2024

• Record 109 of
  
  Title:On-orbit calibration of space camera lens distortion using a single image
  
  Author(s):Zhang, Gaopeng; Wang, Feng; Zhang, Guangdong; Zhang, Zhe; Du, Hubing; Zhao, Zixin; Wang, Changqing; Cao, Jianzhong; Zhao, Jingwei; Li, Yanjie; Lu, Rong
  Source: OPTICS AND LASERS IN ENGINEERING  Volume: 177  Issue:   DOI: 10.1016/j.optlaseng.2024.108140  Published: 2024  
  Abstract:Since space cameras need to withstand the harsh mechanical and thermal conditions in the space environment for a long time, it is necessary to calibrate them in orbit. However, existing calibration methods have various disadvantages, making them impossible to use in orbit. To address this problem, we present an on-orbit calibration of space camera lens distortion with the vanishing points obtained from a single image of the solar panel. First, we propose a parallel-line-extraction method based on collinear constraints to obtain the parallel lines. Then, we train the optimal vanishing point using the common point constraint method. Using the optimal vanishing point, we establish the optimization function of lens distortion based on vanishing point consistency. Finally, we present an improved genetic algorithm to solve the optimization function. Simulations and experiments show that the proposed method is flexible and robust.
  Accession Number: 108140
  ISSN: 0143-8166
  eISSN: 1873-0302
• Record 110 of
  
  Title:Interaction semantic segmentation network via progressive supervised learning
  
  Author(s):Zhao, Ruini; Xie, Meilin; Feng, Xubin; Guo, Min; Su, Xiuqin; Zhang, Ping
  Source: MACHINE VISION AND APPLICATIONS  Volume: 35  Issue: 2  DOI: 10.1007/s00138-023-01500-4  Published: 2024  
  Abstract:Semantic segmentation requires both low-level details and high-level semantics, without losing too much detail and ensuring the speed of inference. Most existing segmentation approaches leverage low- and high-level features from pre-trained models. We propose an interaction semantic segmentation network via Progressive Supervised Learning (ISSNet). Unlike a simple fusion of two sets of features, we introduce an information interaction module to embed semantics into image details, they jointly guide the response of features in an interactive way. We develop a simple yet effective boundary refinement module to provide refined boundary features for matching corresponding semantic. We introduce a progressive supervised learning strategy throughout the training level to significantly promote network performance, not architecture level. Our proposed ISSNet shows optimal inference time. We perform extensive experiments on four datasets, including Cityscapes, HazeCityscapes, RainCityscapes and CamVid. In addition to performing better in fine weather, proposed ISSNet also performs well on rainy and foggy days. We also conduct ablation study to demonstrate the role of our proposed component. Code is available at: https://github.com/Ruini94/ISSNet
  Accession Number: 26
  ISSN: 0932-8092
  eISSN: 1432-1769
• Record 111 of
  
  Title:A semi-supervised cross-modal memory bank for cross-modal retrieval
  
  Author(s):Huang, Yingying; Hu, Bingliang; Zhang, Yipeng; Gao, Chi; Wang, Quan
  Source: NEUROCOMPUTING  Volume: 579  Issue:   DOI: 10.1016/j.neucom.2024.127430  Published: 2024  
  Abstract:The core of semi -supervised cross -modal retrieval tasks lies in leveraging limited supervised information to measure the similarity between cross -modal data. Current approaches assume an association between unlabelled data and pre -defined k -nearest neighbour data, relying on classifier performance for this selection. With diminishing labelled data, classifier performance weakens, resulting in erroneous associations among unlabelled instances. Moreover, the lack of interpretability in class probabilities of unlabelled data hinders classifier learning. Thus, this paper focuses on learning pseudo -labels for unlabelled data, providing pseudosupervision to aid classifier learning. Specifically, a cross -modal memory bank is proposed, dynamically storing feature representations in a common space and class probability representations in a label space for each cross -modal data. Pseudo -labels are derived by computing feature representation similarity and adjusting class probabilities. During this process, imposing constraints on the classification loss between labelled data and contrastive losses between paired cross -modal data is a prerequisite for the successful learning of pseudolabels. This procedure significantly contributes to enhancing the credibility of these pseudo -labels. Empirical findings demonstrate that using only 10% labelled data, compared to prevailing semi -supervised techniques, this method achieves improvements of 2.6%, 1.8%, and 4.9% in MAP@50 on the Wikipedia, NUS -WIDE, and MS-COCO datasets, respectively.
  Accession Number: 127430
  ISSN: 0925-2312
  eISSN: 1872-8286
• Record 112 of
  
