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Photonics
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Machine Learning in Photonics
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Machine Learning in Photonics

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

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 7209

Special Issue Editors

Dr. Shu-Hao Chang

E-Mail Website
Guest Editor
Science and Technology Policy Research and Information Center, National Applied Research Laboratories, 14F., No. 106, Sec. 2, Heping E. Rd., Da'an Dist., Taipei 10636, Taiwan
Interests: optical communication; optics patent analysis; silicon photonics; solar cell; technology and innovation management
Special Issues, Collections and Topics in MDPI journals
Dr. Chin-Yuan Fan

E-Mail Website
Guest Editor
Science and Technology Policy Research and Information Center, National Applied Research Laboratories, 14F., No. 106, Sec. 2, Heping E. Rd., Da'an Dist., Taipei 10636, Taiwan
Interests: sensor analysis; optics patent analysis; silicon photonics; solar cell; technology and innovation management; semiconductor industry analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the emergency of the Internet of Things, the application of machine learning in photonics has become a prospective research field. The last two decades have seen a rapid surge of interest in photonics and machine learning, and scholars have also seen an increase in the potential of combining machine learning and photonics. It is our pleasure to announce a Special Issue that is entirely focused on their combination. The combination of these two fields is indeed drawing large amounts of attention, and its full potential is yet to be disclosed. These results are paving the way for broader and deeper investigations, which we aim to collect here.

This Special Issue is dedicated to theoretical or experimental advances bringing together the fields of optical technologies and machine learning. It is focused on recent advances in frontier technologies, technology trends, and to leverage machine learning in this application. We strongly encourage the submission of papers focusing on the keywords below. However, works on related topics will also be considered.

Dr. Shu-Hao Chang
Dr. Chin-Yuan Fan
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

  • photonics
  • machine learning
  • optical communication
  • silicon photonics
  • technology foresight

Published Papers (3 papers)

