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The temperature response of pavement is not only crucial for assessing the internal stresses within pavement structures but is also an essential parameter in pavement design. Investigating the temperature response of rubberized concrete pavements (RCP) can support the construction of large-scale rubber concrete pavements. This study constructed a pavement monitoring system based on fiber Bragg grating technology to investigate the temperature distribution, temperature strain, temperature effects, and temperature stress of RCP. The results show that the daily temperature-time history curves of concrete pavement exhibit a significant asymmetry, with the heating phase accounting for only one-third of the curve. The temperature at the middle of RCP is 1.8 °C higher than that of ordinary concrete pavement (OCP). The temperature distribution along the thickness of the pavement follows a "spindle-shaped" pattern, with higher temperatures in the center and lower temperatures at the ends. Additionally, the addition of rubber aggregates increases the temperature strain in the pavements, makes the temperature-strain hysteresis effect more pronounced, and increases the curvature of the pavement slab. However, the daily stress range at the bottom of RCP is approximately 0.7 times that of OCP.

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To address the issue of harsh marine background noise impacting the monitoring signal of fiber-optic hydrophones, we propose a low-noise fiber Bragg grating (FBG) hydroacoustic monitoring system with a reference sensor based on genetic algorithm backpropagation (GA-BP). Through theoretical analysis, we deduce the noise suppression steps of the GA-BP algorithm based on the reference sensor and construct train and test sets based on the data from the reference sensor and monitoring sensor at different times, optimizing the GA-BP algorithm to find the best fitting results and thereby obtaining the low-noise monitoring signal. Experimental results from the anechoic tank show that the proposed method can suppress background noise interference on effective signals and that the suppression effect improves as the background noise increases. The sound pressure sensitivity ranges from -173.76 dB to -171.33 dB at frequencies of 8 kHz to 12 kHz, with a response flatness of less than 2.43 dB. The noise suppression effect is obvious under the condition of poor signal-to-noise ratio (SNR), which can reach more than 18.3 dB. The advantages of the proposed algorithm in array signal processing are further demonstrated by the directivity experiment, which proves that the algorithm has a great potential for engineering applications in harsh marine environment.

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An acousto-optic (AO)-based electric field sensor is presented for time domain measurement under magnetic resonance imaging (MRI). A fully MR-compatible sensor is designed and fabricated using a phase-shifted fiber Bragg grating mechanically coupled to a piezoelectric transducer. Mechanical resonance of the piezoelectric transducer is matched to the operating frequencies of commonly used MRI systems to increase the sensitivity of the sensor. Sensitivity of the sensor is measured as 1.27 mV/V/m, with a minimum detectable electric field of 4.4 mV/m/√/Hz. Directivity of the sensor is measured with a 18 dB orthogonal component rejection. The dynamic range of the sensor is calculated as 117 dB/Hz, which allows the measurement of electric fields up to 3.2 kV/m. In MRI studies, the AO sensor was able detect local hot spots around a reference implant accurately with high signal-to-noise ratio. AO sensor exhibited similar or better performance when compared with commercially available MRI compatible electric field sensors. Furthermore, the small size of the sensor with the flexible fiber optic link could allow in situ measurements of electric fields during critical interventional procedures such as pacemaker lead or deep brain stimulator placement as an MRI dosimeter during diagnostic scans.

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Monitoring the integrity of aeronautical structures is fundamental for safety. Structural Health Monitoring Systems (SHMSs) perform real-time monitoring functions, but their performance must be carefully assessed. This is typically done by introducing artificial damages to the components; however, such a procedure requires the production and testing of a large number of structural elements. In this work, the damage detection performance of a strain-based SHMS was evaluated on a composite helicopter rotor blade root, exploiting a Finite Element (FE) model of the component. The SHMS monitored the bonding between the central core and the surrounding antitorsional layer. A damage detection algorithm was trained through FE analyses. The effects of the load's variability and of the damage were decoupled by including a load recognition step in the algorithm, which was accomplished either with an Artificial Neural Network (ANN) or a calibration matrix. Anomaly detection, damage assessment, and localization were performed by using an ANN. The results showed a higher load identification and anomaly detection accuracy using an ANN for the load recognition, and the load set was recognized with a satisfactory accuracy, even in damaged blades. This case study was focused on a real-world subcomponent with complex geometrical features and realistic load conditions, which was not investigated in the literature and provided a promising approach to estimate the performance of a strain-based SHMS.

