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Jingyi Hu, Junfeng Guo, Zhiyuan Rui and Zhiming Wang
To solve the problem that noise seriously affects the online monitoring of parts signals of outdoor machinery, this paper proposes a signal reconstruction method integrating deep neural network and compression sensing, called ADMM-1DNet, and gives a deta...
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Zhangyou Peng and Jingang Liu
In order to reduce the sea clutter interference in the detection of sea surface targets, we propose a bistatic sea clutter suppression method based on compressed sensing optimization in this paper. The proposed method mitigates the interference effect by...
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Yang Shi, Zhenbo Wang, Tim J. LaClair, Chieh (Ross) Wang, Yunli Shao and Jinghui Yuan
The advent of connected vehicle (CV) technology offers new possibilities for a revolution in future transportation systems. With the availability of real-time traffic data from CVs, it is possible to more effectively optimize traffic signals to reduce co...
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Obada Issa and Tamer Shanableh
This paper proposes a novel approach to activity recognition where videos are compressed using video coding to generate feature vectors based on compression variables. We propose to eliminate the temporal domain of feature vectors by computing the mean a...
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Leila Malihi and Gunther Heidemann
Efficient model deployment is a key focus in deep learning. This has led to the exploration of methods such as knowledge distillation and network pruning to compress models and increase their performance. In this study, we investigate the potential syner...
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Jonathan Miquel, Laurent Latorre and Simon Chamaillé-Jammes
Biologging refers to the use of animal-borne recording devices to study wildlife behavior. In the case of audio recording, such devices generate large amounts of data over several months, and thus require some level of processing automation for the raw d...
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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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Juan Cisneros, Alain Lalande, Binnaz Yalcin, Fabrice Meriaudeau and Stephan Collins
Using a high-throughput neuroanatomical screen of histological brain sections developed in collaboration with the International Mouse Phenotyping Consortium, we previously reported a list of 198 genes whose inactivation leads to neuroanatomical phenotype...
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Miu Sakaida, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa and Hiroyuki Sugimori
Convolutional neural networks (CNNs) in deep learning have input pixel limitations, which leads to lost information regarding microcalcification when mammography images are compressed. Segmenting images into patches retains the original resolution when i...
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Carmine Paolino, Alessio Antolini, Francesco Zavalloni, Andrea Lico, Eleonora Franchi Scarselli, Mauro Mangia, Alex Marchioni, Fabio Pareschi, Gianluca Setti, Riccardo Rovatti, Mattia Luigi Torres, Marcella Carissimi and Marco Pasotti
Analog In-Memory computing (AIMC) is a novel paradigm looking for solutions to prevent the unnecessary transfer of data by distributing computation within memory elements. One such operation is matrix-vector multiplication (MVM), a workhorse of many fiel...
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