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Advances in Fiber Optic Sensors: Innovations, Challenges and Applications
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Advances in Fiber Optic Sensors: Innovations, Challenges and Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".

Deadline for manuscript submissions: 28 August 2026 | Viewed by 7839

Special Issue Editor

Dr. Jianzhong Hao

E-Mail Website
Guest Editor
Institute for Infocomm Research, A*STAR, Singapore 138632, Singapore
Interests: fiber optic sensors and applications; sensor packaging; structural health monitoring; predictive maintenance; system integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fiber optic sensors (FOSs) have emerged as a critical technology for real-time, high-precision sensing across diverse fields, including structural health monitoring, biomedical diagnostics, environmental surveillance, and industrial automation. Their inherent advantages—such as high sensitivity, resistance to electromagnetic interference, and adaptability to extreme environments—have driven significant advancements in recent years.

This Special Issue aims to showcase state-of-the-art developments in fiber optic sensing, covering novel sensor designs, advanced interrogation techniques, and new applications in emerging industries. Topics of interest include distributed and point-based sensing, smart fiber sensors for IoT and AI-driven applications, multiplexing strategies for large-scale deployments, and the integration of fiber optics with photonic and nanomaterial-based technologies. Additionally, we encourage the submission of contributions addressing current challenges, such as cost reduction, miniaturization, and improved durability in harsh environments.

We welcome original research articles, reviews, and case studies from both academia and industry to provide a comprehensive outlook on the future of fiber optic sensing. Through this Special Issue, we aim to foster interdisciplinary collaboration and accelerate the translation of cutting-edge research into real-world applications.

Dr. Jianzhong Hao
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 250 words) can be sent to the Editorial Office for assessment.

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. Sensors 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 2600 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

  • fiber optic sensing technologies
  • distributed and multiplexed sensing
  • smart sensors and iot integration
  • optical signal processing and ai-enhanced analytics
  • structural health monitoring and industrial applications
  • biomedical and environmental sensing
  • advanced materials for fiber sensors
  • harsh environment sensing and durability

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Published Papers (4 papers)

