Simulation of the SMILE Soft X-ray Imager response to a southward interplanetary magnetic field turning

The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)Soft X-ray Imager(SXI)will shine a spotlight on magnetopause dynamics during magnetic reconnection.We simulate an event with a southward interplanetary magnetic field turning and produce SXI count maps with a 5-minute integration time.By making assumptions about the magnetopause shape,we find the magnetopause standoff distance from the count maps and compare it with the one obtained directly from the magnetohydrodynamic(MHD)simulation.The root mean square deviations between the reconstructed and MHD standoff distances do not exceed 0.2 RE(Earth radius)and the maximal difference equals 0.24 RE during the 25-minute interval around the southward turning.

Retinal vascular morphological characteristics in diabetic retinopathy: an artificial intelligence study using a transfer learning system to analyze ultra-wide field images

AIM:To investigate the morphological characteristics of retinal vessels in patients with different severity of diabetic retinopathy(DR)and in patients with or without diabetic macular edema(DME).METHODS:The 239 eyes of DR patients and 100 eyes of healthy individuals were recruited for the study.The severity of DR patients was graded as mild,moderate and severe non-proliferative diabetic retinopathy(NPDR)according to the international clinical diabetic retinopathy(ICDR)disease severity scale classification,and retinal vascular morphology was quantitatively analyzed in ultra-wide field images using RU-net and transfer learning methods.The presence of DME was determined by optical coherence tomography(OCT),and differences in vascular morphological characteristics were compared between patients with and without DME.RESULTS:Retinal vessel segmentation using RU-net and transfer learning system had an accuracy of 99%and a Dice metric of 0.76.Compared with the healthy group,the DR group had smaller vessel angles(33.68±3.01 vs 37.78±1.60),smaller fractal dimension(Df)values(1.33±0.05 vs 1.41±0.03),less vessel density(1.12±0.44 vs 2.09±0.36)and fewer vascular branches(206.1±88.8 vs 396.5±91.3),all P<0.001.As the severity of DR increased,Df values decreased,P=0.031.No significant difference between the DME and non-DME groups were observed in vascular morphological characteristics.CONCLUSION:In this study,an artificial intelligence retinal vessel segmentation system is used with 99%accuracy,thus providing with relatively satisfactory performance in the evaluation of quantitative vascular morphology.DR patients have a tendency of vascular occlusion and dropout.The presence of DME does not compromise the integral retinal vascular pattern.

High-resolution imaging of magnetic fields of banknote anti-counterfeiting strip using fiber diamond probe

Counterfeiting of modern banknotes poses a significant challenge,prompting the use of various preventive measures.One such measure is the magnetic anti-counterfeiting strip.However,due to its inherent weak magnetic properties,visualizing its magnetic distribution has been a longstanding challenge.In this work,we introduce an innovative method by using a fiber optic diamond probe,a highly sensitive quantum sensor designed specifically for detecting extremely weak magnetic fields.We employ this probe to achieve high-resolution imaging of the magnetic fields associated with the RMB 50denomination anti-counterfeiting strip.Additionally,we conduct computer simulations by using COMSOL Multiphysics software to deduce the potential geometric characteristics and material composition of the magnetic region within the anti-counterfeiting strip.The findings and method presented in this study hold broader significance,extending the RMB 50 denomination to various denominations of the Chinese currency and other items that employ magnetic anti-counterfeiting strips.These advances have the potential to significantly improve and promote security measures in order to prevent the banknotes from being counterfeited.

Lossless Compression Method for the Magnetic and Helioseismic Imager(MHI)Payload

The Solar Polar-orbit Observatory(SPO),proposed by Chinese scientists,is designed to observe the solar polar regions in an unprecedented way with a spacecraft traveling in a large solar inclination angle and a small ellipticity.However,one of the most significant challenges lies in ultra-long-distance data transmission,particularly for the Magnetic and Helioseismic Imager(MHI),which is the most important payload and generates the largest volume of data in SPO.In this paper,we propose a tailored lossless data compression method based on the measurement mode and characteristics of MHI data.The background out of the solar disk is removed to decrease the pixel number of an image under compression.Multiple predictive coding methods are combined to eliminate the redundancy utilizing the correlation(space,spectrum,and polarization)in data set,improving the compression ratio.Experimental results demonstrate that our method achieves an average compression ratio of 3.67.The compression time is also less than the general observation period.The method exhibits strong feasibility and can be easily adapted to MHI.

