Projects

VesselAI – Enabling Maritime Digitalization by Extreme-scale Analytics, AI and Digital Twins

VesselAI aims at realising a holistic, beyond the state-of-the-art AI-empowered framework for decision-support models, data analytics and visualisations to build digital twins and maritime applications for a diverse set of cases with high impact, including simulating and predicting vessel behaviour and manoeuvring (including the human factor), ship energy design optimisation, autonomous shipping and fleet intelligence.

OPTIMA – Computational Intelligence Methods for Big Mobility Data

OPTIMA – Computational Intelligence Methods for Big Mobility Data

The main objective of this research project is the development of new Computational Intelligence (CI) methodologies, with emphasis on Artificial Neural Networks (ANNs), Evolutionary computation (EC) and Swarm Intelligence (SI), which will be inherently designed tο address the particular characteristics of big mobility data, with the aim of improving public transport services and as a result to enhance people life quality and to maximize environmental benefits. This research project aims to design “attractive” for passengers, and simultaneously “efficient” and “economical” public transports. In order to accomplish this, it is necessary to study tasks such as: bus capacity / passenger load prediction, bus arrival time prediction, bus timetable optimization (scheduling), bus predictive maintenance, bus Eco-driving behavior, safety and collision risk on intelligent transportation sy
SoundScapes – A Toolkit for the Analysis and Synthesis of Soundscapes

SoundScapes – A Toolkit for the Analysis and Synthesis of Soundscapes

The goal of the SOUNDSCAPES project is to develop a framework and tools for the design and production of sound scenes and related sound effects and ambiences. To that end, the project develops a suite of Acoustic Scene classification, Audio Event Detection and Audio Scene synthesis tools aiming at: (i) providing a framework and solutions for the cost-efficient production of static and living soundscapes, background sound textures and foreground sound effects with the aid of cutting-edge ICT and (ii) overcoming obstacles that prevent individuals from incorporating high-quality sound scenes and ambiences into their products or services. SOUNDSCAPES relies on cutting-edge machine learning research with an emphasis on deep neural networks and reinforcement learning algorithms to develop solutions that deal with the research challenges of the exciting field of automatic scene analysis and production.

SPADES: Spatio-textual Data Exploration at Scale

SPADES: Spatio-textual Data Exploration at Scale

SPADES aims to address the limitations of spatio-textual data analysis and processing when applied in the context of Big Spatial Data, as witnessed by the lack of existing systems and techniques for this purpose. Achievement of this goal constitutes a substantial step forward in dealing with challenges emerging from management of Big Spatial Data. At a practical level, the research outcome will benefit applications such as spatio-textual search and retrieval, mining of spatio-textual data, next generation location-based services, and tourism-oriented applications to name a few.

MASTER

MASTER – Multiple ASpects TrajEctoRy management and analysis

MASTER (Multiple ASpects TrajEctoRy management and analysis) is a project funded under the call H2020-MSCA-RISE-2017 with the objective of forming an international and inter-sectoral network of organisations working on a joint research programme to define new methods to build, manage and analyse multiple aspects semantic trajectories.

CHOROLOGOS: Semantic Spatio-textual Data Analysis and Processing

CHOROLOGOS: Semantic Spatio-textual Data Analysis and Processing

CHOROLOGOS aims at advancing the state-of-the-art in spatio-temporal-textual query processing, by introducing a novel framework that tightly combines spatio-textual and spatio-temporal querying with semantic retrieval, focusing on expressive query formulation beyond syntactical matching, efficient indexing and query processing, and scalable analysis of massive spatio-textual data.

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