Zobrazeno 1 - 10
of 1 110
pro vyhledávání: '"Georgios B. Giannakis"'
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2023, Iss 1, Pp 1-21 (2023)
Abstract Graph-based learning and estimation are fundamental problems in various applications involving power, social, and brain networks, to name a few. While learning pair-wise interactions in network data is a well-studied problem, discovering hig
Externí odkaz:
https://doaj.org/article/565e391595c7491ea56afc0d74a9b2cc
Publikováno v:
BMC Bioinformatics, Vol 20, Iss 1, Pp 1-15 (2019)
Abstract Background Gene networks in living cells can change depending on various conditions such as caused by different environments, tissue types, disease states, and development stages. Identifying the differential changes in gene networks is very
Externí odkaz:
https://doaj.org/article/89ea275f4a3446fab9402104d784deae
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2018, Iss 1, Pp 1-17 (2018)
Abstract The present work introduces the hybrid consensus alternating direction method of multipliers (H-CADMM), a novel framework for optimization over networks which unifies existing distributed optimization approaches, including the centralized an
Externí odkaz:
https://doaj.org/article/4e760d8da72647b393e5bd3eca5bf801
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2010 (2010)
The number of active users, their timing offsets, and their (possibly dispersive) channels with the access point are performance-critical parameters for wireless code division multiple access (CDMA). Estimating them as accurately as possible using as
Externí odkaz:
https://doaj.org/article/973a06b947484e889d659a9eb29c2ad4
Autor:
Seung-Jun Kim, Georgios B. Giannakis
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2009 (2009)
Sequential sensing algorithms are developed for OFDM-based hierarchical cognitive radio (CR) systems. Secondary users sense multiple subbands simultaneously for possible spectrum availabilities under hard misdetection constraints to prevent interfere
Externí odkaz:
https://doaj.org/article/a9d9f787d34648ed923864c7ffa8a944
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2009 (2009)
Low-cost estimation of stationary signals and reduced-complexity tracking of nonstationary processes are well motivated tasks than can be accomplished using ad hoc wireless sensor networks (WSNs). To this end, a fully distributed least mean-square (D
Externí odkaz:
https://doaj.org/article/229206bf4f3f48fb89903bf38a593024
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2008 (2008)
We deal with centralized and distributed rate-constrained estimation of random signal vectors performed using a network of wireless sensors (encoders) communicating with a fusion center (decoder). For this context, we determine lower and upper bounds
Externí odkaz:
https://doaj.org/article/e056f3994b644414bfa406fc38657988
Autor:
Georgios B. Giannakis, Xiliang Luo
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2008 (2008)
As low power, low cost, and longevity of transceivers are major requirements in wireless sensor networks, optimizing their design under energy constraints is of paramount importance. To this end, we develop quantizers under strict energy constraints
Externí odkaz:
https://doaj.org/article/53ef4ff792f54d23b20db52f59be3a3d
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2008 (2008)
Combining multisource cooperation and link-adaptive regenerative techniques, a novel protocol is developed capable of achieving diversity order up to the number of cooperating users and large coding gains. The approach relies on a two-phase protocol.
Externí odkaz:
https://doaj.org/article/d06b6825c22a42828967b3ff37f738f2
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 45:1876-1893
Belonging to the family of Bayesian nonparametrics, Gaussian process (GP) based approaches have well-documented merits not only in learning over a rich class of nonlinear functions, but also in quantifying the associated uncertainty. However, most GP
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1080f95b0a97e4edcd516edc50d6ecaa
https://doi.org/10.1109/tpami.2022.3157197
https://doi.org/10.1109/tpami.2022.3157197