Should I Stay or Should I Go: A Learning Approach for Drone-based Sensing Applications

Autor: Polychronis, Giorgos, Koutsoubelias, Manos, Lalis, Spyros

Publikováno v: 2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), Abu Dhabi, United Arab Emirates, 2024, pp. 339-346

Multicopter drones are becoming a key platform in several application domains, enabling precise on-the-spot sensing and/or actuation. We focus on the case where the drone must process the sensor data in order to decide, depending on the outcome, whet

Externí odkaz: http://arxiv.org/abs/2409.04764

Flexible Computation Offloading at the Edge for Autonomous Drones with Uncertain Flight Times

Autor: Polychronis, Giorgos, Lalis, Spyros

Publikováno v: 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), Pafos, Cyprus, 2023, pp. 201-208

An ever increasing number of applications can employ aerial unmanned vehicles, or so-called drones, to perform different sensing and possibly also actuation tasks from the air. In some cases, the data that is captured at a given point has to be proce

Externí odkaz: http://arxiv.org/abs/2310.11895

'Lalis' Business Plan

Autor: Ausejo Rubert, Laura

Treball Final de Grau en Administració d'Empreses. Codi: AE1049. Curs 2020/2021 "Lalis" is a company whose activity consists of selling an experience, an experience that is divided into hosting and carrying out activities. "Lalis" arises from the ne

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______1500::a0818f5d43b7cea944305960666da09b

Artificial neural networks for online error detection

Autor: Vassiliadis, Vassilis, Parasyris, Konstantinos, Antonopoulos, Christos D., Lalis, Spyros, Bellas, Nikolaos

Hardware reliability is adversely affected by the downscaling of semiconductor devices and the scale-out of systems necessitated by modern applications. Apart from crashes, this unreliability often manifests as silent data corruptions (SDCs), affecti

Externí odkaz: http://arxiv.org/abs/2111.13908

IPLS : A Framework for Decentralized Federated Learning

Autor: Pappas, Christodoulos, Chatzopoulos, Dimitris, Lalis, Spyros, Vavalis, Manolis

The proliferation of resourceful mobile devices that store rich, multidimensional and privacy-sensitive user data motivate the design of federated learning (FL), a machine-learning (ML) paradigm that enables mobile devices to produce an ML model with

Externí odkaz: http://arxiv.org/abs/2101.01901