic applications has been carried out. It is observed that artificial neural network (ANN) and genetic algorithm (GA) has been employed extensively for diverse microwave engineering applications [Choudhury et al., 2012]. In contrast, emerging soft computing techniques like particle swarm optimization (PSO) and bacterial foraging optimization (BFO) have not been explored comprehensively for these applications. Hence, soft computing techniques for various microwave engineering applications such as antenna engineering, frequency selective surfaces, radar absorber design applications, microwave devices, etc., are systematically reviewed in this chapter. This chapter also identifies the emerging trends and suitability of different soft computing techniques for various electromagnetic design and optimization problems.
Radar Absorbers
As the name suggests, electromagnetic absorbers are devices that absorb any incident radiation. In other words, the reflection off, and transmission through these devices is zero and the entire incident energy is absorbed by the materials present in the absorbers. The resonant properties of these absorbers are dependent on the constituent material, structure, and morphology. Conventionally, most absorbers are multi-layer in nature and consist of multiple dielectrics stacked one above the other. The thicknesses of these layers play an important role in the performance of the absorber. In addition, advances in the field of metamaterials have resulted in the use of metamaterials in absorber designs. A multi-band metamaterial absorber is given in Fig. 3.1 along with the absorption characteristic. It is seen that each peak in the absorption characteristic corresponds to a ring in the metamaterial structure [Shen et al., 2011]. Therefore, it is clear that designing of radar absorbers is a complicated task and requires careful manipulation of material and structural properties.
Symbols
- Balamati Choudhury, Rakesh Mohan Jha
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- Soft Computing in Electromagnetics
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Acknowledgments
- Balamati Choudhury, Rakesh Mohan Jha
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- Soft Computing in Electromagnetics
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Subject Index
- Balamati Choudhury, Rakesh Mohan Jha
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- Soft Computing in Electromagnetics
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Author Index
- Balamati Choudhury, Rakesh Mohan Jha
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- Soft Computing in Electromagnetics
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Contents
- Balamati Choudhury, Rakesh Mohan Jha
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- Soft Computing in Electromagnetics
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Frontmatter
- Balamati Choudhury, Rakesh Mohan Jha
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- Soft Computing in Electromagnetics
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8 - Multi-Objective Particle Swarm Optimization for Active Terahertz Devices
- Balamati Choudhury, Rakesh Mohan Jha
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- Soft Computing in Electromagnetics
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Summary
The terahertz spectrum is gaining momentum in a wide range of fields including astronomy, material characterization, tumour detection, security and detection of concealed items, etc. among others. The main obstacle in the rapid growth of terahertz applications is the lack of natural dielectrics, and of high power, efficient sources. Emergence of metamaterial science and technology has attracted researchers to design and develop terahertz devices using these artificially engineered materials. These metamaterial structures are highly resonant, restricting the terahertz applications to narrow bands. This issue can be sorted out by designing active metamaterials.
As discussed in the Chapter 5, mathematical formulation for the design of metamaterial based structures is complicated and soft computing has proven to be an efficient method to arrive at design solutions. Further, terahertz devices themselves often are complex designs being multilayer in nature with an embedded metamaterial layer. Further, to make the devices tunable, mechanisms such as the MEMS switches, diodes, etc., should be incorporated in the design. In order to achieve an optimized design with these complex mechanisms one ought to go for a multi-objective optimization computational engine. This issue has been discussed in detail in this chapter via a tunable terahertz absorber design.
Introduction to Terahertz Technology
Terahertz refers to the range of frequencies that lies within the microwave and infrared (IR) bands. It can be loosely defined as the band between 0.1–10 THz. At times, the term “submillimetre band” is used to describe frequencies lying in the 0.3–3 THz band. While systems and applications operating in the microwave and IR ranges have been well established over decades, the development of terahertz technology has been slow. However, in recent times, this technology has evolved rapidly to find major applications relating directly to human lives, especially in the field of biomedical imaging and sample identification.
Properties of terahertz spectrum
The primary reason for this slow developmental trend was the lack of terahertz sources during the early years, coupled with the fact that this range displays a natural break-down point in electric and magnetic properties in conventional materials [Ferguson and Zhang, 2009, Smith et al., 2004]. This property put constraints on the material used in the fabrication of terahertz devices.
