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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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Gharsalli, Leila
Institut Polytechnique des Sciences Avancées
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (6/6 displayed)
- 2022Hybrid Genetic Algorithmscitations
- 2019Hybrid Genetic Algorithms
- 2014Variational Bayesian inversion for microwave imaging applied to breast cancer detection
- 2014Variational Bayesian inversion for microwave breast imaging
- 2014A gradient-like variational Bayesian approach: Application to microwave imaging for breast tumor detectioncitations
- 2013Microwave tomography for breast cancer detection within a Variational Bayesian Approach
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conferencepaper
Microwave tomography for breast cancer detection within a Variational Bayesian Approach
Abstract
We consider a nonlinear inverse scattering problem where the goal is to detect breast cancer from measurements of the scattered field that results from its interaction with a known wave in the microwave frequency range. The modeling of the wave-object interaction is tackled through a domain integral representation of the electric field in a 2D-TM configuration. The inverse problem is solved in a Bayesian framework where the prior information is introduced via a Gauss-Markov-Potts model. A Variational Bayesian Approximation (VBA) technique is adapted to complex valued contrast and applied to compute the posterior estimators and reconstruct maps of both the permittivity and conductivity. Results obtained by means of this approach from synthetic data are compared with those given by a deterministic contrast source inversion method.