Spectroscopic ellipsometry has been widely used in materials science, microelectronics, physical chemistry and biomedicine. In the spectroscopic ellipsometry system, the degree of polarization of the light source subsystem and the polarization sensitivity of the spectrometer subsystem will affect the measurement accuracy of the spectroscopic ellipsometry considering the leakage of polarizer and analyzer. To remove this systematic error, we included the degree of polarization of light from source and the polarization sensitivity of the spectroscopic ellipsometry in our calibration model; a method for measuring the polarization state of light source subsystem and polarization sensitivity of a spectrometer subsystem is proposed. To verify the method, we present the measurement setup and results for a commercial broadband light source and broadband spectrometer.

Aiming at the problem that the light field multi-view image quality is poor which is resulting from the specific lenslet structure of the light field camera and pixel aliasing at the lenslet edge, a light field demosaicing algorithm based on double-guided filtering is proposed. First, the G image is reconstructed by reweighting the gradient based threshold free (GBTF) algorithm with the white image and lenslet mask information. Then, the reconstructed G image is used to double-guide the R/B image for reconstruction. Finally, the reconstructed R, G, and B images are combined into a full color image. The demosaicing result demonstrates that compared with other advanced demosaicing algorithms, the index CPSNR is increased by 1.68%, the index SSIM is increased by 2%, and the light field multi-view image obtained by our method has clear edges and less color artifacts.

This article studied the frame structure of Xilinx FPGA configuration RAM, giving the method of extracting the frame structure and providing the order of frames in the bit stream file. The structure of the intermediate file of SEM IP core is also analyzed to get the positions of essential bits. Performing 0/1 flipping on the essential bits is a way to simulate the single event upset which the circuit is sensitive to under the radiation environment. A PC-side interface is designed to implement a human-machine interaction. The fault injection system is implemented on the FPGA chip, and the read and write of configuration RAM data are realized through ICAP without the need of the processor. The operation of flipping and repairing test classifies essential bits into some categories. The classification results can be used to protect the key bits in subsequent fault repairing.

The variation of peak temperature of metal materials irradiated by continuous wave (CW) laser is studied in this paper. We established a finite element model of metal materials irradiated by CW laser. The variation of peak temperature of aluminum alloy circular plates irradiated by CW laser is studied by simulation analysis method. By analyzing the simulation results under different conditions, such as beam drift, spot diffusion, air convection and material surface oxidation, the effects of various factors on the peak temperature of laser-irradiated materials are given, and the influence of latent heat of phase change on temperature rise is analyzed by using the method of equivalent material specific heat capacity. Finally, according to various conditions, the change of peak temperature of aluminum alloy irradiated by CW laser is given, and the damage of aluminum alloy is analyzed.

An easy line-structured light system calibration method is proposed, which is based on the constructed light plane and homography matrix. In this method, the sequential images of the light plane and calibration target are obtained at different positions by shifting a translating target plane within the depth of camera’s field, then a series of feature points would be extracted from these images to form a light plane. Then, a homography matrix, which is the mapping relationship between the light plane and the image plane of camera, can be calculated. In the experiment, the 3D data can be obtained by using this homography matrix when image coordinates of the light plane are extracted in an arbitrary image. Then the entire object can be measured by using a translation device. For the real data of calibration, the maximum residual error is less than 0.05 mm, standard deviation is less than 0.02 mm, the relative error of the measured distance between the two planes is less than 1.3%. The proposed method can make the entire calibration process easy and flexible to use.

Two problems exist in traditional multi-view geometry method to obtain the three-dimensional structure of the scene. First, the mismatching of the feature points caused by the blurred image and low texture, which reduces the accuracy of reconstruction; second, as the information obtained by monocular camera is lack of scale, the reconstruction results can only determine the unknown scale factor, and cannot get accurate scene structure. This paper proposes a method of equal-scale motion restoration structure based on deep learning. First, the convolutional neural network is used to obtain the depth information of the image; then, to restore the scale information of the monocular camera, an inertial measurement unit (IMU) is introduced, and the acceleration and angular velocity acquired by the IMU and the camera position acquired by the ORB-SLAM2 are demonstrated. The pose is coordinated in both time domain and frequency domain, and the scale information from the monocular camera is acquired in the frequency domain; finally, the depth information of the image and the camera pose with the scale factor are merged to reconstruct the three-dimensional structure of the scene. Experiments show that the monocular image depth map obtained by the Depth CNN network solves the problem that the output image of the multi-level convolution pooling operation has low resolution and lacks important feature information, and the absolute value error reaches 0.192, and the accuracy rate is up to 0.959. The multi-sensor fusion method can achieve a scale error of 0.24 m in the frequency domain, which is more accurate than that of the VIORB method in the frequency domain. The error between the reconstructed 3D model and the real size is about 0.2 m, which verifies the effectiveness of the proposed method.

