Optical Machine Learning Using Time-Lens Deep Neural NetWorks Article Swipe
Luhe Zhang
,
Caiyun Li
,
Jiangyong He
,
Yange Liu
,
Jian Zhao
,
Huiyi Guo
,
Longfei Zhu
,
Mengjie Zhou
,
Kaiyan Zhu
,
Congcong Liu
,
Zhi Wang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/photonics8030078
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/photonics8030078
As a high-throughput data analysis technique, photon time stretching (PTS) is widely used in the monitoring of rare events such as cancer cells, rough waves, and the study of electronic and optical transient dynamics. The PTS technology relies on high-speed data collection, and the large amount of data generated poses a challenge to data storage and real-time processing. Therefore, how to use compatible optical methods to filter and process data in advance is particularly important. The time-lens proposed, based on the duality of time and space as an important data processing method derived from PTS, achieves imaging of time signals by controlling the phase information of the timing signals. In this paper, an optical neural network based on the time-lens (TL-ONN) is proposed, which applies the time-lens to the layer algorithm of the neural network to realize the forward transmission of one-dimensional data. The recognition function of this optical neural network for speech information is verified by simulation, and the test recognition accuracy reaches 95.35%. This architecture can be applied to feature extraction and classification, and is expected to be a breakthrough in detecting rare events such as cancer cell identification and screening.
Related Topics
Concepts
Computer science
Artificial neural network
Lens (geology)
Filter (signal processing)
Artificial intelligence
Data transmission
Process (computing)
Transmission (telecommunications)
Feature extraction
Convolutional neural network
Pattern recognition (psychology)
Computer vision
Optics
Computer hardware
Telecommunications
Operating system
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/photonics8030078
- https://www.mdpi.com/2304-6732/8/3/78/pdf?version=1615814693
- OA Status
- gold
- Cited By
- 10
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3136287790
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3136287790Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/photonics8030078Digital Object Identifier
- Title
-
Optical Machine Learning Using Time-Lens Deep Neural NetWorksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-03-15Full publication date if available
- Authors
-
Luhe Zhang, Caiyun Li, Jiangyong He, Yange Liu, Jian Zhao, Huiyi Guo, Longfei Zhu, Mengjie Zhou, Kaiyan Zhu, Congcong Liu, Zhi WangList of authors in order
- Landing page
-
https://doi.org/10.3390/photonics8030078Publisher landing page
- PDF URL
-
https://www.mdpi.com/2304-6732/8/3/78/pdf?version=1615814693Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2304-6732/8/3/78/pdf?version=1615814693Direct OA link when available
- Concepts
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Computer science, Artificial neural network, Lens (geology), Filter (signal processing), Artificial intelligence, Data transmission, Process (computing), Transmission (telecommunications), Feature extraction, Convolutional neural network, Pattern recognition (psychology), Computer vision, Optics, Computer hardware, Telecommunications, Operating system, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 4, 2023: 1, 2022: 3Per-year citation counts (last 5 years)
- References (count)
-
38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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