Sign Data Derivative Recovery
Joint Authors
Dymnikov, A. D.
Glass, G. A.
Houston, Louis M.
Source
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-02-14
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Given only the signs of signal plus noise added repetitively or sign data, signal amplitudes can be recovered with minimal variance.
However, discrete derivatives of the signal are recovered from sign data with a variance which approaches infinity with decreasing step size and increasing order.
For industries such as the seismic industry, which exploits amplitude recovery from sign data, these results place constraints on processing, which includes differentiation of the data.
While methods for smoothing noisy data for finite difference calculations are known, sign data requires noisy data.
In this paper, we derive the expectation values of continuous and discrete sign data derivatives and we explicitly characterize the variance of discrete sign data derivatives.
American Psychological Association (APA)
Houston, Louis M.& Glass, G. A.& Dymnikov, A. D.. 2012. Sign Data Derivative Recovery. ISRN Applied Mathematics،Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-486584
Modern Language Association (MLA)
Houston, Louis M.…[et al.]. Sign Data Derivative Recovery. ISRN Applied Mathematics No. 2012 (2012), pp.1-7.
https://search.emarefa.net/detail/BIM-486584
American Medical Association (AMA)
Houston, Louis M.& Glass, G. A.& Dymnikov, A. D.. Sign Data Derivative Recovery. ISRN Applied Mathematics. 2012. Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-486584
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-486584