Shyam Devi - Academia.edu (original) (raw)

Papers by Shyam Devi

Research paper thumbnail of Input impedance of rectangular microstrip patch antenna using artificial neural networks

Microwave and Optical Technology Letters, 2002

A new method of using artificial neural networks (ANNs) for the calculation of the input impedanc... more A new method of using artificial neural networks (ANNs) for the calculation of the input impedance of rectangular microstrip patch antennas has been adopted in this paper. The results obtained using ANNs are compared with the experimental findings, theoretical values, and with the simulation results obtained using an IE3D package. The ANNs result are more in agreement with experimental findings. © 2002 John Wiley & Sons, Inc. Microwave Opt Technol Lett 32: 381–383, 2002.

Research paper thumbnail of Calculation of optimized parameters of rectangular microstrip patch antenna using genetic algorithm

Microwave and Optical Technology Letters, 2003

In this paper, the genetic algorithm (GA) has been applied to calculate the optimized length and ... more In this paper, the genetic algorithm (GA) has been applied to calculate the optimized length and width of rectangular microstrip antennas. The inputs to the problem are the desired resonant frequency, dielectric constant, and thickness of the substrate; the outputs are the optimized length and width. The antennas considered are electrically thin. Method of moments (MoM)-based IE3D software from Zealand Inc., USA, and experimental results are used to validate the GA-based code. The results are in good agreement. © 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 37: 431–433, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10940

Research paper thumbnail of Coupling of ANNN with GA for effective optimization of dimensions of rectangular patch antenna on thick substrate microstrip patch antenna on thick substrate

A novel method of optimization by coupling the genetic algorithm (GA) and artificial neural netwo... more A novel method of optimization by coupling the genetic algorithm (GA) and artificial neural network (ANN) is presented in this paper. A trained artificial neural network is taken as objective function in GA for optimization. By utilizing this technique the optimized dimensions of patch antenna on thick substrate has been calculated. It is seen that the results obtained by this method are closer to experimental value compared to earlier results obtained by curve fitting method. To validate this, the results are compared with experimental values for five fabricated antenna. The results are in very good agreement with experimental findings.

Research paper thumbnail of A novel method of using artificial neural networks to calculate input impedance of circular microstrip antenna

KEY TERMS: Input impedance, Rectangular microstrip antenna, Artificial Neural Networks. ABSTRACT:... more KEY TERMS: Input impedance, Rectangular microstrip antenna, Artificial Neural Networks. ABSTRACT: A new method of using artificial neural networks(ANNS) for calculation of input impedance of rectangular microstrip patch antennas has been adopted in this paper. The results obtained using ANNs are compared with the experimental findings, theoretical values. The ANNs results are more in agreement with experimental findings. 1 INTRODUCTION:

Research paper thumbnail of Design of a wideband microstrip antenna and the use of artificial neural networks in parameter calculation

IEEE Antennas and Propagation Magazine, 2005

This paper deals with the design of a multi-slot hole-coupled microstrip antenna on a substrate o... more This paper deals with the design of a multi-slot hole-coupled microstrip antenna on a substrate of 2 mm thickness that gives multi-frequency (wideband) characteristics. The Method of Moments (MoM)-based IE3D software was used to simulate the results for return loss, VSWR, the Smith chart, and the radiation patterns. A tunnel-based artificial neural network (ANN) was also developed to calculate the radiation patterns of the antenna. The radiation patterns were measured experimentally at 10.5 GHz and 12 GHz. The experimental results were in good agreement with the simulated results from IE3D and those of the artificial neural network. A new method of using a genetic algorithm (GA) in an artificial neural network is also discussed. This new method was used to calculate the resonant frequency of a single-shorting-post microstrip antenna. The resonant frequency calculated using the genetic-algorithm-coupled artificial neural network was compared with the analytical and experimental results. The results obtained were in very good agreement with the experimental results.

Research paper thumbnail of A simple and efficient approach to train artificial neural networks using a genetic algorithm to calculate the resonant frequency of an RMA on thick substrate

Microwave and Optical Technology Letters, 2004

Both genetic algorithms (GAs) and artificial neural networks (ANNs) have been used in the field o... more Both genetic algorithms (GAs) and artificial neural networks (ANNs) have been used in the field of computational electromagnetics as the most powerful optimizing tools. In this paper, a simple and efficient method is presented to handle the problem of competing convention while training an ANN by using a GA. This technique is applied to calculate the resonant frequency of a thick-substrate rectangular microstrip antenna (RMA). The training time is less than that of a normal feed-forward backpropagation algorithm. The measured results are in very good agreement with experimental results. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 41: 313–315, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20126

Research paper thumbnail of Offset fed diamond shaped split ring (DSSR) planar metamaterial antenna