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A plasmonic tilted fiber Bragg grating (TFBG)-based sensor for the detection of calcium ion (Ca2+) was proposed and demonstrated experimentally. Hydrogel material was synthesized by utilizing hydrogen bond recombination between cellulose nanocrystals (CNC) and polyvinyl alcohol (PVA). Sodium alginate (SA) was incorporated into this hydrogel material, resulting in a composite membrane with specific binding properties for Ca2+. The membrane was applied as a coating on the surface of a gold-coated TFBG. The CNC/PVA-SA modified gold on the TFBG surface enhanced the localized refractive index changes caused by variations of Ca2+ concentrations. The experimental results demonstrated an impressive limit of detection (LOD) of approximately 0.025 fM, which is five orders of magnitude better than the current LODs of similar Ca2+ sensors. And the proposed Ca2+ sensor exhibited a wide dynamic range of 10-16 M to 10-6 M.

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Flexible needle insertion procedures are common in minimally-invasive surgeries for diagnosing and treating prostate cancer. Bevel-tip needles provide physicians the capability to steer the needle during long insertions to avoid vital anatomical structures in the patient and reduce post-operative patient discomfort. To provide needle placement feedback to the physician, sensors are embedded into needles for determining the real-time 3D shape of the needle during operation without needing to visualize the needle intra-operatively. Through expansive research in fiber optics, a plethora of bio-compatible, MRI-compatible, optical shape-sensors have been developed to provide real-time shape feedback, such as single-core and multicore fiber Bragg gratings. In this paper, we directly compare single-core fiber-based and multicore fiber-based needle shape-sensing through similarly constructed, four-active area sensorized bevel-tip needles inserted into phantom and ex-vivo tissue on the same experimental platform. In this work, we found that for shape-sensing in phantom tissue, the two needles performed identically with a p-value of 0.164 > 0.05, but in ex-vivo real tissue, the single-core fiber sensorized needle significantly outperformed the multicore fiber configuration with a p-value of 0.0005 < 0.05. This paper also presents the experimental platform and method for directly comparing these optical shape sensors for the needle shape-sensing task, as well as provides direction, insight and required considerations for future work in constructively optimizing sensorized needles.

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With the increase in the demand for large-capacity optical communication capacity, multi-core optical fiber (MCF) communication technology has developed, and both the types of MCFs and related devices have become increasingly mature. The application of MCFs in the field of sensing has also received more and more attention, among which MCF fiber Bragg grating (FBG) devices have received more and more attention and have been widely used in various fields. In this paper, the main writing methods of MCF FBGs and their sensing applications are reviewed. The future development of the MCF FBG is also prospected.

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To investigate the effects of long-term prestress loss on concrete box girders strengthened with external prestressing, a large-span box girder, in service for over 20 years and strengthened with external prestressing, was monitored for four months. Prestress loss in the longitudinal, vertical, and transverse directions of the box girder was calculated according to Chinese code requirements. Magnetic flux rope force transducers were used to monitor the prestress loss in the external prestressing cables. Fiber Bragg Grating (FBG) sensors were used to monitor deflection changes at the mid-span of the bridge. Finally, the effect of prestress loss in the longitudinal, vertical, and transverse tendons on mid-span deflection was investigated through simulations using ABAQUS software. The results show that instantaneous prestress loss accounts for most of the total loss compared to long-term loss, and that longitudinal prestress loss has the most significant effect on mid-span deflection. The impact of longitudinal prestress loss on deflection before and after strengthening was also compared. The downward deflection and up-ward arch caused by longitudinal tendon prestress loss were reduced after strengthening, con-firming the effectiveness of the external prestressing method.

