Novel neural approaches to data topology analysis and telemedicine

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen676. INGEGNERIA ELETTRICAnoopenRandazzo, Vincenz

Development of an unsupervised remote sensing methodology of detect surface leakage from terrestrial CO2 storage sites

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Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Geometric and Bayesian models for safe navigation in dynamic environments

Autonomous navigation in open and dynamic environments is an important challenge, requiring to solve several difficult research problems located on the cutting edge of the state of the art. Basically, these problems may be classified into three main categories: (a) SLAM in dynamic environments; (b) detection, characterization, and behavior prediction of the potential moving obstacles; and (c) online motion planning and safe navigation decision based on world state predictions. This paper addresses some aspects of these problems and presents our latest approaches and results. The solutions we have implemented are mainly based on the followings paradigms: multiscale world representation of static obstacles based on the wavelet occupancy grid; adaptative clustering for moving obstacle detection inspired on Kohonen networks and the growing neural gas algorithm; and characterization and motion prediction of the observed moving entities using Hidden Markov Models coupled with a novel algorithm for structure and parameter learnin