In this paper the authors have presented a planar metamaterial antenna using offset fed diamond s... more In this paper the authors have presented a planar metamaterial antenna using offset fed diamond shaped split rings (DSSR). The structure behaves as normal patch antenna when the split rings are excited at normal cuts on Y-axis whereas the same structure exhibits metamaterial characteristics when fed at offset cut at an angle ??=45??. In offset cut the excess inductance and capacitance due to microstrip discontinuities get eliminated and the antenna structure exhibits metamaterial characteristics. The normal patch antenna resonates at 8.11 GHz having bandwidth of 444 MHz. The resonant frequency of metamaterial planar antenna is 8.27 GHz with the bandwidth of 490 MHz. Thus in metamaterial antenna configuration the bandwidth is enhanced by 46 MHz. The equivalent circuit of the offset fed DSSR is presented to justify the theoretical and simulated resonant frequencies. Both normal cut and offset cut antenna structures are simulated using IE3D electromagnetic simulator of Zeland software, incorporation.

Research paper thumbnail of Parameter calculation of rectangular microstrip antenna using Biogeography-Based Optimization

Miniaturized microstrip antennas are of great interest in the recent time especially in wireless ... more Miniaturized microstrip antennas are of great interest in the recent time especially in wireless communication systems and biomedical applications. To enhance efficiency, avoid co-channel, adjacent channel interference and associated complications accurate determination of resonant frequency is an essential requirement. A method to calculate accurately the resonant frequency is of great importance especially on such antennas like microstrip patch antennas that

Research paper thumbnail of Application of a genetic algorithm in an artificial neural network to calculate the resonant frequency of a tunable single-shorting-post rectangular-patch antenna

International Journal of Rf and Microwave Computer-aided Engineering, 2005

In this article, an efficient application of a genetic algorithm (GA) in an artificial neural net... more In this article, an efficient application of a genetic algorithm (GA) in an artificial neural network (ANN) to calculate the resonant frequency of a coaxially-fed tunable rectangular microstrip-patch antenna is presented. For a normal feed-forward back-propagation algorithm, with a compromise between time and accuracy, it is difficult to train the network to achieve an acceptable error tolerance. The selection of suitable parameters of ANNs in a feed-forward network leads to a high number of man-hours necessary to train a network efficiently. However, in the present method, the GA is used to reduce the man-hours while training a neural network using the feed forward-back-propagation algorithm. It is seen that the training time has also been reduced to a great extent while giving high accuracy. The results are in very good agreement with the experimental results. © 2004 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2005.

Research paper thumbnail of Genetic algorithm with artificial neural networks as its fitness function to design rectangular microstrip antenna on thick substrate

Microwave and Optical Technology Letters, 2005

Over the years, genetic algorithms (GAs) have been applied in many applications. But the lack of ... more Over the years, genetic algorithms (GAs) have been applied in many applications. But the lack of a proper fitness function has been a hindrance to its widespread application in many cases. In this paper, a novel technique of using artificial neural networks (ANNs) as the fitness function of a genetic algorithm in order to calculate the design parameters of a thick substrate rectangular microstrip antenna is presented. A multilayer feed-forward neural network is used as the fitness function in a binary-coded genetic algorithm. The results obtained using this method are found to be closer to the experimental value, as compared to previous results obtained using the curve-fitting method. To validate this, the results are compared with the experimental values for five fabricated antennas. The results are in very good agreement with the experimental findings. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 44: 144–146, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20570

Research paper thumbnail of Tunnel-based artificial neural network technique to calculate the resonant frequency of a thick-substrate microstrip antenna

Microwave and Optical Technology Letters, 2002

The mathematical formulation of empirically developed formulas for the calculation of the resonan... more The mathematical formulation of empirically developed formulas for the calculation of the resonant frequency of a thick-substrate (h ≥ 0.08151λ0) microstrip antenna has been analyzed. With the use of tunnel-based artificial neural networks (ANNs), the resonant frequency of antennas with h satisfying the thick-substrate condition are calculated and compared with the existing experimental results and also with the simulation results obtained with the use of an IE3D software package. The artificial neural network results are in very good agreement with the experimental results. © 2002 Wiley Periodicals, Inc. Microwave Opt Technol Lett 34: 460–462, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10495

Research paper thumbnail of Radiation resistance of coax-fed rectangular microstrip patch antenna with the use of artificial neural networks

Microwave and Optical Technology Letters, 2002

A novel method of using artificial neural networks (ANNS) for the calculation of the radiation re... more A novel method of using artificial neural networks (ANNS) for the calculation of the radiation resistance of a coax-fed rectangular microstrip antenna is presented in this Letter. The network is trained with the results of three different antennas and tested for the fourth antenna. The tested results are in very good agreement. © 2002 Wiley Periodicals, Inc. Microwave Opt Technol Lett 34: 51–53, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10370

Research paper thumbnail of Input impedance of rectangular microstrip patch antenna using artificial neural networks

Microwave and Optical Technology Letters, 2002

A new method of using artificial neural networks (ANNs) for the calculation of the input impedanc... more A new method of using artificial neural networks (ANNs) for the calculation of the input impedance of rectangular microstrip patch antennas has been adopted in this paper. The results obtained using ANNs are compared with the experimental findings, theoretical values, and with the simulation results obtained using an IE3D package. The ANNs result are more in agreement with experimental findings. © 2002 John Wiley & Sons, Inc. Microwave Opt Technol Lett 32: 381–383, 2002.

