Applications of machine learning (ML) models in optical communications and networks have been extensively investigated. For an optical wavelength-division-multiplexing (WDM) system, the quality of transmission (QoT) estimation generally depends on many parameters including the number and arrangement of WDM channels; launch power of each channel; number and distribution of fiber spans; attenuation, dispersion, and nonlinearity parameters and length of each fiber span; noise figure; gain and gain tilt of erbium-doped fiber amplifiers; transceiver noise; digital signal processing (DSP) performance; and so on. In recent years, ML-based QoT estimation schemes have gained significant attention. However, nearly all relevant works are conducted through simulations because it is difficult to obtain sufficient and high-quality datasets for training ML models. In this paper, we demonstrate completely automated generation and collection of an ultra-large-scale experimental training dataset for ML-model-based QoT estimation by automation of transceivers and optical link parameters, as well as data transfer and DSP. Implementation details and key codes of automation are presented. Artificial neural network models with one and two hidden layers are trained by the collected dataset, and brief QoT estimation results are evaluated and discussed to verify the performance and stability of the established automated system.

We report for the first time, to the best of our knowledge, regenerated polymer optical fiber Bragg gratings (RPOFBGs) in ZEONEX-based polymer optical fibers (POFs). The regeneration temperature can be adjusted using a heat treatment process on the POF before FBG inscription, enabling a scalable improvement of the thermal stability of the RPOFBGs. Thermal sustainability of the RPOFBGs at high temperature conditions was investigated for their prolonged use in diverse environments. Furthermore, these RPOFBGs can withstand strain levels up to 2.8% while maintaining a good linearity, even at temperature of 110°C. The RPOFBGs are capable of short-term operation at elevated temperatures of up to 132°C, which is the standard temperature for steam sterilization with at least a 4 min exposure period. The distinction in the morphologies of the two grades of ZEONEX (E48R and 480R, ZEON Corp.) used to fabricate the optical fiber together with the characteristics of UV irradiated and regenerated gratings is explained using micro-Raman spectroscopy. Collectively, these findings provide new heights for long-term operation of POF Bragg gratings (POFBGs) at elevated temperature environments and would be applicable to a wide range of disciplines.

A number of textile-based fiber optic sensors have recently been proposed for the continuous monitoring of vital signs. However, some of these sensors are likely unsuitable for conducting direct measurements on the torso as they lack elasticity and are inconvenient. This project provides a novel method for creating a force-sensing smart textile by inlaying four silicone-embedded fiber Bragg grating sensors into a knitted undergarment. The applied force was determined within 3 N after transferring the Bragg wavelength. The results show that the sensors embedded in the silicone membranes achieved enhanced sensitivity to force, as well as flexibility and softness. Additionally, by assessing the degree of FBG response to a range of standardized forces, the linearity (R²) between the shift in the Bragg wavelength and force was found to be above 0.95, with an ICC of 0.97, when tested on a soft surface. Furthermore, the real-time data acquisition could facilitate the adjustment and monitoring of force during the fitting processes, such as in bracing treatment for adolescent idiopathic scoliosis patients. Nevertheless, the optimal bracing pressure has not yet been standardized. This proposed method could help orthotists to adjust the tightness of brace straps and the location of padding in a more scientific and straightforward way. The output of this project could be further extended to determine ideal bracing pressure levels.

Optical fiber magnetic field sensors based on magnetostrictive materials have attracted significant interest. However, most existing sensors utilizing optical fibers and magnetostrictive materials have a limited measurement range up to 200 mT, posing a challenge for applications requiring the detection of strong magnetic fields, such as in maglev train systems. In this study, we introduce two magnetic field sensors: one based on a magnetic shape memory alloy-silica fiber Bragg grating (MSMA-SFBG) and the other on a magnetic shape memory alloy-polymeric fiber Bragg grating (MSMA-PFBG). These sensors couple magnetic field-induced strain in the MSMA transducer with fiber Bragg gratings inscribed in the silica optical fiber and the polymer optical fiber, respectively. We evaluated the performance of these sensors in terms of sensitivity, measurement range, and saturation magnetic field. The MSMA-SFBG sensor exhibited a linear response with a sensitivity of approximately 2 pm/mT over the magnetic field range from 250 mT to 550 mT. The MSMA-PFBG sensor demonstrated a sensitivity of 46.02 pm/mT at 400 mT, outperforming Terfenol-D-based magnetic field sensors by about four times in sensitivity. By applying DC bias fields up to 268 mT, the sensitivity of the MSMA-SFBG sensor decreased from 2.01 pm/mT to 0.56 pm/mT, while the linear measurement range expanded from 250 – 550 mT to 50 – 550 mT. Conversely, the sensitivity of the MSMA-PFBG sensor increased from 2.30 pm/mT to 18.68 pm/mT at weak magnetic field of 150 mT, although both the measurement range and the saturation magnetic field decreased. These findings suggest that the two proposed MSMA-based magnetic field sensors complement one and other, are well-suited for applications requiring extensive-range, and high-sensitivity magnetic field measurements.

Bracing is a widely used conservative treatment for adolescent idiopathic scoliosis (AIS) patients, yet there is no consensus on the optimal amount of force applied. Although a number of different sensors have been developed to continuously monitor the applied pressure and force, they have several limitations, including inadequate overall force distribution and displacement. They also cause discomfort with limited wearability. In this study, body pressure mapping knitwear (BPMK) integrated with fourteen silicone-embedded fiber Bragg grating (FBG) sensors is developed to monitor immediate and overall changes in force during the bracing treatment. A wear trial of the BPMK is conducted by using a validated soft AIS mannequin, and prediction equations have been formulated for the FBG sensors at individual locations. The findings indicate that the measured forces are in good agreement with those obtained from clinical studies, with peak forces around the padding regions reaching approximately 2N. This was further validated by using finite element (FE) models. When comparing X-ray images, the estimated differences in Cobb angles were found to be 0.6° for the thoracic region and 2.1° for the lumbar region. This model is expected to provide valuable insights into optimal force application, thus minimizing the risk of injury and enhancing bracing compliance and efficacy. Ultimately, this innovative approach provides clinicians with data-driven insights for safer and more effective bracing applications, thus improving the quality of life of AIS patients.