Fiber grating sensing technology is a widely used fiber optic sensing technology due to its ability to form a distributed sensing system for measuring multiple parameters. The number of multiplexed gratings and spatial resolution are important performance indicators of fiber Bragg grating (FBG) sensing systems. This paper proposes a fast distributed temperature measurement system based on the Optical Frequency Domain Reflectometry (OFDR) principle, using a single-mode fiber engraved with five identical weak gratings spaced 30cm apart with a reflectivity of 1% as the sensing fiber. To minimize the impact of light source nonlinearity on demodulation, we use the SiO2 process Mach-Zehnder interferometer (MZI) module with a refractive index difference of 1.5 as an auxiliary interferometer and employ cubic spline interpolation FFT transformation for signal processing. This approach simplifies the design of the laser drive circuit and achieves a temperature resolution of 0.5°C.
The measurement of refractive index plays a crucial role in biosensing. This paper proposes a novel solution for refractive index sensing by utilizing seven-core fiber spatial multiplexing to receive the spectrum of a no-core fiber. Compared to existing high-sensitivity fiber sensing structures like core offset and polishing, this solution offers the advantages of easy production and a simplified process. In terms of spectral demodulation methods, this solution employs deep neural networks to replace the traditional approach of tracking spectral peaks or troughs. By utilizing the entire spectrum information, the accuracy of spectral demodulation is significantly improved. Additionally, the use of seven-core fiber with spatial multiplexing characteristics allows for the reception of a larger amount of information compared to single-mode fiber, thereby further enhancing the refractive index sensing capability. The results demonstrate that this solution achieves a remarkable refractive index sensing accuracy rate of 2.25×10-5.
Hyperspectral image (HSI) contains both spatial pattern and spectral information, which has been widely used in food safety, remote sensing, and medical detection. However, the acquisition of HSIs is usually costly due to the complicated apparatus for the acquisition of optical spectrum. Recently, it has been reported that HSI can be reconstructed from single RGB image using convolution neural network (CNN) algorithms. Compared with the traditional hyperspectral cameras, the method based on CNN algorithms is simple, portable, and low cost. In this study, we focused on the influence of the RGB camera spectral sensitivity (CSS) on the HSI. A xenon lamp incorporated with a monochromator was used as the standard light source to calibrate the CSS. And the experimental results show that the CSS plays a significant role in the reconstruction accuracy of an HSI. In addition, we proposed a new HSI reconstruction network where the dimensional structure of the original hyperspectral datacube was modified by 3D matrix transpose to improve the reconstruction accuracy.
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