2024

• Record 277 of
  
  Title:Observation of the colliding process of plasma jets in the double-cone ignition scheme using an x-ray streak camera
  
  Author(s):Liu, Zhengdong(1,2); Wu, Fuyuan(3,4); Zhang, Yapeng(1,2); Yuan, Xiaohui(3,4); Zhang, Zhe(4,5,6); Xu, Xiangyan(7); Xue, Yanhua(7); Tian, Jinshou(7); Zhong, Jiayong(1,2,4); Zhang, Jie(3,4,5)
  Source: Physics of Plasmas  Volume: 31  Issue: 4  DOI: 10.1063/5.0188056  Published: April 1, 2024  
  Abstract:The double-cone ignition scheme is a novel approach with the potential to achieve a high gain fusion with a relatively smaller drive laser energy. To optimize the colliding process of the plasma jets formed by the CHCl/CD shells embedded in the gold cones, an x-ray streak camera was used to capture the spontaneous x-ray emission from the CHCl and CD plasma jets. High-density plasma jets with a velocity of 220 ± 25 km/s are observed to collide and stagnate, forming an isochoric plasma with sharp ends. During the head-on colliding process, the self-emission intensity nonlinearly increases because of the rapid increase in the density and temperature of the plasma jets. The CD colliding plasma exhibited stronger self-emission due to its faster implosion process. These experimental findings effectively agree with the two-dimensional fluid simulations. © 2024 Author(s).
  Accession Number: 20241916063677
• Record 278 of
  
  Title:Room-temperature MoTe2/InSb heterostructure large-area terahertz detector
  
  Author(s):Wang, Jiatong(1); Zhang, Min(1,3); Zhou, Zhiwen(1); Li, Ling(1); Song, Qi(2); Yan, Peiguang(1)
  Source: Infrared Physics and Technology  Volume: 137  Issue:   DOI: 10.1016/j.infrared.2024.105190  Published: March 2024  
  Abstract:As a building block for terahertz system, terahertz detector is expected to achieve high-performance, room-temperature, low-cost and large-area detection available. However, the state-of-the-art technologies still suffer from various drawbacks. This paper presents a MoTe2/InSb heterostructure large-area terahertz detector. With the photoactive region of heterostructure, carriers are allowed to assemble within the interface due to the carrier mobility difference, resulting in detection sensitivity improvement. The structures and bonding of MoTe2/InSb heterostructure were characterized by Raman spectroscopy. Besides, large-scale interdigital electrodes with subwavelength spacing are employed at the bottom of photoactive region, which contrasts with normal electrodes coated on both sides of the active layer, endowing a large effective detection area of 2 mm × 6.65 mm with the detector. Subwavelength electrodes spacing not only facilitates the directional migration of carriers, but also induces electromagnetic induced well (EIW) effects to obtain extraordinary performance. As a result, the detector achieves a noise equivalent power (NEP) of 2.66 pW Hz-1/2 and a detectivity (D*) of 0.53 × 1012 cm Hz1/2 W−1 under 0.1 THz radiation at room temperature. The proposed high-performance terahertz detector exhibits remarkable prospects in varieties of applications. © 2024 The Author(s)
  Accession Number: 20240615520725
• Record 279 of
  
  Title:Compressed Spectrum Reconstruction Method Based on Coding Feature Vector Enhancement
  
