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Photonics
Special Issues
Artificial Intelligence and Machine Learning in Photonics
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Artificial Intelligence and Machine Learning in Photonics

A special issue of Photonics (ISSN 2304-6732).

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 6827

Special Issue Editors

Prof. Dr. Zhuoran Wang

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: LiDAR (light detection and ranging); photonics sensor; deep learning; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Guohui Yuan

E-Mail Website
Guest Editor
Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China
Interests: photonic-integrated circuits; photonics intelligent sensing; semiconductor lasers; artificial intelligence; machine learning; reinforcement learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Photonics invites manuscript submissions in the subject area of “Artificial Intelligence and Machine Learning in Photonics”. The emerging fields of artificial intelligence and machine learning, especially deep learning, have opened up new horizons for extensive technologies coming from the areas of photonic materials, photonic devices, photonic integrated circuits, optical systems, and so on. AI-powered systems show impressive performance and robustness compared with traditional methods. Therefore, the marriage of AI technology and photonics has been attracting a great deal of attention in recent years. The purpose of this Special Issue of Photonics is to highlight the recent progress and trends in developing AI-enhanced photonics technologies. Areas of interest include (but are not limited to):

  • Reinforcement learning to control optical systems.
  • Artificially engineered photonic structures, materials, and devices.
  • Neural networks on photonic integrated platforms and free-space optics.
  • Photonics and intelligent sensing.
  • High-speed optical communication and computing.
  • Super-resolution imaging and 3D imaging.
  • Quantum information processing.
  • Next-generation ultrafast photonics.

Prof. Dr. Zhuoran Wang
Dr. Guohui Yuan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Photonics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • reinforcement learning
  • machine learning
  • artificial intelligence
  • neural networks
  • photonics integrated circuits
  • optical computing
  • intelligent photonics
  • quantum optics

Published Papers (4 papers)

