Abstract:In this paper, we design a total infrared (IR) absorber based on a dispersive band structure of two-dimensional (2D) multiwall carbon nanotube (MWCNTs) square array working from near IR (NIR) to mid IR (MIR) regime. The absorption characteristics have been investigated by the 2D finite-difference time domain (FDTD) method in square lattice photonic crystal (PC) of the multipole Drude-Lorentz model inserted to the dispersive dielectric function of MWCNTs. Dispersive photonic band structure and scattering parameters for the wide range of lattice constants from 15 nm to 3 500 nm with various filling ratios have been calculated. The results show that for large lattice constant (>2 000 nm), the Bragg gap moves to the IR regime and leads to MWCNTs arrays acting as a total absorber. For a structure with lattice constant of 3 500 nm and filling factor of 12%, an enhanced absorption coefficient up to 99% is achieved in the range of 0.35 eV (λ=3.5 μm) nominated in the MIR regime. Also, the absorption spectrum peak can be tuned in the range of 0.27—0.38 eV (λ=4.59—3.26 μm) with a changing filling factor. Our results and methodology can be used to design new MWCNTs based photonic devices for applications like night-vision, thermal detector, and total IR absorbers.

Abstract:The hybrid quantum/classical scheme (HQCS) is used for the photoabsorption analysis of metal nanoparticles. The HQCS divides the structure of interest into quantum and classical subsystems. First, we calculate and report Lorentz parameters for gold (Au), silver (Ag), aluminum (Al), chromium (Cr), and nickel (Ni) permittivities used in the classical subsystem. Then, photoabsorption spectra were obtained from HQCS for the single nanoparticle structures with and without two sodium (Na) atoms. The Au, Ag, Al, Cr, and Ni show strong sensitivity to the presence of the atomic subsystem. This work could pave the way for how coupled plasmon modes between metal nanoparticles and atomic structures can be utilized for sensing devices.

Abstract:The field of view (FOV) of the traditional Cassegrain optical system is small, generally only 0.1°—0.2°. In order to enlarge the FOV, it is usually necessary to add correction groups or change the mirror type, but it introduces color difference and complicates the structure. In this study, an optical digital joint design method is put forward to expand the FOV of the Cassegrain optical system. First, the structural parameters of the system are optimized to control the aberration. Then, based on the wavefront aberration theory, the wavefront aberration model is constructed using Zernike polynomials, and the point spread function model is established using Fourier transform. Finally, the image is processed using the spatial transform deconvolution algorithm. The FOV of the Cassegrain optical system is expanded using only primary and secondary mirror structures. The simulation experiment of the Cassegrain optical system shows that the FOV is expanded approximately 6 times, and the imaging quality is improved. The simulation results indicate that our method is feasible.

Abstract:Cu(InGa)Se2 (CIGS) solar cells become one of the most important thin film photovoltaic devices thus far. The doping of Sb has improved the grain size of CIGS thin film and therefore led to the enhancement of solar cell efficiency. Various approaches have been used for the Sb doping. Not many reports of electrodeposition of In, Ga and Sb alloy have been reported. In this work, the Sb thin film was coated over Cu film surface prior to the In and Ga deposition in order to form a Cu/Sb/In/Ga metal precursor. After selenization, the Sb doped CIGS film was prepared. The structure and morphology of Sb doped CIGS films were investigated compared with the undoped CIGS reference samples. A modified selenization method was proposed, which improved the grain size. Finally, the conversion efficiency of Sb doped CIGS based solar cells has been improved by 1.02%.

Abstract:In this paper, a rare-earth spectral traceability anti-counterfeiting detection technology is proposed. Configure rare-earth samples No.1, No.2 and No.3 with different doping ratios. The spectral signals of these three samples are collected and integrated into a sample library. The traceability anti-counterfeiting detection is to compare the spectral information of the samples to be tested with the established sample library by selecting specific wavelength points for light intensity values. If the sample to be measured meets the light intensity range of the established sample library at a specific wavelength point, the sample model will be output. If it does not meet the light intensity range, the sample to be tested is fake. After testing, the anti-counterfeit rate of samples No.1, No.2 and No.3 can reach 100%. This testing process does not destroy the sample, and the anti-counterfeit effect is unique and reliable.

