Materials Map

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2021Bandwidth Analysis of Dual-Feed Slotted Antenna Using Artificial Neural Networks1citations

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Singh, Vinod Kumar
1 / 3 shared
Lala, Kunal
1 / 1 shared
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2021

Co-Authors (by relevance)

  • Singh, Vinod Kumar
  • Lala, Kunal
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booksection

Bandwidth Analysis of Dual-Feed Slotted Antenna Using Artificial Neural Networks

  • Singh, Vinod Kumar
  • Lala, Archana
  • Lala, Kunal
Abstract

In this chapter, artificial neural network is used for the estimation of bandwidth of a dual feed microstrip antenna. The MLPFFBP-ANN and RBF-ANN are used to implement the neural network model. The simulated values for training and testing the neural network are obtained by simulating the antenna on IE3D software. The results obtained by using ANNs and IE3D simulation are compared and are found quite acceptable, and also it is concluded that RBF network is more accurate and fast as compared to back propagation algorithm of MLPFFBP. The anticipated is applicable to operate in triple band from 2.208GHz-5.35GHz, 2.358GHz-2.736GHz, and 3.815GHz-5.143GHz. The antenna is also fabricated with FR-4 glass epoxy material. The experimental results, simulated results of IE3D, and simulated results of neural network are compared.

Topics
  • impedance spectroscopy
  • simulation
  • glass
  • glass