2024

• Record 169 of
  
  Title:One-Dimensional Gap Soliton Molecules and Clusters in Optical Lattice-Trapped Coherently Atomic Ensembles via Electromagnetically Induced Transparency
  
  Author(s):Chen, Zhiming; Xie, Hongqiang; Zhou, Qi; Zeng, Jianhua
  Source: CRYSTALS  Volume: 14  Issue: 1  DOI: 10.3390/cryst14010036  Published: 2024  
  Abstract:In past years, optical lattices have been demonstrated as an excellent platform for making, understanding, and controlling quantum matters at nonlinear and fundamental quantum levels. Shrinking experimental observations include matter-wave gap solitons created in ultracold quantum degenerate gases, such as Bose-Einstein condensates with repulsive interaction. In this paper, we theoretically and numerically study the formation of one-dimensional gap soliton molecules and clusters in ultracold coherent atom ensembles under electromagnetically induced transparency conditions and trapped by an optical lattice. In numerics, both linear stability analysis and direct perturbed simulations are combined to identify the stability and instability of the localized gap modes, stressing the wide stability region within the first finite gap. The results predicted here may be confirmed in ultracold atom experiments, providing detailed insight into the higher-order localized gap modes of ultracold bosonic atoms under the quantum coherent effect called electromagnetically induced transparency.
  Accession Number: 36
  ISSN:
  eISSN: 2073-4352
• Record 170 of
  
  Title:Interface Contact Thermal Resistance of Die Attach in High-Power Laser Diode Packages
  
  Author(s):Deng, Liting; Li, Te; Wang, Zhenfu; Zhang, Pu; Wu, Shunhua; Liu, Jiachen; Zhang, Junyue; Chen, Lang; Zhang, Jiachen; Huang, Weizhou; Zhang, Rui
  Source: ELECTRONICS  Volume: 13  Issue: 1  DOI: 10.3390/electronics13010203  Published: 2024  
  Abstract:The reliability of packaged laser diodes is heavily dependent on the quality of the die attach. Even a small void or delamination may result in a sudden increase in junction temperature, eventually leading to failure of the operation. The contact thermal resistance at the interface between the die attach and the heat sink plays a critical role in thermal management of high-power laser diode packages. This paper focuses on the investigation of interface contact thermal resistance of the die attach using thermal transient analysis. The structure function of the heat flow path in the T3ster thermal resistance testing experiment is utilized. By analyzing the structure function of the transient thermal characteristics, it was determined that interface thermal resistance between the chip and solder was 0.38 K/W, while the resistance between solder and heat sink was 0.36 K/W. The simulation and measurement results showed excellent agreement, indicating that it is possible to accurately predict the interface contact area of the die attach in the F-mount packaged single emitter laser diode. Additionally, the proportion of interface contact thermal resistance in the total package thermal resistance can be used to evaluate the quality of the die attach.
  Accession Number: 203
  ISSN:
  eISSN: 2079-9292
• Record 171 of
  
  Title:Rapid Determination of Positive-Negative Bacterial Infection Based on Micro-Hyperspectral Technology
  
  Author(s):Du, Jian; Tao, Chenglong; Qi, Meijie; Hu, Bingliang; Zhang, Zhoufeng
  Source: SENSORS  Volume: 24  Issue: 2  DOI: 10.3390/s24020507  Published: 2024  
  Abstract:To meet the demand for rapid bacterial detection in clinical practice, this study proposed a joint determination model based on spectral database matching combined with a deep learning model for the determination of positive-negative bacterial infection in directly smeared urine samples. Based on a dataset of 8124 urine samples, a standard hyperspectral database of common bacteria and impurities was established. This database, combined with an automated single-target extraction, was used to perform spectral matching for single bacterial targets in directly smeared data. To address the multi-scale features and the need for the rapid analysis of directly smeared data, a multi-scale buffered convolutional neural network, MBNet, was introduced, which included three convolutional combination units and four buffer units to extract the spectral features of directly smeared data from different dimensions. The focus was on studying the differences in spectral features between positive and negative bacterial infection, as well as the temporal correlation between positive-negative determination and short-term cultivation. The experimental results demonstrate that the joint determination model achieved an accuracy of 97.29%, a Positive Predictive Value (PPV) of 97.17%, and a Negative Predictive Value (NPV) of 97.60% in the directly smeared urine dataset. This result outperformed the single MBNet model, indicating the effectiveness of the multi-scale buffered architecture for global and large-scale features of directly smeared data, as well as the high sensitivity of spectral database matching for single bacterial targets. The rapid determination solution of the whole process, which combines directly smeared sample preparation, joint determination model, and software analysis integration, can provide a preliminary report of bacterial infection within 10 min, and it is expected to become a powerful supplement to the existing technologies of rapid bacterial detection.
  Accession Number: 507
  ISSN:
  eISSN: 1424-8220
• Record 172 of
  