  Author(s):Cao, Chipeng(1,2); Li, Jie(3); Wang, Pan(1); Qi, Chun(3)
  Source: IEEE Transactions on Geoscience and Remote Sensing  Volume: 62  Issue:   DOI: 10.1109/TGRS.2023.3347220  Published: 2024  
  Abstract:Compressive spectral imaging (CSI) is a snapshot spectral imaging technique that rapidly captures the spectral information of a target in a single exposure and effectively reconstructs high spectral data using reconstruction algorithms. However, due to the presence of a large number of identical pixels in the measured image, which map to different prior spectral information, existing algorithms struggle to establish an accurate pixel separation representation model. To improve the separation effect between pixels and enhance the representation capability of the measured image pixels, we propose a compressed spectral reconstruction method with enhanced encoding feature vectors. By designing encoding information calculation rules based on a combination of linear and nonlinear functions, encoding features are calculated according to the spatial coordinate position information and wavelength information of the pixels, effectively enhancing the separation representation characteristics between channels and neighboring pixels through the addition of encoding features. Furthermore, by utilizing the semantic similarity between the predicted results of the prior model and the prior spectral image, the reconstruction problem is transformed into a total variation (TV) minimization problem between the predicted results of the prior model and the reconstruction results, combined with the alternating direction method of multipliers (ADMMs) to achieve accurate pixel reconstruction. The experimental setup utilizes a dual-camera compressed spectral imaging (DCCHI) system, consisting of a dual-dispersion coded aperture compressed spectral imaging (DD-CASSI) system and a grayscale imaging system. Various experiments have shown that the proposed method outperforms in reconstructing quality and displays superior algorithmic performance. © 1980-2012 IEEE.
  Accession Number: 20240215337320
• Record 280 of
  
  Title:Hybrid Grid Pattern Star Identification Algorithm Based on Multi-Calibration Star Verification
  
  Author(s):Shen, Chao(1); Ma, Caiwen(1); Gao, Wei(1); Wang, Yuanbo(1)
  Source: Sensors  Volume: 24  Issue: 5  DOI: 10.3390/s24051661  Published: March 2024  
  Abstract:In order to solve the star identification problem in the lost space mode for scientific cameras with small fields of view and higher instruction magnitudes, this paper proposes a star identification algorithm based on a hybrid grid pattern. The application of a hybrid pattern generated by multi-calibration stars in the initial matching enables the position distribution features of neighboring stars around the main star to be more comprehensively described and avoids the interference of position noise and magnitude noise as much as possible. Moreover, calibration star filtering is adopted to eliminate incorrect candidates and pick the true matched navigation star from candidate stars in the initial match. Then, the reference star image is utilized to efficiently verify and determine the final identification results of the algorithm via the nearest principle. The performance of the proposed algorithm in simulation experiments shows that, when the position noise is 2 pixels, the identification rate of the algorithm is 96.43%, which is higher than that of the optimized grid algorithm by 2.21% and the grid algorithm by 4.05%; when the magnitude noise is 0.3 mag, the star identification rate of the algorithm is 96.45%, which is superior to the optimized grid algorithm by 2.03% and to the grid algorithm by 3.82%. In addition, in the actual star image test, star magnitude values of ≤12 mag can be successfully identified using the proposed algorithm. © 2024 by the authors.
  Accession Number: 20241115750356
• Record 281 of
  
  Title:Motion detection of swirling multiphase flow in annular space based on electrical capacitance tomography
  
  Author(s):Zhao, Qing(1); Liao, Jiawen(1); Chen, Weining(1)
  Source: Proceedings of SPIE - The International Society for Optical Engineering  Volume: 13090  Issue:   DOI: 10.1117/12.3026097  Published: 2024  
  Abstract:Cyclone multiphase flow in the annular space is widely used in fluid machinery, such as burner and pneumatic conveying. However, the annular flow field is complex, and the related research is not sufficient. To improve the safety and efficiency of equipment, this paper proposes a method for detecting the motion state of swirling fluid in annular space by integrating computational fluid dynamics (CFD) and electrical capacitance tomography (ECT), calculates the motion characteristics of swirling multiphase flow in the annular space using the CFD, and visually measures the distribution and motion state of swirling multiphase flow in the annular space using the ECT. Numerical simulation and experimental results show that the results of the two methods are in good agreement, indicating that the model selected in this paper in the CFD is correct. The CFD effectively reveals the distribution of swirling multiphase flow in the annular pipe, and the ECT can accurately reconstruct the position and size of swirling multiphase flow in the annular space. The combination of these two methods provides a new idea for the study of multiphase flow in annular space. © 2024 SPIE.
  Accession Number: 20241815993004
• Record 282 of
  
  Title:An optimization method for aircraft attitude measurement based on contour matching
  
