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Photonics
Special Issues
Optical Sensing Technologies, Devices and Their Data Applications
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Optical Sensing Technologies, Devices and Their Data Applications

A special issue of Photonics (ISSN 2304-6732). This special issue belongs to the section "Lasers, Light Sources and Sensors".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2524

Special Issue Editors

Dr. Yufei Chu

E-Mail Website
Guest Editor
School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA
Interests: atmospheric detection; boundary layer; lidar; fiber optic detection; non-destructive detection; microfluidics
Dr. Hanqing Guo

E-Mail Website
Guest Editor
Electrical and Computer Engineering Department, University of Hawaii at Manoa, 2540 Dole Street, Holmes Hall 483, Honolulu, HI 96822, USA
Interests: cybersecurity; trustworthy AI; mobile computing; mobile optical sensing
Prof. Dr. Yuanxian Zhang

E-Mail Website
Guest Editor
School of Physics and Astronomy, Yunnan University, Kunming, China
Interests: micro-nano optics; fiber optofluidic laser; optofluidic waveguide; laser biochemical sensing
Dr. Abu Farzan Mitul

E-Mail Website
Guest Editor
1. National Energy Technology Laboratory (NETL), Department of Energy Office of Fossil Energy, Pittsburgh, PA, USA
2. Department of Electrical and Computer Engineering, Saginaw Valley State University (SVSU), Saginaw, MI, USA
Interests: photovoltaic devices; optoelectronics; photonic devices; nanotechnology and nanofabrication

Special Issue Information

Dear Colleagues,

Optical sensing is a rapidly advancing field that leverages the unique advantages of optical technologies to measure a wide range of physical, chemical, and environmental parameters with exceptional accuracy and sensitivity. This technology has diverse applications, from structural health monitoring to environmental science and biomedical engineering. This Special Issue is dedicated to exploring the latest advancements in optical sensing technologies and devices, as well as the innovative applications of the data they generate, particularly in atmospheric and environmental detection, ultrasonic sensing, and optofluidics.

This Special Issue on “Optical Sensing Technologies, Devices and Their Data Applications” invites contributions that highlight cutting-edge developments in the design, deployment, and data utilization of advanced optical sensors. We are particularly interested in research that pushes the boundaries of how these technologies are applied in real-world scenarios. Specific topics of interest include the following:

  1. Development and validation of novel optical sensors and methodologies, especially those incorporating fiber optic sensing, photonic sensing, optical imaging applications, lidar, Doppler lidar, and other optical sensing technologies, offering enhanced capabilities in diverse environments.
  2. Applications of optical sensing in atmospheric and environmental monitoring, including advanced methodologies for environmental surveillance, industrial process control, and public health applications.
  3. Integration of optical sensors with other technologies, such as ultrasonic and nondestructive evaluation, to create multifunctional sensing platforms that deliver comprehensive monitoring solutions.
  4. Advancements in data processing and interpretation, focusing on the application of machine learning, big data analytics, and AI techniques to enhance the accuracy, reliability, and actionable insights derived from optical sensor data.
  5. AI-enhanced optical sensing, including the use of AI models to boost sensing performance, address AI-related security issues in optical sensing, and explore real-world applications that synergize AI with optical sensing technologies.
  6. Exploration of microfluidics, optofluidics, and their industrial applications, focusing on innovations in the chemical, biological, and pharmaceutical industries, and how these technologies can be integrated with optical sensing to drive advancements in industrial processes.

This Special Issue aims to gather groundbreaking research and reviews that will shape the future of optical sensing technologies and their applications across various industries.

Dr. Yufei Chu
Dr. Hanqing Guo
Prof. Dr. Yuanxian Zhang
Dr. Abu Farzan Mitul
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

  • sensing
  • lidar
  • atmospheric and environmental detection
  • optical sensors
  • AI in optical sensing
  • optofluidics
  • fluorescence
  • non-destructive detection
  • ultrasonic detection
  • optical sensing data and their applications

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Published Papers (3 papers)