Research paper thumbnail of Calculation of optimized parameters of rectangular microstrip patch antenna using genetic algorithm

Microwave and Optical Technology Letters, 2003

In this paper, the genetic algorithm (GA) has been applied to calculate the optimized length and ... more In this paper, the genetic algorithm (GA) has been applied to calculate the optimized length and width of rectangular microstrip antennas. The inputs to the problem are the desired resonant frequency, dielectric constant, and thickness of the substrate; the outputs are the optimized length and width. The antennas considered are electrically thin. Method of moments (MoM)-based IE3D software from Zealand Inc., USA, and experimental results are used to validate the GA-based code. The results are in good agreement. © 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 37: 431–433, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10940

Research paper thumbnail of Coupling of ANNN with GA for effective optimization of dimensions of rectangular patch antenna on thick substrate microstrip patch antenna on thick substrate

A novel method of optimization by coupling the genetic algorithm (GA) and artificial neural netwo... more A novel method of optimization by coupling the genetic algorithm (GA) and artificial neural network (ANN) is presented in this paper. A trained artificial neural network is taken as objective function in GA for optimization. By utilizing this technique the optimized dimensions of patch antenna on thick substrate has been calculated. It is seen that the results obtained by this method are closer to experimental value compared to earlier results obtained by curve fitting method. To validate this, the results are compared with experimental values for five fabricated antenna. The results are in very good agreement with experimental findings.

Research paper thumbnail of A novel method of using artificial neural networks to calculate input impedance of circular microstrip antenna

KEY TERMS: Input impedance, Rectangular microstrip antenna, Artificial Neural Networks. ABSTRACT:... more KEY TERMS: Input impedance, Rectangular microstrip antenna, Artificial Neural Networks. ABSTRACT: A new method of using artificial neural networks(ANNS) for calculation of input impedance of rectangular microstrip patch antennas has been adopted in this paper. The results obtained using ANNs are compared with the experimental findings, theoretical values. The ANNs results are more in agreement with experimental findings. 1 INTRODUCTION:

Research paper thumbnail of Design of a wideband microstrip antenna and the use of artificial neural networks in parameter calculation

IEEE Antennas and Propagation Magazine, 2005

This paper deals with the design of a multi-slot hole-coupled microstrip antenna on a substrate o... more This paper deals with the design of a multi-slot hole-coupled microstrip antenna on a substrate of 2 mm thickness that gives multi-frequency (wideband) characteristics. The Method of Moments (MoM)-based IE3D software was used to simulate the results for return loss, VSWR, the Smith chart, and the radiation patterns. A tunnel-based artificial neural network (ANN) was also developed to calculate the radiation patterns of the antenna. The radiation patterns were measured experimentally at 10.5 GHz and 12 GHz. The experimental results were in good agreement with the simulated results from IE3D and those of the artificial neural network. A new method of using a genetic algorithm (GA) in an artificial neural network is also discussed. This new method was used to calculate the resonant frequency of a single-shorting-post microstrip antenna. The resonant frequency calculated using the genetic-algorithm-coupled artificial neural network was compared with the analytical and experimental results. The results obtained were in very good agreement with the experimental results.

Research paper thumbnail of A simple and efficient approach to train artificial neural networks using a genetic algorithm to calculate the resonant frequency of an RMA on thick substrate

Microwave and Optical Technology Letters, 2004

Both genetic algorithms (GAs) and artificial neural networks (ANNs) have been used in the field o... more Both genetic algorithms (GAs) and artificial neural networks (ANNs) have been used in the field of computational electromagnetics as the most powerful optimizing tools. In this paper, a simple and efficient method is presented to handle the problem of competing convention while training an ANN by using a GA. This technique is applied to calculate the resonant frequency of a thick-substrate rectangular microstrip antenna (RMA). The training time is less than that of a normal feed-forward backpropagation algorithm. The measured results are in very good agreement with experimental results. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 41: 313–315, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20126

Research paper thumbnail of Offset fed diamond shaped split ring (DSSR) planar metamaterial antenna

