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Photonics
Special Issues
Optical Fiber Sensors: Recent Progress and Future Prospects
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Optical Fiber Sensors: Recent Progress and Future Prospects

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 998

Special Issue Editors

Dr. Vikas

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy
Interests: optical fiber sensors; plasmonics; nanophotonics; long period gratings
Dr. Paola Saccomandi

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy
Interests: optical fiber sensors; thermal and mechanical measurements; fiber Bragg gratings
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue dealing with the latest developments in optical fiber-based sensing technology. Optical fiber sensors have been rapidly developed due to their small size, excellent sensing performance, large bandwidth, free from electromagnetic interference, environmental ruggedness, and ease of manufacturing multiplexed or distributed sensors. Recent advances in optics and photonics, biochemistry, and biology have increased the utility and demand of optical fiber sensing devices in various fields including security and defense, transportation, point-of-care diagnostics, oil and gas industries, environmental monitoring, and food production. The aim of this Special Issue is to collect scientific contributions on optical fiber-based sensing devices for a wide range of applications, and to make significant progress in the design and fabrication of novel optical fiber sensors.

We invite researchers and scientists from academia and industry to contribute original research articles, letters, and reviews. The research areas for this special issue may include, but are not limited to, the following:

  • optical fiber sensors
  • surface plasmon resonance
  • photonic sensors
  • fiber Bragg gratings (FBGs)
  • long period gratings (LPGs)
  • interferometric optical fiber devices
  • fluorescent sensors
  • light diffuser-integrated sensors
  • evanescent wave sensors
  • fiber laser sensors

We look forward to receiving your contributions.

Dr. Vikas
Dr. Paola Saccomandi
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

  • optical fiber sensors
  • surface plasmon resonance 
  • photonic sensors
  • fiber Bragg gratings (FBGs)
  • long period gratings (LPGs)
  • interferometric optical fiber devices
  • fluorescent sensors
  • light diffuser-integrated sensors
  • evanescent wave sensors
  • fiber laser sensors

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Published Papers (1 paper)

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Research

18 pages, 3382 KiB  
Article
Deep Learning-Enabled De-Noising of Fiber Bragg Grating-Based Glucose Sensor: Improving Sensing Accuracy of Experimental Data
by Harshit Tiwari, Yogendra S. Dwivedi, Rishav Singh, Anuj K. Sharma, Ajay Kumar Sharma, Richa Krishna, Nitin Singh Singha, Yogendra Kumar Prajapati and Carlos Marques
Photonics 2024, 11(11), 1058; https://doi.org/10.3390/photonics11111058 - 12 Nov 2024
Viewed by 679
Abstract
This paper outlines the successful utilization of deep learning (DL) techniques to elevate data quality for assessing Au-TFBG (tilted fiber Bragg grating) sensor performance. Our approach involves a well-structured DL-assisted framework integrating a hierarchical composite attention mechanism. In order to mitigate high variability [...] Read more.
This paper outlines the successful utilization of deep learning (DL) techniques to elevate data quality for assessing Au-TFBG (tilted fiber Bragg grating) sensor performance. Our approach involves a well-structured DL-assisted framework integrating a hierarchical composite attention mechanism. In order to mitigate high variability in experimental data, we initially employ seasonal decomposition using moving averages (SDMA) statistical models to filter out redundant data points. Subsequently, sequential DL models extrapolate the normalized transmittance (Tn) vs. wavelength spectra, which showcases promising results through our SpecExLSTM model. Furthermore, we introduce the AttentiveSpecExLSTM model, integrating a composite attention mechanism to improve Tn sequence prediction accuracy. Evaluation metrics demonstrate its superior performance, including a root mean square error of 1.73 ± 0.05, a mean absolute error of 1.20 ± 0.04, and a symmetric mean absolute percentage error of 2.22 ± 0.05, among others. Additionally, our novel minima difference (Min. Dif.) metric achieves a value of 1.08 ± 0.46, quantifying wavelength for the global minima within the Tn sequence. The composite attention mechanism in the AttentiveSpecExLSTM adeptly captures both high-level and low-level dependencies, refining the model’s comprehension and guiding informed decisions. Hierarchical dot and additive attention within this model enable nuanced attention refinement across model layers; dot attention focuses on high-level dependencies, while additive attention fine-tunes its focus on low-level dependencies within the sequence. This innovative strategy enables accurate estimation of the spectral width (full-width half maxima) of the Tn curve, surpassing raw data’s capabilities. These findings significantly contribute to data quality enhancement and sensor performance analysis. Insights from this study hold promise for future sensor applications, enhancing sensitivity and accuracy by improving experimental data quality and sensor performance assessment. Full article
(This article belongs to the Special Issue Optical Fiber Sensors: Recent Progress and Future Prospects)
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