Abstract. Coronal Mass ejections (CMEs) are enormous eruptions of magnetized plasma expelled from the Sun into the interplanetary space, over the course of hours to days. They can create major disturbances in the interplanetary medium and trigger severe magnetic storms when they collide with the Earth's magnetosphere. It is important to know their real speed, propagation direction and 3-D configuration in order to accurately predict their arrival time at the Earth. Using data from the SECCHI coronagraphs onboard the STEREO mission, which was launched in October 2006, we can infer the propagation direction and the 3-D structure of such events. In this review, we first describe different techniques that were used to model the 3-D configuration of CMEs in the coronagraph field of view (up to 15 R ).Correspondence to: M. Mierla (mmierla@gmail.com) Then, we apply these techniques to different CMEs observed by various coronagraphs. A comparison of results obtained from the application of different reconstruction algorithms is presented and discussed.

We consider the probe of astrophysical signals through radio interferometers with small field of view and baselines with non-negligible and constant component in the pointing direction. In this context, the visibilities measured essentially identify with a noisy and incomplete Fourier coverage of the product of the planar signals with a linear chirp modulation. In light of the recent theory of compressed sensing and in the perspective of defining the best possible imaging techniques for sparse signals, we analyze the related spread spectrum phenomenon and suggest its universality relative to the sparsity dictionary. Our results rely both on theoretical considerations related to the mutual coherence between the sparsity and sensing dictionaries, as well as on numerical simulations.Comment: 10 pages, 3 figures. Version 2 matches version accepted for publication in MNRAS. Changes include minor clarification

This paper addresses the problem of localizing people in low and high density crowds with a network of heterogeneous cameras. The problem is recast as a linear inverse problem. It relies on deducing the discretized occupancy vector of people on the ground, from the noisy binary silhouettes observed as foreground pixels in each camera. This inverse problem is regularized by imposing a sparse occupancy vector, i.e., made of few non-zero elements, while a particular dictionary of silhouettes linearly maps these nonempty grid locations to the multiple silhouettes viewed by the cameras network. The proposed framework is (i) generic to any scene of people, i.e., people are located in low and high density crowds, (ii) scalable to any number of cameras and already working with a single camera, (iii) unconstrained by the scene surface to be monitored, and (iv) versatile with respect to the camera's geometry, e.g., planar or omnidirectional.Qualitative