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A flexible arc-shaped micro-Fiber Bragg Grating (mFBG) array three-dimensional tactile sensor for fingertip signal detection and human pulse monitoring is presented. It is based on a three mFBGs array which is embedded in an arc-shaped poly (dimethylsiloxane) (PDMS) elastomer, which can effectively discriminate the normal force, left force, and right force by monitoring the reflected intensity variation of the three mFBGs. Different from the traditional FBG sensors, this sensor measures force by detecting changes in light intensity, effectively avoiding the wavelength cross-sensitivity impact of temperature variations on the sensor performance. This design strategy simplifies the sensor structure, reduces the system complexity and signal interrogation cost, and enhances reliability and practicality. Through systematic experiments, we successfully validated the sensor's superior performance, achieving a minimum detection force of 0.01 N and providing robust data support for practical applications. In addition, the sensor has been used to monitor human pulse accurately. The successful fabrication and experimental validation of this sensor lay a foundation for its widespread application in fields such as robot perception and human vital signal detection.

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Shape recognition plays a significant role in the field of robot perception. In view of the low efficiency and few types of shape recognition of the fiber tactile sensor applied to flexible skin, a convolutional-neural-network-based FBG tactile sensing array shape recognition method was proposed. Firstly, a sensing array was fabricated using flexible resin and 3D printing technology. Secondly, a shape recognition system based on the tactile sensing array was constructed to collect shape data. Finally, shape classification recognition was performed using convolutional neural network, random forest, support vector machine, and k-nearest neighbor. The results indicate that the tactile sensing array exhibits good sensitivity and perception capability. The shape recognition accuracy of convolutional neural network is 96.58%, which is 6.11%, 9.44%, and 12.01% higher than that of random forest, k-nearest neighbor, and support vector machine. Its F1 is 96.95%, which is 6.3%, 8.73%, and 11.94% higher than random forest, k-nearest neighbor, and support vector machine. The research of FBG shape sensing array based on convolutional neural network provides an experimental basis for shape perception of flexible tactile sensing.

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A hybrid enhanced inverse finite element method (E-iFEM) is proposed for real-time intelligent sensing of discontinuous aerospace structures. The method can improve the flight performance of intelligent aircrafts by feeding back the structural shape information to the control system. Initially, the presented algorithm combines rigid kinematics with the classical iFEM to discretize the aerospace structures into elastic parts and rigid parts, which will effectively overcome structural complexity due to fluctuating bending stiffness and a special aerodynamic section. Subsequently, the rigid parts provide geometric constraints for the iFEM in the shape reconstruction method. Meanwhile, utilizing the Fiber Bragg grating (FBG) strain sensor to obtain real-time strain information ensures lightweight and anti-interference of the monitoring system. Next, the strain data and the geometric constraints are processed by the iFEM for monitoring the full-field elastic deformation of the aerospace structures. The whole procedure can be interpreted as a piecewise sensing technology. Overall, the effectiveness and reliability of the proposed method are validated by employing a comprehensive numerical simulation and experiment.

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Throughout the central nervous system, the spinal cord plays a very important role, namely, transmitting sensory and motor information inwardly so that it can be processed by the brain. There are many different ways this structure can be damaged, such as through traumatic injury or surgery, such as scoliosis correction, for instance. Consequently, damage may be caused to the nervous system as a result of this. There is no doubt that optical devices such as microscopes and cameras can have a significant impact on research, diagnosis, and treatment planning for patients with spinal cord injuries (SCIs). Additionally, these technologies contribute a great deal to our understanding of these injuries, and they are also essential in enhancing the quality of life of individuals with spinal cord injuries. Through increasingly powerful, accurate, and minimally invasive technologies that have been developed over the last decade or so, several new optical devices have been introduced that are capable of improving the accuracy of SCI diagnosis and treatment and promoting a better quality of life after surgery. We aim in this paper to present a timely overview of the various research fields that have been conducted on optical devices that can be used to diagnose spinal cord injuries as well as to manage the associated health complications that affected individuals may experience.

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A molecularly imprinted polymer (MIP) based microfiber differential demodulation sensing system for sodium benzoate (SB) concentration detection is proposed. The specific binding of MIP on the surface of microfibers with SB can lead to changes in local refractive index (RI). RI change induces a drift in the interference wavelength, which can be monitored by the power difference between two fiber Bragg gratings (FBGs). The sensing system can detect SB in the concentration range of 0.1-50 µg/ml, and interference wavelength and FBG power difference sensitivities are 0.55 nm/(µg/ml) and 2.64 dB/(µg/ml) in the low concentration range of 0.1-1 µg/ml, respectively, with a limit of detection (LOD) of 0.1 µg/ml. This microfiber differential demodulation sensing system is not only simple to fabricate, but also simplifies the demodulation equipment to reduce the cost, which providing a simple, reliable and low-cost technique for the quantitative detection of SB concentration in beverages and flavoured foods.