This paper proposes a method of using diffraction phase microscopy combined with microfluidic chip to quantitatively measure waterborne parasites. A diffraction phase microscopy system is built up by combining interferometry with optical microscope to achieve high sensitivity real-time measurement of parasites. Based on soft lithographic techniques, a double-layered microfluidic chip with U-shaped trapping structures is designed and fabricated for high throughput single parasites trapping. Ficoll solution with the same refractive index as polydimethylsiloxane (PDMS) is introduced into the microfluidic chamber to eliminate significant artifacts in phase imaging originating from diffraction at the edges of trapping structures. The accuracy of the system is verified using standard polystyrene microspheres of different diameters, and the error of maximum phase shift does not exceed 3%. 100 Giardia Lamblia (G. Lamblia) cysts and 100 Cryptosporidium Parvum (C. Parvum) oocysts are measured using this system. The phase maps of the parasites are obtained from the interferograms. The morphological parameters and quantitative optical volume difference distribution of the two kind of waterborne parasites are obtained by analyzing the quantitative phase maps. Quantitative data provides the basis for understanding their physiological characteristics. The microfluidic diffraction phase microscopy system has simple structure, good stability and high measurement accuracy, and has great potential for real-time monitoring and label-free quantitative studies of single microorganism.

In order to increase the lifetime of the TDICCD imaging system in space and decrease the impact on the imaging quality for a long-time working in orbit, a system of real-time radiation correction in space is designed. It generates real-time correction parameters of multi-TDICCD channels and pixels in-channel by rotating the focus plane before the calculation of the real-time calibration images, and improves the adaptability and PRNU (pixel response non-uniformity) values of the TDICCD mosaic camera imaging system. FPGA is used to calculate and save the parameters, and an optimization design is implemented to improve the system stability and reliability. This method can calculate the real-time PRNU correction parameters of the TDICCD mosaic camera in-orbit, and the PRNU value of TDICCD mosaic camera in-channel reaches 2.01% after real-time calibration. This method is potentially used in real-time radiation calibration, and has got a better result.

In order to improve the measurement accuracy, a kind of phase shifting digital holographic microscopy based on a long working distance microscopic objective is proposed. In the setup, an LED is adopted as the illumination light source, which can suppress coherent noise effectively and hence improve the measurement accuracy. A michelson quasi-common-path interferometer is constructed by adding a beam-splitter between the long working distance objective and the sample. The layout of the setup is simple and it can be easily adjusted, and thus the interference can be come into being conveniently especially when the sample is illuminated with a partial light source. The blind phase-shifting interferometry is adopted in the reconstruction procedure, and the two-step blind phase-shifting algorithm is used to reconstruct the phase map of the measured sample. In the experiments, the height maps of a reflective USAF 1951 resolution target are measured under LED illumination and He-Ne laser illumination, respectively. The measurement results show that both coincide with each other; the phase noise under LED illumination is, however, reduced by 70% when compared with that under laser illumination. In addition, in order to further verify the effectiveness of the device, the device is used to measure a micro-nano rectangular step engraved on the silicon substrate. The measurement results are in good agreement with the nominal values. This technique can be potentially used in the topographic measurement of micro-structures.

According to the problems of target missed detection and repeated detection in the object detection algorithm, this paper proposes an improved Faster R-CNN algorithm based on dual threshold-non-maximum suppression. The algorithm first uses the deep convolutional network architecture to extract the multi-layer convolution features of the targets, and then proposes the dual threshold-non-maximum suppression (DT-NMS) algorithm in the RPN(region proposal network). The phase extracts the deep information of the target candidate regions, and finally uses the bilinear interpolation method to improve the nearest neighbor interpolation method in the original RoI pooling layer, so that the algorithm can more accurately locate the target on the detection dataset. The experimental results show that the DT-NMS algorithm effectively balances the relationship between the single-threshold algorithm and the target missed detection problem, and reduces the probability of repeated detection. Compared with the soft-NMS algorithm, the repeated detection rate of the DT-NMS algorithm in PASCAL VOC2007 is reduced by 2.4%, and the target error rate of multiple detection is reduced by 2%. Compared with the Faster R-CNN algorithm, the detection accuracy of this algorithm on the PASCAL VOC2007 is 74.7%, the performance is improved by 1.5%, and the performance on the MSCOCO dataset is improved by 1.4%. At the same time, the algorithm has a fast detection speed, reaching 16 FPS.