Search Results (1,224)

A respiration rate (RR) monitoring system was created by integrating a Fibre Bragg Grating (FBG) optical fibre sensor into a respirator mask. The system exploits the sensitivity of an FBG to temperature to identify an individual’s RR by measuring airflow temperature variation near the nostrils and mouth. To monitor the FBG response, a portable, battery-powered, wireless miniature interrogator system was developed to replace a relatively bulky benchtop interrogator used in previous studies. A healthy volunteer study was conducted to evaluate the performance of the developed system (10 healthy volunteers). Volunteers were asked to perform normal breathing whilst simultaneously wearing the system and a reference spirometer for 120 s. Individual breaths are then identified using a peak detection algorithm. The result showed that the number of breaths detected by both devices matched exactly (100%) across all volunteer trials. Full article

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

We investigate the dynamic excitation of surface plasmon polaritons (SPPs) using vector Laguerre–Gauss (LG) beams, which offer unique properties for manipulating the polarization and spatial distribution of light. Our study demonstrates the efficient coupling of SPPs with LG beams, characterized by their azimuthal and radial indices $(m,p)$, as well as polarization distribution type. Numerical simulations reveal that the vector nature of LG beams enables selective excitation of SPPs, depending on the polarization type of the beam. Experimental verification of our simulations is achieved using a gold circular Bragg grating and a spatial light modulator that generates vector LG beams. Leakage radiation imaging demonstrates the potential of vector LG beams for dynamic SPP excitation and manipulation. This study opens novel ways for the control of SPPs in plasmonic devices, such as modulators, and nanophotonic circuits. Full article

Fiber Bragg gratings (FBGs) have gained substantial research interest due to their exceptional sensing capabilities. Traditionally, FBG fabrication has required the use of pre-hydrogenated fibers and high-cost laser systems such as excimer lasers at 193 nm or femtosecond lasers. In this study, we present the first instance of FBG inscription in hydrogen-loaded, standard single-mode silica optical fibers using a more affordable 266 nm solid-state pulsed laser combined with a scanning phase mask lithography technique. We systematically explored the effects of pulse energy and scanning speed on the quality and spectral characteristics of the gratings, achieving reflectivities as high as 99.81%. Additionally, we tracked the spectral evolution during the FBG inscription process, demonstrating uniform growth of the core mode. We also investigated the stability of the core mode during a 24-h thermal annealing process up to 150 °C. The sensitivity was 10.7 pm/°C in the range of 0 to 130 °C. Furthermore, strain measurement was conducted based on the FBG annealed at 100 °C, showing a sensitivity of 0.943 pm/µε in the range of 0 to 1667 µε. Full article

Implementation of edge-filter detection for interrogating optical interferometric ultrasonic sensors is often hindered by the lack of cost-effective laser sources with agile wavelength tunability and good noise performance. The detected signal can also be affected by optical power variations and locking-point drift, negatively affecting the sensor accuracy. Here, we report the use of laser single-sideband generation with a dual-parallel Mach–Zehnder interferometer (DP-MZI) for laser wavelength tuning and locking in edge-filter detection of fiber-optic ultrasonic sensors. We also demonstrate real-time in situ calibration of the sensor response to ultrasound-induced wavelength shift tuning. The DP-MZI is employed to generate a known wavelength modulation of the laser, whose response is used to gauge the sensor response to the ultrasound-induced wavelength shifts in real time and in situ. Experiments were performed on a fiber-optic ultrasonic sensor based on a high-finesse Fabry–Perot interferometer formed by two fiber Bragg gratings. The results demonstrated the effectiveness of the laser locking against laser wavelength drift and temperature variations and the effectiveness of the calibration method against optical power variations and locking-point drift. These techniques can enhance the operational robustness and increase the measurement accuracy of optical ultrasonic sensors. Full article

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 (T_n) vs. wavelength spectra, which showcases promising results through our SpecExLSTM model. Furthermore, we introduce the AttentiveSpecExLSTM model, integrating a composite attention mechanism to improve T_n 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 T_n 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 T_n 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

