Data · collection · 2018
Learning to see through multimode fibers
Listed in DataCite
Deep Neural Networks (DNNs) are used to classify and reconstruct the input images from the intensity of the speckle patterns that result after the inputs propagated through the multimode fiber (MMF).
Description
We were able to demonstrate this result for fibers up to 1km long by training the DNNs with a database of 16,000 handwritten digits. Better recognition accuracy was obtained when the DNNs were first trained to reconstruct the input and then classify based on the recovered image.
We observed remarkable robustness against environmental instabilities and tolerance to deviations of the input pattern from the patterns with which the DNN was originally trained.
Links
Where it is published
- Repository landing page figshare.com/collections/Learning_to_see_through_multimode_fibers/4109672/1 ↗
landing page · from DataCite
- DOI doi.org/10.6084/m9.figshare.c.4109672.v1 ↗
DOI / persistent id · from DataCite
Documentation and papers
- CC BY 4.0 creativecommons.org/licenses/by/4.0 ↗
license · from DataCite
- IsSupplementTo 10.1364/optica.5.000960 doi.org/10.1364/optica.5.000960 ↗
publication · from DataCite
Catalogue records · 2
- DataCite API api.datacite.org/dois/10.6084/m9.figshare.c.4109672.v1 ↗
metadata API · from DataCite
- DataCite Commons commons.datacite.org/doi.org/10.6084/m9.figshare.c.4109672.v1 ↗
catalogue entry · from DataCite
Topics
- Inferred from text
- Communications engineering 70% · Image 75%
Related
Provenance · 1 source records, 10 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| DataCite | 10.6084/m9.figshare.c.4109672.v1 | 14 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · DataCite | connector:datacite@1.0.0 | /data/attributes/rightsList |
| concepts[field].anzsrc:group:4006 | enrichment · DataCite | taxonomy-embedding@1.1.0 | title+keywords+description (70%) |
| concepts[field].fos:electrical-engineering-electronic-engineering-information-engineering | source · DataCite | connector:datacite@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · DataCite | keyword-concept-rules@1.0.0 | title+description (75%) |
| created_date | source · DataCite | connector:datacite@1.0.0 | |
| description | source · DataCite | connector:datacite@1.0.0 | /data/attributes/descriptions |
| license | source · DataCite | connector:datacite@1.0.0 | /data/attributes/rightsList |
| publication_date | source · DataCite | connector:datacite@1.0.0 | /data/attributes/dates |
| title | source · DataCite | connector:datacite@1.0.0 | /data/attributes/titles/0/title |
| updated_date | source · DataCite | connector:datacite@1.0.0 |