Please use this identifier to cite or link to this item:
http://localhost:8080/xmlui/handle/123456789/1076Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Chayanika Sharma, Vijay Kumar | - |
| dc.date.accessioned | 2024-10-10T05:08:57Z | - |
| dc.date.available | 2024-10-10T05:08:57Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/1076 | - |
| dc.description.abstract | Speckle-based deep learning approach for the classification of partially coherent vortex beams is presented. Remarkably, this approach achieved 100% classification accuracy. © 2023 The Author(s) | en_US |
| dc.description.sponsorship | NITW | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Frontiers in Optics + Laser Science © 2023 Optica Publishing Group | en_US |
| dc.subject | Speckled-learned Classification | en_US |
| dc.subject | Partially Coherent Vortex Beams | en_US |
| dc.title | Speckled-learned Classification of Partially Coherent Vortex Beams | en_US |
| dc.type | Other | en_US |
| Appears in Collections: | Physics | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FIO-2023-JTu4A.29.pdf | 1.36 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.