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Centre for Artificial Intelligence and Cybersecurity – AIRI

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A Network for Gravitational Waves, Geophysics and Machine Learning

18.10.2018

The breakthrough discovery of gravitational waves on September 14, 2015 was made possible through synergy of techniques drawing from expertise in physics, mathematics, information science and computing.  At present, there is a rapidly growing interest in Machine Learning (ML), Deep Learning (DL), classification problems, data mining and visualization and, in general, in the development of new techniques and algorithms for efficiently handling the complex and massive data sets found in what has been coined “Big Data”, across a broad range of disciplines, ranging from Social Sciences to Natural Sciences. The rapid increase in computing power at our disposal and the development of innovative techniques for the rapid analysis of data will be vital to the exciting new field of Gravitational Wave (GW) Astronomy, on specific topics such as control and feedback systems for next-generation detectors, noise removal, data analysis and data-conditioning tools.The discovery of GW signals from colliding binary black holes (BBH) and the likely existence of a newly observable population of massive, stellar-origin black holes, has made the analysis of low-frequency GW data a crucial mission of GW science. The low-frequency performance of Earth-based GW detectors is largely influenced by the capability of handling ambient seismic noise suppression. This Cost Action aims at creating a broad network of scientists from four different areas of expertise, namely GW physics, Geophysics, Computing Science and Robotics, with a common goal of tackling challenges in data analysis and noise characterization for GW detectors.

RITEH is a partner in the project.

Project website

Duration:
2018–2022
Contributors:
27 countries

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Latest Projects

Machine Learning for Knowledge Transfer in Medical Radiology

Estimating River Discharges in Highly Stratified Estuaries

Multilayer Framework for the Information Spreading Characterization in Social Media during the COVID-19 Crisis (InfoCoV)

European Network for assuring food integrity using non-destructive spectral sensors

National Competence Centres in the Framework of EuroHPC (EUROCC)

Latest Research Papers

Rule-Based EEG Classifier Utilizing Local Entropy of Time–Frequency Distributions

Rethinking Effects of Innovation in Competition In The Era of New Digital Technologies

A Comparison of Approaches for Measuring the Semantic Similarity of Short Texts Based on Word Embeddings

Indoor Localization Based on Infrared Angle of Arrival Sensor Network

Gravitational-Wave Burst Signals Denoising Based on the Adaptive Modification of Intersection of Confidence Intervals Rule

Latest News

U Rijeci radi Centar za umjetnu inteligenciju, već su u prvoj godini rada povukli šest milijuna u 14 projekata

UNIRI Excellence Awards in Science

Talk on conference “Exploring Digital Legal Landscapes”

ICAIH 2020 conference presentation

International conference “Exploring Digital Legal Landscapes” – 11th of December, 2020

We provide the expertise for solving real world problems using AI

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Center for Artificial Intelligence and Cybersecurity
  • jlerga@airi.uniri.hr
  • +385 51 406 500

University of Rijeka

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