Abstract
Magnetic Resonance Imaging (MRI) exams suffer from undesirable structure replicating and overlapping effects on certain acquisition settings. These are called Spatial Aliasing Artefacts (SAA) and their presence interferes with the segmentation of other anatomical structures. This paper addresses the segmentation of the SAA in T1-weighted MRI image sets, in order to effectively remove their influence over the legitimately positioned body structures. The proposed method comprises an initial thresholding, employing the Triangle method, an aggregation of neighboring voxels through Region Growing. Further refinement of the objects contour is obtained with Convex Hull and a Minimum Path algorithm applied to two orthogonal planes (Sagittal and Axial). Some experiments concerning the extension of the pipeline used are reported and the results seem promising. The average contour distance concerning the Ground Truth (GT) rounds 2.5mm and area metrics point out average overlaps above 64% with the GT. Some issues concerning the fusion between the output from the two planes are to be perfected. Nevertheless, the results seems sufficient to neutralize the influence of SAA and expedite the downstream anatomical segmentation tasks.

Abstract
The development of a three-dimensional (3D) planing tool for breast cancer surgery requires the existence of proper deformable models of the breast, with parameters that can be manipulated to obtain the desired shape. However, modelling breast is a challenging task due to the lack of physical landmarks that remain unchanged after deformation. In this paper, the fitting of a 3D point cloud of the breast to a parametric model suitable for surgery planning is investigated. Regression techniques were used to learn breast deformation functions from exemplar data, resulting in comprehensive models easy to manipulate by surgeons. New breast shapes are modelled by varying the type and degree of deformation of three common deformations: ptosis, turn and top-shape.

Abstract
Surgery planing of breast cancer interventions is gaining importance among physicians, who recognize value in discussing the possible aesthetic outcomes of surgery with patients. Research is been propelled to create patient-specific breast models, but breast image registration algorithms are still limited, particularly for the purpose of matching pre- and post-surgical data of patient's breast surfaces. Yet, this is a fundamental task to learn prediction models of breast healing process after surgery. In this paper, a coarse-to-fine registration strategy is proposed to match breast surface data acquired before and after surgery. Methods are evaluated in their ability to register surfaces in an anatomical reliable way, and results suggest proper alignment adequated to be used as input to train deformable models.

Abstract
In this work, we present parameter regions for the existence of stable plain solitons of the cubic complex Ginzburg-Landau equation (CGLE) with higher-order terms associated with a fourth-order expansion. Using a perturbation approach around the nonlinear Schrdinger equation soliton and a full numerical analysis that solves an ordinary differential equation for the soliton profiles and using the Evans method in the search for unstable eigenvalues, we have found that the minimum equation allowing these stable solitons is the cubic CGLE plus a term known in optics as Raman-delayed response, which is responsible for the redshift of the spectrum. The other favorable term for the occurrence of stable solitons is a term that represents the increase of nonlinear gain with higher frequencies. At the stability boundary, a bifurcation occurs giving rise to stable oscillatory solitons for higher values of the nonlinear gain. These oscillations can have very high amplitudes, with the pulse energy changing more than two orders of magnitude in a period, and they can even exhibit more complex dynamics such as period-doubling.

Abstract
This paper focus on the use of unmanned aerial vehicle teams for performing cooperative perception using Data Distribution Service (DDS) Network. We develop a DDS framework to manage the incoming and out bounding network traffic of multiple types of data that is exchanged inside the UAV network. Experimental results both in laboratory and in actual flight are presented to help characterize the proposed system solution.

Abstract
This paper describes an energy management system that is being developed in the AnyPLACE project to support new energy services, like demand response, in residential buildings. In the project end-user interfaces are designed and implemented to allow the input of preferences regarding the flexible use of shiftable and thermal appliances. Monitoring and self-learning algorithm are used to allow additional information to be collected and an automation platform is available for the management and control of appliances. An energy management algorithm is presented that processes end-user preferences and devices characteristics to produce an optimal dispatch considering demand response incentives. Results show the successful implementation of an optimized energy scheduling.