  Title:Classification of self-limited epilepsy with centrotemporal spikes by classical machine learning and deep learning based on electroencephalogram data
  
  Author(s):Liu, Xi; Zhang, Xinming; Yu, Tao; Dang, Ruochen; Li, Jian; Hu, Bingliang; Wang, Quan; Luo, Rong
  Source: BRAIN RESEARCH  Volume: 1830  Issue:   DOI: 10.1016/j.brainres.2024.148813  Published: 2024  
  Abstract:Electroencephalogram (EEG) has been widely utilized as a valuable assessment tool for diagnosing epilepsy in hospital settings. However, clinical diagnosis of patients with self -limited epilepsy with centrotemporal spikes (SeLECTS) is challenging due to the presence of similar abnormal discharges in EEG displays compared to other types of epilepsy (non-SeLECTS) patients. To assist the diagnostic process of epilepsy, a comprehensive classification study utilizing machine learning or deep learning techniques is proposed. In this study, clinical EEG was collected from 33 patients diagnosed with either SeLECTS or non-SeLECTS, aged between 3 and 11 years. In the realm of classical machine learning, sharp wave features (including upslope, downslope, and width at half maximum) were extracted from the EEG data. These features were then combined with the random forest (RF) and extreme random forest (ERF) classifiers to differentiate between SeLECTS and non-SeLECTS. Additionally, deep learning was employed by directly inputting the EEG data into a deep residual network (ResNet) for classification. The classification results were evaluated based on accuracy, F1 -score, area under the curve (AUC), and area under the precision -recall curve (AUPRC). Following a 10 -fold cross -validation, the ERF classifier achieved an accuracy of 73.15 % when utilizing sharp wave feature extraction for classification. The F1 -score obtained was 0.72, while the AUC and AUPRC values were 0.75 and 0.63, respectively. On the other hand, the ResNet model achieved a classification accuracy of 90.49 %, with an F1 -score of 0.90. The AUC and AUPRC values for ResNet were found to be 0.96 and 0.92, respectively. These results highlighted the significant potential of deep learning methods in SeLECTS classification research, owing to their high accuracy. Moreover, feature extraction -based methods demonstrated good reliability and could assist in identifying relevant biological features of SeLECTS within EEG data.
  Accession Number: 148813
  ISSN: 0006-8993
  eISSN: 1872-6240
• Record 113 of
  
  Title:Singularity engineering of the resonant perfect absorber
  
  Author(s):Ming, Xianshun; Ren, Dezheng; Shi, Lei; Sun, Qibing; Sun, Liqun; Wang, Leiran
  Source: RESULTS IN PHYSICS  Volume: 58  Issue:   DOI: 10.1016/j.rinp.2024.107500  Published: 2024  
  Abstract:The metal-dielectric-metal (MDM) perfect absorber (PA) is an important kind of resonant metasurface with promising applications in selective thermal emitting, solar energy harvesting, biosensing and so on. Establishing a direct link between resonant features and structural parameters is essential for guiding design processes and exploring novel applications. In this paper, we conduct a comprehensive investigation of the MDM PA, utilizing scattering singularity (pole/zero) engineering. We propose a straightforward design methodology to achieve a MDM PA operating at a specific wavelength, and demonstrate a design example with a maximum absorption of 99.93 % at 1200 nm and a full width of half maximum of about 155 nm, which is subsequently experimentally validated. The results indicate high absorption across a wide range of angles. This study sheds new light on fast design and analysis of MDM PAs.
  Accession Number: 107500
  ISSN: 2211-3797
  eISSN:
• Record 114 of
  
  Title:Pakistan's 2022 floods: Spatial distribution, causes and future trends from Sentinel-1 SAR observations
  
