To achieve data-driven intelligence in engineering applications, the key requirements for distributed optical fiber sensor networks are large capacity, long distance, dense distribution, fast response, and high signal-to-noise ratio (SNR). Combining the traditional optical reflectometry techniques with the large-scale fiber Bragg grating (FBG) array fabricated on-line by a fiber drawing tower, multiplexing and demodulation of thousands of FBG sensors is successfully demonstrated, which greatly expands the application of FBG sensors to the field of distributed sensing. In this work, the fabrication, demodulation, and applications of large-scale FBG arrays are reviewed. Firstly, the on-line fabrication technology and process of large-scale FBG arrays are introduced. Secondly, the demodulation techniques including wavelength demodulation based on optical time-domain reflectometry (OTDR) and optical frequency-domain reflectometry (OFDR), as well as phase demodulation based on phase-sensitive OTDR are reviewed, where we mainly focus on the key parameters such as multiplexing capacity, demodulation speed, spatial resolution, and SNR. Last, the applications of the large-scale FBG arrays are briefly discussed.

In this paper, to develop a high-performance fiber-optic hydrophone for large-scale hydrophone arrays, a general sensing model is theoretically built from mandrel-wound structures of fiber-optic hydrophones. By analyzing and optimizing the structural parameters of the model, a high-performance fiber-optic hydrophone is designed from two aspects. First, the structure of the mandrel is optimized by elastic theory and finite element analysis approach, and the structural design of fiber-optic hydrophone with high acoustic pressure sensitivity and wide frequency response range is realized. Then, the winding ratio of the fiber is optimized by mathematical simulation to realize an Omni-directivity fiber optic hydrophone design. The feasibility of the designed fiber-optic hydrophone is further experimentally demonstrated. The results indicate that the proposed hydrophone achieves a high average acoustic pressure sensitivity of $-$ 113 dB ( $re$ 1 rd / $\mu$ Pa) within the flatness of $\pm$ 1.5 dB from 10 Hz to 1000 Hz, and a horizontal directivity of $\pm$ 1 dB at 1 kHz. Moreover, the average minimum detectable acoustic pressure is achieved as low as 14.1 $\mu$ Pa. The capability of weak acoustic signal detection and large-scale multiplexing offers great potential for application in underwater faint target detection.

Heart rate variability (HRV) is widely investigated to provide early warning signs for cardiovascular diseases (CVDs). However, traditional HRV monitoring methods are inconvenient in daily long-term continuous monitoring due to the uncomfortable direct contact of the devices with the skin. In this work, we present a ballistocardiogram (BCG)-based system for long-term HRV monitoring in a non-contact mode. BCG signals can reflect the mechanical activity of the heart, from which the beat-to-beat interval (BBI) can be extracted for HRV analysis. An effective solution is demonstrated for accurate and steady BCG signal detection using a highly-sensitive fiber optic sensor. A robust heartbeat extraction algorithm based on the deep learning method is proposed to improve the accuracy of the BBI extraction. Continuous wavelet transform (CWT) is introduced to facilitate feature extraction against the variation of morphological characteristics of BCG signal. The accuracy of BBI estimation presents a median error of 4.4 ms, indicating the effectiveness and feasibility of the proposed system. This work is expected to pave a new and practical pathway for household HRV monitoring during sleep.

Under realistic scenarios, more fiber Bragg gratings (FBGs) are always expected to be multiplexed in one sensor array to share the expensive optical components and electrical devices. However, either the sensing number or the interrogation frequency is limited in previous works due to the huge amount of data generated from large-scale sensing arrays. This paper presents a field-programmable gate array (FPGA)-based dynamic wavelength interrogation system for thousands of identical FBGs. With the advantages of parallel controlling and pipeline processing, FPGA can accelerate the data-processing rate of the wavelength interrogation, realizing a continuous-running and real-time sensing system. The signal-processing system precisely synchronizes the generation of interrogation pulses, the acquisition of reflected signals, and the processing of the wavelength-related data, making the interrogation frequency fundamentally limited by the round-trip time of light pulses traveling in the fiber. Multiple sensing arrays can be independently carried out simultaneously, affecting hardly the interrogation frequency. Experimental results show that over 4000 FBGs with a 3-m spatial resolution in four channels are interrogated with a 150-Hz sensing frequency, 3-nm dynamic range, and ±5.9-pm sensing precision, greatly improving the interrogation frequency while ensuring the multiplexing number.