  Author(s):Qin, Ruijiao(1,2); Tang, Huijun(3)
  Source: Proceedings of SPIE - The International Society for Optical Engineering  Volume: 12978  Issue:   DOI: 10.1117/12.3019432  Published: 2024  
  Abstract:The pose information of aircraft is an important index to study flight status and aircraft performance[1]. This article mainly focuses on the research of aircraft attitude estimation based on contour matching, intending to achieve pose estimation of non-contact long-distance moving objects under the rigorous formula system of photogrammetry. The rationality of the algorithm proposed in this article has been proven through the analysis of experimental results. © 2024 COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
  Accession Number: 20240615524021
• Record 283 of
  
  Title:Consumer Camera Demosaicking and Denoising With a Collaborative Attention Fusion Network
  
  Author(s):Yuan, Nianzeng(1); Li, Junhuai(2); Sun, Bangyong(3,4)
  Source: IEEE Transactions on Consumer Electronics  Volume: 70  Issue: 1  DOI: 10.1109/TCE.2023.3342035  Published: February 1, 2024  
  Abstract:For the consumer cameras with Bayer filter array, raw color filter array (CFA) data collected in real-world is sampled with signal-dependent noise. Various joint denoising and demosaicking (JDD) methods are utilized to reconstruct full-color and noise-free images. However, some artifacts (e.g., remaining noise, color distortion, and fuzzy details) still exist in the reconstructed images by most JDD models, mainly due to the highly related challenges of low sampling rate and signal-dependent noise. In this paper, a collaborative attention fusion network (CAF-Net), with two key modules, is proposed to solve this issue. Firstly, a multi-weight attention module is proposed to efficiently extract image features by realizing the interaction of spatial, channel, and pixel attention mechanisms. By designing a local feedforward network and mask convolution aggregation of multiple receptive fields, we then propose an effective dual-branch feature fusion module, which enhances image details and spatial correlation. Accordingly, the proposed two modules significantly facilitate our CAF-Net to recover a high-quality image, by accurately inferring the correlations of color, noise, and the spatial distribution of the CFA data. Extensive experiments on demosaicking, synthetic, and real image JDD tasks prove that the proposed CAF-Net can achieve advanced performance in terms of objective evaluation index metrics and visual perception. © 2023 IEEE.
  Accession Number: 20235115239885
• Record 284 of
  
  Title:A Detection Method for Typical Component of Space Aircraft Based on YOLOv3 Algorithm
  
  Author(s):He, Bian(1,2,3); Jianzhong, Cao(1,3); Cheng, Li(1,3); Junpeng, Dong(1,3); Zhongling, Ruan(1,3); Chao, Mei(1,3)
  Source: 2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024  Volume:   Issue:   DOI: 10.1109/EEBDA60612.2024.10485846  Published: 2024  
  Abstract:A solar panel recognition method based on YOLOv3 deep learning algorithm is proposed to address issues such as inaccurate recognition of traditional algorithms in space solar panel detection. First, this paper scales the dataset images to 416 × 416, then uses Labelme to annotate the data and transform the bounding box position information, and finally uses the YOLOv3 algorithm framework for model training. The results show that the recall, F1 score and accuracy of YOLOv3 algorithm are all above 80%. The YOLOv3 deep learning algorithm meets the requirements for real-time detection of solar panels in terms of accuracy. © 2024 IEEE.
  Accession Number: 20241715982706
• Record 285 of
  
  Title:High-performance architecture for real-time high-definition short-wave infrared streaming video processing and its field programmable gate array prototype
  
