Barbara Nicolas

Mikael Carmona, Olivier Michel, Jean-Louis Lacoume et al.

Sensor network attitude problem consists in retrieving the attitude of each sensor of a network knowing some relative orientations between pairs of sensors. The attitude of a sensor is its orientation in an absolute axis system. We present in this paper a method for solving the sensor network attitude problem using quaternion formalism which allows to apply linear algebra tools. The proposed algorithm solves the problem when all of the relative attitudes are known. A complete characterisation of the algorithm is established: spatial complexity, time complexity and robustness. Our algorithm is validated in simulations and with real experiments.

Tatiana Gelvez-Barrera, Barbara Nicolas, Bruno Gilles et al.

Passive acoustic mapping (PAM) is a key imaging technique for characterizing cavitation activity in therapeutic ultrasound applications. Recent model-based beamforming algorithms offer high reconstruction quality and strong physical interpretability. However, their computational burden and limited temporal resolution restrict their use in applications with time-evolving cavitation. To address these challenges, we introduce a PAM beamforming framework based on a novel convolutional formulation in the time domain, which enables efficient computation. In this framework, PAM is formulated as an inverse problem in which the forward operator maps spatiotemporal cavitation activity to recorded radio-frequency signals accounting for time-of-flight delays defined by the acquisition geometry. We then formulate a regularized inversion algorithm that incorporates prior knowledge on cavitation activity. Experimental results demonstrate that our framework outperforms classical beamforming methods, providing higher temporal resolution than frequency-domain techniques while substantially reducing computational burden compared with iterative time-domain formulations.