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

Manickam Umadevi; Seshachalam Srinivasulu

doi:10.1117/1.JRS.7.073517

22 August 2013 Online sensors and wavelet-based filter approach for tsunami case study

Manickam Umadevi, Seshachalam Srinivasulu

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 7, Issue 1, 073517 (August 2013). https://doi.org/10.1117/1.JRS.7.073517

Abstract

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.

Citation Download Citation

Manickam Umadevi and Seshachalam Srinivasulu "Online sensors and wavelet-based filter approach for tsunami case study," Journal of Applied Remote Sensing 7(1), 073517 (22 August 2013). https://doi.org/10.1117/1.JRS.7.073517

Published: 22 August 2013

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