Browsing by Author "Narasimhan, S.V."

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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.
A new approach for channel blind identification based on second order cyclostationary statistics and the group delay has been proposed. In this, two methods are proposed. In both the methods, the correction is applied to the basic phase estimate for both the poles and zeros, in the group delay domain. The basic phase estimate is derived from the spectral correlation density (SCD) of the system output. In the first method, the phase correction is based on magnitude group delay. In the second method, not only the phase correction but also an improved system magnitude estimate of better variance and frequency resolution is derived based on modified magnitude group delay. The results indicate a significant improvement in performance for both the methods. For the first method in the absence of noise, the percentage normalized mean square error is reduced by about 85% over that of the existing non-parametric method. The second method in the presence of noise (SNR=5 dB), provides a reduction of 74% over the existing non-parametric method and 57% over the existing combined parametric and non-parametric methods. © 2005 Elsevier B.V. All rights reserved.
(Channel blind identification based on cyclostationarity and group delay) Narasimhan, S.V.; Hazarathaiah, M.; Giridhar, P.V.S.
2005
A new approach has been proposed for improving the performance of the Wigner-Ville distribution. This approach is based on signal decomposition and modified magnitude group delay function. Signal decomposition achieved by perfect reconstruction filter bank reduces significantly the existence of crossterms. The Gibbs ripple effect is due to truncation of the Wigner-Ville distribution kernel. The modified magnitude group delay function overcomes this effect without applying any window. Compared to those of Pseudo Wigner-Ville distribution and its versions, the proposed method has significantly improved performance in both time and frequency resolution as there is no time and frequency smoothing. Further, this method obeys better the desirable properties of time-frequency representation and has a better noise immunity. © 2003 Elsevier B.V. All rights reserved.
(Improved Wigner-Ville distribution performance by signal decomposition and modified group delay) Narasimhan, S.V.; Nayak, M.B.
2003
An improved system blind identification method based on second-order cyclostationary statistics and the properties of group delay, has been proposed. This is achieved by applying a correction to the estimated phase (by the spectral correlation density of the system output) for the poles, in the group delay domain. The results indicate a significant improvement in system blind identification, in terms of root mean square error. Depending upon the signal-to-noise ratio, the improvement in percentage normalized mean square error ranges between 20 and 50%.
(Improved system blind identification based on second-order cyclostationary statistics: A group delay approach) Giridhar, P.V.S.; Narasimhan, S.V.
2000
Autoregressive modeling of the Wigner-Ville distribution based on signal decomposition and modified group delay
(2004) Nayak, M.B.; Narasimhan, S.V.
An autoregressive modeling of the Wigner-Ville distribution (WVD), based on signal decomposition (SD) by a perfect reconstruction filter bank (PRFB) and the modified magnitude group delay function (MMGD), has been proposed. The SD and MMGD, respectively, reduce the existence of crossterms (without any time smoothing) and the Gibb's ripple effect (due to truncation of the WVD kernel, without applying any window), significantly. In view of this, the modeling is not affected by either the crossterms or the Gibb's ripple and the window that would have been used. The proposed method represents actual time-frequency information parsimoniously and compared to the existing WVD modeling methods, its performance is significantly better in terms of both time and frequency resolution (as there is no time and frequency smoothing) and noise immunity/variance and is computationally efficient. � 2003 Elsevier B.V. All rights reserved.
Autoregressive modeling of the Wigner-Ville distribution based on signal decomposition and modified group delay
(2004) Nayak, M.B.; Narasimhan, S.V.
An autoregressive modeling of the Wigner-Ville distribution (WVD), based on signal decomposition (SD) by a perfect reconstruction filter bank (PRFB) and the modified magnitude group delay function (MMGD), has been proposed. The SD and MMGD, respectively, reduce the existence of crossterms (without any time smoothing) and the Gibb's ripple effect (due to truncation of the WVD kernel, without applying any window), significantly. In view of this, the modeling is not affected by either the crossterms or the Gibb's ripple and the window that would have been used. The proposed method represents actual time-frequency information parsimoniously and compared to the existing WVD modeling methods, its performance is significantly better in terms of both time and frequency resolution (as there is no time and frequency smoothing) and noise immunity/variance and is computationally efficient. Â© 2003 Elsevier B.V. All rights reserved.
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.
Estimation of evolutionary spectrum based on short time Fourier transform and modified group delay
(2004) Narasimhan, S.V.; Pavanalatha, S.
