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Optical sensors using fiber Bragg gratings (FBGs) have become an alternative to traditional electronic sensors thanks to their immunity against electromagnetic interference, their applicability in harsh environments, and other advantages. However, the complexity and high cost of the FBG interrogation systems pose a challenge for the wide deployment of such sensors. Herein, we present a clean and cost-effective method for interrogating an FBG temperature sensor using a micro-chip called the waveguide spectral lens (WSL) and a standard CMOS camera. This interrogation system can project the FBG transmission spectrum onto the camera without any free-space optical components. Based on this system, an FBG temperature sensor is developed, and the results show good agreement with a commercial optical spectrum analyzer (OSA), with the respective wavelength-temperature sensitivity measured as 6.33 pm/°C for the WSL camera system and 6.32 pm/°C for the commercial OSA. Direct data processing on the WSL camera system translates this sensitivity to 0.44 μm/°C in relation to the absolute spatial shift of the FBG spectra on the camera. Furthermore, a deep neural network is developed to train the spectral dataset, achieving a temperature resolution of 0.1 °C from 60 °C to 120 °C, while direct processing on the valley/dark line detection yields a resolution of 7.84 °C. The proposed hardware and the data processing method may lead to the development of a compact, practical, and low-cost FBG interrogator.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11509316 | PMC |
http://dx.doi.org/10.3390/mi15101206 | DOI Listing |
Opt Express
March 2025
A compact fiber Bragg grating (FBG) interrogator, integrating an arrayed waveguide grating (AWG) chip, photodiode (PD) array, and demodulation circuits within an 85 × 60 × 40 mm volume, has been designed and fabricated. Utilizing what we believe to be a novel three-point demodulation technique, precise center wavelength demodulation is achieved. The conventional thermoelectric cooler (TEC) is supplanted by a look-up table (LUT) in conjunction with interpolation for temperature compensation.
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July 2025
Perm Federal Research Center of the Ural Branch of the Russian Academy of Sciences (PFRC UB RAS), 13a Lenin St., Perm 614000, Russia.
Measuring pH is a critical parameter in environmental monitoring, biomedical diagnostics, food safety, and industrial processes. Optical fiber sensors have proven highly effective for pH detection due to their exceptional sensitivity, rapid response, and resistance to electromagnetic interference, making them well suited for real-time monitoring. This review offers a comprehensive analysis of recent advances in optical fiber-based pH sensors, covering key techniques such as fluorescence-based, absorbance-based, evanescent wave, and interferometric methods.
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July 2025
Department of Physics, School of Science, Kitasato University, Sagamihara 252-0373, Kanagawa, Japan.
This article reports on the interrogation of fiber Bragg grating (FBG)-based sensors that have been multiplexed in a ladder topology. In each line of this topology, FBGs with different wavelengths are connected. In addition, delay fibers have been inserted between each line to enable reflections from different lines to be distinguished.
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July 2025
Department of Physics, School of Science, Kitasato University, Sagamihara 252-0373, Kanagawa, Japan.
This article reports on the interrogation of fiber Bragg grating (FBG)-based sensors that are multiplexed in an asymmetric tree topology. At each stage in the topology, FBGs are connected at one output port of a 50:50 coupler with fibers of different lengths. This asymmetric structure allows the simultaneous interrogation of long-distance and parallel sensor networks to be realized.
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June 2025
Department of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
This study introduces a novel deep neural network (DNN) framework tailored to breaking the sampling limit for high-frequency vibration recognition using fiber Bragg grating (FBG) sensors in conjunction with low-power, low-sampling-rate FBG interrogators. These interrogators, while energy-efficient, are inherently limited by constrained acquisition rates, leading to severe undersampling and the obfuscation of fine spectral details essential for accurate vibration analysis. The proposed method circumvents this limitation by operating solely on raw time-domain signals, learning to recognize high-frequency and extremely close vibrational components accurately.
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