Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Jiahe Cui"'
Autor:
Yantao Wu, Hao Li, Jiahe Cui, Ying Han, Hangyu Li, Bailing Miao, Yongkang Tang, Zhiyong Li, Jinghui Zhang, Lixin Wang, Cunzhu Liang
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
Livestock presence impacts plant biodiversity (species richness) in grassland ecosystems, yet extent and direction of grazing impacts on biodiversity vary greatly across inter-annual periods. In this study, an 8-year (2014-2021) grazing gradient expe
Externí odkaz:
https://doaj.org/article/411b62a914634a70aa5c871cf37e37e9
Publikováno v:
Land, Vol 13, Iss 6, p 754 (2024)
Driven by continuously evolving precipitation shifts and temperature increases, the frequency and intensity of droughts have increased. There is an obvious need to accurately monitor drought. With the popularity of machine learning, many studies have
Externí odkaz:
https://doaj.org/article/b030f6527d7f4ce684ca3598bf72d3c0
Publikováno v:
Leida xuebao, Vol 12, Iss 1, Pp 154-172 (2023)
Facing the increasingly complex electromagnetic interference environment, Synthetic Aperture Radar (SAR) interference suppression has become an urgent problem to be solved. The existing mainstream synthetic aperture radar nonparametric/parametric int
Externí odkaz:
https://doaj.org/article/24fcc08c0a1a491ea1af77afe867737c
Publikováno v:
Light: Science & Applications, Vol 11, Iss 1, Pp 1-4 (2022)
Prof. Martin Booth, as one of the first people to explore the practical use of adaptive optics in microscopy, shares with us his valuable experience and insight
Externí odkaz:
https://doaj.org/article/50bb5c561d694003a14556d53668cf04
Autor:
Ying Han, Yantao Wu, Jiahe Cui, Hangyu Li, Hao Li, Jinghui Zhang, Bailing Miao, Lixin Wang, Zhiyong Li, Cunzhu Liang
Publikováno v:
Agronomy, Vol 13, Iss 8, p 2030 (2023)
Environmental change is a crucial driver shaping grassland biodiversity and stability. Both environmental change and stability contain multiple dimensions. Nonetheless, few studies examined multiple dimensions of stability in response to environmenta
Externí odkaz:
https://doaj.org/article/562ef42f4f4f4e75bcb142fb7e06a002
Publikováno v:
IEEE Photonics Journal, Vol 11, Iss 3, Pp 1-10 (2019)
In this letter, we propose an indoor visible light positioning technique that combines deep neural network based on the Bayesian Regularization (BR-DNN) with sparse diagonal training data set. Unlike other neural networks, which require a large numbe
Externí odkaz:
https://doaj.org/article/223c65c87044435f93c8640d9506a83e
Publikováno v:
Photonics, Vol 9, Iss 8, p 547 (2022)
We show that there is an intrinsic link between the use of Walsh aberration modes in adaptive optics (AO) and the mathematics of lattices. The discrete and binary nature of these modes means that there are infinite combinations of Walsh mode coeffici
Externí odkaz:
https://doaj.org/article/bc717657411b44f2afd01b2d1d81c0a2
Publikováno v:
Sensors, Vol 19, Iss 10, p 2324 (2019)
In this letter, we propose an indoor visible light positioning technique using a Modified Momentum Back-Propagation (MMBP) algorithm based on received signal strength (RSS) with sparse training data set. Unlike other neural network algorithms that re
Externí odkaz:
https://doaj.org/article/06f788aa70694be8a6cd78b77284d87b
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 23:12439-12450
Object detection is the most critical and foundational sensing module for the autonomous movement platform. However, most of the existing deep learning solutions are based on GPU servers, which limits their actual deployment. We present an efficient
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d7156dcb06fef1b7162ca47b28a258f4
https://doi.org/10.1109/tits.2021.3114062
https://doi.org/10.1109/tits.2021.3114062
Publikováno v:
Information Fusion. 77:172-183
Commercialization of self-driving applications requires precision and reliability of the perception system due to the highly dynamic and complex road environment. Early perception systems either rely on the camera or on LiDAR for moving obstacle dete
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a6004c52239cf436d16bf6075544d414
https://doi.org/10.1016/j.inffus.2021.07.004
https://doi.org/10.1016/j.inffus.2021.07.004