219 research outputs found
Modular Middleware for Gestural Data and Devices Management
In the last few years, the use of gestural data has become a key enabler for human-computer interaction (HCI) applications. The growing diffusion of low-cost acquisition devices has thus led to the development of a class of middleware aimed at ensuring a fast and easy integration of such devices within the actual HCI applications. The purpose of this paper is to present a modular middleware for gestural data and devices management. First, we describe a brief review of the state of the art of similar middleware. Then, we discuss the proposed architecture and the motivation behind its design choices. Finally, we present a use case aimed at demonstrating the potential uses as well as the limitations of our middleware
A Novel Technique for Fingerprint Classification based on Fuzzy C-Means and Naive Bayes Classifier
Fingerprint classification is a key issue in
automatic fingerprint identification systems. One of the main
goals is to reduce the item search time within the fingerprint
database without affecting the accuracy rate. In this paper, a
novel technique, based on topological information, for
efficient fingerprint classification is described. The proposed
system is composed of two independent modules: the former
module, based on Fuzzy C-Means, extracts the best set of
training images; the latter module, based on Fuzzy C-Means
and Naive Bayes classifier, assigns a class to each processed
fingerprint using only directional image information. The
proposed approach does not require any image enhancement
phase. Experimental trials, conducted on a subset of the free
downloadable PolyU database, show a classification rate of
91% over a 100 images test database using only 12 training
examples
VisIVOWeb: A WWW Environment for Large-Scale Astrophysical Visualization
This article presents a newly developed Web portal called VisIVOWeb that aims
to provide the astrophysical community with powerful visualization tools for
large-scale data sets in the context of Web 2.0. VisIVOWeb can effectively
handle modern numerical simulations and real-world observations. Our
open-source software is based on established visualization toolkits offering
high-quality rendering algorithms. The underlying data management is discussed
with the supported visualization interfaces and movie-making functionality. We
introduce VisIVOWeb Network, a robust network of customized Web portals for
visual discovery, and VisIVOWeb Connect, a lightweight and efficient solution
for seamlessly connecting to existing astrophysical archives. A significant
effort has been devoted for ensuring interoperability with existing tools by
adhering to IVOA standards. We conclude with a summary of our work and a
discussion on future developments
Integrating virtual reality and gis tools for geological mapping, data collection and analysis: An example from the metaxa mine, santorini (Greece)
In the present work we highlight the effectiveness of integrating different techniques and tools for better surveying, mapping and collecting data in volcanic areas. We use an Immersive Virtual Reality (IVR) approach for data collection, integrated with Geographic Information System (GIS) analysis in a well-known volcanological site in Santorini (Metaxa mine), a site where volcanic processes influenced the island’s industrial development, especially with regard to pumice mining. Specifically, we have focused on: (i) three-dimensional (3D) high-resolution IVR scenario building, based on Structure from Motion photogrammetry (SfM) modeling; (ii) subsequent geological survey, mapping and data collection using IVR; (iii) data analysis, e.g., calculation of extracted volumes, as well as production of new maps in a GIS environment using input data directly from the IVR survey; and finally, (iv) presentation of new outcomes that highlight the importance of the Metaxa Mine as a key geological and volcanological geosite
Clustering analysis for muon tomography data elaboration in the Muon Portal project
Clustering analysis is one of multivariate data analysis techniques which allows to gather statistical data units into groups, in order to minimize the logical distance within each group and to maximize the one between different groups. In these proceedings, the authors present a novel approach to the muontomography data analysis based on clustering algorithms. As a case study we present the Muon Portal project that aims to build and operate a dedicated particle detector for the inspection of harbor containers to hinder the smuggling of nuclear materials. Clustering techniques, working directly on scattering points, help to detect the presence of suspicious items inside the container, acting, as it will be shown, as a filter for a preliminary analysis of the data
The Impact of SARS-COVID-19 Outbreak on European Cities Urban Mobility
The global outbreak of the SARS-COVID-19 pandemic has changed our lives, driving an unprecedented transformation of our habits. In response, the authorities have enforced several measures, including social distancing and travel restrictions that lead to the temporary closure of activities centered around schools, companies, local businesses to those pertaining to the recreation category. As such, with a mobility reduction, the life of our cities during the outbreak changed significantly. In this paper, we aim at drawing attention to this problem and perform an analysis for multiple cities through crowdsensed information available from datasets such as Apple Maps, to shed light on the changes undergone during both the outbreak and the recovery. Specifically, we exploit data characterizing many mobility modes like driving, walking, and transit. With the use of Gaussian Processes and clustering techniques, we uncover patterns of similarity between the major European cities. Further, we perform a prediction analysis that permits forecasting the trend of the recovery process and exposes the deviation of each city from the trend of the cluster. Our results unveil that clusters are not typically formed by cities with geographical ties, but rather on the spread of the infection, lockdown measures, and citizens’ reactions
CAESAR source finder: recent developments and testing
A new era in radioastronomy will begin with the upcoming large-scale surveys
planned at the Australian Square Kilometre Array Pathfinder (ASKAP). ASKAP
started its Early Science program in October 2017 and several target fields
were observed during the array commissioning phase. The SCORPIO field was the
first observed in the Galactic Plane in Band 1 (792-1032 MHz) using 15
commissioned antennas. The achieved sensitivity and large field of view already
allow to discover new sources and survey thousands of existing ones with
improved precision with respect to previous surveys. Data analysis is currently
ongoing to deliver the first source catalogue. Given the increased scale of the
data, source extraction and characterization, even in this Early Science phase,
have to be carried out in a mostly automated way. This process presents
significant challenges due to the presence of extended objects and diffuse
emission close to the Galactic Plane. In this context we have extended and
optimized a novel source finding tool, named CAESAR , to allow extraction of
both compact and extended sources from radio maps. A number of developments
have been done driven by the analysis of the SCORPIO map and in view of the
future ASKAP Galactic Plane survey. The main goals are the improvement of
algorithm performances and scalability as well as of software maintainability
and usability within the radio community. In this paper we present the current
status of CAESAR and report a first systematic characterization of its
performance for both compact and extended sources using simulated maps. Future
prospects are discussed in light of the obtained results.Comment: 15 pages, 10 figure
The CORONA business in modern cities
As a response to the global outbreak of the SARS-COVID-19 pandemic, authorities have enforced a number of measures including social distancing, travel restrictions that lead to the "temporary" closure of activities stemming from public services, schools, industry to local businesses. In this poster we draw the attention to the impact of such measures on urban environments and activities. For this, we use crowdsensed information available from datasets like Google Popular Times and Apple Maps to shed light on the changes undergone during the outbreak and the recovery
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