A critical issue in image restoration is noise removal, whose state-of-art algorithm, NonLocal Means, is highly demanding in terms of computational time. Aim of the present paper is to boost its performance by an efficient algorithm tailored to GPU hardware architectures. This algorithm adapts itself to several variants of the methodologies in terms of different strategies for estimating the involved filtering parameter, type of noise affecting data, multicomponent signals, spatial dimension of the images. Numerical experiments on brain Magnetic Resonance images are provided.

Nature routinely presents us with spectacular demonstrations of organization and orchestrated motion in living species. Efficient information transfer among the individuals is known to be instrumental to the emergence of spatial patterns (e.g. V-shaped formations for birds or diamond-like shapes for fishes), responding to a specific functional goal such as predatory avoidance or energy savings. Such functional patterns materialize whenever individuals appoint one of them as a leader with the task of guiding the group towards a prescribed target destination.

Unlike for systems in equilibrium, a straightforward definition of a metastable set in the non-stationary, non-equilibrium case may only be given case-by-case-and therefore it is not directly useful any more, in particular in cases where the slowest relaxation time scales are comparable to the time scales at which the external field driving the system varies. We generalize the concept of metastability by relying on the theory of coherent sets.

An algorithm for coupling a classical Finite Volume (FV) approach, that discretize the Navier-Stokes equations on a block structured Eulerian grid, with the weakly-compressible SPH is presented. The coupling procedure aims at applying each solver in the region where its intrinsic characteristics can be exploited in the most efficient and accurate way: the FV solver is used to resolve the bulk flow and the wall regions, whereas the SPH solver is implemented in the free surface region to capture details of the front evolution.

In this paper we propose a multiperiod optimization model based on the maximal covering location problem in order to support safety policies within urban areas. In particular, we focus on the field of car accidents control, by considering the problem of the optimal location of intersection safety cameras (ISC) on an urban traffic network to maximize road control and reduce the number and the impact of car accidents. The effectiveness of accidents prevention programs can be increased by changing periodically the position of the available ISCs on a given time horizon.

The purpose of this article is to present a methodology to identify the sources of activity in brain networks from functional magnetic resonance imaging (fMRI) data using the multiset canonical correlation analysis algorithm. The aim is to lay the foundations for a screening marker to be used as indicator of mental diseases. Group analysis blind source separation methods have proved reliable to extract the latent sources underlying the brain activities but currently there is no recognized biomarker for mental disorders.

Results from direct numerical simulations (DNS) of particle relative dispersion in three-dimensional homogeneous and isotropic turbulence at Reynolds number Re?~300 are presented. We study point-like passive tracers and heavy particles, at Stokes number St=0.6,1 and 5. Particles are emitted from localised sources, in bunches of thousands, periodically in time, allowing an unprecedented statistical accuracy to be reached, with a total number of events for two-point observables of the order of 1011.

Intermittency effects are numerically studied in turbulent bubbling Rayleigh-Benard (RB) flow and compared to the standard RB case. The vapour bubbles are modelled with a Euler-Lagrangian scheme and are two-way coupled to the flow and temperature fields, both mechanically and thermally. To quantify the degree of intermittency we use probability density functions, structure functions, extended self-similarity (ESS) and generalized extended self-similarity (GESS) for both temperature and velocity differences.