Zobrazeno 1 - 10
of 116
pro vyhledávání: '"Marija Furdek"'
Autor:
Yuchuan Fan, Xiaodan Pang, Aleksejs Udalcovs, Carlos Natalino, Lu Zhang, Sandis Spolitis, Vjaceslavs Bobrovs, Richard Schatz, Xianbin Yu, Marija Furdek, Sergei Popov, Oskars Ozolins
Publikováno v:
IEEE Photonics Journal, Vol 14, Iss 4, Pp 1-8 (2022)
Error vector magnitude (EVM) is a metric for assessing the quality of m-ary quadrature amplitude modulation (mQAM) signals. Recently proposed deep learning techniques, e.g., feedforward neural networks (FFNNs) -based EVM estimation scheme leverage fa
Externí odkaz:
https://doaj.org/article/f0698daa211d482d9386b7ec066aa8d5
Publikováno v:
IEEE Transactions on Reliability. 71:616-629
Design of reliable wireless backhaul networks is challenging due to the inherent vulnerability of wireless backhauling to random fluctuations of the wireless channel. Considerable studies deal with modifying and designing the network topology to meet
Autor:
Carlos Natalino da Silva, Lluis Gifre Renom, Francisco-Javier Moreno-Muro, Sergio Gonzalez Diaz, Ricard Vilalta, Raul Muñoz, Paolo Monti, Marija Furdek
Publikováno v:
Journal of Optical Communications and Networking.
Autor:
Christine Tremblay, Émile Archambault, Rodney G. Wilson, Stewart Clelland, Marija Furdek, Lena Wosinska
Publikováno v:
2022 IEEE Future Networks World Forum (FNWF).
Publikováno v:
Zibar, D, Turitsyn, S, Jalali, B, Kojima, K & Furdek, M 2022, ' Editorial Introduction to JSTQE Special Issue on Machine Learning in Photonic Communication and Measurement Systems ', IEEE Journal of Selected Topics in Quantum Electronics, vol. 28, no. 4, 9851897, pp. 3-3 . https://doi.org/10.1109/JSTQE.2022.3191912
The papers in this special section focus on machine learning in photonic communication and measurement systems. From being a niche field within computer science, the field of machine learning has gone mainstream within the last couple of years. The r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::828449dcadca2ad77f8f35131f1bbc2b
https://orbit.dtu.dk/en/publications/f39ef844-bb85-431b-92ad-62aac0d8c0d8
https://orbit.dtu.dk/en/publications/f39ef844-bb85-431b-92ad-62aac0d8c0d8
Publikováno v:
IEEE Communications Letters. 25:1583-1586
Accurate and efficient anomaly detection is a key enabler for the cognitive management of optical networks, but traditional anomaly detection algorithms are computationally complex and do not scale well with the amount of monitoring data. Therefore,
Autor:
Marija Furdek, Paolo Monti, Lena Wosinska, Stefan Melin, Anders Lindgren, Renzo Diaz, Carlos Natalino, Ehsan Etezadi
An exponential growth of bandwidth demand, spurred by emerging network services, often with diverse characteristics and stringent performance requirements, drive the need for more dynamic operation of optical networks, efficient use of spectral resou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ca3e4833482e28c7294a3bd54ea4b6b
https://doi.org/10.36227/techrxiv.20013458.v1
https://doi.org/10.36227/techrxiv.20013458.v1
Autor:
Polina Bayvel, Matthew Brand, Francesco Da Ros, Camille Delezoide, Qirui Fan, Marija Furdek, Christian Häger, Takeshi Hoshida, Luyao Huang, Memedhe Ibrahimi, Ognjen Jovanovic, Boris Karanov, Faisal Nadeem Khan, Toshiaki Koike-Akino, Keisuke Kojima, Alan Pak Tao Lau, Patricia Layec, Zhengxuan Li, Chao Lu, Carlos Natalino, Petros Ramantanis, Cristina Rottondi, Marc Ruiz, Laurent Schmalen, Behnam Shariati, Yingheng Tang, Takahito Tanimura, Massimo Tornatore, Alba P. Vela, Luis Velasco, Ye Wang, Wanting Xu, Yongxin Xu, Metodi Yankov, Lilin Yi, Shaoliang Zhang, Darko Zibar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d4fd040b3b2aa2f85798415b0acef8ca
https://doi.org/10.1016/b978-0-32-385227-2.00006-1
https://doi.org/10.1016/b978-0-32-385227-2.00006-1
Autor:
Yuchuan Fan, Xiaodan Pang, Aleksejs Udalcovs, Carlos Natalino, Lu Zhang, Sandis Spolitis, Vjaceslavs Bobrovs, Richard Schatz, Xianbin Yu, Marija Furdek, Sergei Popov, Oskars Ozolins
Publikováno v:
Conference on Lasers and Electro-Optics.
We experimentally demonstrate the effectiveness of a simple linear regression scheme for optical performance monitoring when applied after modulation format identification. It outperforms the FFNN-based benchmark scheme providing 0.2% mean absolute e