  Title:CMID: Crossmodal Image Denoising via Pixel-Wise Deep Reinforcement Learning
  
  Author(s):Guo, Yi; Gao, Yuanhang; Hu, Bingliang; Qian, Xueming; Liang, Dong; Vozel, Benoit
  Source: SENSORS  Volume: 24  Issue: 1  DOI: 10.3390/s24010042  Published: 2024  
  Abstract:Removing noise from acquired images is a crucial step in various image processing and computer vision tasks. However, the existing methods primarily focus on removing specific noise and ignore the ability to work across modalities, resulting in limited generalization performance. Inspired by the iterative procedure of image processing used by professionals, we propose a pixel-wise crossmodal image-denoising method based on deep reinforcement learning to effectively handle noise across modalities. We proposed a similarity reward to help teach an optimal action sequence to model the step-wise nature of the human processing process explicitly. In addition, We designed an action set capable of handling multiple types of noise to construct the action space, thereby achieving successful crossmodal denoising. Extensive experiments against state-of-the-art methods on publicly available RGB, infrared, and terahertz datasets demonstrate the superiority of our method in crossmodal image denoising.
  Accession Number: 42
  ISSN:
  eISSN: 1424-8220
• Record 173 of
  
  Title:Rapid Solidification of Invar Alloy
  
  Author(s):He, Hanxin; Yao, Zhirui; Li, Xuyang; Xu, Junfeng
  Source: MATERIALS  Volume: 17  Issue: 1  DOI: 10.3390/ma17010231  Published: 2024  
  Abstract:The Invar alloy has excellent properties, such as a low coefficient of thermal expansion, but there are few reports about the rapid solidification of this alloy. In this study, Invar alloy solidification at different undercooling (Delta T) was investigated via glass melt-flux techniques. The sample with the highest undercooling of Delta T = 231 K (recalescence height 140 K) was obtained. The thermal history curve, microstructure, hardness, grain number, and sample density of the alloy were analyzed. The results show that with the increase in solidification undercooling, the XRD peak of the sample shifted to the left, indicating that the lattice constant increased and the solid solubility increased. As the solidification of undercooling increases, the microstructure changes from large dendrites to small columnar grains and then to fine equiaxed grains. At the same time, the number of grains also increases with the increase in the undercooling. The hardness of the sample increases with increasing undercooling. If Delta T >= 181 K (128 K), the grain number and the hardness do not increase with undercooling.
  Accession Number: 231
  ISSN:
  eISSN: 1996-1944
• Record 174 of
  
  Title:Hybrid Fiber-Single Crystal Fiber Chirped-Pulse Amplification System Emitting More Than 1.5 GW Peak Power With Beam Quality Better Than 1.3
  
  Author(s):Li, Feng; Zhao, Wei; Li, Qianglong; Zhao, Hualong; Wang, Yishan; Yang, Yang; Wen, Wenlong; Cao, Xue
  Source: JOURNAL OF LIGHTWAVE TECHNOLOGY  Volume: 42  Issue: 1  DOI: 10.1109/JLT.2023.3312399  Published: 2024  
  Abstract:A hybrid chirped pulse amplification system composed by the monolithic fiber pre-amplifier and a two-stage single-pass single crystal fiber amplifier was demonstrated. A maximum power of 68 W at the repetition rate of 100 kHz was obtained. The laser pulses were amplified and then compressed using a 1600 line/mm grating pair compressor. A short pulse duration of 358 fs and a power of 54 W were obtained at 100 kHz, corresponding to a peak power of 1.508 GW, to the best of our knowledge, this is the highest peak power ever obtained from single crystal fiber at repetition rate above 100 kHz due to the consideration of the third order dispersion which was engraved in the stretcher and the tuning capacity of higher-order dispersion compensation of chirped fiber Bragg grating. Additionally, the beam quality better than 1.3 was obtained. This high peak power CPA system with excellent comprehensive parameters will find various applications in scientific research and industrial applications.
  Accession Number:
  ISSN: 0733-8724
  eISSN: 1558-2213
• Record 175 of
  
  Title:Biomedical Image Segmentation Using Denoising Diffusion Probabilistic Models: A Comprehensive Review and Analysis
  
