This paper proposes an intrusion pattern recognition scheme based on Gramian Angular Field (GAF) and convolutional neural network (CNN) for the dual Mach-Zehnder Interference (DMZI) distributed fiber perimeter security system, which possesses the advantages of fast recognition speed and high recognition accuracy rate. Compared with traditional recognition algorithms with the inadequate ability to extract deep features, GAF algorithm is used to transform the intrusion signals of one-dimensional time series to two-dimensional images which can present more deep features and maintain the time domain dependence of the signals, meanwhile, each intrusion signal corresponds to a unique fingerprint. Then, this GAF algorithm combined with the appropriate CNN model is able to extract deep features and recognize the intrusion patterns accurately. Besides, GAF algorithm is insensitive to the fluctuation of power source in the optical path, so the robustness as well as the practicability of the system are improved effectively. The experiments demonstrated that the average recognition accuracy rate of three common natural intrusion events and three human intrusion events on fence (wind blowing, light rain, heavy rain, knocking, impacting and slapping) can reach to 97.67%, and the detection response time is only about 0.58 s, which can meet the requirements of real-time emergency monitoring.

A novel all-polymer fiber-optic pH sensor using a UV-cured pH-sensitive hydrogel, poly(ethylene glycol) diacrylate (PEGDA), coated on a polymer fiber Bragg grating was developed. The PEGDA increased in volume according to the pH value of the surrounding fluid, which subsequently induced a lateral stress in the polymer fiber Bragg grating. The proposed pH sensor exhibits a pH sensitivity of up to −0.41 nm/pH and a fast response time of 30 s.

Accurately detecting man-made intrusion from different events is of great significance for distributed optical fiber sensing perimeter security system. Most traditional algorithms lack the ability to reject various events of unknown class which are mainly from natural disturbance, and greatly decline the accuracy of intrusion recognition in field application. In order to solve this problem, we proposed a novel robust intrusion event recognition scheme based on convolutional prototype network (CPL), which realized end-to-end feature extraction and recognition based on the similarity of intrusion signals by integrating relevant variables of prototype learning into the training process of multiscale convolutional neural network (MSCNN) as trainable parameters, and had the ability to recognize and reject the unknown disturbance events. In field experiments, the average recognition accuracy of intrusion events as known class can reach 84.67%, with the rejection rate of disturbance events as unknown class is about 83.75%, which ensure the accuracy of intrusion events monitoring in complex field environments. And the recognition response time is about 17 ms, which also meets the need of real-time monitoring.

A simple, compact, and highly sensitive gas pressure sensor based on a Fabry–Perot interferometer (FPI) with a silicone rubber (SR) diaphragm is demonstrated. The SR diaphragm is fabricated on the tip of a silica tube using capillary action followed by spin coating. This process ensures uniformity of its inner surface along with reproducibility. A segment of single mode fiber (SMF) inserted into this tube forms the FPI which produces an interference pattern with good contrast. The sensor exhibits a high gas pressure sensitivity of −0.68 nm/kPa along with a low temperature cross-sensitivity of ≈ 1.1 kPa/°C.

In this study, we present first-time fabrication of FBGs in all ZEONEX-based SMPOFs with a single 25 ns pulse of 248 nm UV irradiation over a 12-month period, which opens up new frontiers in optics and photonics for the effective fabrication of polymer optical fiber Bragg gratings (POFBGs), permitting mass producibility of them. POFBGs were characterized by subjecting them to various physical parameters including temperature and tensile strain. Strain responses of FBGs with similar grating strengths fabricated with 248 nm and 325 nm He-Cd laser irradiations were explored over a year to demonstrate their long-term stability and applicability. Owing to the unique features of the proposed sensing device fabricated by embedding POFBGs in silicone rubber, a good performance in the detection of human heart rate with an amplitude of 4 pm, which is 4 times higher compared to that of silica single mode fiber (SMF) was demonstrated. The response of the sensing device during a human respiration process was also explored where exhalation and inhalation were monitored and distinguished while the breath was held. These revelations signify the importance of ZEONEX-based POFBGs, which allow consistent and effective grating fabrication and are highly promising in the foreseeable future for biomedical applications.

A fiber Bragg grating (FBG) inscribed Zeonex-based novel, to the best of our knowledge, side hole polymer optical fiber (SHPOF) is proposed and demonstrated for low gas pressure measurement above and below the atmospheric pressure. Two different grades of Zeonex have been used to fabricate the core and cladding of this fiber, thereby making it dopant free. The side hole introduced in the cladding is parallel to the fiber core. A few ultrashort pulses with nanosecond duration have been used to write the FBG in the core of this fiber. The incorporation of the side hole leads to enhancement of pressure sensitivity as well as low hysteresis and performance repeatability compared to Zeonex-based conventional polymer optical fiber (CPOF). Above the atmospheric pressure, the proposed probe shows a pressure sensitivity of 0.47 pm/kPa, which is 80% more compared to the Zeonex-based CPOF and 0.48 pm/kPa for regime below atmospheric pressure. The sensor has a resolution of 2.12 kPa and exhibited very low hysteresis.