Super-structured photonic crystal fiber Bragg grating biosensor image model based on sparse matrix

Abstract

Abstract Fiber grating (FBG) is an important optical device of fiber, which is widely used in optical fiber communication and sensing. At the present stage, the fiber grating is almost prepared in the static state of the fiber, and then the grating is welded into the fiber grating array or network. With the continuous improvement of the application effect of fiber grating, has become one of the most promising, representative and fastest developing fiber passive devices. In this paper, fiber grating is classified according to the refractive index distribution of grating axis. The central wavelength of Bragg fiber grating is modulated by using external parameters (temperature or stress strain). The wave equation of insulating medium is obtained by the reflection characteristic and coupling mode theory of fiber grating. Using sparse matrix model of nerve action potential signal with wavelet decomposition layers, nerve action potential signal reconstruction of the relative error between the value and the original value contrast found that reconstructed and original signals are very close. Good results have been obtained for the sampling reconstruction of the filtered high signal-to-noise ratio neural action potential signal. Researchers have conducted extensive and in-depth research on fiber grating sensing technology, and achieved gratifying results. But with the of the engineering application of technical requirements, they need real-time monitoring. Due to the cross sensitivities of fiber grating, it became the bottleneck of multiple parameter measurement.