Zobrazeno 1 - 10
of 140
pro vyhledávání: '"B. Letaief"'
Publikováno v:
Entropy, Vol 26, Iss 2, p 102 (2024)
In recent years, semantic communication has received significant attention from both academia and industry, driven by the growing demands for ultra-low latency and high-throughput capabilities in emerging intelligent services. Nonetheless, a comprehe
Externí odkaz:
https://doaj.org/article/8c7b847d659e497784edde2646100547
Publikováno v:
Entropy, Vol 25, Iss 8, p 1205 (2023)
As a promising distributed learning paradigm, federated learning (FL) faces the challenge of communication–computation bottlenecks in practical deployments. In this work, we mainly focus on the pruning, quantization, and coding of FL. By adopting a
Externí odkaz:
https://doaj.org/article/901eb90a0d8b44bdbb8943b10c97910d
Precision medicine in the era of artificial intelligence: implications in chronic disease management
Autor:
Murugan Subramanian, Anne Wojtusciszyn, Lucie Favre, Sabri Boughorbel, Jingxuan Shan, Khaled B. Letaief, Nelly Pitteloud, Lotfi Chouchane
Publikováno v:
Journal of Translational Medicine, Vol 18, Iss 1, Pp 1-12 (2020)
Abstract Aberrant metabolism is the root cause of several serious health issues, creating a huge burden to health and leading to diminished life expectancy. A dysregulated metabolism induces the secretion of several molecules which in turn trigger th
Externí odkaz:
https://doaj.org/article/7474f0910e8b4a6cb0f759f4b08eb8a3
Publikováno v:
IEEE Open Journal of the Communications Society, Vol 1, Pp 77-91 (2020)
Millimeter-wave (mm-wave) communication is a key technology for future wireless networks. To combat significant path loss and exploit the abundant mm-wave spectrum, effective beamforming is crucial. Nevertheless, conventional fully digital beamformin
Externí odkaz:
https://doaj.org/article/6aa9ce391a5a4eb79c1976b4fcf88653
Publikováno v:
Entropy, Vol 25, Iss 1, p 48 (2022)
This paper focuses on the ultimate limit theory of image compression. It proves that for an image source, there exists a coding method with shapes that can achieve the entropy rate under a certain condition where the shape-pixel ratio in the encoder/
Externí odkaz:
https://doaj.org/article/264816375a874579ba7f811d7335550c
Publikováno v:
IEEE Internet of Things Journal. 10:9482-9497
Autor:
Xuanyu Cao, Tamer Başar, Suhas Diggavi, Yonina C. Eldar, Khaled B. Letaief, H. Vincent Poor, Junshan Zhang
Publikováno v:
IEEE Journal on Selected Areas in Communications. 41:845-850
Publikováno v:
IEEE Transactions on Wireless Communications. 22:2744-2759
Autor:
Xuanyu Cao, Tamer Başar, Suhas Diggavi, Yonina C. Eldar, Khaled B. Letaief, H. Vincent Poor, Junshan Zhang
Publikováno v:
IEEE Journal on Selected Areas in Communications. 41:851-873
Publikováno v:
IEEE Journal on Selected Areas in Communications. 41:170-185
Existing deep learning-enabled semantic communication systems often rely on shared background knowledge between the transmitter and receiver that includes empirical data and their associated semantic information. In practice, the semantic information