In this paper the authors have presented a planar metamaterial antenna using offset fed diamond s... more In this paper the authors have presented a planar metamaterial antenna using offset fed diamond shaped split rings (DSSR). The structure behaves as normal patch antenna when the split rings are excited at normal cuts on Y-axis whereas the same structure exhibits metamaterial characteristics when fed at offset cut at an angle ??=45??. In offset cut the excess inductance and capacitance due to microstrip discontinuities get eliminated and the antenna structure exhibits metamaterial characteristics. The normal patch antenna resonates at 8.11 GHz having bandwidth of 444 MHz. The resonant frequency of metamaterial planar antenna is 8.27 GHz with the bandwidth of 490 MHz. Thus in metamaterial antenna configuration the bandwidth is enhanced by 46 MHz. The equivalent circuit of the offset fed DSSR is presented to justify the theoretical and simulated resonant frequencies. Both normal cut and offset cut antenna structures are simulated using IE3D electromagnetic simulator of Zeland software, incorporation.

Research paper thumbnail of Parameter calculation of rectangular microstrip antenna using Biogeography-Based Optimization

Miniaturized microstrip antennas are of great interest in the recent time especially in wireless ... more Miniaturized microstrip antennas are of great interest in the recent time especially in wireless communication systems and biomedical applications. To enhance efficiency, avoid co-channel, adjacent channel interference and associated complications accurate determination of resonant frequency is an essential requirement. A method to calculate accurately the resonant frequency is of great importance especially on such antennas like microstrip patch antennas that

Research paper thumbnail of Application of a genetic algorithm in an artificial neural network to calculate the resonant frequency of a tunable single-shorting-post rectangular-patch antenna

International Journal of Rf and Microwave Computer-aided Engineering, 2005

In this article, an efficient application of a genetic algorithm (GA) in an artificial neural net... more In this article, an efficient application of a genetic algorithm (GA) in an artificial neural network (ANN) to calculate the resonant frequency of a coaxially-fed tunable rectangular microstrip-patch antenna is presented. For a normal feed-forward back-propagation algorithm, with a compromise between time and accuracy, it is difficult to train the network to achieve an acceptable error tolerance. The selection of suitable parameters of ANNs in a feed-forward network leads to a high number of man-hours necessary to train a network efficiently. However, in the present method, the GA is used to reduce the man-hours while training a neural network using the feed forward-back-propagation algorithm. It is seen that the training time has also been reduced to a great extent while giving high accuracy. The results are in very good agreement with the experimental results. © 2004 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2005.

Research paper thumbnail of Genetic algorithm with artificial neural networks as its fitness function to design rectangular microstrip antenna on thick substrate

Microwave and Optical Technology Letters, 2005

Over the years, genetic algorithms (GAs) have been applied in many applications. But the lack of ... more Over the years, genetic algorithms (GAs) have been applied in many applications. But the lack of a proper fitness function has been a hindrance to its widespread application in many cases. In this paper, a novel technique of using artificial neural networks (ANNs) as the fitness function of a genetic algorithm in order to calculate the design parameters of a thick substrate rectangular microstrip antenna is presented. A multilayer feed-forward neural network is used as the fitness function in a binary-coded genetic algorithm. The results obtained using this method are found to be closer to the experimental value, as compared to previous results obtained using the curve-fitting method. To validate this, the results are compared with the experimental values for five fabricated antennas. The results are in very good agreement with the experimental findings. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 44: 144–146, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20570

Research paper thumbnail of Tunnel-based artificial neural network technique to calculate the resonant frequency of a thick-substrate microstrip antenna

Microwave and Optical Technology Letters, 2002

The mathematical formulation of empirically developed formulas for the calculation of the resonan... more The mathematical formulation of empirically developed formulas for the calculation of the resonant frequency of a thick-substrate (h ≥ 0.08151λ0) microstrip antenna has been analyzed. With the use of tunnel-based artificial neural networks (ANNs), the resonant frequency of antennas with h satisfying the thick-substrate condition are calculated and compared with the existing experimental results and also with the simulation results obtained with the use of an IE3D software package. The artificial neural network results are in very good agreement with the experimental results. © 2002 Wiley Periodicals, Inc. Microwave Opt Technol Lett 34: 460–462, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10495

Research paper thumbnail of Radiation resistance of coax-fed rectangular microstrip patch antenna with the use of artificial neural networks

Microwave and Optical Technology Letters, 2002

A novel method of using artificial neural networks (ANNS) for the calculation of the radiation re... more A novel method of using artificial neural networks (ANNS) for the calculation of the radiation resistance of a coax-fed rectangular microstrip antenna is presented in this Letter. The network is trained with the results of three different antennas and tested for the fourth antenna. The tested results are in very good agreement. © 2002 Wiley Periodicals, Inc. Microwave Opt Technol Lett 34: 51–53, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10370