  Author(s):Chen, Fang; Zhang, Meimei; Zhao, Hang; Guan, Weigui; Yang, Aqiang
  Source: REMOTE SENSING OF ENVIRONMENT  Volume: 304  Issue:   DOI: 10.1016/j.rse.2024.114055  Published: 2024  
  Abstract:Floods are a great threat to Pakistan with increasing concern. As the consequences of increased extreme weather related to climate change, Pakistan experiences severe floods almost every year. This study aims to explore and analysis the actual inundated situation, magnitude, the possible causes of the 2022 devastating floods, and future trends. We presented an enhanced nationwide flood mapping method and compared with other pixel-based image processing techniques including active contours and change detection. These algorithms were applied to Sentinel-1 Ground Range Detected (GRD) Synthetic Aperture Radar (SAR) imagery (10 m spatial resolution) with various land types and inundation scenarios in Pakistan, and were evaluated using other reference flood products. Accuracy evaluation analysis demonstrated that our algorithm has high robustness and accuracy, with the overall accuracy (OA) higher than 0.83 and critical success index (CSI) up to 0.91, and is suitable for automated flood monitoring in near real time. Nearly one-third of the lands were flooded in 2022, and more than half were inundated croplands. Punjab and Sindh provinces were the most severely affected regions, with the proportions of inundated area in 2022 (21.26% and 20.55%) nearly twice of that in 2010 (11.40% and 12.70%), indicating an intensified flooding trend. Analysis of possible influential factors showed that the intense and cumulative rainfall during the monsoon season (June to August) was the major cause of the 2022 flood event. Although the snow melted rapidly in June (the average change in snow depth is similar to 10 mm), the overall ablation contributed insignificant amount to the flood water. The glacial lake outburst floods (GLOFs) induced by abnormal April-May heatwave provide water flowed into the tributaries of the Indus River, but are difficult to spread for thousands of kilometers from mountains to the plain downstream. The combination of the intrinsic arid climate and extreme floods exacerbate the already severe situation.
  Accession Number: 114055
  ISSN: 0034-4257
  eISSN: 1879-0704
• Record 115 of
  
  Title:Nanosecond pulse X-ray emission source based on ultrafast laser modulation
  
  Author(s):Li Yun; Su Tong; Sheng Li-Zhi; Zhang Rui-Li; Liu Duo; Liu Yong-An; Qiang Peng-Fei; Yang Xiang-Hui; Xu Ze-Fang
  Source: ACTA PHYSICA SINICA  Volume: 73  Issue: 4  DOI: 10.7498/aps.73.20231505  Published: 2024  
  Abstract:In response to the growing demand for miniaturized ultrafast pulsed X-ray sources in the fields of fundamental science and space applications, we design and develop an ultrafast pulsed X-ray generator based on a laser-modulated light source and a photoelectric cathode. This innovative technology addresses the limitations commonly encountered in traditional X-ray emission devices, such as low repetition rate, insufficient time stability, and suboptimal pulse characteristics. Our effort is to study and develop the ultrafast modulation control module for the pulsed X-ray generator. This effort results in achieving high levels of time accuracy and stability in ultrafast time-varying photon signals. Moreover, we successfully generate nanosecond pulsed X-rays by using a laser-controlled light source. Theoretically, we establish a comprehensive time response model for the pulsed X-ray generator in response to short pulses. This includes a thorough analysis of the time characteristics of the emitted pulsed X-rays in the time domain. Experimentally, we conduct a series of tests related to various time-related parameters of the laser-controlled light source. Additionally, we design and implemente an experimental test system for assessing the time characteristics of pulsed X-rays, by using an ultrafast scintillation detector. The experimental results clearly demonstrate that our pulsed X-ray generator achieves impressive capabilities, including high repetition rates (12.5 MHz), ultrafast pulses (4 ns), and exceptional time stability (400 ps) in X-ray emission. These results closely align with our established theoretical model. Compared with traditional modulation techniques, our system exhibits significant improvement in pulse time parameters, thereby greatly expanding its potential applications. This research provides a valuable insight into achieving ultra-high time stability and ultrafast pulsed X-ray emission sources. These advances will further enhance the capabilities of X-ray technology for scientific research and space applications.
  Accession Number: 40701
  ISSN: 1000-3290
  eISSN:
• Record 116 of
  
  Title:Tetherless Optical Neuromodulation: Wavelength from Orange-red to Mid-infrared
  