  Author(s):Zhou, Feng(1,2,3); Chen, Zhiqiang(1,2,3); Xie, Qingsheng(1,3); Kong, Fanzi(1,2,3); Chen, Yaohong(1,3); Wang, Huawei(1,3)
  Source: Optical Engineering  Volume: 63  Issue: 2  DOI: 10.1117/1.OE.63.2.023103  Published: February 1, 2024  
  Abstract:Image detail enhancement is critical to the performance of short-wave infrared (SWIR) imaging systems. Recently, the requirement for real-time processing of high-definition (HD) SWIR video has shown rapid growth. Nevertheless, the research on field programmable gate array (FPGA) implementation of HD SWIR streaming video processing architecture is relatively few. This work proposes a real-time FPGA architecture of SWIR video enhancement by combining the difference of Gaussian filter and plateau equalization. To accelerate the algorithm and reduce memory bandwidth, two efficient key architectures, namely edge information extraction and equalization and remapping architecture, are proposed to sharpen edges and improve dynamic range. The experimental results demonstrated that the proposed architecture achieved a real-time processing of 1280 × 1024@60Hz with 2.7K lookup tables, 2.5K Slice Reg, and about 350 kb of block RAM consumption, and their utilization reached 12.5%, 19.2%, and 12.5% for the XC7A200T FPGA board, respectively. Moreover, the proposed architecture is fully pipelined and synchronized to the pixel clock of output video, meaning that it can be seamlessly integrated into diverse real-time video processing systems. © 2024 Society of Photo-Optical Instrumentation Engineers (SPIE)
  Accession Number: 20241115712686
• Record 286 of
  
  Title:An efficient multi-scale transformer for satellite image dehazing
  
  Author(s):Yang, Lei(1,2); Cao, Jianzhong(1,2); Chen, Weining(1); Wang, Hao(1); He, Lang(3,4,5)
  Source: Expert Systems  Volume:   Issue:   DOI: 10.1111/exsy.13575  Published: 2024  
  Abstract:Given the impressive achievement of convolutional neural networks (CNNs) in grasping image priors from extensive datasets, they have been widely utilized for tasks related to image restoration. Recently, there is been significant progress in another category of neural architectures—Transformers. These models have demonstrated remarkable performance in natural language tasks and higher-level vision applications. Despite their ability to address some of CNNs limitations, such as restricted receptive fields and adaptability issues, Transformer models often face difficulties when processing images with a high level of detail. This is because the complexity of the computations required increases significantly with the image's spatial resolution. As a result, their application to most high-resolution image restoration tasks becomes impractical. In our research, we introduce a novel Transformer model, named DehFormer, by implementing specific design modifications in its fundamental components, for example, the multi-head attention and feed-forward network. Specifically, the proposed architecture consists of the three modules, that is, (a) multi-scale feature aggregation network (MSFAN), (b) the gated-Dconv feed-forward network (GFFN), (c) and the multi-Dconv head transposed attention (MDHTA). For the MDHTA module, our objective is to scrutinize the mechanics of scaled dot-product attention through the utilization of per-element product operations, thereby bypassing the need for matrix multiplications and operating directly in the frequency domain for enhanced efficiency. For the GFFN module, which enables only the relevant and valuable information to advance through the network hierarchy, thereby enhancing the efficiency of information flow within the model. Extensive experiments are conducted on the SateHazelk, RS-Haze, and RSID datasets, resulting in performance that significantly exceeds that of existing methods. © 2024 John Wiley & Sons Ltd.
  Accession Number: 20241315812824
• Record 287 of
  
  Title:Exploring the Connection between Eye Movement Parameters and Eye Fatigue
  
  Author(s):Sun, Weifeng(1,2,3); Wang, Yuqi(1,3); Hu, Bingliang(1,3); Wang, Quan(1,3)
  Source: Journal of Physics: Conference Series  Volume: 2722  Issue: 1  DOI: 10.1088/1742-6596/2722/1/012013  Published: 2024  
  Abstract:Eye fatigue, a prominent symptom of computer vision syndrome (CVS), has gained significant attention in various domains due to the increasing diversification of electronic display devices and their widespread usage scenarios. The COVID-19 pandemic has further intensified the reliance on these devices, leading to prolonged screen time. This study aimed to investigate the effectiveness of utilizing eye movement patterns in discriminating fatigue during the usage of electronic display devices. Eye movement data was collected from subjects experiencing different levels of fatigue, and their fatigue levels were recorded using the T/CVIA-73-2019 scale. The analysis revealed that features related to the pupils demonstrated a high level of confidence and reliability in distinguishing fatigue, especially related to pupil size. However, features associated with fixations, such as fixation duration and frequency, did not significantly contribute to fatigue discrimination. Furthermore, the study explored the influence of subjective awareness on fatigue discrimination. By modifying the experimental settings and considering the subjects' subjective perception, it was observed that individual consciousness and self-awareness played a crucial role in fatigue discrimination. The implications of these findings extend beyond the field of computer vision syndrome, offering potential applications in developing interventions and strategies to alleviate eye fatigue and promote eye health among individuals who extensively use electronic display devices. © Published under licence by IOP Publishing Ltd.
  Accession Number: 20241916032392
• Record 288 of
  
