Zobrazeno 1 - 10
of 679
pro vyhledávání: '"Sarigiannidis P"'
Autor:
Evgenidis, Nikos G., Mitsiou, Nikos A., Tegos, Sotiris A., Diamantoulakis, Panagiotis D., Sarigiannidis, Panagiotis, Krikidis, Ioannis, Karagiannidis, George K.
Publikováno v:
IEEE Transactions on Wireless Communications, vol. 23, no. 9, pp. 10926-10940, Sept. 2024
Semantic communications are considered a promising beyond-Shannon/bit paradigm to reduce network traffic and increase reliability, thus making wireless networks more energy efficient, robust, and sustainable. However, the performance is limited by th
Externí odkaz:
http://arxiv.org/abs/2410.01379
Autor:
Siniosoglou, Ilias, Argyriou, Vasileios, Fragulis, George, Fouliras, Panagiotis, Papadopoulos, Georgios Th., Lytos, Anastasios, Sarigiannidis, Panagiotis
The time-consuming nature of training and deploying complicated Machine and Deep Learning (DL) models for a variety of applications continues to pose significant challenges in the field of Machine Learning (ML). These challenges are particularly pron
Externí odkaz:
http://arxiv.org/abs/2409.06904
Autor:
Stefanidou, Artemis, Cani, Jorgen, Papadopoulos, Thomas, Radoglou-Grammatikis, Panagiotis, Sarigiannidis, Panagiotis, Varlamis, Iraklis, Papadopoulos, Georgios Th.
Over the recent years, the protection of the so-called `soft-targets', i.e. locations easily accessible by the general public with relatively low, though, security measures, has emerged as a rather challenging and increasingly important issue. The co
Externí odkaz:
http://arxiv.org/abs/2408.17136
Autor:
Li, Vladislav, Tsoumplekas, Georgios, Siniosoglou, Ilias, Argyriou, Vasileios, Lytos, Anastasios, Fountoukidis, Eleftherios, Sarigiannidis, Panagiotis
Current methods for low- and few-shot object detection have primarily focused on enhancing model performance for detecting objects. One common approach to achieve this is by combining model finetuning with data augmentation strategies. However, littl
Externí odkaz:
http://arxiv.org/abs/2408.10940
Autor:
Alimisis, Panagiotis, Mademlis, Ioannis, Radoglou-Grammatikis, Panagiotis, Sarigiannidis, Panagiotis, Papadopoulos, Georgios Th.
Image data augmentation constitutes a critical methodology in modern computer vision tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; thereby, improving the performance and robustness of machine learnin
Externí odkaz:
http://arxiv.org/abs/2407.04103
Autor:
Spyridis, Yannis, Argyriou, Vasileios, Sarigiannidis, Antonios, Radoglou, Panagiotis, Sarigiannidis, Panagiotis
The escalating volumes of textile waste globally necessitate innovative waste management solutions to mitigate the environmental impact and promote sustainability in the fashion industry. This paper addresses the inefficiencies of traditional textile
Externí odkaz:
http://arxiv.org/abs/2405.10696
Autor:
Kokkinos, Kyriazis, Polymenidis, Ioannis, Siniosoglou, Ilias, Liatifis, Athanasios, Sarigiannidis, Panagiotis
Data exchange through mobile devices is rapidly increasing due to the high information demands of today's applications. The need for monitoring the exchanged traffic becomes important in order to control and optimize the device and network performanc
Externí odkaz:
http://arxiv.org/abs/2406.18251
Autor:
Evgenidis, Nikos G., Mitsiou, Nikos A., Tegos, Sotiris A., Diamantoulakis, Panagiotis D., Sarigiannidis, Panagiotis, Rekanos, Ioannis T., Karagiannidis, George K.
In response to the increasing number of devices anticipated in next-generation networks, a shift toward over-the-air (OTA) computing has been proposed. Leveraging the superposition of multiple access channels, OTA computing enables efficient resource
Externí odkaz:
http://arxiv.org/abs/2405.20877
Autor:
Tsoumplekas, Georgios, Siniosoglou, Ilias, Argyriou, Vasileios, Moscholios, Ioannis D., Sarigiannidis, Panagiotis
The increased availability of medical data has significantly impacted healthcare by enabling the application of machine / deep learning approaches in various instances. However, medical datasets are usually small and scattered across multiple provide
Externí odkaz:
http://arxiv.org/abs/2405.20430
Autor:
Bouzinis, Pavlos S., Radoglou-Grammatikis, Panagiotis, Makris, Ioannis, Lagkas, Thomas, Argyriou, Vasileios, Papadopoulos, Georgios Th., Sarigiannidis, Panagiotis, Karagiannidis, George K.
Federated learning (FL) is a decentralized learning technique that enables participating devices to collaboratively build a shared Machine Leaning (ML) or Deep Learning (DL) model without revealing their raw data to a third party. Due to its privacy-
Externí odkaz:
http://arxiv.org/abs/2405.13062