Abstract
Wireless capsule endoscopy is a relatively novel technique used for imaging of the gastrointestinal tract. Unlike traditional approaches, it allows painless visualisation of the whole of the gastrointestinal tract, including the small bowel, a region of difficult access. Endoscopic capsules record for about 8h, producing around 60,000 images. These are analysed by an expert that identifies abnormalities present in the frames, a process that is very tedious and prone to errors. Thus there is a clear need to develop systems that automatically analyse this data and detect lesions. Lesion detection achieved a precision of 0.94 and a recall of 0.93 by fmetuning the pre-trained DenseNet-161 model. (C) 2019 The Authors. Published by Elsevier B.V.

Abstract
The Sternum is a human bone located in the anterior area of the thoracic cage. It is present in most of the axial cuts provided from the Magnetic Resonance Imaging (MRI) acquisitions. used in the medical field. Detecting the Sternum is relevant as it contains rigid key-points for 3D model reconstructions, assisting in the planning and evaluation of several surgical procedures, and for atlas development by segmenting structures in anatomical proximity. In the absence of applicable approaches for this specific problem. this paper focuses on two distinct automated methods for Sternum segmentation in MRI. The first. relies on K-Means (Clustering) to perform the segmentation, while the second encompasses the closed Minimum Path over the elliptical transformation of Gradient images. A dataset of 14 annotated acquisitions was used for evaluation. The results favored the Gradient approach over Clustering.

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Abstract
The estimation of atmospheric turbulence parameters is of relevance for the following: (a) site evaluation and characterization; (b) prediction of the point spread function; (c) live assessment of error budgets and optimization of adaptive optics performance; (d) optimization of fringe trackers for long baseline optical interferometry. The ubiquitous deployment of Shack-Hartmann wavefront sensors in large telescopes makes them central for atmospheric turbulence parameter estimation via adaptive optics telemetry. Several methods for the estimation of the Fried parameter and outer scale have been developed, most of which are based on the fitting of Zernike polynomial coefficient variances reconstructed from the telemetry. The non-orthogonality of Zernike polynomial derivatives introduces modal cross coupling, which affects the variances. Furthermore, the finite resolution of the sensor introduces aliasing. In this article the impact of these effects on atmospheric turbulence parameter estimation is addressed with simulations. It is found that cross-coupling is the dominant bias. An iterative algorithm to overcome it is presented. Simulations are conducted for typical ranges of the outer scale (4-32 m), Fried parameter (10 cm) and noise in the variances (signal-to-noise ratio of 10 and above). It is found that, using the algorithm, both parameters are recovered with sub-per cent accuracy.

Abstract
We found stable soliton solutions for two generalizations of the cubic complex Ginzburg-Landau equation, namely, one that includes the term that, in optics, represents a delayed response of the nonlinear gain and the other including the self-steepening term, also in the optical context. These solutions do not require the presence of the delayed response of the nonlinear refractive index, such that, they exist regardless of the term previously considered essential for stabilization. The existence of these solitons was predicted by a perturbation approach, and then confirmed by solving the ordinary differential equations, resulting from a similarity reduction, and also by applying a linear stability analysis. We found that these solitons exist for a large region of the parameter space and possess very asymmetric amplitude profiles as well as a complicated chirp characteristic.