###### Abstract  
A high-performance, low-cost demodulation system is essential for fiber-optic sensor-based measurement applications. This paper presents a demodulation system for FBG sensors based on a long-period fiber grating (LPG) driven by artificial intelligence techniques. The LPG is applied as an edge filter to convert the spectrum drift of the FBG sensor into transmitted intensity variation, which is subsequently fed to the proposed sensor demodulation network to provide high-precision wavelength interrogation. The sensor demodulation network consists of a generative adversarial network (GAN) for data augmentation and a dense neural network (DNN) for wavelength interrogation, the former addresses the drawback that traditional machine learning models rely on a large-scale dataset for satisfactory performance, while the latter is used to model the relationship between transmitted intensity and wavelength for demodulation. Experiments demonstrate that the proposed system has excellent performance and can achieve wavelength interrogation precision of \(\pm\)3 pm. In addition, the effectiveness of the GAN is demonstrated. With a wide demodulation range, high performance, and low cost, the system can provide a new platform for fiber-optic sensor-based measurement applications. fiber Bragg grating; long-period grating; demodulation system; generative adversarial network; neural network +
Footnote †: journal: Medical Imaging and Biomedicine and Bio  
demodulation range. [PERSON] et al. [8] achieved a low-loss all-fiber system by demodulating FBGs using the reflective spectral ramp of a wavelength-division multiplexer. However, these methods are subject to certain limitations, such as the tedious demodulation process due to the uncertainty of the interferometric spectrum of the F-P interference filter and the existence of a large flat region in the spectrum of the WDM/CWDM that severely limits the demodulation range. Long-period fiber gratings (LPGs) are widely used in all-fiber FBG demodulation systems due to their low insertion loss, wide bandwidth, and low cost. [PERSON] et al. [11] used LPGs to demodulate small wavelength shifts. They cascaded LPG as filters in FBGs multiplexing to provide a low-cost demodulation system for FBG sensors. [PERSON] et al. [12] built an FBG demodulation system based on LPG and photodiodes that can be used for level, specific gravity, and static and dynamic measurement applications. [PERSON] et al. [13] developed an FBG demodulation system based on a cascaded long-period fiber grating (CLPG) technique for simultaneous demodulation of two wavelength-matched FBG sensors, and the proposed system can be used in dynamic strain measurements. [PERSON] et al. [14] used a long-period grating-based FBG demodulation system to monitor seismic waves, providing a highly sensitive, high-speed detection system that can be used for seismic exploration. Yet, attempts to improve the precision of these demodulation systems often rely on cascading more LPG or providing higher-specification equipment, which is contrary to the expectation of low cost and low complexity. With the rapid development of artificial intelligence (AI) in recent years, intelligent photonics has attracted the attention of more and more researchers [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. For example, AI techniques are applied to unique optical fiber design [24], optical imaging [16], and fiber optic sensor demodulation systems [18]. AI algorithms, especially machine learning (ML), have also been introduced in LPG-based FBG interrogation systems. For example, [PERSON] et al. [27; 28] used two LPG combined with artificial neural networks (ANNs) to simultaneously measure temperature and curvature and an LPG-ANN-based design to sense many different parameters, such as strain, etc. [PERSON] et al. [29] completed FBG-array-based LPG sensor interrogation using an ANN.