Haotong Qi, Jianyang Hu, Chang Li, Xuyao Zhang, Chen Chen, Danlin Cao, Jie Lin, Yiqun Wang, Peng Jin
{"title":"All-Optical Physical Field Recognition Via Sparse Feature Extraction","authors":"Haotong Qi, Jianyang Hu, Chang Li, Xuyao Zhang, Chen Chen, Danlin Cao, Jie Lin, Yiqun Wang, Peng Jin","doi":"10.1002/lpor.202400376","DOIUrl":null,"url":null,"abstract":"Optical computing has been proven to have the ability to process information with ultra-high speed. Here, an all-optical feature extraction system via sparse representation (AFE-SR) is introduced. The AFE-SR, which is achieved by multiple diffractive optical elements (DOEs), can realize the recognition of generated physical fields with the speed of light. The sparse representation simplifies the target and improves the recognition accuracy. With the mathematical analysis principle of sparse optical features extraction and optical integration, the identification accuracy of the generation of physical fields is 100% in 2100 frames of the experimental videos. The application field of optical computing systems is extended to state recognition.","PeriodicalId":204,"journal":{"name":"Laser & Photonics Reviews","volume":null,"pages":null},"PeriodicalIF":9.8000,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Laser & Photonics Reviews","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1002/lpor.202400376","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0
Abstract
Optical computing has been proven to have the ability to process information with ultra-high speed. Here, an all-optical feature extraction system via sparse representation (AFE-SR) is introduced. The AFE-SR, which is achieved by multiple diffractive optical elements (DOEs), can realize the recognition of generated physical fields with the speed of light. The sparse representation simplifies the target and improves the recognition accuracy. With the mathematical analysis principle of sparse optical features extraction and optical integration, the identification accuracy of the generation of physical fields is 100% in 2100 frames of the experimental videos. The application field of optical computing systems is extended to state recognition.
期刊介绍:
Laser & Photonics Reviews is a reputable journal that publishes high-quality Reviews, original Research Articles, and Perspectives in the field of photonics and optics. It covers both theoretical and experimental aspects, including recent groundbreaking research, specific advancements, and innovative applications.
As evidence of its impact and recognition, Laser & Photonics Reviews boasts a remarkable 2022 Impact Factor of 11.0, according to the Journal Citation Reports from Clarivate Analytics (2023). Moreover, it holds impressive rankings in the InCites Journal Citation Reports: in 2021, it was ranked 6th out of 101 in the field of Optics, 15th out of 161 in Applied Physics, and 12th out of 69 in Condensed Matter Physics.
The journal uses the ISSN numbers 1863-8880 for print and 1863-8899 for online publications.