Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Ayman Mukhaimar"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-16 (2023)
Abstract Frictional pressure drop has been grasping the attention of many industrial applications associated with multi-phase and academia. Alongside the United Nations, the 2030 Agenda for Sustainable Development calls for the exigency of giving att
Externí odkaz:
https://doaj.org/article/eb5372d18cf247d6bb5ef28e33c35ffc
Publikováno v:
IEEE Access, Vol 10, Pp 21541-21553 (2022)
Point clouds produced by either 3D scanners or multi-view images are often imperfect and contain noise or outliers. This paper presents an end-to-end robust spherical harmonics approach to classifying 3D objects. The proposed framework first uses the
Externí odkaz:
https://doaj.org/article/e5da2491c901499db92f849d568587b3
Publikováno v:
Intelligent Systems with Applications, Vol 17, Iss , Pp 200162- (2023)
The task of learning from point cloud data is always challenging due to the often occurrence of noise and outliers in the data. Such data inaccuracies can significantly influence the performance of state-of-the-art deep learning networks and their ab
Externí odkaz:
https://doaj.org/article/1d4d2cca130740d6829e87692490ef3c
Publikováno v:
IEEE Access, Vol 7, Pp 163757-163766 (2019)
Three-dimensional point clouds produced by 3D scanners are often noisy and contain outliers. Such data inaccuracies can significantly affect current deep learning-based methods and reduce their ability to classify objects. Most deep neural networks-b
Externí odkaz:
https://doaj.org/article/ff744f3faf994f53bda1b7689e1b4453
Autor:
Ayman Mukhaimar, Yuan Miao, Zora Vrcelj, Bruce Gu, Ang Yang, Jun Zhao, Malindu Sandanayake, Melissa Chan
Publikováno v:
2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3defd128182153cfa98e65dfd9002098
https://doi.org/10.1109/vrw58643.2023.00139
https://doi.org/10.1109/vrw58643.2023.00139
Frictional pressure drop has been grasping the attention of many industrial applications associated with multi-phase and academia. Alongside the United Nations, the 2030 Agenda for Sustainable Development calls for the exigency of giving attention to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ae50f7b8c5eaf2214415ee1ebe83f7bc
https://doi.org/10.21203/rs.3.rs-2542905/v1
https://doi.org/10.21203/rs.3.rs-2542905/v1
Publikováno v:
Plasmonics. 16:1-8
Solar cell utilizes a small portion of solar spectrum leaving higher energy (> band gap, Eg) as thermalization loss and lower energy (< band gap, Eg) as absorption loss. Wavelength-sensitive engineered absorbing layer such as nanometric absorber hold
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c4644178bb42116961c325b601d6606c
https://doi.org/10.1007/s11468-020-01250-3
https://doi.org/10.1007/s11468-020-01250-3
Publikováno v:
IEEE Access, Vol 7, Pp 163757-163766 (2019)
Three-dimensional point clouds produced by 3D scanners are often noisy and contain outliers. Such data inaccuracies can significantly affect current deep learning-based methods and reduce their ability to classify objects. Most deep neural networks-b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4bc52243a1e017309908ab2b8017d79f
https://ieeexplore.ieee.org/document/8895750/
https://ieeexplore.ieee.org/document/8895750/
The task of learning from point cloud data is always challenging due to the often occurrence of noise and outliers in the data. Such data inaccuracies can significantly influence the performance of state-of-the-art deep learning networks and their ab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b13e638c4eb54f03d6cf68c89950fa7
Publikováno v:
ICIP
Classification of 3D shapes into physically meaningful categories is one of the most important tasks in understanding the immediate environment. Methods that leverage the recent advancements in deep learning have shown to outperform the traditional a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e85459bc687e2d3ac352757655aa5432
https://doi.org/10.1109/icip.2019.8803345
https://doi.org/10.1109/icip.2019.8803345