This paper proposes a new estimator for evolutionary spectrum (ES) based on short time Fourier transform (STFT) and modified group delay function (GDFM). The STFT due to its built-in averaging suppresses the crossterms and the GDFM preserves the frequency resolution of the rectangular window as it reduces the Gibbs ripple without using any window function. The new estimator is applicable to random signals as the GDFM removes the effect of the zeros due to input noise driving the time-varying system and provides the system information effectively. The GDFM also provides signal-to-noise ratio enhancement as it removes the zeros due to the associated noise. The performance of the method is illustrated for linear chirp signals, frequency shift keying and for time-varying random process which indicate that its frequency resolution is better than evolutionary periodogram (EP) and STFT and nearer to that of Wigner Ville distribution. Further, its noise immunity is better than those of EP and STFT. � 2004 Elsevier B.V. All rights reserved.
Estimation of evolutionary spectrum based on short time Fourier transform and modified group delay
(Elsevier, 2004) Narasimhan, S.V.; Pavanalatha, S.
This paper proposes a new estimator for evolutionary spectrum (ES) based on short time Fourier transform (STFT) and modified group delay function (GDFM). The STFT due to its built-in averaging suppresses the crossterms and the GDFM preserves the frequency resolution of the rectangular window as it reduces the Gibbs ripple without using any window function. The new estimator is applicable to random signals as the GDFM removes the effect of the zeros due to input noise driving the time-varying system and provides the system information effectively. The GDFM also provides signal-to-noise ratio enhancement as it removes the zeros due to the associated noise. The performance of the method is illustrated for linear chirp signals, frequency shift keying and for time-varying random process which indicate that its frequency resolution is better than evolutionary periodogram (EP) and STFT and nearer to that of Wigner Ville distribution. Further, its noise immunity is better than those of EP and STFT. Â© 2004 Elsevier B.V. All rights reserved.
Estimation of evolutionary spectrum based on STFT and modified group delay
(2003) Narasimhan, S.V.; Pavanalatha, S.
This paper proposes a new estimator for Evolutionary Spectrum (ES) based on short time Fourier transform (STFT) and modified group delay (MGD). Here, the STFT enables crossterm suppression and the MGD preserves the frequency resolution of the rectangular window. It is applicable to deterministic and random signals generated by time varying systems. The proposed method provides signal to noise ratio enhancement due to the use of MGD. The results indicate that for linear chirp signals and for time varying random process, its frequency resolution is close to that of WVD and better than Evolutionary periodogram (EP) and STFT. Further, its noise immunity is better than those of EP and STFT.
Estimation of evolutionary spectrum based on STFT and modified group delay
(2003) Narasimhan, S.V.; Pavanalatha, S.
This paper proposes a new estimator for Evolutionary Spectrum (ES) based on short time Fourier transform (STFT) and modified group delay (MGD). Here, the STFT enables crossterm suppression and the MGD preserves the frequency resolution of the rectangular window. It is applicable to deterministic and random signals generated by time varying systems. The proposed method provides signal to noise ratio enhancement due to the use of MGD. The results indicate that for linear chirp signals and for time varying random process, its frequency resolution is close to that of WVD and better than Evolutionary periodogram (EP) and STFT. Further, its noise immunity is better than those of EP and STFT.
Feedback active noise control based on forward-backward LMS predictor
(2013) Pavithra, S.; Narasimhan, S.V.
In this paper, a new feedback active noise control (FBANC) system based on forward-backward error LMS (FBLMS) predictor is proposed. The misadjustment of the FBLMS predictor is about half that of the forward error LMS (FLMS) predictor. The new ANC system employs FBLMS predictors both for its main path (MP) predictor and for the noise canceler (NC) for the secondary path (SP) identification (SPI). To realize the MP predictor based on the FBLMS concept, a new FXLMS structure is proposed. But for the NC for the SPI, the FBLMS predictor is directly used. The MP predictor based on FBLMS reduces its misadjustment. Further the use of FBLMS predictor for the NC, as it gives a good prediction of primary noise component in the error (residual noise), improves the SNR for SPI. Thus, the improved SP estimate and the reduced misadjustment for the MP predictor achieved result in a significantly better overall noise reduction (of about 8 dB) over the ANC that uses the MP predictor and noise canceler for SPI, both based only on the forward error LMS algorithm. The computational load for the proposed algorithm is about twice that of FBANC that uses only forward error. © 2012 Springer-Verlag London Limited.