In the field of fiber-optic sensing, effectively reducing the noise of sensing spectra and achieving a high signal-to-noise ratio (SNR) has consistently been a focal point of research. This study proposes a deep-learning-based denoising method for fiber-optic sensors, which involves pre-processing the sensor spectrum into a 2D image and training with a cycle-consistent generative adversarial network (Cycle-GAN) model. The pre-trained algorithm demonstrates the ability to effectively denoise various spectrum types and noise profiles. This study evaluates the denoising performance of simulated spectra obtained from four different types of fiber-optic sensors: fiber Fabry–Perot interferometer (FPI), regular fiber Bragg grating (FBG), chirped FBG, and FBG pair. Compared to traditional denoising algorithms such as wavelet transform (WT) and empirical mode decomposition (EMD), the proposed method achieves an SNR improvement of up to $13.71 \mathrm{dB}$, an RMSE that is up to three times smaller, and a minimum correlation coefficient ($R^2$) of no less than 99.70% with the original high-SNR signals. Additionally, the proposed algorithm was tested for multimode noise reduction, demonstrating an excellent linearity in temperature response with a $R^2$ of 99.95% for its linear fitting and $99.74\%$ for the temperature response obtained from single-mode fiber sensors. The proposed denoising approach effectively reduces the impact of various noises from the sensing system, enhancing the practicality of fiber-optic sensing, especially for specialized fiber applications in research and industrial domains. Full article

Strong mine pressure often emerges when the working face of the lower coal seam in a closely spaced coal seam system passes through the remaining coal pillar in the overlying goaf. This study investigates the law of overburden movement and the manifestation of mine pressure during mining under the remaining coal pillar. A physical model measuring 2.5 × 0.2 × 1.503 m is constructed. Fiber Bragg grating sensing technology (FBG) and Brillouin optical time domain analysis technology (BOTDA) are employed in the physical model experiment to monitor the internal strain of the overlying rock as the working face advances. This study determines the laws of overlying rock fracture and working face pressure while mining coal seams beneath the remaining coal pillar. It analyzes the relationship between the pressure at the working face and the strain characteristics of the horizontally distributed optical fiber. A fiber grating characterization method is established for the stress evolution law of overlying rock while passing the remaining coal pillar. The experimental results indicated that the fracture angle of overlying rock gradually decreases during the mining stage through and after the coal pillar. In the mining stage through the coal pillar, the cycle pressure step distance of the working face is reduced by 33.3% compared to the stage after mining through the coal pillar. Initially, the strain pattern of the horizontal optical fiber is unimodal when pressure is first applied to the working face, and it transitions from unimodal to bimodal during periodic pressure. The peak value of fiber Bragg grating compressive strain and the range of influence of advanced support pressure are 3.6 times and 4.8 times, respectively, before passing through the remaining coal pillar. Finally, the accuracy of the FBG characterization method is verified by comparing it to the monitoring curve of the coal seam floor pressure sensor. The research results contribute to applying fiber optic sensing technology in mining physical model experiments. Full article

Industrial microwave-heating systems are pivotal in various sectors, including food processing and materials manufacturing, where precise temperature control and safety are critical. Conventional systems often struggle with uneven heat distribution and high fire risks due to the intrinsic properties of microwave heating. In this work, a fiber-optic-sensor-assisted monitoring system is presented to tackle the pressing challenges associated with uneven heating and fire hazards in industrial microwave systems. The core innovation lies in the development of a sophisticated fiber-optic 2D temperature distribution sensor and a dedicated fire detector, both designed to significantly mitigate risks and optimize the heating process. Experimental results set the stage for future innovations that could transform the landscape of industrial heating technologies toward better process quality. Full article

A new-type vibration sensor based on a fiber Bragg grating combined with a special structure-packaged design is proposed for monitoring the mechanical vibration signals. Three different sensing structures, including the film squeeze type, new film squeeze type, and elastic tape squeeze type are proposed for measuring the vibration signals with the frequency range from tens to thousands of Hz. In the comparison to experimental results, the new film squeeze structure has a nice sensing performance in the range from 100 to 1000 Hz with a sensitivity of 0.302 mV/g. For the elastic tape squeeze structure, the elastic tape is designed to encapsulate the optical fiber with a good frequency response from 1100 to 3100 Hz. In addition, by using the new film squeeze structure to measure the steady-state and non-steady-state vibration signals, the spectral components of sensing signals are analyzed by using the wavelet transformation for confirming the testing signals. These vibration fiber sensors can be applied in the measurement of high-end manufacture-facility vibration or earthquake vibrations etc. Full article

