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Elena Loli Piccolomini, Marco Prato, Margherita Scipione and Andrea Sebastiani
In this paper, we propose a new deep learning approach based on unfolded neural networks for the reconstruction of X-ray computed tomography images from few views. We start from a model-based approach in a compressed sensing framework, described by the m...
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Qingcheng Fan, Sicong Liu, Chunjiang Zhao and Shuqin Li
Feature selection is crucial in classification tasks as it helps to extract relevant information while reducing redundancy. This paper presents a novel method that considers both instance and label correlation. By employing the least squares method, we c...
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Zongxiang Liu, Chunmei Zhou and Junwen Luo
The random finite set (RFS) approach for multi-target tracking is widely researched because it has a rigorous theoretical basis. However, many prior parameters such as the clutter density, survival probability and detection probability of the target, pru...
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Po-Wei Li, Shenghan Hu and Mengyao Zhang
This study applies the space?time generalized finite difference scheme to solve nonlinear dispersive shallow water waves described by the modified Camassa?Holm equation, the modified Degasperis?Procesi equation, the Fornberg?Whitham equation, and its mod...
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Yunpiao Cai, Weixing Qian, Jiaqi Zhao, Jiayi Dong and Tianxiao Shen
In this paper, we propose a novel visual?inertial simultaneous localization and mapping (SLAM) method for intelligent navigation systems that aims to overcome the challenges posed by dynamic or large-scale outdoor environments. Our approach constructs a ...
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