  Title:NVPCA Image Enhancement-Based Detection Method for Sidelobe Peak Parameters in Weak Signal Regions
  
  Author(s):Wang, Zhengzhou(1); Wang, Li(1); Duan, Yaxuan(1); Li, Gang(1); Wei, Jitong(1)
  Source: Zhongguo Jiguang/Chinese Journal of Lasers  Volume: 51  Issue: 6  DOI: 10.3788/CJL231185  Published: 2024  
  Abstract:Objective The primary application of the host device involves research in high-energy density physics and inertial confinement fusion, handling energies up to 100000 joules. A significant challenge encountered during these experiments is the simultaneous detection of strong and weak signals in the far-field focal spot. Specifically, accurately measuring weak signals in the sidelobe area of the far-field focal spot has proven difficult. To address this, we introduce a peak parameter detection method for weak signal regions in the sidelobe, leveraging neighborhood vector principal component analysis (NVPCA) for image enhancement. Methods Our optimization strategy includes several steps. First, we treat each pixel in the sidelobe image and its eight neighboring pixels as a column vector to construct a 9-dimensional data cube. The first dimension post-PCA transformation, the NVPCA image, is then selected. Next, we employ angle transformation to detect various peak parameters of the one-dimensional sidelobe curve in all directions, facilitating the quantification of energy distribution in the sidelobe’s weak signal area. Subsequently, we identify the maximum position points of each sidelobe peak in all directions, linking these to form a maximum ring for each peak and calculating the grayscale mean of these rings. The smallest grayscale mean exceeding the LCM target separation threshold is identified as the minimum measurable signal for the entire sidelobe beam. Results and Discussions 1) We propose a sidelobe weak signal detection method using NVPCA image enhancement. This approach successfully isolates and extracts the minimum measurable signal from the 5th peak ring on the sidelobe image’s periphery, increasing the dynamic range ratio to 1.528 times. This method enhances the peak’s maximum value in any direction, ensuring the extraction of the minimum measurable signal from the peripheral 5th peak loop. 2) The LCM target detection threshold formula is employed to segregate the minimum measurable signal. This formula, tailored to the characteristics of far-field focal lobe images, effectively separates background noise. 3) We validate the one-dimensional curve peak parameters in various directions using a two-dimensional plane display method. Combining two-dimensional and one-dimensional displays, this method not only showcases the peak parameter distribution of one-dimensional sidelobe curves from multiple perspectives but also differentiates adjacent sampling angles’peak positions. The validation using equations (11) – (13) yields rising edge, falling edge, and pulse width consistent with those in Table 5, confirming the two-dimensional display method’s efficacy in verifying one-dimensional curve peak parameters. Conclusions Addressing the challenge of extracting the smallest measurable signal in the sidelobe image’s periphery for strong laser far-field focal spot measurements, we introduce a sidelobe weak signal region peak parameter detection method based on NVPCA image enhancement. Our findings demonstrate this method’s capability to isolate and extract the minimum measurable signal from sidelobe image peripheral peaks, increasing the dynamic range ratio to 1.528 times. This approach is crucial for accurately measuring weak signal areas in sidelobe beams, understanding their energy distribution, and laying the groundwork for future precise measurements of strong laser far-field focal spots in large-scale laser devices. © 2024 Science Press. All rights reserved.
  Accession Number: 20241215768417