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Electronics
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Machine Learning and Photonics Cooperation: Principles, Algorithms, and Systems
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Machine Learning and Photonics Cooperation: Principles, Algorithms, and Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Optoelectronics".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 14194

Special Issue Editor

Prof. Dr. Nianqiang Li

E-Mail Website
Guest Editor
School of Optoelectronic Science and Engineering, Soochow University, Suzhou 215006, China
Interests: microwave photonics; nonlinear dynamics; semiconductor lasers; optical chaos; secure communications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last two decades, machine learning has been recognized for its enormous potential across many scientific disciplines and economic sectors. In particular, through combination with photonics technology, machine learning has flourished in various application fields, such as optical communication, objection recognition/detection, and measurement systems. On the one hand, optical technologies provide a well-established platform for countless applications in our everyday life as well as in several areas of basic research. On the other hand, the strength of machine learning is to discover effective ways for solving problems that are highly complex. The combination of machine learning and photonics is indeed raising considerable attention, and its full potential is yet to be uncovered.

Motivated by the above achievements, this Special Issue will focus on the fundamental theory, frameworks, techniques, and applications of machine learning/deep learning combined with photonics, with the aim of sharing and discussing recent advances and future trends. The topics of interest include but are not limited to the following:

Optical components

  • Semiconductor lasers and fiber-based lasers devices
  • Programmable multi-purpose photonic integrated circuits
  • Fibers
  • Optical amplifiers

Photonic neuromorphic computing and neural networks

  • High-performance computing
  • Optics for neuromorphic and reservoir computing
  • Optical convolutional neural network
  • Programmable photonics
  • Optical unitary conversion

Typical systems and applications

  • Biomedical imaging
  • Objection recognition/detection
  • Machine learning applications
  • Optical communication systems

Prof. Dr. Nianqiang Li
Guest Editor

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. Electronics is an international peer-reviewed open access semimonthly 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

  • Semiconductor lasers and fiber-based lasers devices
  • Programmable multi-purpose photonic integrated circuits
  • Fibers
  • Optical amplifiers
  • High-performance computing
  • Optics for neuromorphic and reservoir computing
  • Optical convolutional neural network
  • Programmable photonics
  • Optical unitary conversion
  • Biomedical imaging
  • Objection recognition/detection
  • Machine learning applications
  • Optical communication systems

Published Papers (6 papers)

