Zobrazeno 1 - 10
of 124
pro vyhledávání: '"Nguyen Linh Trung"'
Publikováno v:
Patterns, Vol 4, Iss 6, Pp 100759- (2023)
Summary: In this paper, we propose two new provable algorithms for tracking online low-rank approximations of high-order streaming tensors with missing data. The first algorithm, dubbed adaptive Tucker decomposition (ATD), minimizes a weighted recurs
Externí odkaz:
https://doaj.org/article/fed8199ad35b4d8283802bcb2e0a3ec2
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 30, Pp 1548-1556 (2022)
Correct detection of peaks in electroencephalogram (EEG) signals is of essence due to the significant correlation of those potentials with cognitive performance and disorders. This paper proposes a novel and non-parametric approach to detect predicti
Externí odkaz:
https://doaj.org/article/3b688517575d4cd3b59beea9b7523c87
Publikováno v:
IEEE Access, Vol 8, Pp 166503-166512 (2020)
The Cramér-Rao Bound (CRB) is a powerful tool to assess the performance limits of a parameter estimation problem for a given statistical model. In particular, the Gaussian CRB (i.e., the CRB obtained assuming the data are Gaussian) corresponds to t
Externí odkaz:
https://doaj.org/article/f2e6330aaeb7450789affc90ace3088b
Publikováno v:
Machine Learning with Applications, Vol 6, Iss , Pp 100184- (2021)
This paper presents a method of ranking the effectiveness of brain region of interest (ROI) in order to separate Normal Control (NC) from Alzheimer’s disease (AD) brains Positron Emission Tomography (PET) images based on AutoEncoder (AE) networks.
Externí odkaz:
https://doaj.org/article/4a878da8de0946fa9c85bc1aac657b9d
Fake path injection is an emerging paradigm for inducing privacy over wireless networks. In this paper, fake paths are injected by the transmitter into a SIMO multipath communication channel to preserve her physical location from an eavesdropper. A n
Externí odkaz:
http://arxiv.org/abs/2402.01198
Publikováno v:
IEEE Transactions on Signal Processing. 70:4305-4320
Tensor decomposition has been demonstrated to be successful in a wide range of applications, from neuroscience and wireless communications to social networks. In an online setting, factorizing tensors derived from multidimensional data streams is how
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f264cd45a65cc6f99ecc551553997181
https://doi.org/10.36227/techrxiv.20105966
https://doi.org/10.36227/techrxiv.20105966
Publikováno v:
2022 RIVF International Conference on Computing and Communication Technologies (RIVF).
Big data streaming analytics has recently attracted much attention in the signal and information processing communities due to the fact that massive streaming datasets have been collected over the years. Among them, many modern data streams are repre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a0b494888da715464f0ee9805a46ac98
https://doi.org/10.36227/techrxiv.21551367
https://doi.org/10.36227/techrxiv.21551367
In this paper, we propose two new provable algorithms for tracking online low-rank approximations of high-order streaming tensors with missing data. The first algorithm, dubbed adaptive Tucker decomposition (ATD), minimizes a weighted recursive least
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3afc9f5f849e76101134fea89f6c1f53
https://doi.org/10.36227/techrxiv.19704034.v2
https://doi.org/10.36227/techrxiv.19704034.v2