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Igor Paz, Bernard Willinger, Auguste Gires, Bianca Alves de Souza, Laurent Monier, Hervé Cardinal, Bruno Tisserand, Ioulia Tchiguirinskaia and Daniel Schertzer
Recent studies have highlighted the need for high resolution rainfall measurements for better modelling of urban and peri-urban catchment responses. In this work, we used a fully-distributed model called ?Multi-Hydro? to study small-scale rainfall variab...
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Haoran Liu, Kehui Xu, Bin Li, Ya Han and Guandong Li
Machine learning classifiers have been rarely used for the identification of seafloor sediment types in the rapidly changing dredge pits for coastal restoration. Our study uses multiple machine learning classifiers to identify the sediment types of the C...
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Tianhao Gao, Meng Zhang, Yifan Zhu, Youjian Zhang, Xiangsheng Pang, Jing Ying and Wenming Liu
Classifying sports videos is complex due to their dynamic nature. Traditional methods, like optical flow and the Histogram of Oriented Gradient (HOG), are limited by their need for expertise and lack of universality. Deep learning, particularly Convoluti...
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Nadia Brancati and Maria Frucci
To support pathologists in breast tumor diagnosis, deep learning plays a crucial role in the development of histological whole slide image (WSI) classification methods. However, automatic classification is challenging due to the high-resolution data and ...
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Moiz Hassan, Kandasamy Illanko and Xavier N. Fernando
Single Image Super Resolution (SSIR) is an intriguing research topic in computer vision where the goal is to create high-resolution images from low-resolution ones using innovative techniques. SSIR has numerous applications in fields such as medical/sate...
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Hexin Lu, Xiaodong Zhu, Jingwei Cui and Haifeng Jiang
The process of iris recognition can result in a decline in recognition performance when the resolution of the iris images is insufficient. In this study, a super-resolution model for iris images, namely SwinGIris, which combines the Swin Transformer and ...
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Lei Li, Xiaobao Zeng, Xinpeng Pan, Ling Peng, Yuyang Tan and Jianxin Liu
Microseismic monitoring plays an essential role for reservoir characterization and earthquake disaster monitoring and early warning. The accuracy of the subsurface velocity model directly affects the precision of event localization and subsequent process...
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Xiaoqin Xue, Chao Ren, Anchao Yin, Ying Zhou, Yuanyuan Liu, Cong Ding and Jiakai Lu
In the domain of remote sensing research, the extraction of roads from high-resolution imagery remains a formidable challenge. In this paper, we introduce an advanced architecture called PCCAU-Net, which integrates Pyramid Pathway Input, CoordConv convol...
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Minxing Dong, Jichao Yang, Yushan Fu, Tengfei Fu, Qing Zhao, Xuelei Zhang, Qinzeng Xu and Wenquan Zhang
The soft coral order Alcyonacea is a common coral found in the deep sea and plays a crucial role in the deep-sea ecosystem. This study aims to predict the distribution of Alcyonacea in the western Pacific Ocean using four machine learning-based species d...
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Jason Cornelius, Sven Schmitz, Jose Palacios, Bernadine Juliano and Richard Heisler
This work details the development and validation of a methodology for high-resolution rotor models used in hybrid Blade Element Momentum Theory Unsteady Reynolds Averaged Navier?Stokes (BEMT-URANS) CFD. The methodology is shown to accurately predict sing...
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