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pro vyhledávání: '"Channel capacity maximisation"'
Due to copyright restrictions, the access to the full text of this article is only available via subscription. In this study, the authors investigate the information theoretical limits on the performance of point-to-point single-carrier acoustic syst
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::18b27ef9a34929f75fb650d202bc0555
https://hdl.handle.net/10679/519
https://hdl.handle.net/10679/519
Akademický článek
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Autor:
Fuentes, Jesús, Obregón, Octavio
As an application of generalised statistical mechanics, it is studied a possible route toward a consistent generalised information theory in terms of a family of non-extensive, non-parametric entropies $H^\pm_D(P)$. Unlike other proposals based on no
Externí odkaz:
http://arxiv.org/abs/2103.11759
Publikováno v:
2016 24th European Signal Processing Conference (EUSIPCO); 2016, p1083-1087, 5p
Autor:
Vanthana, K.
Publikováno v:
2016 International Conference on Information Communication & Embedded Systems (ICICES); 2016, p1-12, 12p
Publikováno v:
ICASSP
In this paper the effects of quantisation on distributed convex optimisation algorithms are explored via the lens of monotone operator theory. Specifically, by representing transmission quantisation via an additive noise model, we demonstrate how qua
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e6ea0cf7c9c33a4b6ebaf371a842bd7a
https://doi.org/10.1109/icassp.2018.8461782
https://doi.org/10.1109/icassp.2018.8461782
Publikováno v:
2016 24th European Signal Processing Conference, EUSIPCO 2016
EUSIPCO
EUSIPCO
In this paper, we focus on the challenge of processing data generated within decentralised wireless sensor networks in a distributed manner. When the desired operations can be expressed as globally constrained separable convex optimisation problems,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::22427190f69c65e69ee7922a66dae084
http://resolver.tudelft.nl/uuid:86a7f178-fe77-4607-9cfe-77f1bc3b90d8
http://resolver.tudelft.nl/uuid:86a7f178-fe77-4607-9cfe-77f1bc3b90d8
This book constitutes the thoroughly refereed proceedings of the First International Conference on Machine Learning for Networking, MLN 2018, held in Paris, France, in November 2018. The 22 revised full papers included in the volume were carefully re