AIoT enabled resampling filter for temperature extraction of the Brillouin gain spectrum

Abstract

The Artificial Intelligence of Things (AIoT) turns passive fiber sensors into learning machines. It can be used to integrate intelligent nodes into a multi-dimensional sensing system. In this study, the temperature measurement system based on Brillouin Gain Spectrum (BGS) test setup is creatively implemented with the AIoT architecture; the training and testing stages of the neural network are divided into different layers of equipment to improve performance and reduce the network traffic. To enable the lightweight and low-power consumption edge computing device to extract accurate temperature from the BGS during testing, we have integrated the post-processing method inspired by curve fitting into the machine learning and proposed the efficient digital resampling filter. The resampling filter approach is achieved by the peak range algorithm with Gauss differential operator and digital band-pass filter. The evaluation results for different methods on the BGS datasets show the superior performance of our approach. Notably, the approach can increase temperature extraction accuracy and compress the sampling data. The RMSEA of the extraction temperature is 0.5635, which can bring an almost 21% accuracy increase over the classic method. Compared with the classic method of processing the original data on the same hardware platform, the amount of data post-processed by the filter is reduced by 75%; the time consumption is reduced by 22%.