Abstract
A high sensitivity temperature sensor based on spot pattern demodulation of rare doped dual-core fiber (RDCF) is proposed. By utilizing the interference and scattering in RDCF, the related temperature variations can be captured by optical field. Furthermore, In order to improve the sensitivity, the RDCF is given a certain amount of bending. Due to the change of the spot mode caused by bending, the temperature changes more to the refractive index of the fiber core, so that the spot of the fiber changes more to facilitate the subsequent spot identification. In addition, considering the global and subtle nature of spot changes, a deep learning algorithm based on AlexNet transfer model is used to process image recognition at multiple temperatures. The experimental results show that the sensor can perceive different temperature in range of 30–199 °C, and the resolution is higher than 0.05 °C. These outstanding results indicate that the bending assisted temperature sensor based on spot pattern decoding of RDCF has potential applications in temperature quantitative sensing and artificial intelligence perception.
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