Online sensors and wavelet-based filter approach for tsunami case study

The Tsunami classification model with real-time sensors placed at different locations and at different depths is proposed. To exclude the artifact effects in the sensor values, a wavelet-based denoising scheme is integrated in the model. In addition, a downsampling approach has been proposed to achi...

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Bibliographic Details
Published inJournal of applied remote sensing Vol. 7; no. 1; p. 073517
Main Authors Umadevi, Manickam, Srinivasulu, Seshachalam
Format Journal Article
LanguageEnglish
Published Society of Photo-Optical Instrumentation Engineers 22.08.2013
Subjects
noise removal filter
tsunami
downsampling
conjugate quadrature filter
wavelet transform
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ISSN1931-3195
1931-3195
DOI10.1117/1.JRS.7.073517

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Summary:The Tsunami classification model with real-time sensors placed at different locations and at different depths is proposed. To exclude the artifact effects in the sensor values, a wavelet-based denoising scheme is integrated in the model. In addition, a downsampling approach has been proposed to achieve maximum flat delay response, and the present results are compared with the Pc McClellan method. Various parameters such as conductivity, salinity, pressure, temperature, and dissolved oxygen are measured and sensed using multisensor grid architecture. Our results show that by sensing the above parameters and subject them online, it is possible to clearly distinguish the pre- and post-tsunami behaviors.
ISSN:1931-3195
1931-3195
DOI:10.1117/1.JRS.7.073517