  Author(s):Sun, Chao; Fan, Qi; Xie, Rougang; Luo, Ceng; Hu, Bingliang; Wang, Quan
  Source: NEUROSCIENCE BULLETIN  Volume:   Issue:   DOI: 10.1007/s12264-024-01179-1  Published: 2024  
  Abstract:Optogenetics, a technique that employs light for neuromodulation, has revolutionized the study of neural mechanisms and the treatment of neurological disorders due to its high spatiotemporal resolution and cell-type specificity. However, visible light, particularly blue and green light, commonly used in conventional optogenetics, has limited penetration in biological tissue. This limitation necessitates the implantation of optical fibers for light delivery, especially in deep brain regions, leading to tissue damage and experimental constraints. To overcome these challenges, the use of orange-red and infrared light with greater tissue penetration has emerged as a promising approach for tetherless optical neuromodulation. In this review, we provide an overview of the development and applications of tetherless optical neuromodulation methods with long wavelengths. We first discuss the exploration of orange-red wavelength-responsive rhodopsins and their performance in tetherless optical neuromodulation. Then, we summarize two novel tetherless neuromodulation methods using near-infrared light: upconversion nanoparticle-mediated optogenetics and photothermal neuromodulation. In addition, we discuss recent advances in mid-infrared optical neuromodulation.
  Accession Number:
  ISSN: 1673-7067
  eISSN: 1995-8218
• Record 117 of
  
  Title:All-optical neural network nonlinear activation function based on the optical bistability within a micro-ring resonator
  
  Author(s):Zhang, Hui; Wen, Jin; Wu, Zhengwei; Wang, Qian; Yu, Huimin; Zhang, Ying; Pan, Yu; Yin, Lan; Wang, Chenglong; Qu, Shuangchao
  Source: OPTICS COMMUNICATIONS  Volume: 558  Issue:   DOI: 10.1016/j.optcom.2024.130374  Published: 2024  
  Abstract:Training all-optical neural networks in itself remains an unresolved problem, and the challenges compound when the problem is turned into the hardware implementations. In this paper, we propose a nonlinear activation function based on optical bistability within a micro-ring resonator (MRR), achieving threshold control without external modulation. Furthermore, a convolutional neural network similar to the Le-Net-5 architecture is designed, in which all nonlinear activation functions are composed of optical bistable hysteresis loop. The numerical simulation results demonstrate that the recognition rate on the Fashion-MNIST dataset can achieve 91.3%, which means that the optical neuromorphic computation can be implemented by utilizing the nonlinear optical effects themselves in the all-optical hardware. Such a scheme promises access to the all-optical neural network training in the optical hardware environment compared to numerical activation functions.
  Accession Number: 130374
  ISSN: 0030-4018
  eISSN: 1873-0310
• Record 118 of
  
  Title:Iodide-based glass with combination of high transparency and conductivity: A novel promising candidate for transparent microwave absorption and radar stealth
  
  Author(s):Guo, Chen; Chen, Chao; Wan, Rui; Yang, Liqing; Guan, Yongmao; Wang, Pengfei
  Source: CHEMICAL ENGINEERING JOURNAL  Volume: 484  Issue:   DOI: 10.1016/j.cej.2024.148930  Published: 2024  
  Abstract:The rapid development of the electronics industry has sparked widespread interest in transparent microwave-absorbing materials. Herein, iodide-based transparent conductive glass was used as a candidate material for transparent microwave absorption. AgI-AgPO3-WO3 glasses with varying AgI content were synthesized employing a quench-melting method. Their structures, optical and electrical properties, microwave absorption performance, and radar cross section (RCS) reduction were thoroughly investigated. The 45AgI-45AgPO(3)-10WO(3) sample exhibited satisfactory microwave absorption, achieving a minimum reflection loss (RLmin) of - 47.18 dB, effective absorption bandwidth (EAB) of 1.97 GHz, and RCS reduction of 31.46 dB m(2) in the X band. This was attributed to the synergistic effects of dielectric and magnetic losses, and impedance matching and electromagnetic attenuation. It also manifested acceptable performance in the Ku band (RLmin = - 14.58 dB, EAB = 1.38 GHz, and RCS reduction = 13.37 dB m(2)), which was primarily attributed to dielectric loss and electromagnetic attenuation. The conductive glass exhibited an optical transmittance of similar to 80 % in the range of 500-2000 nm. In summary, this study highlights the potential use of transparent conductive glasses as transparent microwave-absorbing media for electromagnetic interference shielding applications in optical windows and domes, and stealth applications in high-performance optical cameras and optical detection device systems.
  Accession Number: 148930
  ISSN: 1385-8947
  eISSN: 1873-3212
• Record 119 of
  
