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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Hunter, A.
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Publications (5/5 displayed)
- 2023Electronic Structure of Few-Layer Black Phosphorus from μ-ARPEScitations
- 2020An evaluation of ultrasonic arrays for the static and dynamic measurement of wheel rail contact pressure and area
- 2020Non-intrusive measurement of strip thickness and roll-bite length in metal rolling
- 2015Nano-enabled orientation alignment via extreme shear strainscitations
- 2012Autofocus imaging
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document
Autofocus imaging
Abstract
<p>The quality of an ultrasonic array image, especially for anisotropic material, depends on accurate information about acoustic properties. Inaccuracy of acoustic properties causes image degradation, e.g., blurring, errors in locating of reflectors and introduction of artifacts. In this paper, for an anisotropic austenitic steel weld, an autofocus imaging technique is presented. The array data from a series of beacons is captured and then used to statistically extract anisotropic weld properties by using a Monte-Carlo inversion approach. The beacon and imaging systems are realized using two separated arrays; one acts as a series of beacons and the other images these beacons. Key to the Monte-Carlo inversion scheme is a fast forward model of wave propagation in the anisotropic weld and this is based on the Dijkstra algorithm. Using this autofocus approach a measured weld map was extracted from an austenitic weld and used to reduce location errors, initially greater than 6mm, to less than 1mm. © 2012 American Institute of Physics.</p>