Abstract

Abstract
Current solutions for delivering adapted multimedia content in mobile environments take into account only a limited set of contextual information, and can be regarded as passive solutions. We propose a new solution that anticipates user's needs based on the contexts of use and preferences to deliver media content to mobile users. This paper describes the profiling approach of the proposed solution, and a context-aware content-based recommendation for smart devices. Recommendations are issued based on user history, driven by real-time contextual conditions.

Abstract
Penetration of distributed generation systems in conventional power systems leads to power quality (PQ) disturbances. This paper provides an improved PQ disturbances classification, which is associated with load changes and environmental factors. Various forms of PQ disturbances, including sag, swell, notch, and harmonics, are taken into account. Several features are obtained through hyperbolic S-transform, out of which the optimal features are selected using a genetic algorithm. These optimal features are used for PQ disturbances classification by employing support vector machines (SVMs) and decision tree (DT) classifiers. The study is supported by three different case studies, considering the experimental setup prototypes for wind energy and photovoltaic systems, as well as the modified Nordic 32-bus test system. The robustness and precision of DT and SWM are performed with noise and harmonics in the disturbance signals, thus providing comprehensive results.

Abstract
In this paper we explore a novel feature for the segmentation of heart sounds: the entropy gradient. We are motivated by the fact that auscultations in real environments are highly contaminated by noise and results reinforce our suspicions that the entropy gradient is not only robust to such noise but maintains a high sensitivity to the S1 and S2 components of the signal. Our whole approach consists of three stages, out of which the last two are novel contributions to this field. The first stage consists of typical pre-processing and wavelet reconstruction to obtain the Shannon energy envelogram. On the second stage we use an embedding matrix to track the dynamics of the system, which is formed by delay vectors with higher dimension than the corresponding attractor. On the third stage, we use the eigenvalues and eigenvectors of the embedding matrix to estimate the entropy of the envelogram. Finite differences are used to estimate entropy gradients, in which standard peak picking approaches are used for heart sound segmentation. Experiments are performed on a public dataset of pediatric auscultations obtained in real environments and results show the promising potential of this novel feature for such noisy scenarios.

Abstract

Abstract
The GRAVITY acquisition camera has four 9x9 Shack-Hartmann sensors operating in the near-infrared. It measures the slow variations of a quasi-distorted wavefront of four telescope beams simultaneously, by imaging the Galactic Center field. The Shack-Hartmann lenslet images of the Galactic Center are generated. Since the lenslet array images are filled with the crowded Galactic Center stellar field, an extended object, the local shifts of the distorted wavefront have to be estimated with a correlation algorithm. In this paper we report on the accuracy of six existing centroid algorithms for the Galactic Center stellar field. We show the VLTI tunnel atmospheric turbulence phases are reconstructed back with a precision of 100 nm at 2 s integration.