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Intrinsic feature extraction in the COI of wavelet power spectra ofclimatic signals
Beijing Normal University.
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL.
2011 (English)In: Proceedings of IEEE 4th International Conference on Image and Signal Processing, 2380-2382 p.Article in journal (Refereed) Published
Abstract [en]

Since the wavelet power spectra are distorted at databoundaries (the cone of influence, COI), using traditionalmethods, one cannot judge whether there is a significant regionin COI or not. In this paper, with the help of a first-order autoregressive (AR1) extension and using our simple andrigorous method, we can obtain realistic significant regions andintrinsic feature in the COI of wavelet power spectra. We verifyour method using the 300 year record of ice extent in the Baltic Sea.

Place, publisher, year, edition, pages
2011. 2380-2382 p.
National Category
Climate Research
Research subject
Physical Geography
Identifiers
URN: urn:nbn:se:uu:diva-190132OAI: oai:DiVA.org:uu-190132DiVA: diva2:583074
Available from: 2013-01-07 Created: 2013-01-07 Last updated: 2013-01-08Bibliographically approved

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http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=06100753

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Moore, John C

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