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Tunnel excavation induces the stress redistribution of the surrounding rock. Structural cracks may develop in the secondary lining due to this stress redistribution and bias pressure, consequently affecting the overall construction safety of the tunnel. This paper aims to achieve real-time monitoring of the excavation stability of the lining structure by integrating two monitoring technologies: structural deformation monitoring and fiber grating strain monitoring. Additionally, it proposes a method to simultaneously measure the thermal strain and applied stress-strain of the structure. By analyzing the displacement and deformation of the lining structure, its stability can be preliminarily evaluated in the short term. To achieve long-term real-time monitoring and a more accurate assessment of the tunnel structure's stability, the paper introduces fiber Bragg grating (FBG) strain sensor monitoring technology. First, based on the geological stratigraphy information obtained from the exploration, a simulation model of the tunnel under different section bias angles is established. The displacement and stress concentration areas of the lining structure are then analyzed to optimize the sensor deployment array and provide a theoretical basis for the sensor arrangement. FBG strain sensors are installed on the surface of the structure to measure thermal strain and loading stress-strain, whereas FBG temperature sensors measure local temperature. The findings indicate that following tunnel excavation, the maximum daily strain differences at K107+043 and K107+240 were 126.87 µÎµ and 209.38 µÎµ, respectively. After a period of rock disturbance, the average daily strain differences due to applied stress-strain were 16.8 µÎµ and 12.65 µÎµ, respectively. The thermal strain was close to the daily strain difference. Therefore, after the rock disturbance subsided, the strain fluctuations in the lining structure were mainly caused by local temperature changes, and the surrounding rock tended to stabilize. This offers a viable method for evaluating structural stability post-tunnel excavation.

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To measure vibration signals, a low-frequency fiber Bragg grating (FBG) acceleration sensor featuring a flexible hinge with a spring support and symmetric compensation structure has been designed. Based on the mechanical model of the sensor's structure, the expressions for sensitivity and resonant frequency of the sensor are derived. The structural parameters of the sensor are optimized, and a simulation analysis is conducted using ANSYS 19.2 software. According to the results of simulation analysis and size optimization, the sensor prototype is constructed. Subsequently, its amplitude-frequency response, sensitivity, and temperature characteristics are investigated through vibration experiments. The experimental results show that the resonant frequency of the sensor is 73 Hz, the operating frequency range is 0~60 Hz, and the sensitivity measures 24.24 pm/g. This design meets the requirements for measuring vibration signals at low frequencies.

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This paper reports on the testing and evaluation of a passive autoranging (AR) method designed to dynamically extend the measurement range of a photonic current transducer (PCT) to pave the way toward a realization of a combined metering- and protection-class current sensor. The PCT utilizes a current transformer (CT), a piezoelectric transducer (PZT), and a fiber Bragg grating (FBG) to enable current measurement at multiple points in an electrical power network whereby multiple sensors are deployed and interrogated serially using a single optical fiber. The autoranging technique relies on incorporating static MOSFET switches to instantaneously short individual serially connected CT burdens in response to a measured current magnitude exceeding pre-set thresholds. The AR circuit switching events produce distinctive signal features that are used by the proposed switching algorithm to apply appropriate scaling factors to reconstruct the measured current from the optical signal. It is shown through laboratory experiments that the AR circuit correctly reacts to pre-set burden current thresholds of 130% of the nominal value and 22 times the nominal value, signifying its "metering" and "protection" range boundaries. The circuit reaction time is below 4 ms, rendering it suitable for standard power system protection purposes. Moreover, the operation of the AR circuit is demonstrated for burden currents of up to 100 A for over 1 s, satisfying a test procedure for the secondary CT circuit, as required by some power system operators. It is demonstrated that the proposed switching algorithm allows for a correct reconstruction of the burden currents from the optical signal acquired by the FBG interrogator, offering the potential to realize a dual-class optical current sensor.