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Research

13 pages, 2896 KiB  
Article
Enhanced Prediction Performance of Reservoir Computing Based on Mutually Delay-Coupled Semiconductor Lasers via Parameter Mismatch
by Deyu Cai, Yigong Yang, Pei Zhou and Nianqiang Li
Electronics 2022, 11(16), 2577; https://doi.org/10.3390/electronics11162577 - 17 Aug 2022
Viewed by 1313
Abstract
As an efficient information processing method, reservoir computing (RC) is essential to artificial neural networks (ANNs). Via the Santa Fe time series prediction task, we numerically investigated the effect of the mismatch of some critical parameters on the prediction performance of the RC [...] Read more.
As an efficient information processing method, reservoir computing (RC) is essential to artificial neural networks (ANNs). Via the Santa Fe time series prediction task, we numerically investigated the effect of the mismatch of some critical parameters on the prediction performance of the RC based on two mutually delay-coupled semiconductor lasers (SLs) with optical injection. The results show that better prediction performance can be realized by setting appropriate parameter mismatch scenarios. Especially for the situation with large prediction errors encountered in the RC with identical laser parameters, a suitable parameter mismatch setting can achieve computing performance improvement of an order of magnitude. Our research is instructive for the hardware implementation of laser-based RC, where the parameter mismatch is unavoidable. Full article
(This article belongs to the Special Issue Machine Learning and Photonics Cooperation: Principles, Algorithms, and Systems)
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13 pages, 4280 KiB  
Article
Spiking VGG7: Deep Convolutional Spiking Neural Network with Direct Training for Object Recognition
by Shuiying Xiang, Shuqing Jiang, Xiaosong Liu, Tao Zhang and Licun Yu
Electronics 2022, 11(13), 2097; https://doi.org/10.3390/electronics11132097 - 04 Jul 2022
Cited by 12 | Viewed by 3254
Abstract
We propose a deep convolutional spiking neural network (DCSNN) with direct training to classify concrete bridge damage in a real engineering environment. The leaky-integrate-and-fire (LIF) neuron model is employed in our DCSNN that is similar to VGG. Poisson encoding and convolution encoding strategies [...] Read more.
We propose a deep convolutional spiking neural network (DCSNN) with direct training to classify concrete bridge damage in a real engineering environment. The leaky-integrate-and-fire (LIF) neuron model is employed in our DCSNN that is similar to VGG. Poisson encoding and convolution encoding strategies are considered. The gradient surrogate method is introduced to realize the supervised training for the DCSNN. In addition, we have examined the effect of observation time step on the network performance. The testing performance for two different spike encoding strategies are compared. The results show that the DCSNN using gradient surrogate method can achieve a performance of 97.83%, which is comparable to traditional CNN. We also present a comparison with STDP-based unsupervised learning and a converted algorithm, and the proposed DCSNN is proved to have the best performance. To demonstrate the generalization performance of the model, we also use a public dataset for comparison. This work paves the way for the practical engineering applications of the deep SNNs. Full article
(This article belongs to the Special Issue Machine Learning and Photonics Cooperation: Principles, Algorithms, and Systems)
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8 pages, 3327 KiB  
Article
Influence of Linewidth Enhancement Factor on the Nonlinear Dynamics and TDS Concealment of Semiconductor Ring Lasers
by Yichen Wang, Xianglong Wang, Penghua Mu, Gang Guo, Xintian Liu, Kun Wang, Pengfei He, Guoying Hu and Gang Jin
Electronics 2022, 11(13), 2007; https://doi.org/10.3390/electronics11132007 - 27 Jun 2022
Viewed by 1201
Abstract
In this paper, the influences of linewidth enhancement factor on the output characteristics of a semiconductor ring laser (SRL) are numerically investigated. By constructing a master–slave injection model, we discuss the influence of linewidth enhancement factor on the output characteristics of SRL. In [...] Read more.
In this paper, the influences of linewidth enhancement factor on the output characteristics of a semiconductor ring laser (SRL) are numerically investigated. By constructing a master–slave injection model, we discuss the influence of linewidth enhancement factor on the output characteristics of SRL. In addition, the 0–1 chaos test is introduced to study the effects of linewidth enhancement factor, feedback strength, feedback time delay and normalized injection current on the dynamic characteristics of the master laser. Furthermore, a simulation study is carried out on the suppression of time delay characteristics by the linewidth enhancement factor. The results show that selecting a proper linewidth enhancement factor has a significant effect on the chaotic output of SRL, and a larger linewidth enhancement factor is beneficial for the concealment of time delay signature. Such results are beneficial for achieving the security chaos communication and physical random generators. Full article
(This article belongs to the Special Issue Machine Learning and Photonics Cooperation: Principles, Algorithms, and Systems)
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13 pages, 3474 KiB  
Article
High-Speed Reservoir Computing Based on Circular-Side Hexagonal Resonator Microlaser with Optical Feedback
by Tong Zhao, Wenli Xie, Yanqiang Guo, Junwei Xu, Yuanyuan Guo and Longsheng Wang
Electronics 2022, 11(10), 1578; https://doi.org/10.3390/electronics11101578 - 15 May 2022
Cited by 1 | Viewed by 1459
Abstract
In the current environment of the explosive growth in the amount of information, the demand for efficient information-processing methods has become increasingly urgent. We propose and numerically investigate a delay-based high-speed reservoir computing (RC) using a circular-side hexagonal resonator (CSHR) microlaser with optical [...] Read more.
