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Model for Spatiotemporal Point-cloud Stream Forecasting, AAAI 2021 [11] D. Bega, M. Gramaglia, M. Fiore, A. Banchs, X. Costa-Perez, AZTEC: Anticipatory Capacity Allocation for Zero-Touch Network Slicing
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) and Edge Computing are undergoing a major transformation. Systems that once relied heavily on cloud-based processing and passive data collection are evolving into distributed networks of intelligent
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. The PhD candidates will focus on developing methods that utilize remote sensing and AI supporting precision forestry. Remotely sensed data, such as images, lidar, and photogrammetric point clouds acquired
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abundance of computation power residing in the network. The focus in our group is research, development and implementation of networking and edge/cloud computing solutions, primarily in the context of 5G and
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to move key AI applications from Cloud datacenters to Edge devices. In addition to reducing demand for datacenters, Edge AI also has the potential to improve upon Cloud AI by reducing latency and
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(i.e., an SFI ) in which NTNU, the University of Oslo, and key industry players work together towards developing the knowledge and technology required to move key AI applications from Cloud datacenters
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dynamic, optically unpredictable environments, dominated by waves, specular reflection, cloud-dependent illumination, and variable scattering. These factors significantly degrade sensor reliability, leading
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Contrail formation remains one of the least understood yet most climate-relevant impacts of aviation. Persistent contrails can evolve into cirrus-like clouds that significantly affect Earth's
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-performance and cloud computing; (iii) skills in mathematical modeling and scientific computing; (iv) an ability to work independently; (v) strong publication record (at least five first-authored publications
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and