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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Shilpa Mehta
Presidency University
Computer Science & IT Electrical & Electronics Engineering Engineering

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Frequency reconfigurable rectangular patch antenna for cognitive radio applications Manjuanatha Kurugodu Hanumanthappa; Shilpa Mehta
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp579-589

Abstract

A frequency reconfigurable microstrip transformed rectangular patch antenna consisting of two slots able to radiate in S-band and C-band is proposed. Spectrum occupancy is first analyzed using the data from literature and internet sources and hence spectrum holes are identified. A rectangular radiating patch is then designed for 5.8 GHz resonant frequency. A coaxial feed is used in the bottom by a suitable feed point. Two slots at an angle of +45 degree are made at the two corners. The electrical length of the patch is changed by using two varactor diodes in the slots. The varactors enable frequency reconfiguration in the band of frequencies that are unused or the spectral occupancy is very less. The return loss, voltage standing wave ratio (VSWR), and 2D-radiation patterns are analyzed for various values of the capacitances. high-frequency structure simulator (HFSS) is used for simulation. FR4 substrate which is economical, is used with height, h=1.6 mm, width W=25.33 mm, and length L=21.34 mm. On the substrate the rectangular patch is of width 15.73 mm and length 11.74 mm. The return loss and radiation patterns for different values of capacitances is presented. The tunability ratio obtained is 1.93. The results obtained agree with the standards.
Text detection and recognition through deep learning-based fusion neural network Sunil Kumar Dasari; Shilpa Mehta
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1396-1406

Abstract

Text recognition task involves recognizing the text from the natural image; it possesses various application, which aids information extraction through data mining from street view like images. Scene text recognition involves two stages i.e., text detection and text recognition, in the past several mechanisms has been proposed for accurate identification, these mechanisms are either traditional approach or deep learning-based. All the existing deep-learning methodology fails as this comprises character data and image data, further this research develops an optimal architecture fusion neural network (FNN) for text identification and recognition. FNN comprises several layers of convolutional neural network (CNN) as well as recurrent neural network (RNN). Within FNN architecture convolutional layer is utilized for the feature extraction and recurrent layer is utilized for attaining the feature classification prediction. Further, an optimal training architecture is established for the enhancement of classification accuracy. Here Devanagari MLT-19 dataset is utilized for the evaluation of FNN. Three different parameters are considered during evaluation i.e., script word identification, character recognition rate (CRR) and word recognition rate (WRR). Further comparison with existing models is performed to establish the proposed model efficiency and it shows FNN methodology observes 98.67% of script identification accuracy, 84.65% of WRR and 92.93% of CRR.
Co-Authors Manjuanatha Kurugodu Hanumanthappa Sunil Kumar Dasari