Faculty Publications

Now showing 1 - 4 of 4

Improved phase estimation based on complete bispectrum and modified group delay
(2008) Narasimhan, S.V.; Basumallick, N.; Chaitanya, R.
In this paper, a new method for extracting the system phase from the bispectrum of the system output has been proposed. This is based on the complete bispectral data computed in the frequency domain and modified group delay. The frequency domain bispectrum computation improves the frequency resolution and the modified group delay reduces the variance preserving the frequency resolution. The use of full bispectral data also reduces the variance as it is used for averaging. For the proposed method at a signal to noise ratio of 5dB, the reduction in root mean square error is in the range of 1.5-7 times over the other methods considered. © 2008 Springer-Verlag London Limited.
Discrete cosine harmonic wavelet transform and its application to signal compression and subband spectral estimation using modified group delay
(2009) Narasimhan, S.V.; Harish, M.; Haripriya, A.R.; Basumallick, N.
This paper proposes a new harmonic wavelet transform (HWT) based on discrete cosine transform (DCTHWT) and its application for signal or image compression and subband spectral estimation using modified group delay (MGD). Further, the existing DFTHWT has also been explored for image compression. The DCTHWT provides better quality decomposed decimated signals, which enable improved compression and MGD processing. For signal/image compression, compared to the HWT based on DFT (DFTHWT), the DCTHWT reduces the reconstruction error. Compared to DFTHWT for the speech signal considered for a compression factor of 0.62, the DCTWHT provides a 30% reduction in reconstruction error. For an image, the DCTHWT algorithm due to its real nature, is computationally simple and more accurate than the DFTHWT. Further compared to Cohen-Daubechies-Feauveau 9/7 biorthogonal symmetric wavelet, the DCTHWT, with its computational advantage, gives a better or comparable performance. For an image with 6.25% coefficients, the reconstructed image by DFTHWT is significantly inferior in appearance to that by DCTHWT which is reflected in the error index as its values are 3.0 and 2.65%, respectively. For spectral estimation, DCTHWT reduces the bias both in frequency (frequency resolution) and spectral magnitude. The reduction in magnitude bias in turn improves the signal detectability. In DCTHWT, the improvement in frequency resolution and the signal detectability is not only due to good quality DCT subband signals but also due to their stretching (decimation) in the wavelet transform. The MGD reduces the variance while preserving the frequency resolution achieved by DCT and decimation. In view of these, the new spectral estimator facilitates a significant improvement both in magnitude and frequency bias, variance and signal detection ability; compared to those of MGD processing of both DFT and DCT fullband and DFT subband signals. © Springer-Verlag London Limited 2008.
2D-spectral estimation based on DCT and modified magnitude group delay
(2013) Sandeep, P.; Shreyamsha Kumar, B.K.; Narasimhan, S.V.
This paper proposes two new 2D-spectral estimation methods. The 2D-modified magnitude group delay (MMGD) is applied to 2D-discrete Fourier transform (2D-DFT) for the first and to the analytic 2D-discrete Cosine transform for the second. The analytic 2D-DCT preserves the desirable properties of the DCT (like, improved frequency resolution, leakage and detectability) and is realized by a 2D-discrete cosine transform (2D-DCT) and its Hilbert transform. The 2D-MMGD is an extension from 1D to 2D, and it reduces the variance preserving the original frequency resolution of 2D-DFT or 2D-analytic DCT, depending upon to which is applied. The first and the second methods are referred to as DFT-MMGD and DCT-MMGD, respectively. The proposed methods are applied to 2D sinusoids and 2D AR process, associated with Gaussian white noise. The performance of the DCT-MMGD is found to be superior to that of DFT-MMGD in terms of variance, frequency resolution and detectability. The performance of DFT-MMGD and DCT-MMGD is better than that of 2D-LP method even when the signal to noise ratio is low. © 2012 Springer-Verlag London Limited.
Short-term wind speed forecasting using S-transform with compactly supported kernel
(John Wiley and Sons Ltd, 2021) Kamath, P.R.; Senapati, K.
This paper presents a modified S-transform (ST) based on a compactly supported kernel. A version of Cheriet-Belochrani (CB) kernel is chosen for this purpose. It is shown that the proposed modified S-transform (CBST) offers better frequency resolution than the traditional ST. It is used to decompose the wind speed time series into frequency-based subseries. Further, artificial neural network (ANN) is applied to each of the subseries for an hour ahead prediction. Finally, forecast for the original wind speed series is obtained by combining the prediction result of all the subseries. Initially, increasing the number of subseries results in a decrease in prediction error. However, when the number of subseries is sufficiently large, no significant change in prediction error is observed if the number is further increased. It is also observed that, for a model based on neural-network, involving decomposition of wind speed time series, the proposed model offers low prediction error. A comparative study with the methods based on wavelet transform (WT) and empirical mode decomposition (EMD) demonstrates the effectiveness of the proposed method. For this study, we have used simulated wind speed data generated by nonhydrostatic mesoscale model and data recorded using anemometer and LiDAR instrument at different heights to evaluate the short-term forecasting results. © 2020 The Authors. Wind Energy published by John Wiley & Sons Ltd

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