Feedback active noise control based on transform-domain forward-backward LMS predictor
(Springer London, 2014) Pavithra, S.; Narasimhan, S.V.
In this paper, a new feedback active noise control (FBANC) system based on the transform-domain forward-backward LMS (TFBLMS) predictor has been proposed. The new ANC system employs the TFBLMS predictor for its main-path (MP) predictor as well as for the noise canceller. To overcome the ill effect of the primary noise field, which acts as an observation noise for the secondary-path (SP) identification, the noise canceller is used. As the main-path predictor is based on the TFBLMS, its convergence rate improves due to its input orthogonalization. Further, its FBLMS nature reduces misadjustment. The use of TFBLMS predictor for noise canceller also gives a good prediction of primary noise at a faster rate, enabling improved SP identification. This improved SP identification indirectly aids the MP predictor to achieve an improved performance. A new filtered-x LMS structure has been proposed to realize the new MP predictor to accommodate the TFBLMS algorithm. The TFBLMS algorithm is applied directly to the noise canceller for SP identification. The proposed new ANC system has been found to have a significantly better noise reduction (by 14.6 dB) over the FBANC system based on tapped delay line time-domain FBLMS algorithm. © 2012 Springer-Verlag London Limited.
Harmonic wavelet transform signal decomposition and modified group delay for improved Wigner-Ville distribution
(2004) Narasimhan, S.V.; Kumar, B.K.S.
A new approach for the Wigner-Ville Distribution (WVD) based on signal decomposition by harmonic wavelet transform (SDHWT) and the modified magnitude group delay function (MMGD) has been proposed. The SDHWT directly provides subband signals and the WVD of these components are concatenated to get the overall WVD without using antialias and image rejection filtering. The SDHWT and the MMGD remove the existence of crossterms (CT) and the ripple effect due to truncation of the WVD kernel without applying any window, respectively. Since there is no time and frequency smoothing, the proposed method has a better performance in terms of both time and frequency resolution and desirable properties of a time-frequency representation (TFR) than the Pseudo WVD (PWVD). Further, it has a relatively better noise immunity compared to that of PWVD. In the WVD, for signal decomposition, the use of SDHWT, compared to that of a filter bank, provides almost similar results but has a significant (72%) computational advantage. � 2004 IEEE.
Harmonic wavelet transform signal decomposition and modified group delay for improved Wigner-Ville distribution
(2004) Narasimhan, S.V.; Shreyamsha Kumar, B.K.S.
A new approach for the Wigner-Ville Distribution (WVD) based on signal decomposition by harmonic wavelet transform (SDHWT) and the modified magnitude group delay function (MMGD) has been proposed. The SDHWT directly provides subband signals and the WVD of these components are concatenated to get the overall WVD without using antialias and image rejection filtering. The SDHWT and the MMGD remove the existence of crossterms (CT) and the ripple effect due to truncation of the WVD kernel without applying any window, respectively. Since there is no time and frequency smoothing, the proposed method has a better performance in terms of both time and frequency resolution and desirable properties of a time-frequency representation (TFR) than the Pseudo WVD (PWVD). Further, it has a relatively better noise immunity compared to that of PWVD. In the WVD, for signal decomposition, the use of SDHWT, compared to that of a filter bank, provides almost similar results but has a significant (72%) computational advantage. Â© 2004 IEEE.
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.
Improved Wigner-Ville distribution performance based on DCT/DFT harmonic wavelet transform and modified magnitude group delay
(2008) Narasimhan, S.V.; Haripriya, A.R.; Shreyamsha Kumar, B.K.
A new Wigner-Ville distribution (WVD) estimation is proposed. This improved and efficient WVD is based on signal decomposition (SD) by DCT or DFT harmonic wavelet transform (DCTHWT or DFTHWT) and the modified magnitude group delay (MMGD). The MMGD processing can be either in fullband or subband. The SD by DCTHWT provides better quality low leakage decimated subband components. The concatenation of WVDs of the subbands results in an overall WVD, significantly free from crossterms and Gibbs ripple. As no smoothing window is used for the instantaneous autocorrelation (IACR), MMGD removes or reduces the Gibbs ripple preserving the frequency resolution achieved by the DCT/DFT HWT. The SD by DCTHWT compared to that of DFTHWT, has improved frequency resolution and detectability. These are due to the symmetrical data extension and the consequential low leakage (bias and variance). As the zeros due to the associated white noise are removed by the MMGD effectively in subband domain than in fullband, the proposed WVD based on subband has a better noise immunity. Compared to fullband WVD, the subband WVD is computationally efficient and achieves a significantly better: frequency resolution, detectability of low-level signal in the presence of high-level one and variance. The SD-based methods, however cannot bring out the frequency transition path from band to band clearly, as there will be gap in the contour plot at the transition. For the proposed methods, the heart rate variability (HRV) real data is also considered as an example. © 2007 Elsevier B.V. All rights reserved.