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Research

11 pages, 1995 KiB  
Article
Angle-Tunable Method for Optimizing Rear Reflectance in Fabry–Perot Interferometers and Its Application in Fiber-Optic Ultrasound Sensing
by Yufei Chu, Mohammed Alshammari, Xiaoli Wang and Ming Han
Photonics 2024, 11(12), 1100; https://doi.org/10.3390/photonics11121100 - 21 Nov 2024
Viewed by 218
Abstract
With the introduction of advanced Fiber Bragg Grating (FBG) technology, Fabry–Pérot (FP) interferometers have become widely used in fiber-optic ultrasound detection. In these applications, the slope of the reflectance is a critical factor influencing detection results. Due to the intensity limitations of the [...] Read more.
With the introduction of advanced Fiber Bragg Grating (FBG) technology, Fabry–Pérot (FP) interferometers have become widely used in fiber-optic ultrasound detection. In these applications, the slope of the reflectance is a critical factor influencing detection results. Due to the intensity limitations of the laser source in fiber-optic ultrasound detection, the reflectance of the FBG is generally increased to enhance the signal-to-noise ratio (SNR). However, increasing reflectance can cause the reflectance curve to deviate from a sinusoidal shape, which in turn affects the slope of the reflectance and introduces greater errors. This paper first investigates the relationship between the transmission curve of the FP interferometer and reflectance, with a focus on the errors introduced by simplified assumptions. Further research shows that in sensors with asymmetric reflectance slopes, their transmittance curves deviate significantly from sinusoidal signals. This discrepancy highlights the importance of achieving symmetrical slopes to ensure consistent and accurate detection. To address this issue, this paper proposes an innovative method to adjust the rear-end reflectance of the FP interferometer by combining stress modulation, UV adhesive, and a high-reflectivity metal disk. Additionally, by adjusting the rear-end reflectance to ensure that the transmittance curve approximates a sinusoidal signal, the symmetry of the slope is maintained. Finally, through practical ultrasound testing, by adjusting the incident wavelength to the positions of slope extrema (or zero) at equal intervals, the expected ultrasound signals at extrema (or zero) can be detected. This method converts the problem of approximating a sinusoidal signal into a problem of the slope adjustment of the transmittance curve, making it easier and more direct to determine its impact on detection results. The proposed method not only improves the performance of fiber-optic ultrasound sensors but also reduces costs, paving the way for broader applications in medical diagnostics and structural health monitoring. Full article
(This article belongs to the Special Issue Optical Sensing Technologies, Devices and Their Data Applications)
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5 pages, 1128 KiB  
Communication
Modeling a Fully Polarized Optical Fiber Suitable for Photonic Integrated Circuits or Sensors
by Wenbo Sun
Photonics 2024, 11(10), 961; https://doi.org/10.3390/photonics11100961 - 14 Oct 2024
Viewed by 546
Abstract
A method is developed to make an optical fiber that only transmits fully linearly polarized light and maintains the polarization state. The method for efficient ingesting laser into this fiber is also reported. Using an optical fiber with a prism head, we can [...] Read more.
A method is developed to make an optical fiber that only transmits fully linearly polarized light and maintains the polarization state. The method for efficient ingesting laser into this fiber is also reported. Using an optical fiber with a prism head, we can compress a plane wave into the thin rectangular cross-section fiber, and the light intensity within the fiber is much larger than that of the incidence wave. Our finite-difference time-domain (FDTD) simulation results show that the compressed light in the fiber becomes fully polarized and maintains the polarization state, and can be well coupled out by the resonance rings. This method is suitable for developing highly efficient polarization-maintaining optical fibers in a much simpler way, for applications in photonic integrated circuits or optical sensors. Full article
(This article belongs to the Special Issue Optical Sensing Technologies, Devices and Their Data Applications)
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16 pages, 2550 KiB  
Article
A Study on the Improvement of YOLOv5 and the Quality Detection Method for Cork Discs
by Liguo Qu, Guohao Chen, Ke Liu and Xin Zhang
Photonics 2024, 11(9), 825; https://doi.org/10.3390/photonics11090825 - 1 Sep 2024
Viewed by 643
Abstract
Combining machine vision and deep learning, optical detection technology can achieve intelligent inspection. To address the issues of low efficiency and poor consistency in the quality classification of cork discs used for making badminton heads, research on optimizing the YOLOv5 image-processing algorithm was [...] Read more.
Combining machine vision and deep learning, optical detection technology can achieve intelligent inspection. To address the issues of low efficiency and poor consistency in the quality classification of cork discs used for making badminton heads, research on optimizing the YOLOv5 image-processing algorithm was conducted and applied to cork disc quality detection. Real-time images of cork discs were captured using industrial cameras, and a dataset was independently constructed. A GAN-based defect synthesis algorithm was employed to resolve the lack of defect samples. An attention mechanism was embedded in the YOLOv5 backbone network to enhance feature representation. The number of anchors in the YOLOv5 detection layer was reduced to address similar sample sizes, a center-matching strategy was designed to balance positive samples, and a shortest-distance label assignment algorithm was developed to eliminate ambiguities, improving accuracy and reducing postprocessing complexity. Detection results were integrated into quality classification. Experiments on the NVIDIA RTX3080 GPU demonstrated that the optimized algorithm improved the original YOLOv5 F1 score by 2.4% and mF1 score by 9.0%, achieving a quality classification F1 score of 95.1%, a processing speed of 178.5 FPS, and an mAP of 81.5%. Comparative experiments showed that the improved algorithm achieved the best detection accuracy on the cork disc dataset while maintaining high processing speed. Full article
(This article belongs to the Special Issue Optical Sensing Technologies, Devices and Their Data Applications)
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