In this work, we propose a fiber Bragg grating (FBG)-based sensor for curvature measurements. Two gratings are inscribed through the protective coating in a specialty optical fiber using focused femtosecond laser pulses and point-by-point direct writing technology. One grating is inscribed on the central core adjacent to an air channel, while the other is inscribed on the eccentric core. The bending characteristics of the two-core fiber strongly depend on the bending direction due to the asymmetry of the fiber cores. A bending sensitivity of 58 $\mathrm{p}\mathrm{m}/{\mathrm{m}}^{-1}$ is achieved by the FBG in the eccentric fiber core over the curvature range of 0–50 ${\mathrm{m}}^{-1}$. Temperature and humidity cross-sensitivity could be significantly reduced by analyzing the differences in peak shifts between the two gratings. The sensor features a large sensing range and good robustness due to the presence of its protective buffer coating, which makes it a good candidate for curvature sensing in engineering fields. Full article

This paper presents a new type of phase-shifted Fiber Bragg Grating (FBG): the sliced-FBG (SFBG). The fabrication process involves cutting a standard FBG inside its grating region. As a result, the last grating pitch is shorter than the others. The optical output signal consists of the overlap between the FBG reflection and the reflection at the fiber-cleaved tip. This new fiber optic device has been studied as a vibration sensor, allowing for the characterization of this sensor in the frequency range of 150 Hz to 70 kHz. How the phase shift in the FBG can be controlled by changing the length of the last pitch is also shown. This device can be used as a filter and a sensing element. As a sensing element, we will demonstrate its application as a vibration sensor that can be utilized in various applications, particularly in monitoring mechanical structures. Full article

This study introduces a Sagnac Interferometer (SI) fiber sensor that integrates Polarization-Maintaining Fibers (PMFs) with a Tilted Fiber Bragg Grating (TFBG) for the dual-parameter measurement of strain and lateral pressure. By incorporating a 6° TFBG with PMFs into the SI sensor, its sensitivity is significantly enhanced, enabling advanced multi-parameter sensing capabilities. The sensor demonstrates a temperature sensitivity of −1.413 nm/°C and a lateral pressure sensitivity of −4.264 dB/kPa, as validated by repeated experiments. The results exhibit excellent repeatability and high precision, underscoring the sensor’s potential for robust and accurate multi-parameter sensing applications. Full article

By employing the whale optimization algorithm’s (WOA) capability to reduce the probability of being stuck in a locally optimal solution, this study proposed an improved WOA-DQN algorithm based on the Deep Q-Network algorithm (DQN). Firstly, the mathematical model of Fiber Bragg Grating (FBG) sensor placement was established to calculate the reward of DQN. Secondly, the effectiveness and applicability of WOA-DQN were validated through experiments in nine cases. It indicated that the algorithm is far superior to other methods (Noisy DQN, Prioritized DQN, DQN, WOA), especially with the learning rate of 0.001, the initial noise 0.4, the hidden layer 3–512, and the updated frequency of 20. Finally, the FBG sensors were placed at [0°, 27°, 30°, 47°, 51°, 111°, 126°, 219°, 221°, 289°] to detect the accurate deformation of the tunnel with the maximum error 8.66 mm, which is better than the traditional placement. In conclusion, the algorithm provides a theoretical foundation for sensor placement and improves monitoring accuracy. It further shows great promise for deformation monitoring in tunnels. Full article

Free-space optics communication (FSO) can be used as a transmission medium for fiber optic sensing signals to make fiber optic sensing easier to implement; however, interference with the sensing signals caused by the optical turbulence and scattering of airborne particles in the FSO path is a potential problem. This work aims to deep denoise sensed signals from fiber Bragg grating (FBG) sensors based on FSO link transmission using advanced denoising deep learning techniques, such as stacked denoising autoencoders (SDAE). Furthermore, it will demodulate the sensed wavelength of FBGs by applying the deep belief network (DBN) technique. This is the first time the real FBG sensing experiment has utilized the actual noise interference caused by the environmental turbulence from an FSO link rather than adding noise through numerical processing. Consequently, the spectrum of the FBG sensors is clearly modulated by the noise and the issue with peak power variation. This complicates the determination of the center wavelengths of multiple stacked FBG spectra, requiring the use of machine learning techniques to predict these wavelengths. The results indicate that SDAE is efficient in denoising from the FBG spectrum, and DBN is effective in demodulating the central wavelength of the overlapped FBG spectrum. Thus, it is beneficial to implement an FSO link-based FBG sensing system in adverse weather conditions or atmospheric turbulence. Full article