Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Longyu, Jiang"'
Autor:
Longyu Jiang, Quan Jin, Feng Hua, Xingjie Jiang, Zeyu Wang, Wei Gao, Fuhua Huang, Can Fang, Yongzeng Yang
Publikováno v:
Frontiers in Marine Science, Vol 11 (2024)
The receptive field (RF) plays a crucial role in convolutional neural networks (CNNs) because it determines the amount of input information that each neuron in a CNN can perceive, which directly affects the feature extraction ability. As the number o
Externí odkaz:
https://doaj.org/article/4e38cb8164d942f8ba47f2ade52eca8e
Separating raypaths in a multipath shallow-water environment is a challenge problem due to the interferences between them and colored noise existing in ocean environment, especially for two raypaths arrive close to each other. Thus, in this paper, a
Externí odkaz:
http://arxiv.org/abs/2103.14206
Publikováno v:
IEEE Access, Vol 8, Pp 102540-102553 (2020)
Object detection in sonar images has always been a challenge due to the low resolution of sonar images and the strong noise existing in them. Although convolutional neural networks (CNNs) have been applied to detect objects in sonar images, successfu
Externí odkaz:
https://doaj.org/article/34074c04fa3a49359efcb8d32635e818
Publikováno v:
IEEE Access, Vol 6, Pp 4941-4954 (2018)
Low-rank matrix completion with phase constraints have been applied to the single-channel magnetic resonance imaging (MRI) reconstruction. In this paper, the reconstruction of sparse parallel imaging with smooth phase in each coil is formulated as th
Externí odkaz:
https://doaj.org/article/b06074cd5f7c437dad5dbf758c0d4375
Autor:
Xiong Huang, Wenling Wang, Ting Gong, David Wickell, Li-Yaung Kuo, Xingtan Zhang, Jialong Wen, Hoon Kim, Fachuang Lu, Hansheng Zhao, Song Chen, Hui Li, Wenqi Wu, Changjiang Yu, Su Chen, Wei Fan, Shuai Chen, Xiuqi Bao, Li Li, Dan Zhang, Longyu Jiang, Xiaojing Yan, Zhenyang Liao, Gongke Zhou, Yalong Guo, John Ralph, Ronald R. Sederoff, Hairong Wei, Ping Zhu, Fay-Wei Li, Ray Ming, Quanzi Li
Publikováno v:
Nature Plants. 8:500-512
To date, little is known about the evolution of fern genomes, with only two small genomes published from the heterosporous Salviniales. Here we assembled the genome of Alsophila spinulosa, known as the flying spider-monkey tree fern, onto 69 pseudoch
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a203b579388a1b0ccf162b2204029770
https://doi.org/10.1038/s41477-022-01146-6
https://doi.org/10.1038/s41477-022-01146-6
Publikováno v:
OCEANS 2022, Hampton Roads.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0d3731c956e7e7965facd660e168f5a5
https://doi.org/10.1109/oceans47191.2022.9977055
https://doi.org/10.1109/oceans47191.2022.9977055
Publikováno v:
JASA Express Letters
JASA Express Letters, 2022, 2, ⟨10.1121/10.0011810⟩
JASA Express Letters, 2022, 2, ⟨10.1121/10.0011810⟩
Separating raypaths in a multipath shallow-water environment is a challenge due to the interferences between them and the colored noise that exists in an ocean environment, especially for two raypaths that arrive close to each other. Thus, in this pa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::46d6302c4605a0375ae0e7a223684463
https://hal.science/hal-03996305
https://hal.science/hal-03996305
Publikováno v:
Pattern Recognition. 138:109363
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f47740439b5bd74b2bddf76d585245c8
https://doi.org/10.1016/j.patcog.2023.109363
https://doi.org/10.1016/j.patcog.2023.109363
Publikováno v:
IEEE Access, Vol 8, Pp 102540-102553 (2020)
Object detection in sonar images has always been a challenge due to the low resolution of sonar images and the strong noise existing in them. Although convolutional neural networks (CNNs) have been applied to detect objects in sonar images, successfu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4aaa0da1e0b62ea73e61ccbf198b0238
https://doi.org/10.1109/access.2020.2999341
https://doi.org/10.1109/access.2020.2999341