Machine learning algorithm partially reconfigured on FPGA for an image edge detection system

Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)signal.However,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or spoofing.One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images.But a high effort is required for image edge extraction.Here a support vector regression(SVR)model is proposed to reduce this computational load and processing time.The dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable region.Results show that the implementation in hardware is 68 times faster than that in software.This architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without DPR.This is also the lowest power consumption in current machine learning hardware implementations.Besides,the circuitry area is 41 times smaller.SVR with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning trajectory.This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.

A study on the discrete image method for calculation of transient electromagnetic fields in geological media

We conducted a study on the numerical calculation and response analysis of a transient electromagnetic field generated by a ground source in geological media. One solution method, the traditional discrete image method, involves complex operation, and its digital filtering algorithm requires a large number of calculations. To solve these problems, we proposed an improved discrete image method, where the following are realized： the real number of the electromagnetic field solution based on the Gaver-Stehfest algorithm for approximate inversion, the exponential approximation of the objective kernel function using the Prony method, the transient electromagnetic field according to discrete image theory, and closed-form solution of the approximate coefficients. To verify the method, we tentatively calculated the transient electromagnetic field in a homogeneous model and compared it with the results obtained from the Hankel transform digital filtering method. The results show that the method has considerable accuracy and good applicability. We then used this method to calculate the transient electromagnetic field generated by a ground magnetic dipole source in a typical geoelectric model and analyzed the horizontal component response of the induced magnetic field obtained from the ＂ground excitation-stratum measurement method. We reached the conclusion that the horizontal component response of a transient field is related to the geoelectric structure, observation time, spatial location, and others. The horizontal component response of the induced magnetic field reflects the eddy current field distribution and its vertical gradient variation. During the detection of abnormal objects, positions with a zero or comparatively large offset were selected for the drill- hole measurements or a comparatively long observation delay was adopted to reduce the influence of the ambient field on the survey results. The discrete image method and forward calculation results in this paper can be used as references for relevant research.

Characterization for elastic constants of fused deposition modelling-fabricated materials based on the virtual fields method and digital image correlation

Fused deposition modelling(FDM), a widely used rapid prototyping process, is a promising technique in manufacturing engineering. In this work, a method for characterizing elastic constants of FDM-fabricated materials is proposed. First of all, according to the manufacturing process of FDM, orthotropic constitutive model is used to describe the mechanical behavior. Then the virtual fields method(VFM) is applied to characterize all the mechanical parameters(Q, Q, Q, Q) using the full-field strain,which is measured by digital image correlation(DIC). Since the principal axis of the FDM-fabricated structure is sometimes unknown due to the complexity of the manufacturing process, a disk in diametrical compression is used as the load configuration so that the loading angle can be changed conveniently. To verify the feasibility of the proposed method, finite element method(FEM) simulation is conducted to obtain the strain field of the disk. The simulation results show that higher accuracy can be achieved when the loading angle is close to 30?. Finally, a disk fabricated by FDM was used for the experiment. By rotating the disk, several tests with different loading angles were conducted. To determine the position of the principal axis in each test, two groups of parameters(Q, Q, Q, Q) are calculated by two different groups of virtual fields. Then the corresponding loading angle can be determined by minimizing the deviation between two groups of the parameters. After that, the four constants(Q, Q, Q, Q) were determined from the test with an angle of 27?.

Measurement and evaluation of strain fields in T23 steel based on digital image correlation method

Surface strain fields of the designed compact tension(CT)specimens were investigated by digital image correlation(DIC)method.An integrative computer program was developed based on DIC algorithms to characterize the strain fields accurately and graphically.Strain distribution of the CT specimen was predicted by finite element method(FEM).Good agreement is observed between the surface strain fields measured by DIC and predicted by FEM,which reveals that the proposed method is practical and effective to determine the strain fields of CT specimens.Moreover,strain fields of the CT specimens with various compressive loads and notch diameters were studied by DIC.The experimental results can provide effective reference to usage of CT specimens in triaxial creep test by appropriately selecting specimen and experiment parameters.