List of Figures
- Balamati Choudhury, Rakesh Mohan Jha
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1 - Introduction
- Balamati Choudhury, Rakesh Mohan Jha
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Summary
Today's world can be best described by the term global village. Exploration, trade, commerce and scientific advancements have resulted in the breakdown of physical boundaries like mountains and oceans. While the credit for this virtual shrinking of the world is often attributed to the aforementioned factors, the biggest leap in this direction is a direct consequence of the discovery and subsequent improvements in communication systems—particularly wireless communication systems. The performance of these systems is an outcome of their constituent elements—elements whose designs are governed by electromagnetic (EM) formulations. Therefore, the contribution of electromagnetics in shaping the world as we currently know it, cannot be ignored. The demand for better communication systems has resulted in a demand for high performance electromagnetic structures, and as well as accurate, reliable and fast techniques to solve electromagnetic problems.
Almost in parallel with this demand for high performance in electromagnetics, a novel computing technique, called soft computing, is gaining popularity in a multitude of applications. This technique differs from conventional computing techniques by not relying on strict mathematical formulations. In fact, soft computing technique often seeks to emulate biological systems like neural networks, swarm behaviour, etc. Today, soft computing is increasingly being used to tackle non-linear, computationally intensive problems in engineering. Therefore, it is not surprising that these techniques find a comfortable niche in the field of electromagnetics, where there is a ubiquitous need for optimization.
Design and Optimization Scenarios
While the initial intention of soft computing was to address the problems in engineering design and optimization, the versatility of these techniques resulted in its application to almost all areas of day-to-day life including finance, humanities and medicine. A brief outlook on soft computing for these applications is discussed here.
Engineering applications
Soft computing plays an important role in providing cost effective and efficient means towards attaining the final objective of any engineering application, i.e., actual hardware realization. Hence, industrial applications of soft computing techniques in various fields such as microwave engineering, aerospace engineering, power systems, robotics, etc., are discussed here.
Design and optimization in microwave engineering applications is an important aspect that has been explored by various researchers.
Preface
- Balamati Choudhury, Rakesh Mohan Jha
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Summary
At this point, we are at the throes of two revolutions — one is the information revolution and the other less visible one…. is the intelligent systems revolution.
—Lofti ZadehEver since the days of Aristotle, classical scientific thinking has been based on strict logic, well-constructed definitions and mathematical expressions. This approach to science changed drastically when Dr Lofti Zadeh published his famous paper ‘Fuzzy sets. Information and Control’ in 1965. By introducing imprecision in science, Dr Zadeh created in-roads into developing greater understanding in the field of artificial intelligence and even certain areas of philosophy and psychology! This imprecision, he claims, had led to a revolution in intelligent systems that has affected the way we live.
Today, the idea conceived by Dr Zadeh has grown into a whole new field of science—the field of soft-computing. Algorithms that attempt to mimic animal and human behaviour, evolution, etc., have been developed and implemented in problems ranging from scientific ones to even problems in economics and humanities! Certain researchers have also noted that soft computing techniques offer an alternate methodology to solve mathematically intensive problems.
The extension of this wondrous computation technique into one of sciences most mathematically challenging field, that of electromagnetics, is not surprising. This book address the implementation of soft computing in numerous, common electromagnetic problems. In doing so, computationally intensive, time consuming, three-dimensional electromagnetic simulations may be replaced by these fast-converging algorithms, thereby simplifying the process of electromagnetic design. This realization has led to a concerted effort by the Center for Electromagnetics, CEM (to which the authors are affiliated) towards improving existing research in soft computing. This book is a culmination of these efforts.
Accurate, reliable and fast optimization techniques are a priori requirements to cater to the demand for high performance, real time electromagnetic design objectives. Soft computing techniques are emerging as important tools in design and optimization of various complex electromagnetic problems. In view of this, an attempt has been made in this book to cover soft-computing based solutions to such EM problems. A brief overview of the topics covered in the book is given below.
Resolving problems such as fault detection and compensation in active antenna arrays are important for the aerospace community; finding out real time, cost effective solutions to these problems will help in handling critical situations.
9 - Soft Computing based CAD Packages for EM Applications
- Balamati Choudhury, Rakesh Mohan Jha
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- Soft Computing in Electromagnetics
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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.