WSN optimization for sampling-based signal estimation using semi-binarized variational autoencoder

DOI10.1016/j.ins.2021.12.022OpenAlexW4200017925MaRDI QIDQ6154778

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Publication date: 16 February 2024

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2021.12.022

zbMATH Keywords

deep learninggenerative modelsubmodular optimizationbinarized neural networkssparse samplingsparse signal estimation

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Cites Work

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• The Optimal Hard Threshold for Singular Values is <inline-formula> <tex-math notation="TeX">\(4/\sqrt {3}\) </tex-math></inline-formula>
• Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns
• An Introduction to Variational Autoencoders
• Sparse Sampling for Inverse Problems With Tensors
• On Information and Sufficiency

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