  Author(s):Liu, Zengxin; Ma, Caiwen; She, Wenji; Xie, Meilin
  Source: APPLIED SCIENCES-BASEL  Volume: 14  Issue: 2  DOI: 10.3390/app14020632  Published: 2024  
  Abstract:Biomedical image segmentation plays a pivotal role in medical imaging, facilitating precise identification and delineation of anatomical structures and abnormalities. This review explores the application of the Denoising Diffusion Probabilistic Model (DDPM) in the realm of biomedical image segmentation. DDPM, a probabilistic generative model, has demonstrated promise in capturing complex data distributions and reducing noise in various domains. In this context, the review provides an in-depth examination of the present status, obstacles, and future prospects in the application of biomedical image segmentation techniques. It addresses challenges associated with the uncertainty and variability in imaging data analyzing commonalities based on probabilistic methods. The paper concludes with insights into the potential impact of DDPM on advancing medical imaging techniques and fostering reliable segmentation results in clinical applications. This comprehensive review aims to provide researchers, practitioners, and healthcare professionals with a nuanced understanding of the current state, challenges, and future prospects of utilizing DDPM in the context of biomedical image segmentation.
  Accession Number: 632
  ISSN:
  eISSN: 2076-3417
• Record 176 of
  
  Title:Study on Stray Light Testing and Suppression Techniques for Large-Field of View Multispectral Space Optical Systems
  
  Author(s):Lu, Yi; Xu, Xiping; Zhang, Ning; Lv, Yaowen; Xu, Liang
  Source: IEEE ACCESS  Volume: 12  Issue:   DOI: 10.1109/ACCESS.2024.3369471  Published: 2024  
  Abstract:To evaluate the ability of space optical systems to suppress off-axis stray light, this paper proposes a stray light testing method for large-field of view, multispectral spatial optical systems based on point source transmittance (PST). And a stray light testing platform was developed using a high-brightness simulated light source, large-aperture off-axis reflective collimator, high-precision positioning mechanism and a double column tank to evaluate the stray light PST index of spatial optical system. On the basis of theoretical analyses, a set of calibration lenses and stray light elimination structures such as hoods, baffle and stop are designed for the accuracy calibration of stray light testing systems. The theoretical PST values of the calibration lens at different off-axis angles are analyzed by Trace Pro software simulation and compared with the measured values to calibrate the accuracy of the system. The testing results show that the PST measurement range of the system reaches 10(-3)similar to 10(-10) when the off-axis angles of the calibration lens are in the range of +/- 5 degrees similar to +/- 60 degrees. The stray light test system has the advantages of wide working band, high automation and large dynamic range, and its test results can be used in the correction of lens hood and other applications.
  Accession Number:
  ISSN: 2169-3536
  eISSN:
• Record 177 of
  
  Title:Complex Noise-Based Phase Retrieval Using Total Variation and Wavelet Transform Regularization
  
  Author(s):Qin, Xing; Gao, Xin; Yang, Xiaoxu; Xie, Meilin
  Source: PHOTONICS  Volume: 11  Issue: 1  DOI: 10.3390/photonics11010071  Published: 2024  
  Abstract:This paper presents a phase retrieval algorithm that incorporates sparsity priors into total variation and framelet regularization. The proposed algorithm exploits the sparsity priors in both the gradient domain and the spatial distribution domain to impose desirable characteristics on the reconstructed image. We utilize structured illuminated patterns in holography, consisting of three light fields. The theoretical and numerical analyses demonstrate that when the illumination pattern parameters are non-integers, the three diffracted data sets are sufficient for image restoration. The proposed model is solved using the alternating direction multiplier method. The numerical experiments confirm the theoretical findings of the lighting mode settings, and the algorithm effectively recovers the object from Gaussian and salt-pepper noise.
  Accession Number: 71
  ISSN:
  eISSN: 2304-6732
• Record 178 of
  
  Title:Attention Network with Outdoor Illumination Variation Prior for Spectral Reconstruction from RGB Images
  