Steiglitz-McBride adaptive notch filter based on a variable-step-size LMS algorithm and its application to active noise control
(John Wiley and Sons Ltd Southern Gate Chichester, West Sussex PO19 8SQ, 2016) Roopa, S.; Narasimhan, S.V.; Babloo, B.
Summary This paper proposes a new Steiglitz-McBride (SM) adaptive notch filter (SM-ANF) based on a robust variable-step-size least-mean-square algorithm and its application to active noise control (ANC). The proposed SM-ANF not only has fast convergence but also has small misadjustment. The variable-step-size algorithm uses the sum of the squared cross correlation between the error signal and the delayed inputs corresponding to the adaptive weights. The cross correlation provides robustness to the broadband signal, which plays the role of noise. The proposed SM-ANF is computationally simpler than the existing Newton/recursive least-squares-type ANF. The frequency response of the new SM-ANF has a notch depth of about -25 dB (for each of the three frequencies considered) and has spectral flatness within 5 dB (peak to peak). This robust notch filter algorithm is used as an observation noise canceller for the secondary path estimation of an ANC system based on the SM method. The ANC with proposed SM-ANF provides not only faster convergence but also an 11-dB improvement in noise attenuation over the SM-based ANC without such a SM-ANF. © © 2015 John Wiley & Sons, Ltd.
Virtual sensor based Feedback Active Noise Control for neonates in NICU
(2013) Veena, S.; Pavithra, S.; Lokesha, H.; Narasimhan, S.V.; More, N.V.
In this paper, a novel approach based on Feedback Active Noise Control with Virtual sensing has been proposed to reduce the Neonatal Intensive Care Unit (NICU) noise for infants in incubator. In NICU, the noise is due to infant monitoring medical equipments located in the vicinity of incubators. To address this, the algorithms in the literature are based on feedforward approach which requires a good reference to achieve effective attenuation. In an NICU environment, these algorithms require more than one reference microphone, which increases the complexity of the algorithm. This is eliminated by the proposed Feedback Active Noise Control approach as it generates its own reference from the error signal. The baby's ears must fall in the zone of silence (ZOS) to ensure noise reduction at its ears. But the volume of ZOS is inversely proportional to noise frequency and for frequencies above 1 KHz the ZOS is less than an inch. This constraint is overcome by the virtual sensing technique, which focuses the ZOS at baby's ears. The algorithm is evaluated with noise record from Neonatal Intensive Care Unit and it has resulted in 7dB more reduction at the baby's ears compared to existing algorithms. � 2013 IEEE.
Virtual sensor based Feedback Active Noise Control for neonates in NICU
(IEEE Computer Society, 2013) Veena, S.; Pavithra, S.; Lokesha, H.; Narasimhan, S.V.; More, N.V.
In this paper, a novel approach based on Feedback Active Noise Control with Virtual sensing has been proposed to reduce the Neonatal Intensive Care Unit (NICU) noise for infants in incubator. In NICU, the noise is due to infant monitoring medical equipments located in the vicinity of incubators. To address this, the algorithms in the literature are based on feedforward approach which requires a good reference to achieve effective attenuation. In an NICU environment, these algorithms require more than one reference microphone, which increases the complexity of the algorithm. This is eliminated by the proposed Feedback Active Noise Control approach as it generates its own reference from the error signal. The baby's ears must fall in the zone of silence (ZOS) to ensure noise reduction at its ears. But the volume of ZOS is inversely proportional to noise frequency and for frequencies above 1 KHz the ZOS is less than an inch. This constraint is overcome by the virtual sensing technique, which focuses the ZOS at baby's ears. The algorithm is evaluated with noise record from Neonatal Intensive Care Unit and it has resulted in 7dB more reduction at the baby's ears compared to existing algorithms. Â© 2013 IEEE.