In the current environment of the explosive growth in the amount of information, the demand for efficient information-processing methods has become increasingly urgent. We propose and numerically investigate a delay-based high-speed reservoir computing (RC) using a circular-side hexagonal resonator (CSHR) microlaser with optical feedback and injection. In this RC system, a smaller time interval can be obtained between virtual nodes, and a higher information processing rate (Rinf) can also be achieved, due to the ultra-short photon lifetime and wide bandwidth of the CSHR microlaser. The performance of the RC system was tested with three benchmark tasks (Santa-Fe chaotic time series prediction task, the 10th order Nonlinear Auto Regressive Moving Average task and Nonlinear channel equalization task). The results show that the system achieves high-accuracy prediction, even with a small number of virtual nodes (25), and is more feasible, with lower requirements for arbitrary waveform generators at the same rate. Significantly, at the high rate of 10 Gbps, low error predictions can be achieved over a large parameter space (e.g., frequency detuning in the interval 80 GHz, injected strength in the range of 0.9 variation and 2% range for feedback strength). Interestingly, it has the potential to achieve Rinf of 25 Gbps under technical advancements. Additionally, its shorter external cavity length and cubic micron scale size make it an excellent choice for large-scale photonic integration reservoir computing. Full article
(This article belongs to the Special Issue Machine Learning and Photonics Cooperation: Principles, Algorithms, and Systems)
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10 pages, 1323 KiB  
Article
Prompt Frequency Stabilization of Ultra-Stable Laser via Improved Mean Shift Algorithm
by Le Fan, Dongdong Jiao, Jun Liu, Long Chen, Guanjun Xu, Linbo Zhang, Jie Liu, Ruifang Dong, Tao Liu and Shougang Zhang
Electronics 2022, 11(9), 1319; https://doi.org/10.3390/electronics11091319 - 21 Apr 2022
Cited by 3 | Viewed by 1335
Abstract
In many scientific fields, the continuous operation of ultra-stable lasers is crucial for applications. To speed up the frequency stabilization process in case of the occurence of unexpected interruptions, a prompt frequency stabilization approach based on an improved mean shift algorithm is proposed [...] Read more.
In many scientific fields, the continuous operation of ultra-stable lasers is crucial for applications. To speed up the frequency stabilization process in case of the occurence of unexpected interruptions, a prompt frequency stabilization approach based on an improved mean shift algorithm is proposed and verified with a homemade laser system. We developed a double-loop feedback controller to steer the laser frequency with fast and slow channels, respectively. In this study, an improved mean shift algorithm is utilized to intelligently search for the transmission signal, which involves adaptively updating the sliding window radius and incorporating a Gaussian kernel function to update the shift vector. The number of lock points on the left and right sides of the central point determines the scanning direction to search for the transmission signal quickly. The laser is intentionally interrupted 306 times within 10,000 s to evaluate the relocking performance. The median auto-locking time of the laser is improved from 16 s to 4 s. By beating with another ultra-stable laser system, the laser frequency instability is measured to be less than 2.1×10−14 and the linewidth is 5 Hz. This work improves the adaptation and relocking ability of the ultra-stable laser in a complex environment. Full article
(This article belongs to the Special Issue Machine Learning and Photonics Cooperation: Principles, Algorithms, and Systems)
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15 pages, 989 KiB  
Article
Human Detection in Aerial Thermal Images Using Faster R-CNN and SSD Algorithms
by K. R. Akshatha, A. Kotegar Karunakar, Satish B. Shenoy, Abhilash K. Pai, Nikhil Hunjanal Nagaraj and Sambhav Singh Rohatgi
Electronics 2022, 11(7), 1151; https://doi.org/10.3390/electronics11071151 - 06 Apr 2022
Cited by 22 | Viewed by 4894
Abstract
The automatic detection of humans in aerial thermal imagery plays a significant role in various real-time applications, such as surveillance, search and rescue and border monitoring. Small target size, low resolution, occlusion, pose, and scale variations are the significant challenges in aerial thermal [...] Read more.
The automatic detection of humans in aerial thermal imagery plays a significant role in various real-time applications, such as surveillance, search and rescue and border monitoring. Small target size, low resolution, occlusion, pose, and scale variations are the significant challenges in aerial thermal images that cause poor performance for various state-of-the-art object detection algorithms. Though many deep-learning-based object detection algorithms have shown impressive performance for generic object detection tasks, their ability to detect smaller objects in the aerial thermal images is analyzed through this study. This work carried out the performance evaluation of Faster R-CNN and single-shot multi-box detector (SSD) algorithms with different backbone networks to detect human targets in aerial view thermal images. For this purpose, two standard aerial thermal datasets having human objects of varying scale are considered with different backbone networks, such as ResNet50, Inception-v2, and MobileNet-v1. The evaluation results demonstrate that the Faster R-CNN model trained with the ResNet50 network architecture out-performed in terms of detection accuracy, with a mean average precision (mAP at 0.5 IoU) of 100% and 55.7% for the test data of the OSU thermal dataset and AAU PD T datasets, respectively. SSD with MobileNet-v1 achieved the highest detection speed of 44 frames per second (FPS) on the NVIDIA GeForce GTX 1080 GPU. Fine-tuning the anchor parameters of the Faster R-CNN ResNet50 and SSD Inception-v2 algorithms caused remarkable improvement in mAP by 10% and 3.5%, respectively, for the challenging AAU PD T dataset. The experimental results demonstrated the application of Faster R-CNN and SSD algorithms for human detection in aerial view thermal images, and the impact of varying backbone network and anchor parameters on the performance improvement of these algorithms. Full article
(This article belongs to the Special Issue Machine Learning and Photonics Cooperation: Principles, Algorithms, and Systems)
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