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Research

16 pages, 3775 KB  
Article
Adaptive Layer-Dependent Threshold Function for Wavelet Denoising of ECG and Multimode Fiber Cardiorespiratory Signals
by Yuanfang Zhang, Kaimin Yu, Chufeng Huang, Ruiting Qu, Zhichun Fan, Peibin Zhu, Wen Chen and Jianzhong Hao
Sensors 2025, 25(24), 7644; https://doi.org/10.3390/s25247644 - 17 Dec 2025
Cited by 3 | Viewed by 739
Abstract
This paper proposes an adaptive layer-dependent threshold function (ALDTF) for denoising electrocardiogram (ECG) and multimode optical fiber-based cardiopulmonary signals. Based on wavelet transform, the method employs a layer-dependent threshold function strategy that utilizes the non-zero periodic peak (NZOPP) of the signal’s normalized autocorrelation [...] Read more.
This paper proposes an adaptive layer-dependent threshold function (ALDTF) for denoising electrocardiogram (ECG) and multimode optical fiber-based cardiopulmonary signals. Based on wavelet transform, the method employs a layer-dependent threshold function strategy that utilizes the non-zero periodic peak (NZOPP) of the signal’s normalized autocorrelation function to adaptively determine the optimal threshold for each decomposition layer. The core idea applies soft thresholding at lower layers (high-frequency noise) to suppress pseudo-Gibbs oscillations, and hard thresholding at higher layers (low-frequency noise) to preserve signal amplitude and morphology. The experimental results show that for ECG signals contaminated with baseline wander (BW), electrode motion (EM) artifacts, muscle artifacts (MA), and mixed (MIX) noise, ALDTF outperforms existing methods—including SWT, DTCWT, and hybrid approaches—across multiple metrics. It achieves a ΔSNR improvement of 1.68–10.00 dB, ΔSINAD improvement of 1.68–9.98 dB, RMSE reduction of 0.02–0.56, and PRD reduction of 2.88–183.29%. The method also demonstrates excellent performance on real ECG and optical fiber cardiopulmonary signals, preserving key diagnostic features like QRS complexes and ST segments while effectively suppressing artifacts. ALDTF provides an efficient, versatile solution for physiological signal denoising with strong potential in wearable real-time monitoring systems. Full article
(This article belongs to the Special Issue Advances in Fiber Optic Sensors: Innovations, Challenges and Applications)
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18 pages, 1570 KB  
Article
Moisture Content Detection in Mango (Mangifera indica L., cv. Ataulfo) and Papaya (Carica papaya) Slices During Drying Using an MMI-Based Sensor
by Guadalupe López-Morales, Yuliana M. Espinosa-Sánchez, Ariel Flores-Rosas and Héber Vilchis
Sensors 2025, 25(22), 6902; https://doi.org/10.3390/s25226902 - 12 Nov 2025
Viewed by 1058
Abstract
Monitoring moisture content in agricultural products during the drying process is critical for ensuring quality, preserving nutritional value, and optimizing energy consumption. This study presents the design and implementation of an optical fiber sensor based on multimode interference (MMI) for non-destructive detection of [...] Read more.
Monitoring moisture content in agricultural products during the drying process is critical for ensuring quality, preserving nutritional value, and optimizing energy consumption. This study presents the design and implementation of an optical fiber sensor based on multimode interference (MMI) for non-destructive detection of moisture content in mango (Mangifera indica L., cv. Ataulfo) and papaya (Carica papaya) slices during convective drying at 57 °C. Two sensors were designed and fabricated: one operates in the 975 nm range and the other in the 1414.25 nm range. These sensors detect variations in the refractive index caused by moisture loss, which directly affects the MMI spectral response. The sensor output was correlated with reference gravimetric measurements, demonstrating a dependence in tracking the output power as a function of the reduction in humidity over time. The results confirm the feasibility of the MMI-based optical fiber sensor as a reliable tool for in situ monitoring of drying dynamics in tropical fruits, offering potential applications in agri-food processing and quality control. Full article
(This article belongs to the Special Issue Advances in Fiber Optic Sensors: Innovations, Challenges and Applications)
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18 pages, 6388 KB  
Article
Spatial–Temporal Hotspot Management of Photovoltaic Modules Based on Fiber Bragg Grating Sensor Arrays
by Haotian Ding, Rui Guo, Huan Xing, Yu Chen, Jiajun He, Junxian Luo, Maojie Chen, Ye Chen, Shaochun Tang and Fei Xu
Sensors 2025, 25(15), 4879; https://doi.org/10.3390/s25154879 - 7 Aug 2025
Cited by 1 | Viewed by 1741
Abstract
Against the backdrop of an urgent energy crisis, solar energy has attracted sufficient attention as one of the most inexhaustible and friendly types of environmental energy. Faced with long service and harsh environment, the poor performance ratios of photovoltaic arrays and safety hazards [...] Read more.
Against the backdrop of an urgent energy crisis, solar energy has attracted sufficient attention as one of the most inexhaustible and friendly types of environmental energy. Faced with long service and harsh environment, the poor performance ratios of photovoltaic arrays and safety hazards are frequently boosted worldwide. In particular, the hot spot effect plays a vital role in weakening the power generation performance and reduces the lifetime of photovoltaic (PV) modules. Here, our research reports a spatial–temporal hot spot management system integrated with fiber Bragg grating (FBG) temperature sensor arrays and cooling hydrogels. Through finite element simulations and indoor experiments in laboratory conditions, a superior cooling effect of hydrogels and photoelectric conversion efficiency improvement have been demonstrated. On this basis, field tests were carried out in which the FBG arrays detected the surface temperature of the PV module first, and then a classifier based on an optimized artificial neural network (ANN) recognized hot spots with an accuracy of 99.1%. The implementation of cooling hydrogels as a feedback mechanism achieved a 7.7 °C reduction in temperature, resulting in a 5.6% enhancement in power generation efficiency. The proposed strategy offers valuable insights for conducting predictive maintenance of PV power plants in the case of hot spots. Full article
(This article belongs to the Special Issue Advances in Fiber Optic Sensors: Innovations, Challenges and Applications)
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24 pages, 4120 KB  
Article
Real-Time Railway Hazard Detection Using Distributed Acoustic Sensing and Hybrid Ensemble Learning
by Yusuf Yürekli, Cevat Özarpa and İsa Avcı
Sensors 2025, 25(13), 3992; https://doi.org/10.3390/s25133992 - 26 Jun 2025
Cited by 11 | Viewed by 3464
Abstract
Rockfalls on railways are considered a natural disaster under the topic of landslides. It is an event that varies regionally due to landforms and climate. In addition to traffic density, the Karabük–Yenice railway line also passes through mountainous areas, river crossings, and experiences [...] Read more.
Rockfalls on railways are considered a natural disaster under the topic of landslides. It is an event that varies regionally due to landforms and climate. In addition to traffic density, the Karabük–Yenice railway line also passes through mountainous areas, river crossings, and experiences heavy seasonal rainfall. These conditions necessitate the implementation of proactive measures to mitigate risks such as rockfalls, tree collapses, landslides, and other geohazards that threaten the railway line. Undetected environmental events pose a significant threat to railway operational safety. The study aims to provide early detection of environmental phenomena using vibrations emitted through fiber optic cables. This study presents a real-time hazard detection system that integrates Distributed Acoustic Sensing (DAS) with a hybrid ensemble learning model. Using fiber optic cables and the Luna OBR-4600 interrogator, the system captures environmental vibrations along a 6 km railway corridor in Karabük, Türkiye. CatBoosting, Support Vector Machine (SVM), LightGBM, Decision Tree, XGBoost, Random Forest (RF), and Gradient Boosting Classifier (GBC) algorithms were used to detect the incoming signals. However, the Voting Classifier hybrid model was developed using SVM, RF, XGBoost, and GBC algorithms. The signaling system on the railway line provides critical information for safety by detecting environmental factors. Major natural disasters such as rockfalls, tree falls, and landslides cause high-intensity vibrations due to environmental factors, and these vibrations can be detected through fiber cables. In this study, a hybrid model was developed with the Voting Classifier method to accurately detect and classify vibrations. The model leverages an ensemble of classification algorithms to accurately categorize various environmental disturbances. The system has proven its effectiveness under real-world conditions by successfully detecting environmental events such as rockfalls, landslides, and falling trees with 98% success for Precision, Recall, F1 score, and accuracy. Full article
(This article belongs to the Special Issue Advances in Fiber Optic Sensors: Innovations, Challenges and Applications)
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