Universal linear intensity transformations using spatially incoherent diffractive processors
Published 2023 View Full Article
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Title
Universal linear intensity transformations using spatially incoherent diffractive processors
Authors
Keywords
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Journal
Light-Science & Applications
Volume 12, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2023-08-15
DOI
10.1038/s41377-023-01234-y
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- Analogue computing with metamaterials
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- Performing Mathematical Operations with Metamaterials
- (2014) A. Silva et al. SCIENCE
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