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Research

20 pages, 5182 KiB  
Article
Dual-Tree Complex Wavelet Input Transform for Cyst Segmentation in OCT Images Based on a Deep Learning Framework
by Reza Darooei, Milad Nazari, Rahele Kafieh and Hossein Rabbani
Photonics 2023, 10(1), 11; https://doi.org/10.3390/photonics10010011 - 23 Dec 2022
Cited by 4 | Viewed by 2381
Abstract
Optical coherence tomography (OCT) represents a non-invasive, high-resolution cross-sectional imaging modality. Macular edema is the swelling of the macular region. Segmentation of fluid or cyst regions in OCT images is essential, to provide useful information for clinicians and prevent visual impairment. However, manual [...] Read more.
Optical coherence tomography (OCT) represents a non-invasive, high-resolution cross-sectional imaging modality. Macular edema is the swelling of the macular region. Segmentation of fluid or cyst regions in OCT images is essential, to provide useful information for clinicians and prevent visual impairment. However, manual segmentation of fluid regions is a time-consuming and subjective procedure. Traditional and off-the-shelf deep learning methods fail to extract the exact location of the boundaries under complicated conditions, such as with high noise levels and blurred edges. Therefore, developing a tailored automatic image segmentation method that exhibits good numerical and visual performance is essential for clinical application. The dual-tree complex wavelet transform (DTCWT) can extract rich information from different orientations of image boundaries and extract details that improve OCT fluid semantic segmentation results in difficult conditions. This paper presents a comparative study of using DTCWT subbands in the segmentation of fluids. To the best of our knowledge, no previous studies have focused on the various combinations of wavelet transforms and the role of each subband in OCT cyst segmentation. In this paper, we propose a semantic segmentation composite architecture based on a novel U-net and information from DTCWT subbands. We compare different combination schemes, to take advantage of hidden information in the subbands, and demonstrate the performance of the methods under original and noise-added conditions. Dice score, Jaccard index, and qualitative results are used to assess the performance of the subbands. The combination of subbands yielded high Dice and Jaccard values, outperforming the other methods, especially in the presence of a high level of noise. Full article
(This article belongs to the Special Issue Machine Learning in Photonics)
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16 pages, 2011 KiB  
Article
Novel Inversion Algorithm for the Atmospheric Aerosol Extinction Coefficient Based on an Improved Genetic Algorithm
by Minghuan Hu, Shun Li, Jiandong Mao, Juan Li, Qiang Wang and Yi Zhang
Photonics 2022, 9(8), 554; https://doi.org/10.3390/photonics9080554 - 07 Aug 2022
Cited by 2 | Viewed by 1328
Abstract
As an important atmospheric component, aerosols play a very important role in the radiation budget balance of the earth–atmosphere system. To study the optical characteristics of aerosols, it is necessary to use an inversion algorithm to process the lidar return signal to obtain [...] Read more.
As an important atmospheric component, aerosols play a very important role in the radiation budget balance of the earth–atmosphere system. To study the optical characteristics of aerosols, it is necessary to use an inversion algorithm to process the lidar return signal to obtain both the aerosol extinction coefficient and the backscattering coefficient. However, the lidar return power equation is ill-conditioned and contains two unknown parameters, meaning that traditional inversion algorithms must be solved by adopting certain assumptions (e.g., a uniform atmosphere and the lidar ratio), which to a certain extent can seriously affect the inversion accuracy. Here, to improve the accuracy of the aerosol extinction coefficient inversion, an inversion method based on an improved genetic algorithm is proposed. Using the U.S. Standard Atmosphere model and the return power equation, the aerosol extinction coefficient and the backscattering coefficient are independent variables that randomly provide initial values to simulate the theoretical lidar power. Then, the genetic algorithm is used to approximate the theoretical lidar power to the measured lidar return power with height; when the two are infinitely close, the values of the corresponding two independent variables (i.e., the extinction and backscattering coefficients) are inverted. Experiments performed to compare the different effects between a simple genetic algorithm and the improved genetic algorithm showed the proposed method capable of inverting the aerosol extinction coefficient without reliance on traditional inversion methods, representing a novel approach to the inversion of the aerosol extinction coefficient and the backscattering coefficient. Full article
(This article belongs to the Special Issue Machine Learning in Photonics)
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12 pages, 3385 KiB  
Article
Multicore Photonic Complex-Valued Neural Network with Transformation Layer
by Ruiting Wang, Pengfei Wang, Chen Lyu, Guangzhen Luo, Hongyan Yu, Xuliang Zhou, Yejin Zhang and Jiaoqing Pan
Photonics 2022, 9(6), 384; https://doi.org/10.3390/photonics9060384 - 28 May 2022
Cited by 3 | Viewed by 2504
Abstract
Photonic neural network chips have been widely studied because of their low power consumption, high speed and large bandwidth. Using amplitude and phase to encode, photonic chips can accelerate complex-valued neural network computations. In this article, a photonic complex-valued neural network (PCNN) chip [...] Read more.
Photonic neural network chips have been widely studied because of their low power consumption, high speed and large bandwidth. Using amplitude and phase to encode, photonic chips can accelerate complex-valued neural network computations. In this article, a photonic complex-valued neural network (PCNN) chip is designed. The scale of the single-core PCNN chip is limited because of optical losses, and the multicore architecture of the chip is used to improve computing capability. Further, for improving the performance of the PCNN, we propose the transformation layer, which can be implemented by the designed photonic chip to transform real-valued encoding to complex-valued encoding, which has richer information. Compared with real-valued input, the transformation layer can effectively improve the classification accuracy from 93.14% to 97.51% of a 64-dimensional input on the MNIST test set. Finally, we analyze the multicore computation of the PCNN. Compared with the single-core architecture, the multicore architecture can improve the classification accuracy by implementing larger neural networks and has better phase noise robustness. The proposed architecture and algorithms are beneficial to promote the accelerated computing of photonic chips for complex-valued neural networks and are promising for use in many applications, such as image recognition and signal processing. Full article
(This article belongs to the Special Issue Machine Learning in Photonics)
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