Fusing PLSA model and Markov random fields for automatic image annotation

A novel image auto-annotation method is presented based on probabilistic latent semantic analysis(PLSA) model and multiple Markov random fields(MRF).A PLSA model with asymmetric modalities is first constructed to estimate the joint probability between images and semantic concepts,then a subgraph is extracted served as the corresponding structure of Markov random fields and inference over it is performed by the iterative conditional modes so as to capture the final annotation for the image.The novelty of our method mainly lies in two aspects:exploiting PLSA to estimate the joint probability between images and semantic concepts as well as multiple MRF to further explore the semantic context among keywords for accurate image annotation.To demonstrate the effectiveness of this approach,an experiment on the Corel5 k dataset is conducted and its results are compared favorably with the current state-of-the-art approaches.

A Remote Sensing Image Semantic Segmentation Method by Combining Deformable Convolution with Conditional Random Fields

Currently,deep convolutional neural networks have made great progress in the field of semantic segmentation.Because of the fixed convolution kernel geometry,standard convolution neural networks have been limited the ability to simulate geometric transformations.Therefore,a deformable convolution is introduced to enhance the adaptability of convolutional networks to spatial transformation.Considering that the deep convolutional neural networks cannot adequately segment the local objects at the output layer due to using the pooling layers in neural network architecture.To overcome this shortcoming,the rough prediction segmentation results of the neural network output layer will be processed by fully connected conditional random fields to improve the ability of image segmentation.The proposed method can easily be trained by end-to-end using standard backpropagation algorithms.Finally,the proposed method is tested on the ISPRS dataset.The results show that the proposed method can effectively overcome the influence of the complex structure of the segmentation object and obtain state-of-the-art accuracy on the ISPRS Vaihingen 2D semantic labeling dataset.

Effects of gradient high-field static magnetic fields on diabetic mice

Although 9.4 T magnetic resonance imaging(MRI) has been tested in healthy volunteers,its safety in diabetic patients is unclear.Furthermore,the effects of high static magnetic fields(SMFs),especially gradient vs.uniform fields,have not been investigated in diabetics.Here,we investigated the consequences of exposure to 1.0-9.4 T high SMFs of different gradients(>10 T/m vs.0-10 T/m)on type 1 diabetic(T1D) and type 2 diabetic(T2D) mice.We found that 14 h of prolonged treatment of gradient(as high as 55.5 T/m) high SMFs(1.0-8.6 T) had negative effects on T1D and T2D mice,including spleen,hepatic,and renal tissue impairment and elevated glycosylated serum protein,blood glucose,inflammation,and anxiety,while 9.4 T quasi-uniform SMFs at 0-10 T/m did not induce the same effects.In regular T1D mice(blood glucose>16.7 mmol/L),the>10 T/m gradient high SMFs increased malondialdehyde(P<0.01) and decreased superoxide dismutase(P<0.05).However,in the severe T1D mice(blood glucose≥30.0 mmol/L),the>10 T/m gradient high SMFs significantly increased tissue damage and reduced survival rate.In vitro cellular studies showed that gradient high SMFs increased cellular reactive oxygen species and apoptosis and reduced MS-1 cell number and proliferation.Therefore,this study showed that prolonged exposure to high-field(1.0-8.6 T)>10 T/m gradient SMFs(35-1 380 times higher than that of current clinical MRI)can have negative effects on diabetic mice,especially mice with severe T1D,whereas 9.4 T high SMFs at 0-10T/m did not produce the same effects,providing important information for the future development and clinical application of SMFs,especially high-field MRI.

Data compression of displacement fields in digital image correlation by non-integer quantization

To solve the real-time transmission problem of displacement fields in digital image correlation,two compression coding algorithms based on a discrete cosine transform(DCT)and discrete wavelet transform(DWT)are proposed.Based on the Joint Photographic Experts Group(JPEG)and JPEG 2000 standards,new non-integer and integer quantizations are proposed in the quantization procedure of compression algorithms.Displacement fields from real experiments were used to evaluate the compression ratio and computational time of the algorithm.The results show that the compression ratios of the DCT-based algorithm are mostly below 10%,which are much less than that of the DWT-based algorithm,and the computational speed is also significantly higher than that of the latter.These findings prove the algorithm s effectiveness in real-time displacement field wireless transmission.