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A two-stage decoupling model based on an artificial neural network with polynomial regression is proposed for the six-component force sensor load decoupling problem in the case of multidimensional mixed loading. The six-dimensional load categorization stage model constructed in the first stage combines 63 load category label sets with a deep BP neural network. The six-dimensional load regression stage model was constructed by combining polynomial regression with a BP neural network in the second stage. Meanwhile, the six-component force sensor with a fiber Bragg grating (FBG) sensor as the sensitive element was designed, and the elastomer simulation and calibration experimental dataset was established to realize the validation of the two-stage decoupling model. The results based on the simulation data show that the accuracy of the classification stage is 93.65%. The MAPE for the force channel in the regression stage is 6.29%, and 3.24% for the moment channel. The results based on experimental data show that the accuracy of the classification stage is 87.80%. The MAPE for the force channel in the regression phase is 5.63%, and 4.82% for the moment channel.

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The temperature and strain fields monitoring during the preparation process of buoyancy materials, as well as the health status after molding, are important for mastering the mechanical properties of buoyancy materials and ensuring the safety of operators and equipment. This paper proposes a short and high-density femtosecond fiber Bragg grating (fs-FBG) array based on different temperature coefficients fibers. By optimizing the parameters of femtosecond laser point-by-point writing technology, high-performance fs-FBG arrays with millimeter level gating length and millimeter level spatial resolution were prepared on two types of fibers. These were successfully embedded in buoyancy materials to achieve in-situ online monitoring of the curing process and after molding. The experimental results show that the fs-FBG array sensor has good anti-chirp performance and achieves online monitoring of millimeter-level spatial resolution. Intelligent buoyancy materials can provide real-time feedback on the health status of equipment in harsh underwater environments. The system can achieve temperature monitoring with an accuracy of 0.56 °C and deformation monitoring with sub-millimeter accuracy; the error is in the order of micrometers, which is of great significance in the field of deep-sea exploration.

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The constant monitoring and control of various health, infrastructure, and natural factors have led to the design and development of technological devices in a wide range of fields. This has resulted in the creation of different types of sensors that can be used to monitor and control different environments, such as fire, water, temperature, and movement, among others. These sensors detect anomalies in the input data to the system, allowing alerts to be generated for early risk detection. The advancement of artificial intelligence has led to improved sensor systems and networks, resulting in devices with better performance and more precise results by incorporating various features. The aim of this work is to conduct a bibliometric analysis using the PRISMA 2020 set to identify research trends in the development of machine learning applications in fiber optic sensors. This methodology facilitates the analysis of a dataset comprised of documents obtained from Scopus and Web of Science databases. It enables the evaluation of both the quantity and quality of publications in the study area based on specific criteria, such as trends, key concepts, and advances in concepts over time. The study found that deep learning techniques and fiber Bragg gratings have been extensively researched in infrastructure, with a focus on using fiber optic sensors for structural health monitoring in future research. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. This presents an opportunity for future studies.

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Arterial stiffness has been proved to be an important parameter in the evaluation of cardiovascular diseases, and Pulse Wave Velocity (PWV) is a strong indicator of arterial stiffness. Compared to regional PWV (PWV among different arteries), local PWV (PWV within a single artery) outstands in providing higher precision in indicating arterial properties, as regional PWVs are highly affected by multiple parameters, e.g., variations in blood vessel lengths due to individual differences, and multiple reflection effects on the pulse waveform. However, local PWV is less-developed due to its high dependency on the temporal resolution in synchronized signals with usually low signal-to-noise ratios. This paper presents a method for the noninvasive simultaneous measurement of two local PWVs in both left and right radial arteries based on the Fiber Bragg Grating (FBG) technique via correlation analysis of the pulse pairs at the fossa cubitalis and at the wrist. Based on the measurements of five male volunteers at the ages of 19 to 21 years old, the average left radial PWV ranged from 9.44 m/s to 12.35 m/s and the average right radial PWV ranged from 11.50 m/s to 14.83 m/s. What is worth mentioning is that a stable difference between the left and right radial PWVs was observed for each volunteer, ranging from 2.27 m/s to 3.04 m/s. This method enables the dynamic analysis of local PWVs and analysis of their features among different arteries, which will benefit the diagnosis of early-stage arterial stiffening and may bring more insights into the diagnosis of cardiovascular diseases.