Optical Sensors: Science and 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: closed (30 January 2024) | Viewed by 2927

Special Issue Editor


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Guest Editor
Center of Material Science, National University of Defense Technology, Changsha 410073, China
Interests: biomimetic fiber optic sensor; new fiber optic functional devices; extreme environmental monitoring; engine monitoring; marine environmental monitoring

Special Issue Information

Dear Colleagues,

Optical sensors have attracted worldwide attention because of their high sensitivity, good electrical insulation, anti-electromagnetic interference, small size, light weight, low transmission loss, large transmission capacity, remote control monitoring and multi-point distributed measurement. In many situations, the optical sensor can effectively perceive the environment due to its superior performance, measuring various parameters of the marine environment in the sea water, or sensing temperature, strain and pressure in the extremely high-temperature environment, etc. The optical fiber sensor can even create bionic skin to sense the biological environment to mimic the biological skin’s perception of the environment.

To effectively utilize the advantages of optical sensors over traditional sensors, it is vital to study and explore the sensing ability of optical sensors in a range of situations.

This Special Issue aims to showcase the latest research on the perception function of optical sensors in different scenarios, with a particular emphasis on the perception of the external environment (such as the marine environment, high-temperature environment, geomagnetic field environment, and biomimetic perception of the environment by optical sensors). We seek papers on the use of optical sensors in different scenarios, such as extreme environmental monitoring, marine environmental monitoring, biological environmental awareness, etc. Potential topics include (but are not limited to):

New optical fiber functional devices;

Extreme environmental monitoring;

Marine environmental monitoring;

Earth's magnetic field monitoring;

Biomimetic environmental monitoring;

Multi parameter cooperative sensing.

Dr. Yang Yu
Guest Editor

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Keywords

  • new optical fiber functional devices
  • extreme environmental monitoring
  • marine environmental monitoring
  • earth's magnetic field monitoring
  • biomimetic environmental monitoring
  • multi-parameter cooperative sensing

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Published Papers (1 paper)

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Research

16 pages, 6463 KiB  
Article
A Machine Learning Approach for Automated Detection of Critical PCB Flaws in Optical Sensing Systems
by Pinliang Chen and Feng Xie
Photonics 2023, 10(9), 984; https://doi.org/10.3390/photonics10090984 - 29 Aug 2023
Cited by 10 | Viewed by 2510
Abstract
The circuit boards in the fields of optical sensors and optical devices require extremely high levels of precision and performance. For instance, applications such as fiber optic communication, optical computing, biomedical devices, and high-performance computing devices all necessitate meticulous optical components. Any minute [...] Read more.
The circuit boards in the fields of optical sensors and optical devices require extremely high levels of precision and performance. For instance, applications such as fiber optic communication, optical computing, biomedical devices, and high-performance computing devices all necessitate meticulous optical components. Any minute defect on the circuit boards of these components has the potential to adversely impact the performance of the entire device. Traditional circuit defect detection methods require manual inspection, which is very labor-intensive and time-consuming. The defect detection method based on deep learning can automatically learn features and more accurately find defects in printed circuit boards, improve detection efficiency, and reduce the workload, bringing better economic and social benefits. Based on the popular YOLOv8 model, this paper uses the open-source circuit defect dataset, introduces Wise IoU, proposes the W–YOLOv8 model, and uses the gradient gain allocation strategy of a dynamic non-monotonic focusing mechanism to make the model focus on ordinary-quality anchor boxes, which improves the performance of the original model. Experimental data show that the mAP50 of W–YOLOv8 is 97.3%, which is 1.35% higher than that of YOLOv8, and the mAP50-95 is 55.4%, which is 3.94% higher than that of YOLOv8. Full article
(This article belongs to the Special Issue Optical Sensors: Science and Applications)
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