Zobrazeno 1 - 10
of 5 224
pro vyhledávání: '"Bechir, A."'
This review paper synthesizes the latest research on performance optimization strategies for serverless applications deployed on AWS Lambda. By examining recent studies, we highlight the challenges, solutions, and best practices for enhancing the per
Externí odkaz:
http://arxiv.org/abs/2407.10397
Radio Frequency (RF) device fingerprinting has been recognized as a potential technology for enabling automated wireless device identification and classification. However, it faces a key challenge due to the domain shift that could arise from variati
Externí odkaz:
http://arxiv.org/abs/2403.04036
Autor:
Johnson, Benjamin, Hamdaoui, Bechir
RF data-driven device fingerprinting through the use of deep learning has recently surfaced as a possible method for enabling secure device identification and authentication. Traditional approaches are commonly susceptible to the domain adaptation pr
Externí odkaz:
http://arxiv.org/abs/2402.10044
Next-generation networks aim for comprehensive connectivity, interconnecting humans, machines, devices, and systems seamlessly. This interconnectivity raises concerns about privacy and security, given the potential network-wide impact of a single com
Externí odkaz:
http://arxiv.org/abs/2402.05332
Publikováno v:
Romanian Journal of Oral Rehabilitation, Vol 16, Iss 3, Pp 370-377 (2024)
Aim of the study The aim of this study was to compare the results of the treatment of the gingival smile by two techniques, Botox injection (in the first group of patients) and laser therapy (the second group of patients). Materials and methods The s
Externí odkaz:
https://doaj.org/article/7860b89737a34dee9c0984b2ad094615
Autor:
Johnson, Benjamin, Hamdaoui, Bechir
RF data-driven device fingerprinting through the use of deep learning has recently surfaced as a potential solution for automated network access authentication. Traditional approaches are commonly susceptible to the domain adaptation problem where a
Externí odkaz:
http://arxiv.org/abs/2308.07925
Deep learning (DL)-based RF fingerprinting (RFFP) technology has emerged as a powerful physical-layer security mechanism, enabling device identification and authentication based on unique device-specific signatures that can be extracted from the rece
Externí odkaz:
http://arxiv.org/abs/2308.04467
Autor:
Mohammad Kanan, Bechir Wannassi, Bechir Azouz, Mohamed Ben Hassen, Ramiz Assaf, Ahmad S. Barham
Publikováno v:
Case Studies in Chemical and Environmental Engineering, Vol 10, Iss , Pp 100849- (2024)
In this paper, we propose a new mechanical process (opener machine) mostly adaptive to recycle waste yarn fibers. We study the effect of the process design and the number of passages on the overall fiber quality and process yield for colored denim ya
Externí odkaz:
https://doaj.org/article/8f70e2448c314353a1ef8bb96544f141
Deep learning-based RF fingerprinting offers great potential for improving the security robustness of various emerging wireless networks. Although much progress has been done in enhancing fingerprinting methods, the impact of device hardware stabiliz
Externí odkaz:
http://arxiv.org/abs/2308.00156
Deep learning-enabled device fingerprinting has proven efficient in enabling automated identification and authentication of transmitting devices. It does so by leveraging the transmitters' unique features that are inherent to hardware impairments cau
Externí odkaz:
http://arxiv.org/abs/2306.07878