Convolution Models with Shift-invariant kernel based on Matlab-GPU platform for Fast Acoustic Imaging

Abstract : Acoustic imaging is an advanced technique for acoustic source localization and power reconstruc-tion from limited noisy measurements at microphone sensors. This technique not only involves in a forward model of acoustic propagation from sources to sensors, but also its numerical solution of an ill-posed inverse problem. Nowadays, the Bayesian inference methods in inverse methods have been widely investigated for robust acoustic imaging, but most of Bayesian methods are time-consuming, and one of the reasons is that the forward model causes heavy matrix multiplication. In this paper, we focus on the acceleration of the forward model by using a 2D-invariant convo-lution and a separable convolution respectively; For hardware acceleration, the Matlab-Graphics Processing Unit application are discussed. For method validation, we use the simulated and real data from the wind tunnel experiment in automobile industry.
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Communication dans un congrès
ISAV 2014, Dec 2014, Tehran, Iran. Proceedings of the 4th International Conference on Acoustics and Vibration, 2014
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Ning Chu, Nicolas Gac, José Picheral, Ali Mohammad-Djafari. Convolution Models with Shift-invariant kernel based on Matlab-GPU platform for Fast Acoustic Imaging. ISAV 2014, Dec 2014, Tehran, Iran. Proceedings of the 4th International Conference on Acoustics and Vibration, 2014. 〈hal-01103819〉

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