Abstract:In order to solve the problem that blade fixing bolt cannot be detected quickly and conveniently in the field in actual production, this paper proposed a field rapid detection method of wind turbine blade fixing bolt defects based on field programmable gate array (FPGA), and Yolov4-tiny is selected as the basic algorithm. Nonetheless, the original Yolov4-tiny was not suitable for detecting small defects, so this paper improved the Yolov4-tiny to enhance the detection effect. Next, the convolutional operations in the algorithm were encapsulated into intellectual property (IP) cores by high-level synthesis (HLS) and Vivado, and parallel computation was realized using FPGA features. In the end, using Python to call the IP core and the FPGA hardware library, this paper achieved the purpose of rapid detection. Compared with traditional detection methods and other algorithms, the accuracy and speed of this method are significantly improved, which has a good application value.

Abstract:A semi-supervised convolutional neural network segmentation method of medical images based on contrastive learning is proposed. The cardiac magnetic resonance imaging (MRI) images to be segmented are preprocessed to obtain positive and negative samples by labels. The U-Net shrinks network is applied to extract features of the positive samples, negative samples, and input samples. In addition, an unbalanced contrastive loss function is proposed, which is weighted with the binary cross-entropy loss function to obtain the total loss function. The model is pre-trained with labeled samples, and unlabeled images are predicted by the pre-trained model to generate pseudo-labels. A pseudo-label post-processing algorithm for removing disconnected regions and hole filling of pseudo-labels is proposed to guide the training process of semi-supervised networks. The results on the Sunnybrook dataset show that the segmentation results of this model are better, with a higher dice coefficient, accuracy, and recall rate.

Abstract:An adaptive thresholds algorithm is proposed in this letter, which is used to determine the global optimal thresholds for multi-bit quanta image sensor (MB-QIS). Firstly, the senor model of MB-QIS is set up. Then global optimal thresholds theory is analyzed and a thresholds optimization algorithm based on the binary search is designed to determine the optimal global thresholds. Finally, the high dynamic range (HDR) images are reconstructed by the non-iterative maximum likelihood estimation (MLE) image reconstruction method. The results of simulation prove that HDR imaging of MB-QIS is realized by the proposed method effectively.

Abstract:An adaptive thresholds algorithm is proposed in this letter, which is used to determine the global optimal thresholds for multi-bit quanta image sensor (MB-QIS). Firstly, the senor model of MB-QIS is set up. Then global optimal thresholds theory is analyzed and a thresholds optimization algorithm based on the binary search is designed to determine the optimal global thresholds. Finally, the high dynamic range (HDR) images are reconstructed by the non-iterative maximum likelihood estimation (MLE) image reconstruction method. The results of simulation prove that HDR imaging of MB-QIS is realized by the proposed method effectively.

Abstract:Recognition algorithms have been widely used in brain computer interface (BCI) for neural paradigms classification. To improve the classification and recognition effect of motor imagery with motor observation (O-MI) in BCI rehabilitation technology, this paper explores the function of convolutional neural network (CNN) combined with synchrosqueezed wavelet transform (SST) and long short-term memory (LSTM) in the recognition and classification of neural activities in the brain motor area. Combining the advantages of SST in signal feature extraction in the pretreatment stage and the ability of LSTM network in time series information modeling, the purpose is to make up for CNN's shortcomings in both aspects. This paper verifies the algorithm on the self-collected O-MI experimental datasets and the public datasets (BCI competition IV datasets 2a). The results show that the composite CNN algorithm incorporating SST and LSTM achieves higher classification accuracy than classic algorithms and the similar new method which is CNN combined with discrete wavelet transform (DWT) and power spectral density (PSD), so it is convenient for practical application in O-MI BCI system.

Abstract:Cylindrical density depressions generated by femtosecond laser pulses filamenting in air for different energy depositions are investigated numerically, by using a set of hydrodynamic equations. The evolution of density profile is calculated for different temperature elevations. The results indicate that the gas density hole is getting shallower and wider with the increasing temperature elevations. A simulation of the propagation inside low-density channel implies a new way to generate a type of bottle beam.