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All Journal International Journal of Electrical and Computer Engineering
Neela Rayavarapu
Symbiosis International (Deemed University)
Computer Science & IT Electrical & Electronics Engineering

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On the performance analysis of rainfall prediction using mutual information with artificial neural network Shilpa Hudnurkar; Neela Rayavarapu
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2101-2113

Abstract

Monsoon rainfall prediction over a small geographic region is indeed a challenging task. This paper uses monthly means of climate variables, namely air temperature (AT), sea surface temperature (SST), and sea level pressure (SLP) over the globe, to predict monthly and seasonal summer monsoon rainfall over the state of Maharashtra, India. Mutual information correlates the temperature and pressure from a grid of 10° longitude X 10° latitude with Maharashtra’s monthly rainfall time series. Based on the correlations, selected features over the respective latitude and longitudes are given as inputs to an artificial neural network. It was observed that AT and SLP could predict monthly monsoon rainfall with excellent accuracy. The performance of the test dataset was evaluated through mean absolute error; root mean square error, correlation coefficient, Nash Sutcliffe model efficiency coefficient, and maximum rainfall prediction capability of the network. The individual climate variable model for AT performed better in all evaluation parameters except maximum rainfall capability, where the combined model 2 with AT, SLP and SST as predictors outperformed. The SLP-only model’s performance was comparable to the AT-only model. The combined model 1 with AT and SLP as predictors was found better than the combined model 2.
A novel frequency reconfigurable antenna for smart grid applications in TV white space band Sanjeev Kumar; Neela Rayavarapu; Praveen Vummadisetty Naidu
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.pp611-618

Abstract

This paper presents the design and analysis of a frequency reconfigurable, aperture coupled rectangular patch antenna for use in smart grid applications in TV white space bands. The proposed antenna model has been realized on multi-substrate layers of Polylactic acid (PLA) material (εr=2.65, tanδ=0.003) with a ground plane sandwiched in between them. An aperture has been made in the ground plane for coupling energy to the patch. The overall system dimensions are 270×270 mm. The feature of frequency reconfigurability has been achieved by incorporating a switch and varying the reactance of the feed line on the bottom substrate. A rectangular slot on the long feed line improves impedance matching. The ON and OFF states of the switch provide two operating frequency bands namely 630.13 to 636.7 MHz and 619.16 to 625.3 MHz respectively. The proposed aperture coupled reconfigurable system operates with a maximum gain of 6.4 dB and average efficiency of 78.5% in both bands. The measured results are satisfactory and the proposed antenna will be suitable for operation in the smart grid environment.
Compressive speech enhancement using semi-soft thresholding and improved threshold estimation Smriti Sahu; Neela Rayavarapu
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2788-2800

Abstract

Compressive speech enhancement is based on the compressive sensing (CS) sampling theory and utilizes the sparsity of the signal for its enhancement. To improve the performance of the discrete wavelet transform (DWT) basis-function based compressive speech enhancement algorithm, this study presents a semi-soft thresholding approach suggesting improved threshold estimation and threshold rescaling parameters. The semi-soft thresholding approach utilizes two thresholds, one threshold value is an improved universal threshold and the other is calculated based on the initial-silence-region of the signal. This study suggests that thresholding should be applied to both detail coefficients and approximation coefficients to remove noise effectively. The performances of the hard, soft, garrote and semi-soft thresholding approaches are compared based on objective quality and speech intelligibility measures. The normalized covariance measure is introduced as an effective intelligibility measure as it has a strong correlation with the intelligibility of the speech signal. A visual inspection of the output signal is used to verify the results. Experiments were conducted on the noisy speech corpus (NOIZEUS) speech database. The experimental results indicate that the proposed method of semi-soft thresholding using improved threshold estimation provides better enhancement compared to the other thresholding approaches.
Co-Authors Praveen Vummadisetty Naidu Sanjeev Kumar Shilpa Hudnurkar Smriti Sahu