Abstract
Assessing the quality of aperture synthesis maps is relevant for benchmarking image reconstruction algorithms, for the scientific exploitation of data from optical long-baseline interferometers, and for the design/upgrade of new/existing interferometric imaging facilities. Although metrics have been proposed in these contexts, no systematic study has been conducted on the selection of a robust metric for quality assessment. This article addresses the question: what is the best metric to assess the quality of a reconstructed image? It starts by considering several metrics and selecting a few based on general properties. Then, a variety of image reconstruction cases are considered. The observational scenarios are phase closure and phase referencing at the Very Large Telescope Interferometer (VLTI), for a combination of two, three, four and six telescopes. End-to-end image reconstruction is accomplished with the MIRA software, and several merit functions are put to test. It is found that convolution by an effective point spread function is required for proper image quality assessment. The effective angular resolution of the images is superior to naive expectation based on the maximum frequency sampled by the array. This is due to the prior information used in the aperture synthesis algorithm and to the nature of the objects considered. The l(1)-norm is the most robust of all considered metrics, because being linear it is less sensitive to image smoothing by high regularization levels. For the cases considered, this metric allows the implementation of automatic quality assessment of reconstructed images, with a performance similar to human selection.

Abstract

Abstract
Major rivers have traditionally been linked with important human settlements throughout history. The growth of cities over recent river deposits makes necessary the use of multidisciplinary approaches to characterize the evolution of drainage networks in urbanized areas. Since under-consolidated fluvial sediments are especially sensitive to compaction, their spatial distribution, thickness, and mechanical behavior must be studied. Here, we report on subsidence in the city of Seville (Southern Spain) between 2003 and 2010, through the analysis of the results obtained with the Multi-Temporal InSAR (MT-InSAR) technique. In addition, the temporal evolution of the subsidence is correlated with the rainfall, the river water column and the piezometric level. Finally, we characterize the geotechnical parameters of the fluvial sediments and calculate the theoretical settlement in the most representative sectors. Deformation maps clearly indicate that the spatial extent of subsidence is controlled by the distribution of under-consolidated fine-grained fluvial sediments at heights comprised in the range of river level variation. This is clearly evident at the western margin of the river and the surroundings of its tributaries, and differs from rainfall results as consequence of the anthropic regulation of the river. On the other hand, this influence is not detected at the eastern margin due to the shallow presence of coarse-grain consolidated sediments of different terrace levels. The derived results prove valuable for implementing urban planning strategies, and the InSAR technique can therefore be considered as a complementary tool to help unravel the subsidence tendency of cities located over under-consolidated fluvial deposits. Copyright (c) 2017 John Wiley & Sons, Ltd.

Abstract

Abstract
Medical data is particularly interesting as a subject for relational data mining due to the complex interactions which exist between different entities. Furthermore, the ambiguity of medical imaging causes interpretation to be complex and error-prone, and thus particularly amenable to improvement through automated decision support. Probabilistic Inductive Logic Programming (PILP) is a particularly well-suited tool for this task, since it makes it possible to combine the relational nature of this field with the ambiguity inherent in human interpretation of medical imaging. This work presents a PILP setting for breast cancer data, where several clinical and demographic variables were collected retrospectively, and new probabilistic variables and rules reflecting domain knowledge were introduced. A PILP predictive model was built automatically from this data and experiments show that it can not only match the predictions of a team of experts in the area, but also consistently reduce the error rate of malignancy prediction, when compared to other non-relational techniques.

Abstract
This paper describes the calibration of an underwater navigation system in enclosed scenarios. The work was performed in the context of the VAMOS project addressing the development of robotic solutions for flooded open pit mine exploration. An algorithm for calibration of extrinsic parameters for DVL and USBL systems is presented. Field experiments were performed with the ROAZ autonomous surface vehicle equipped with the underwater sensors and using precision IMU/GNSS fused data as groundtruth. The tests were performed in Douro River and in the Bejanca open pit mine, one of the VAMOS test sites, both in northern Portugal. The procedure was validated in the operational scenarios and results are presented showing the error correction and navigation quality improvement.