  Author(s):Song, Liyao; Li, Haiwei; Liu, Song; Chen, Junyu; Fan, Jiancun; Wang, Quan; Chanussot, Jocelyn
  Source: REMOTE SENSING  Volume: 16  Issue: 1  DOI: 10.3390/rs16010180  Published: 2024  
  Abstract:Hyperspectral images (HSIs) are widely used to identify and characterize objects in scenes of interest, but they are associated with high acquisition costs and low spatial resolutions. With the development of deep learning, HSI reconstruction from low-cost and high-spatial-resolution RGB images has attracted widespread attention. It is an inexpensive way to obtain HSIs via the spectral reconstruction (SR) of RGB data. However, due to a lack of consideration of outdoor solar illumination variation in existing reconstruction methods, the accuracy of outdoor SR remains limited. In this paper, we present an attention neural network based on an adaptive weighted attention network (AWAN), which considers outdoor solar illumination variation by prior illumination information being introduced into the network through a basic 2D block. To verify our network, we conduct experiments on our Variational Illumination Hyperspectral (VIHS) dataset, which is composed of natural HSIs and corresponding RGB and illumination data. The raw HSIs are taken on a portable HS camera, and RGB images are resampled directly from the corresponding HSIs, which are not affected by illumination under CIE-1964 Standard Illuminant. Illumination data are acquired with an outdoor illumination measuring device (IMD). Compared to other methods and the reconstructed results not considering solar illumination variation, our reconstruction results have higher accuracy and perform well in similarity evaluations and classifications using supervised and unsupervised methods.
  Accession Number: 180
  ISSN:
  eISSN: 2072-4292
• Record 179 of
  
  Title:Adaptive Kalman Filter Based on Online ARW Estimation for Compensating Low-Frequency Error of MHD ARS
  
  Author(s):Su, Yunhao; Han, Junfeng; Ma, Caiwen; Wu, Jianming; Wang, Xuan; Zhu, Qinghua; Shen, Jie
  Source: IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT  Volume: 73  Issue:   DOI: 10.1109/TIM.2024.3375962  Published: 2024  
  Abstract:Magnetohydrodynamic angular rate sensor (MHD ARS) can precisely detect angular vibration information with a bandwidth of up to one kilohertz. However, due to secondary flow and viscous force, it experiences performance degradation when measuring low-frequency angular vibrations. This article presents an adaptive Kalman filter that uses online angular random walk (ARW) estimation to correct for the low-frequency error of MHD ARS, where a microelectromechanical system (MEMS) gyroscope is used to measure low-frequency vibrations. The proposed algorithm determines the signal frequency based on the ARW coefficients and adjusts the measurement noise covariance to achieve accurate fusion results. Thus, the method solves the problem of frequency-dependent variation of the amplitude response of the sensors in data fusion. Initially, the algorithm calculates the ARW coefficient recursively utilizing the measurement signals of both sensors. Then, the operational frequencies of both sensors are determined by analyzing the correlation between the ARW coefficient and frequency. Subsequently, in the Sage-Husa adaptive Kalman filter (SHAKF), the Kalman gain matrix is adjusted by modifying the measurement noise variances of both sensor signals individually. Moreover, the stability of the proposed algorithm is achieved by introducing an adaptive matrix to constrain the measurement noise covariance estimation. In the experiment, the fusion effects of single-frequency and mixed-frequency signals are tested separately. The experimental results show that for frequency variation and frequency mixing, the proposed algorithm in this study significantly improves the fusion results.
  Accession Number: 9509510
  ISSN: 0018-9456
  eISSN: 1557-9662
• Record 180 of
  
  Title:Intelligent Space Object Detection Driven by Data from Space Objects
  
  Author(s):Tang, Qiang; Li, Xiangwei; Xie, Meilin; Zhen, Jialiang
  Source: APPLIED SCIENCES-BASEL  Volume: 14  Issue: 1  DOI: 10.3390/app14010333  Published: 2024  
  Abstract:With the rapid development of space programs in various countries, the number of satellites in space is rising continuously, which makes the space environment increasingly complex. In this context, it is essential to improve space object identification technology. Herein, it is proposed to perform intelligent detection of space objects by means of deep learning. To be specific, 49 authentic 3D satellite models with 16 scenarios involved are applied to generate a dataset comprising 17,942 images, including over 500 actual satellite Palatino images. Then, the five components are labeled for each satellite. Additionally, a substantial amount of annotated data is collected through semi-automatic labeling, which reduces the labor cost significantly. Finally, a total of 39,000 labels are obtained. On this dataset, RepPoint is employed to replace the 3 x 3 convolution of the ElAN backbone in YOLOv7, which leads to YOLOv7-R. According to the experimental results, the accuracy reaches 0.983 at a maximum. Compared to other algorithms, the precision of the proposed method is at least 1.9% higher. This provides an effective solution to intelligent recognition for spatial target components.
  Accession Number: 333
  ISSN:
  eISSN: 2076-3417