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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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Lingji Xu, Lixing Chen, Yaan Li and Weihua Jiang
Due to the complex ocean propagation environments, the underwater acoustic (UWA) multipath channel often exhibits block sparse time-varying features, and while dynamic compressed sensing (DCS) can mitigate the time-varying effects of the UWA channel, DCS...
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Subramanyam Shashi Kumar and Prakash Ramachandran
Nowadays, healthcare is becoming very modern, and the support of Internet of Things (IoT) is inevitable in a personal healthcare system. A typical personal healthcare system acquires vital parameters from human users and stores them in a cloud platform f...
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Fangming Zhou, Lulu Zhao, Limin Li, Yifei Hu, Xinglong Jiang, Jinpei Yu and Guang Liang
The recently-emerging compressed sensing (CS) theory makes GNSS signal processing at a sub-Nyquist rate possible if it has a sparse representation in certain domain. The previously proposed code-domain compression acquisition algorithms have high computa...
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Xiang Li, Linlu Dong, Biao Li, Yifan Lei and Nuwen Xu
Microseismic signal denoising is of great significance for P wave, S wave first arrival picking, source localization, and focal mechanism inversion. Therefore, an Empirical Mode Decomposition (EMD), Compressed Sensing (CS), and Soft-thresholding (ST) com...
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