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Can I read High Speed Imaging of Antipersonnel Land Mines by the Convexification Algorithm for a Simplified Mathematical Model in Two Dimensions on EtoBox?
High Speed Imaging of Antipersonnel Land Mines by the Convexification Algorithm for a Simplified Mathematical Model in Two Dimensions by J. Xin; M. V. Klibanov is a Mathematics article available to read on EtoBox.
What is High Speed Imaging of Antipersonnel Land Mines by the Convexification Algorithm for a Simplified Mathematical Model in Two Dimensions about?
We address the efficiency issue for the globally convergent convexification algorithm for coefficient inverse problems. By properly choosing the upper limit for pseudo-frequency and with quadratic polynomial approximations of the quantities which depend on the pseudo-frequency, we show that the algorithm can be made dramatically faster relative to the previous "tail-free" implementation. Numerical results from imaging and the mathematical modeling of antipersonnel land mines demonstrate that the algorithm can detect the location(s) of the inclusion(s) from the background medium, as well as correctly identify the material property of the inclusion(s) and the background. This indicates that the convexification algorithm may be applied in real-time to detect and image mine-like targets in the field.
Who reads High Speed Imaging of Antipersonnel Land Mines by the Convexification Algorithm for a Simplified Mathematical Model in Two Dimensions?
It is typically read by researchers, students, and practitioners in Mathematics.
- Author
- J. Xin; M. V. Klibanov
- Publisher
- Walter de Gruyter GmbH
- Published
- 2009
- Language
- EN
- Field
- Mathematics (Physical Sciences)