  Title:Spectral encoder to extract the efficient features of Raman spectra for reliable and precise quantitative analysis
  
  Author(s):Gao, Chi; Fan, Qi; Zhao, Peng; Sun, Chao; Dang, Ruochen; Feng, Yutao; Hu, Bingliang; Wang, Quan

  Source: SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY  Volume: 312  DOI: 10.1016/j.saa.2024.124036  Published: 2024  

  Abstract:Raman spectroscopy has become a powerful analytical tool highly demanded in many applications such as microorganism sample analysis, food quality control, environmental science, and pharmaceutical analysis, owing to its non-invasiveness, simplicity, rapidity and ease of use. Among them, quantitative research using Raman spectroscopy is a crucial application field of spectral analysis. However, the entire process of quantitative modeling largely relies on the extraction of effective spectral features, particularly for measurements on complex samples or in environments with poor spectral signal quality. In this paper, we propose a method of utilizing a spectral encoder to extract effective spectral features, which can significantly enhance the reliability and precision of quantitative analysis. We built a latent encoded feature regression model; in the process of utilizing the autoencoder for reconstructing the spectrometer output, the latent feature obtained from the intermediate bottleneck layer is extracted. Then, these latent features are fed into a deep regression model for component concentration prediction. Through detailed ablation and comparative experiments, our proposed model demonstrates superior performance to common methods on single -component and multicomponent mixture datasets, remarkably improving regression precision while without needing user -selected parameters and eliminating the interference of irrelevant and redundant information. Furthermore, in-depth analysis reveals that latent encoded feature possesses strong nonlinear feature representation capabilities, low computational costs, wide adaptability, and robustness against noise interference. This highlights its effectiveness in spectral regression tasks and indicates its potential in other application fields. Sufficient experimental results show that our proposed method provides a novel and effective feature extraction approach for spectral analysis, which is simple, suitable for various methods, and can meet the measurement needs of different real -world scenarios.

  Accession Number: 124036

  ISSN: 1386-1425

  eISSN: 1873-3557
• Record 120 of
  
  Title:Performance improvement of a discrete dynode electron multiplication system through the optimization of secondary electron emitter and the adoption of double-grid dynode structure
  
  Author(s):Liu, Biye; Li, Jie; Chen, Song; Yang, Jishi; Hu, Wenbo; Tian, Jinshou; Wu, Shengli
  Source: NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT  Volume: 1062  Issue:   DOI: 10.1016/j.nima.2024.169162  Published: 2024  
  Abstract:The discrete dynode electron multiplication system (DD-EMS) is the core part of commonly used photomultiplier tubes and electron multipliers, and it has a great influence on the signal amplification capability of these devices. In this work, the sputtering time of Mg target during the deposition of the surface MgO layer of the MgO/ (MgO-Au)/Au multilayer film as the secondary electron emitter was optimized, and the strategy of double-grid structures applied at the 7th and 8th dynodes was proposed with the intention of improving the gain and stability of nine-stage DD-EMS under electron bombardment to satisfy the requirements of detecting the single photon or single charged particle. The investigation results show that the DD-EMS fabricated by using the MgO/(MgO-Au)/ Au film with a Mg target's sputtering time of 3600 s has the highest maximal gain of 1.22 x 106 and the lowest gain attenuation rate of 15.7%/mC under electron bombardment. In addition, the DD-EMS with the double-grid structure has a higher maximal gain of 1.62 x 106 and a lower gain attenuation rate of 11.6%/mC under continuous electron bombardment, which are 32.8% increased and 17.7% reduced respectively in comparison with that of the single-grid structure.
  Accession Number: 169162
  ISSN: 0168-9002
  eISSN: 1872-9576