To achieve synchronous demodulation of a large-capacity Fiber Bragg Grating (FBG) sensor network, a FBG demodulation system based on modulated grating Y-branch (MG-Y) tunable laser is designed, combining wavelength division multiplexing and space division multiplexing technologies. The system consists of MG-Y tunable laser control module and FBG reflection spectrum acquisition module. By calibrating the MG-Y tunable laser, it can output quasi-continuous light with a bandwidth of 40nm and wavelength spacing of 20pm to meet the requirements of wavelength division multiplexing technology. In terms of space division multiplexing technology implementation, the spectrum acquisition module is designed for 16 channels. Finally, experiments were conducted using an Fabry-Perot (F-P) etalon to validate the functionality of the system.
To monitor the space attitude of the spacecraft key load under on-orbit conditions and obtain three-dimensional angular deflection data, a folding-reflecting self-collimator was designed based on the Cassegrain optical system, and the evaluation indexes and imaging quality of the optical system were systematically analyzed, with the MTF of 100 lp/mm being better than 0.4, aberration being less than 0.0368%, and diffraction-limited dispersion spot diameter within 8.6 um, which meets the requirements for monitoring on-orbit. The principle of using multiple devices to combine to measure the three-dimensional angular deflection of the target load is analyzed, and the correspondence between the spot change and the target deflection angle is deduced. It shows that this method has a good development prospect in the field of space attitude measurement.
Convolutional neural networks (CNN) has significant advantages in processing image classification and was widely used in image analysis in the fields of autonomous driving, aerospace, and biomedicine. However, image classification and analysis need large matrix multiplication, which imposes many challenges to the realization of high performance and low power consumption of CNNS. Here, a photoelectric hybrid neural network (PHNN) was developed to reduce the CNN’s power consumption. The optical interference unit (OIU) composed of Mach-Zehnder interferometers (MZI) arrays, used as convolution kernel, performs multiplication and accumulation operations. The convolution kernel is split and reorganized effectively to form a new unitary matrix to reduce the number of MZIs. Simultaneously, this method can modularize the OIU, which is beneficial to field-programmable gate array (FPGA) encoding and modulation. FPGA realizes nonlinear calculation, data scheduling and storage, and phase encoding and modulation. Our PHNN has an accuracy rate of 93.3%, which reduces power consumption by 3 times of magnitude compared with traditional electronic products.
To achieve low-power convolutional neural networks, we develop a photoelectric hybrid neural network (PHNN), which consists of the optical interference unit (OIU) and field-programmable gate array (FPGA). The OIU composed of Mach–Zehnder interferometers (MZI) arrays, used as convolution kernels, performs multiplication and accumulation operations. The convolution kernel is split and reorganized, forming a new unitary matrix, which reduces MZI quantity. FPGA realizes nonlinear calculation, data scheduling and storage, and phase encoding and modulation. Our PHNN has an accuracy rate of 88.79%, and the energy efficiency ratio is 1.73 times that of traditional electronic products.
To realize the high spatial resolution measurement of the temperature field, a temperature sensor array composed of eight apodized short fiber Bragg gratings with grating length of 1 mm and spatial spacing of 2 mm are fabricated. To suppress the side lobes, apodization methods by single-slit diffraction are designed. The sensor array is proposed to measure the real-time temperature distribution. The experimental results show that the temperature-sensing array can accurately measure the temperature field distribution with high spatial resolution and rapid response.
This paper proposes a new method which is called variable weighted centroid method for locating the center of retro reflective target. It is based on centroid method. This method adapts itself to different measurement environments by varying the weight coefficients α, which is determined by different factors associated with measuring environment. The coefficient α is optimized by experiment. The evaluation criterion (lower is better) under the proposed method is reduced by at least 25% compared with the traditional method. Experiment results show that this variable weighted centroid method provides higher locating accuracy than old methods.
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