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Sadhana : Volume 42

Sadhana : Volume 41

Sadhana : Volume 41, Issue 12, December 2016

Sadhana : Volume 41, Issue 11, November 2016

Optimum short-time polynomial regression for signal analysis

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Fusion of sparse reconstruction algorithms for multiple measurement vectors

New approach for ensuring cloud computing security: using data hiding methods

Novel quantum inspired binary neural network algorithm

Transient pressure measurements at part load operating condition of a high head model Francis turbine

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Sadhana : Volume 41, Issue 3, March 2016

Sadhana : Volume 41, Issue 2, February 2016

Sadhana : Volume 41, Issue 1, January 2016

Optimum short-time polynomial regression for signal analysis

Content Provider	Springer Nature Link
Author	Murthy, A Sreenivasa Seelamantula, Chandra Sekhar Sreenivas, T V
Copyright Year	2016
Abstract	We propose a short-time polynomial regression (STPR) for time-varying signal analysis. The advantage of using polynomials is that the notion of a spectrum is not needed and the signals can be analyzed in the time domain over short durations. In the presence of noise, such modeling becomes important, because the polynomial approximation performs smoothing leading to noise suppression. The problem of optimal smoothing depends on the duration over which a fixed-order polynomial regression is performed. Considering the STPR of a noisy signal, we derive the optimal smoothing window by minimizing the mean-square error (MSE). For a fixed polynomial order, the smoothing window duration depends on the rate of signal variation, which, in turn, depends on its derivatives. Since the derivatives are not available a priori, exact optimization is not feasible. However, approximate optimization can be achieved using only the variance expressions and the intersection-of-confidence-intervals (ICI) technique. The ICI technique is based on a consistency measure across confidence intervals corresponding to different window lengths. An approximate asymptotic analysis to determine the optimal confidence interval width shows that the asymptotic expressions are the same irrespective of whether one starts with a uniform sampling grid or a nonuniform one. Simulation results on sinusoids, chirps, and electrocardiogram (ECG) signals, and comparisons with standard wavelet denoising techniques, show that the proposed method is robust particularly in the low signal-to-noise ratio regime.
Starting Page	1245
Ending Page	1260
Page Count	16
File Format	PDF
ISSN	02562499
Journal	Sadhana
Volume Number	41
Issue Number	11
e-ISSN	09737677
Language	English
Publisher	Springer India
Publisher Date	2016-11-12
Publisher Place	New Delhi
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Short-time polynomial regression (STPR) mean-square error bias–variance trade-off confidence interval intersection of confidence intervals (ICI) technique Engineering
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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