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.

With the development of economy, the construction of modern infrastructures has been gradually improved. Some infrastructures, such as factories and airports, have large construction volume and high security requirements. Once an intrusion occurs, it will pose a great threat to the safety of personnel and property. Therefore, the perimeter security system based on Internet of Things (IoT) devices and technology is important. Distributed optical fiber sensor (DOFS) is an emerging infrastructure for IoT. Owing to multipoint sensing at the same time, it has attracted extensive attention in the field of perimeter security. However, with the ambient interference, intrusion signals and ambient signals form mixed signals, which brings great difficulty to the efficient and correct identification of intrusion signals. In order to establish a perimeter security system in complex environment, this article proposes an intrusion detection scheme based on DOFS, IoT, and pattern separation technology. The scheme realizes the separation of ambient signals and intrusion signals for the first time. Experiments have proved that this method can separate and recover intrusion signals from mixtures with the average SDR and SI-SDR of 12.35 dB and 11.61 dB, which effectively lays a foundation for subsequent feature extraction and identification. Besides, the response time is only about 0.5 s. This work makes a significant step toward achieving high performance of the perimeter security system in complex environment.

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.

Tactile is an important source of information for people to explore the external environment. In the past few decades, researchers have paid special attention to tactile sensors and have made good practical applications in fields such as robotics. The widely proposed tactile sensors are mostly electromagnetic types, inevitably affected by electromagnetic interference. Fiber optic tactile sensors are considered a possible solution to the problem. Related research has attracted increasing attention in recent years. This article reviews the progress of research on fiber optic tactile sensing technology in recent years, including those based on fiber Bragg gratings, micro-/nanofibers, fiber specklegram patterns, and Fabry-Perot interferometers. In addition, the advantages, disadvantages, and application prospects of each type of sensor were analyzed. Finally, we summarized the research status and challenges of fiber optic tactile sensors and provided some clues for future work.

A DBR fiber grating laser acoustic sensor based on polarization beat signal modulation analysis has been demonstrated for directional acoustic signal measurement. The acoustic sensor was fabricated in birefringent erbium-doped fiber, and the influences of external-acoustic pressure on fiber grating laser sensor were analyzed, considering the effect of relative orientation of the acoustic wave on the degrees of birefringence modulation. In experiment, the birefringence in sensing fiber was modulated by ultrasonic pressure. Agreement between theoretical and experimental results was obtained for ultrasound wave propagating from different directions (0-360 degrees in 15 degrees intervals) corresponding to a nonlinearly change in beat frequency modulation rates. The results demonstrate that the DBR fiber grating laser acoustic sensor has an orientation recognizable ability, offering a potential for acoustic vector signal detection.