On the basis of the photoelectric properties and hydrophilicity and multimode interference of Graphene Oxide (GO), this study proposes an all-fiber humidity sensor. Two core-offset regions are constructed with a fiber fusion splicer, and a GO film is coated on a single-mode fiber (SMF) between the core-offset regions to form a humidity-sensitive Mach-Zehnder interference structure. As the external humidity environment changes, the refractive index of the GO changes, and the light in the SMF is modulated by humidity. External humidity is measured by detecting the changes in the wavelength of the transmission spectrum of the sensing structure. A saturated salt solution is used to build a stable humidity environment. The sensitivity of the sensor is 0.05 nm/%relative humidity (RH) at 35.3%-95.8% RH, and the linear relationship between the characteristic wavelength and RH is 88%. In addition, the linear relationship between the light intensity and the RH of the sensor at a single wavelength at 1571 nm is analyzed, and the linearity is 97.45%. According to this finding, photoelectric conversion is used to convert the light intensity signal into a voltage signal. The data are processed by a computer to achieve the real-time and visual monitoring of the humidity environment. The fiber optic humidity sensor proposed in this work has a simple structure, simple production, low cost, and high sensitivity. This work provides a new method for humidity sensing.
Satellite remote sensing images contain adequate ground object information, making them distinguishable from natural images. Due to the constraint hardware capability of the satellite remote sensing imaging system, coupled with the surrounding complex electromagnetic noise, harsh natural environment, and other factors, the quality of the acquired image may not be ideal for follow-up research to make suitable judgment. In order to obtain clearer images, we propose a dual-path adversarial generation network model algorithm that particularly improves the accuracy of the satellite remote sensing image super-resolution. This network involves a dual-path convolution operation in a generator structure, a feature mapping attention mechanism that first extracts important feature information from a low-resolution image, and an enhanced deep convolutional network to extract the deep feature information of the image. The deep feature information and the important feature information are then fused in the reconstruction layer. Furthermore, we also improve the algorithm structure of the loss function and discriminator to achieve a relatively optimal balance between the output image and the discriminator, so as to restore the super-resolution image closer to human perception. Our algorithm was validated on the public UCAS-AOD datasets, and the obtained results showed significantly improved performance compared to other methods, thus exhibiting a real advantage in supporting various image-related field applications such as navigation monitoring.
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