University of Trento

About: University of Trento is a education organization based out in Trento, Italy. It is known for research contribution in the topics: Population & Context (language use). The organization has 10527 authors who have published 30978 publications receiving 896614 citations. The organization is also known as: Universitá degli Studi di Trento & Universita degli Studi di Trento.

Earthquake Damage Assessment of Buildings Using VHR Optical and SAR Imagery

Abstract: Rapid damage assessment after natural disasters (eg, earthquakes) and violent conflicts (eg, war-related destruction) is crucial for initiating effective emergency response actions Remote-sensing satellites equipped with very high spatial resolution (VHR) multispectral and synthetic aperture radar (SAR) imaging sensors can provide vital information due to their ability to map the affected areas with high geometric precision and in an uncensored manner In this paper, we present a novel method that detects buildings destroyed in an earthquake using pre-event VHR optical and post-event detected VHR SAR imagery The method operates at the level of individual buildings and assumes that they have a rectangular footprint and are isolated First, the 3-D parameters of a building are estimated from the pre-event optical imagery Second, the building information and the acquisition parameters of the VHR SAR scene are used to predict the expected signature of the building in the post-event SAR scene assuming that it is not affected by the event Third, the similarity between the predicted image and the actual SAR image is analyzed If the similarity is high, the building is likely to be still intact, whereas a low similarity indicates that the building is destroyed A similarity threshold is used to classify the buildings We demonstrate the feasibility and the effectiveness of the method for a subset of the town of Yingxiu, China, which was heavily damaged in the Sichuan earthquake of May 12, 2008 For the experiment, we use QuickBird and WorldView-1 optical imagery, and TerraSAR-X and COSMO-SkyMed SAR data

Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization

Abstract: The aim of this paper is twofold. First, we present a thorough experimental study to show the superiority of the generalization capability of the support vector machine (SVM) approach in the automatic classification of electrocardiogram (ECG) beats. Second, we propose a novel classification system based on particle swarm optimization (PSO) to improve the generalization performance of the SVM classifier. For this purpose, we have optimized the SVM classifier design by searching for the best value of the parameters that tune its discriminant function, and upstream by looking for the best subset of features that feed the classifier. The experiments were conducted on the basis of ECG data from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database to classify five kinds of abnormal waveforms and normal beats. In particular, they were organized so as to test the sensitivity of the SVM classifier and that of two reference classifiers used for comparison, i.e., the k-nearest neighbor (kNN) classifier and the radial basis function (RBF) neural network classifier, with respect to the curse of dimensionality and the number of available training beats. The obtained results clearly confirm the superiority of the SVM approach as compared to traditional classifiers, and suggest that further substantial improvements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. On an average, over three experiments making use of a different total number of training beats (250, 500, and 750, respectively), the PSO-SVM yielded an overall accuracy of 89.72% on 40438 test beats selected from 20 patient records against 85.98%, 83.70%, and 82.34% for the SVM, the kNN, and the RBF classifiers, respectively.

Visibility A Category for the Social Sciences

Abstract: Can visibility be counted as a general category for the social sciences? The attempt to provide an answer to this question entails both describing actual phenomena of visibility, and defining the c...

Understanding Mashup Development

Abstract: Web mashups are Web applications developed using contents and services available online. Despite rapidly increasing interest in mashups, comprehensive development tools and frameworks are lacking, and in most cases mashing up a new application implies a significant manual programming effort. This article overviews current tools, frameworks, and trends that aim to facilitate mashup development. The authors use a set of characteristic dimensions to highlight the strengths and weaknesses of some representative approaches.