Image Deformation by User-Defined Force Fields

We present a new method for image deformation. The warping technique provides smooth distortion with intuitive and easy manipulation. Driven by a restrained force field, the input image is deformed gradually and continuously. The method allows us to customize the force fields and the region of interest manually through some simple steps. Experimental results demonstrate the effectiveness and convenience of the approach.

An Adaptive Edge Detection Algorithm for Weed Image Analysis

Weeds are one of the utmost damaging agricultural annoyers that have a major influence on crops.Weeds have the responsibility to get higher production costs due to the waste of crops and also have a major influence on the worldwide agricultural economy.The significance of such concern got motivation in the research community to explore the usage of technology for the detection of weeds at early stages that support farmers in agricultural fields.Some weed methods have been proposed for these fields;however,these algorithms still have challenges as they were implemented against controlled environments.Therefore,in this paper,a weed image analysis approach has been proposed for the system of weed classification.In this system,for preprocessing,a Homomorphic filter is exploited to diminish the environmental factors.While,for feature extraction,an adaptive feature extraction method is proposed that exploited edge detection.The proposed technique estimates the directions of the edges while accounting for non-maximum suppression.This method has several benefits,including its ease of use and ability to extend to other types of features.Typically,low-level details in the formof features are extracted to identify weeds,and additional techniques for detecting cultured weeds are utilized if necessary.In the processing of weed images,certain edges may be verified as a footstep function,and our technique may outperform other operators such as gradient operators.The relevant details are extracted to generate a feature vector that is further given to a classifier for weed identification.Finally,the features have been used in logistic regression for weed classification.The model was assessed against logistic regression that accurately identified different kinds of weed images in naturalistic domains.The proposed approach attained weighted average recognition of 98.5%against the weed images dataset.Hence,it is assumed that the proposed approach might help in the weed classification system to accurately identify narrow and broad weeds taken captured in real environments.

Underwater Inhomogeneous Light Field Based on Improved Convolutional Neural Net Fish Image Recognition

In this paper, artificial intelligence image recognition technology is used to improve the recognition rate of individual domestic fish and reduce the recognition time, aiming at the problem that it is difficult to easily observe the species and growth of domestic fish in the underwater non-uniform light field environment. First, starting from the image data collected by polarizing imaging technology, this paper uses subpixel convolution reconstruction to enhance the image, uses image translation and fill technology to build the family fish database, builds the Adam-Dropout-CNN (A-D-CNN) network model, and its convolution kernel size is 3 × 3. The maximum pooling was used for downsampling, and the discarding operation was added after the full connection layer to avoid the phenomenon of network overfitting. The adaptive motion estimation algorithm was used to solve the gradient sparse problem. The experiment shows that the recognition rate of A-D-CNN is 96.97% when the model is trained under the domestic fish image database, which solves the problem of low recognition rate and slow recognition speed of domestic fish in non-uniform light field.

基于改进YOLOv5的遥感图像目标检测

目前目标检测技术虽然已经趋于成熟,但是对遥感图像的检测仍存在不少挑战。针对遥感图像的背景复杂、目标尺度差异大、目标方向任意等特点造成目标检测精度低下的问题,提出一种基于改进YOLOv5的遥感图像目标检测算法。首先,构建一种联合注意力的多尺度特征增强网络,充分融合高低层特征,使特征层具有语义信息的同时包含丰富的细节信息,并在融合过程中利用设计的特征聚焦模块帮助模型选择关键特征,抑制无关信息。其次,使用感受野模块(RFB)对融合后的特征图进行更新,扩大特征图的感受野,减少特征信息损失。最后,对目标增加旋转角度,并采用圆形平滑标签将回归问题转化成分类问题,提高遥感目标定位的准确性。在用于航拍图像目标检测的大规模数据集(DOTA)上的实验结果表明,与YOLOv5算法相比,所提算法的交并比(Io U)为0.5和0.5~0.95时的平均精度均值(m AP@0.5和m AP@0.5∶0.95)分别提高了7.3和3.3个百分点,能够明显提高复杂背景下遥感图像目标的检测精度,并改善对遥感目标的漏检和误检情况。

