PROPOSING A CRISS-CROSS METAMATERIAL STRUCTURE FOR IMPROVEMENT OF PERFORMANCE PARAMETERS OF MICROSTRIP ANTENNAS

2014 ◽  
Vol 52 ◽  
pp. 145-152 ◽  
Author(s):  
Kirti Inamdar ◽  
Yogesh Pasad Kosta ◽  
Suprava Patnaik
Keyword(s):  
Microstrip Antennas ◽  
Performance Parameters ◽  
Metamaterial Structure

scholarly journals Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Taimoor Khan ◽  
Asok De
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prior Knowledge ◽  
Neural Model ◽  
Microstrip Antennas ◽  
Performance Parameters ◽  
Dual Frequency ◽  
Knowledge Based ◽  
Slot Size ◽  
Good Agreement

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results.

Antenna Miniaturization Using Flexible PBG Materials

2003 ◽  
Author(s):  
Pekka Salonen ◽  
Lauri Syda¨nheimo ◽  
Mikko Keskilammi
Keyword(s):  
Stop Band ◽  
Microstrip Antennas ◽  
Major Effect ◽  
Radiation Efficiency ◽  
Performance Parameters ◽  
Market Place ◽  
Integrated Antennas ◽  
Improved Performance ◽  
Flexible Antenna ◽  
High Dielectric

Antennas play a paramount role today’s communication centered market place. Recently the demands for miniaturization of electronic devices have increased rapidly in which a miniaturization of integrated antennas has confronted the same development. However, antennas and especially the performance parameters of antennas obey physical laws in which the electrical dimensions of an antenna have a major effect on these parameters such as voltage standing wave ratio (SWR) and radiation efficiency. Recently, a new, multidisciplinary field of study called “Electromagnetic BandGap” (EBG) structures have been developed. An EBG structure forms a lattice whose period determines its resonant frequency i.e. the range of frequencies where the stop band exists for transmission of microwave signals. Antennas physical dimensions can be made noticeably smaller applying EBG materials. These advantages of EBG structures allow us to design smaller antennas with high radiation efficiency on high-dielectric substrates such as ceramics. This paper presents how conventional microstrip antennas can be miniaturized using EBG materials with improved performance parameters. In addition, a novel flexible antenna is presented which can be rolled up during e.g. transportation.

Performance parameters prediction of slotted microstrip antennas with modified ground plane using support vector machine

2016 ◽  
Vol 9 (5) ◽  
pp. 1169-1177 ◽  
Author(s):  
Chandan Roy ◽  
Taimoor Khan ◽  
Binod Kumar Kanaujia
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Resonant Frequency ◽  
Ground Plane ◽  
Microstrip Antennas ◽  
Support Vector ◽  
Performance Parameters ◽  
Local Minima ◽  
Vector Machines ◽  
Good Agreement

Artificial neural networks (ANNs) have acquired enormous importance in computing of the performance parameters of microstrip antennas due to their generalized and adaptive features. However, recently the concept of support vector machines (SVMs) has become very much popular in performance parameters computation due to several attractive features over ANNs. Specifically, SVMs outreach ANNs noticeably in terms of execution time. Likewise, ANNs are having multiple local minima problem, whereas a global and unique solution is provided by SVMs. In this paper, several performance parameters like: resonant frequency, gain, directivity, and radiation efficiency of slotted microstrip antennas with modified ground plane are computed with the help of SVM formulation. Comparisons of different parameters of simulated and computed values are illustrated. The achieved radiation patterns at particular resonant frequency in different planes are included as well. A prototype of the optimized antenna is also fabricated and characterized. A good agreement is attained among the computed, simulated, and measured results.

scholarly journals Estimation of Different Performance Parameters of Slotted Microstrip Antennas with Air-Gap Using Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Taimoor Khan ◽  
Asok De
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Neural Model ◽  
Microstrip Antennas ◽  
Air Gap ◽  
Performance Parameters ◽  
Dual Frequency ◽  
The Past ◽  
Resonance Frequencies ◽  
Good Agreement

Over the past decade, artificial neural networks have emerged as fast computational medium for predicting different performance parameters of microstrip antennas due to their learning and generalization features. This paper illustrates a neural network model for instantly predicting the resonance frequencies, gains, directivities, antenna efficiencies, and radiation efficiencies for dual-frequency operation of slotted microstrip antennas with air-gap. The proposed neural model is valid for any arbitrary slot-dimensions and inserted air-gap within their specified ranges. A prototype is fabricated using Roger’s substrate and its performance is measured for validation. A very good agreement is achieved in simulated, predicted, and measured results.

scholarly journals A Generalized ANN Model for Analyzing and Synthesizing Rectangular, Circular, and Triangular Microstrip Antennas

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Taimoor Khan ◽  
Asok De
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Microstrip Antennas ◽  
Computational Technique ◽  
Performance Parameters ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

Since last one decade, artificial neural network (ANN) models have been used as fast computational technique for different performance parameters of microstrip antennas. Recently, the concept of creating a generalized neural approach for different performance parameters has been motivated in microstrip antennas. This paper illustrates a generalized neural approach for analyzing and synthesizing the rectangular, circular, and triangular MSAs, simultaneously. Such approach is very much required for the antenna designers for getting instant answer for the required parameters. Here, total seven performance parameters of three different MSAs are computed using generalized neural approach as such a method is rarely available in the open literature even for computing more than three performance parameters, simultaneously. The results thus obtained are in very good agreement with the measured results available in the referenced literature for all seven cases.

scholarly journals Hardware Neural Networks Modeling for Computing Different Performance Parameters of Rectangular, Circular, and Triangular Microstrip Antennas

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Taimoor Khan ◽  
Asok De
Keyword(s):  
Neural Networks ◽  
Low Cost ◽  
Microstrip Antennas ◽  
Performance Parameters ◽  
Neural Models ◽  
Hardware Platform ◽  
Generalized Neural Networks ◽  
Field Programmable ◽  
The Neural Networks ◽  
Good Agreement

In the last one decade, neural networks-based modeling has been used for computing different performance parameters of microstrip antennas because of learning and generalization features. Most of the created neural models are based on software simulation. As the neural networks show massive parallelism inherently, a parallel hardware needs to be created for creating faster computing machine by taking the advantages of the parallelism of the neural networks. This paper demonstrates a generalized neural networks model created on field programmable gate array- (FPGA-) based reconfigurable hardware platform for computing different performance parameters of microstrip antennas. Thus, the proposed approach provides a platform for developing low-cost neural network-based FPGA simulators for microwave applications. Also, the results obtained by this approach are in very good agreement with the measured results available in the literature.

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