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Research

11 pages, 2657 KiB  
Article
High-Linear Frequency-Swept Lasers with Data-Driven Control
by Haohao Zhao, Dachao Xu, Zihan Wu, Liang Sun, Guohui Yuan and Zhuoran Wang
Photonics 2023, 10(9), 1056; https://doi.org/10.3390/photonics10091056 - 18 Sep 2023
Viewed by 922
Abstract
The frequency-swept laser (FSL) is applied widely in various sensing systems in the scientific and industrial fields, especially in the light detection and ranging (Lidar) area. However, the inherent nonlinearity limits its performance in application systems, especially in the broadband frequency-swept condition. In [...] Read more.
The frequency-swept laser (FSL) is applied widely in various sensing systems in the scientific and industrial fields, especially in the light detection and ranging (Lidar) area. However, the inherent nonlinearity limits its performance in application systems, especially in the broadband frequency-swept condition. In this work, from the perspective of data-driven control, we adopt the reinforcement learning-based broadband frequency-swept linearization method (RL-FSL) to optimize the control policy and generate the modulation signals. The nonlinearity measurement system and the system simulator are established. Since the powerful learning ability of the reinforcement learning algorithm, the linearization policy is optimized off-line and the generated modulation signals reduce the nonlinearity almost 20 times, compared to the case without control. In the long-term operation, the regular updated modulation signals perform better than the traditional iteration results, demonstrating the efficiency of the proposed data-driven control method in application systems. Therefore, the RL-FSL method has the potential to be the candidate of optical system control. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Photonics)
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12 pages, 2692 KiB  
Communication
Handwritten Digits Recognition Based on a Parallel Optoelectronic Time-Delay Reservoir Computing System
by Dianzuo Yue, Yushuang Hou, Chunxia Hu, Cunru Zang and Yingzhe Kou
Photonics 2023, 10(3), 236; https://doi.org/10.3390/photonics10030236 - 22 Feb 2023
Cited by 3 | Viewed by 1525
Abstract
In this work, the performance of an optoelectronic time-delay reservoir computing system for performing a handwritten digit recognition task is numerically investigated, and a scheme to improve the recognition speed using multiple parallel reservoirs is proposed. By comparing four image injection methods based [...] Read more.
In this work, the performance of an optoelectronic time-delay reservoir computing system for performing a handwritten digit recognition task is numerically investigated, and a scheme to improve the recognition speed using multiple parallel reservoirs is proposed. By comparing four image injection methods based on a single time-delay reservoir, we find that when injecting the histograms of oriented gradient (HOG) features of the digit image, the accuracy rate (AR) is relatively high and is less affected by the offset phase. To improve the recognition speed, we construct a parallel time-delay reservoir system including multi-reservoirs, where each reservoir processes part of the HOG features of one image. Based on 6 parallel reservoirs with each reservoir possessing 100 virtual nodes, the AR can reach about 97.8%, and the reservoir processing speed can reach about 1 × 106 digits per second. Meanwhile, the parallel reservoir system shows strong robustness to the parameter mismatch between multi-reservoirs. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Photonics)
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17 pages, 6944 KiB  
Article
Blind Restoration of Images Distorted by Atmospheric Turbulence Based on Deep Transfer Learning
by Yiming Guo, Xiaoqing Wu, Chun Qing, Changdong Su, Qike Yang and Zhiyuan Wang
Photonics 2022, 9(8), 582; https://doi.org/10.3390/photonics9080582 - 18 Aug 2022
Cited by 4 | Viewed by 1607
Abstract
Removing space-time varying blur and geometric distortions simultaneously from an image is a challenging task. Recent methods (including physical-based methods or learning-based methods) commonly default the turbulence-degraded operator as a fixed convolution operator. Obviously, the assumption does not hold in practice. According to [...] Read more.
Removing space-time varying blur and geometric distortions simultaneously from an image is a challenging task. Recent methods (including physical-based methods or learning-based methods) commonly default the turbulence-degraded operator as a fixed convolution operator. Obviously, the assumption does not hold in practice. According to the situation that the real turbulence distorted operator has double uncertainty in space and time dimensions, this paper reports a novel deep transfer learning (DTL) network framework to address this problem. Concretely, the training process of the proposed approach contains two stages. In the first stage, the GoPro Dataset was used to pre-train the Network D1 and freeze the bottom weight parameters of the model; in the second stage, a small amount of the Hot-Air Dataset was employed for finetuning the last two layers of the network. Furthermore, residual fast Fourier transform with convolution block (Res FFT-Conv Block) was introduced to integrate both low-frequency and high-frequency residual information. Subsequently, extensive experiments were carried out with multiple real-world degraded datasets by implementing the proposed method and four existing state-of-the-art methods. In contrast, the proposed method demonstrates a significant improvement over the four reported methods in terms of alleviating the blur and distortions, as well as improving the visual quality. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Photonics)
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11 pages, 6703 KiB  
Article
Imaging Complex Targets through a Scattering Medium Based on Adaptive Encoding
by Enlai Guo, Yingjie Shi, Lianfa Bai and Jing Han
Photonics 2022, 9(7), 467; https://doi.org/10.3390/photonics9070467 - 04 Jul 2022
Cited by 2 | Viewed by 1476
Abstract
The scattering of light after passing through a complex medium poses challenges in many fields. Any point in the collected speckle will contain information from the entire target plane because of the randomness of scattering. The detailed information of complex targets is submerged [...] Read more.
The scattering of light after passing through a complex medium poses challenges in many fields. Any point in the collected speckle will contain information from the entire target plane because of the randomness of scattering. The detailed information of complex targets is submerged in the aliased signal caused by random scattering, and the aliased signal causes the quality of the recovered target to be degraded. In this paper, a new neural network named Adaptive Encoding Scattering Imaging ConvNet (AESINet) is constructed by analyzing the physical prior of speckle image redundancy to recover complex targets hidden behind the opaque medium. AESINet reduces the redundancy of speckle through adaptive encoding which effectively improves the separability of data; the encoded speckle makes it easier for the network to extract features, and helps restore the detailed information of the target. The necessity for adaptive encoding is analyzed, and the ability of this method to reconstruct complex targets is tested. The peak signal-to-noise ratio (PSNR) of the reconstructed target after adaptive encoding can be improved by 1.8 dB. This paper provides an effective reference for neural networks combined with other physical priors in scattering processes. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Photonics)
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