基于Transformer的多尺度遥感语义分割网络

为了提升遥感图像语义分割效果,本文针对分割目标类间方差小、类内方差大的特点,从全局上下文信息和多尺度语义特征2个关键点提出一种基于Transformer的多尺度遥感语义分割网络(muliti-scale Transformer network,MSTNet)。其由编码器和解码器2个部分组成,编码器包含基于Transformer改进的视觉注意网络(visual attention network,VAN)主干和基于空洞空间金字塔池化(atrous spatial pyramid pooling, ASPP)结构改进的多尺度语义特征提取模块(multi-scale semantic feature extraction module, MSFEM)。解码器采用轻量级多层感知器(multi-layer perception,MLP)配合编码器设计,充分分析所提取的包含全局上下文信息和多尺度表示的语义特征。MSTNet在2个高分辨率遥感语义分割数据集ISPRS Potsdam和LoveDA上进行验证,平均交并比(mIoU)分别达到79.50%和54.12%,平均F1-score(m F1)分别达到87.46%和69.34%,实验结果验证了本文所提方法有效提升了遥感图像语义分割的效果。

定向断裂控制爆破下层理页岩的致裂机理

为探究定向断裂控制爆破下层理页岩的爆破致裂机理,采用切缝药包,对四种切缝角度下的页岩立方体试件进行爆破试验,采用数字图像相关技术(DIC)对页岩试件表面应变场的演化过程进行监测,分析了微裂纹孕育至宏观裂纹贯通的内在机理,并基于盒维数理论计算了不同切缝角度下页岩试件表面裂纹的分形维数,采用Matlab软件对爆后块度的筛分方法进行了编程分析,开发了全自动的粒径分析程序,实现了粒径圈定的可视化。试验结果表明:试件在不同比例爆距内的裂纹总密度与比例爆距之间存在负相关的幂函数关系,切缝方向与层理弱面的夹角对微观损伤区域出现的位置影响显著,当层理弱面与切缝方向平行时,损伤区域多集中于层理弱面处,对宏观裂纹的扩展路径影响显著,易于形成单一裂纹;层理弱面处的能量泄漏是造成页岩爆破破碎效果较差的重要因素,当切缝方向与层理弱面一致时,试件爆后的大块占比较高,爆后块度的分形维数平均值在各组间最低,仅为0.7843,而当切缝方向与层理面垂直时,试件的爆后块度分布较为均匀,爆后块度的分形维数平均值达到了2.5233,爆破破碎效果相对较好。

同面阵列电极敏感场优化方法

同面阵列电极敏感场的非线性特性导致图像重建过程存在严重的不适定性,为提高同面3×4阵列电容成像系统的成像质量与稳定性,提出了一种数据降维并结合平方增敏的方法对敏感场进行优化。该方法依据最大熵原理实现敏感场的自适应降维寻优,重构得到的特征敏感场与实际敏感场具有更高的特征匹配度。另外,平方化的增敏方式可以增强敏感场特征数据与和非特征数据的对比度,同时还可以减弱敏感场矢量求解造成的特征边缘波动性。最后通过同面3×4阵列电容成像系统,设计了多组实验对所提优化方法的有效性进行验证,结果表明敏感场优化后的重建图像质量与稳定性明显提高。

大口径爆炸激波管出口冲击波流场图像测量

为了获取大视场下爆炸冲击波的纹影图像以及冲击波与被测模型相互作用后的流场演化特征,开展了基于聚焦纹影的反射式高速纹影测试。利用激光光源和高反射膜建立了系统所需的光源输入,并通过简化刀口栅设计,实现了远距离大口径爆炸激波管出口冲击波流场图像演化特征以及与固体模型相互作用的高时空分辨特征。研究结果表明,设计的聚焦式反射纹影系统在80 m以上距离处获取了大口径大当量爆炸激波管出口处6 m×3 m视场的冲击波特征,以及与固体相互作用的高时空分辨图像和状态数据;相比传统聚焦纹影系统,该系统具有结构简单、可实现性强、测试距离远,覆盖视场大等特点;通过设计不同光源输入,探讨了激光光源在远距离拍摄中的优势;通过冲击波阵面与固体模型的相互作用特征,揭示了冲击波作用于被测固体模型后的形成、演化和绕流状态。该测量结果能够为武器装备在受到冲击波作用后的状态评估和响应特性分析提供直观的图像数据支撑。