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.

基于改进的GAF算法的EEG情感识别

利用脑电图(EEG)信号对人类的情感进行识别一直是一个重要且具有挑战性的研究领域。传统的方法都是对一维EEG信号进行分析,然后提取特征进行识别;但这种方法需要提取许多时域或频域上的特征才能取得较好的识别效果。考虑到二维图像蕴含的信息要远远比一维信号蕴含的信息丰富,因此将一维信号转换成二维图像可以提取更加有效的特征进行识别。为此,该文提出了一种基于改进的Gramian Angular Field(GAF)算法的EEG情感识别方法。首先,从EEG信号中提取alpha、beta、gama三个频段的子带信号;然后,提出了一种基于马氏距离加权的改进GAF算法将一维EEG信号转换成二维特征图像;接着,从二维图像中提取奇异值熵、图能量等特征;最后,利用卷积神经网络(CNN)对提取的EEG特征进行分类识别。基于广泛使用的DEAP数据集,针对四分类(HAHV、LAHV、LALV和HALV)情感识别任务,对该模型进行了验证。实验结果表明:所提算法的平均分类准确率达到92.63%,与现有的识别方法对比具有一定的优势。

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.

基于双通道GAF和深度残差网络的电能质量复合扰动识别

针对传统电能质量扰动(power quality disturbances,PQDs)识别过程中存在的信号特征提取复杂、算法识别能力不足和复合扰动区分困难等问题,提出了一种利用格拉姆角场(Gramain angular fields,GAF)和深度残差网络(residual network,ResNet)进行复合扰动识别的方法。首先对一维时间序列PQDs信号进行标准化与极坐标编码,然后采用双通道GAF方法保留信号时序特征并映射成为二维图像,形成信息充足、特征明显的双通道图像训练集,在此基础上利用ResNet进行深层次的特征提取,构造适用于复合PQDs分类的网络框架。仿真实验表明该方法特征提取能力强,且抗噪性能好,并且对复合扰动识别率高。

基于GAF与卷积神经网络的配电网故障选线新方法

消弧线圈接地配电网的暂态零序电流特征和选线结果呈现复杂的非线性关系,传统选线方法不适用于弱故障特征提取。为提高配电网的故障选线准确性,提出了一种基于格拉米角场GAF(Gramian angular field)与卷积神经网络CNN(convolutional neural network)的故障选线新方法。首先对不同工况下的零序电流进行GAF变换,利用变换后的GASF(Gramian angular summation field)和GADF(Gramian angular difference field)特征图样本集训练CNN,用于判定待测馈线是否故障。为提高选线方法的普适性,除了10 kV辐射状配电网样本库,另外建立了包含3种完全不同拓扑模型样本的混合库。最后,所提方法在辐射状模型库和混合库都得到了验证,判定结果证实了该方法的有效性和准确性。

Integral imaging-based tabletop light field 3D display with large viewing angle

Light field 3D display technology is considered a revolutionary technology to address the critical visual fatigue issues in the existing 3D displays.Tabletop light field 3D display provides a brand-new display form that satisfies multi-user shared viewing and collaborative works,and it is poised to become a potential alternative to the traditional wall and portable display forms.However,a large radial viewing angle and correct radial perspective and parallax are still out of reach for most current tabletop light field 3D displays due to the limited amount of spatial information.To address the viewing angle and perspective issues,a novel integral imaging-based tabletop light field 3D display with a simple flat-panel structure is proposed and developed by applying a compound lens array,two spliced 8K liquid crystal display panels,and a light shaping diffuser screen.The compound lens array is designed to be composed of multiple three-piece compound lens units by employing a reverse design scheme,which greatly extends the radial viewing angle in the case of a limited amount of spatial information and balances other important 3D display parameters.The proposed display has a radial viewing angle of 68.7°in a large display size of 43.5 inches,which is larger than the conventional tabletop light field 3D displays.The radial perspective and parallax are correct,and high-resolution 3D images can be reproduced in large radial viewing positions.We envision that this proposed display opens up possibility for redefining the display forms of consumer electronics.

Modified Maximum Likelihood Estimation of the Spatial Resolution for the Elliptical Gamma Camera SPECT Imaging Using Binary Inhomogeneous Markov Random Fields Models

In this work a complete approach for estimation of the spatial resolution for the gamma camera imaging based on the [1] is analyzed considering where the body distance is detected (close or far way). The organ of interest most of the times is not well defined, so in that case it is appropriate to use elliptical camera detection instead of circular. The image reconstruction is presented which allows spatially varying amounts of local smoothing. An inhomogeneous Markov random field (M.r.f.) model is described which allows spatially varying degrees of smoothing in the reconstructions and a re-parameterization is proposed which implicitly introduces a local correlation structure in the smoothing parameters using a modified maximum likelihood estimation (MLE) denoted as one step late (OSL) introduced by [2].

Single-cell volumetric imaging with light field microscopy: Advances in systems and algorithms

Single-cell volumetric imaging is essential for researching individual characteristics of cells.As a nonscanning imaging technique,lighteld microscopy(LFM)is a critical tool to achieve realtime three-dimensional imaging with the advantage of single-shot.To address the inherent limits including nonuniform resolution and block-wise artifacts,various modied LFM strategies have been developed to provide new insights into the structural and functional information of cells.This review will introduce the principle and development of LFM,discuss the improved approaches based on hardware designs and 3D reconstruction algorithms,and present the applications in single-cell imaging.

Impact of cognition-related single nucleotide polymorphisms on brain imaging phenotype in Parkinson’s disease

Multiple single nucleotide polymorphisms may contribute to cognitive decline in Parkinson’s disease. However, the mechanism by which these single nucleotide polymorphisms modify brain imaging phenotype remains unclear. The aim of this study was to investigate the potential effects of multiple single nucleotide polymorphisms on brain imaging phenotype in Parkinson’s disease. Forty-eight Parkinson’s disease patients and 39 matched healthy controls underwent genotyping and 7 T magnetic resonance imaging. A cognitive-weighted polygenic risk score model was designed, in which the effect sizes were determined individually for 36 single nucleotide polymorphisms. The correlations between polygenic risk score, neuroimaging features, and clinical data were analyzed. Furthermore, individual single nucleotide polymorphism analysis was performed to explore the main effects of genotypes and their interactive effects with Parkinson’s disease diagnosis. We found that, in Parkinson’s disease, the polygenic risk score was correlated with the neural activity of the hippocampus, parahippocampus, and fusiform gyrus, and with hippocampal-prefrontal and fusiform-temporal connectivity, as well as with gray matter alterations in the orbitofrontal cortex. In addition, we found that single nucleotide polymorphisms in α-synuclein(SNCA) were associated with white matter microstructural changes in the superior corona radiata, corpus callosum, and external capsule. A single nucleotide polymorphism in catechol-O-methyltransferase was associated with the neural activities of the lingual, fusiform, and occipital gyri, which are involved in visual cognitive dysfunction. Furthermore, DRD3 was associated with frontal and temporal lobe function and structure. In conclusion, imaging genetics is useful for providing a better understanding of the genetic pathways involved in the pathophysiologic processes underlying Parkinson’s disease. This study provides evidence of an association between genetic factors, cognitive functions, and multi-modality neuroimaging biomarkers in Parkinson’s disease.

Prospects of microwave-induced thermoacoustic imaging

Microwave-induced thermoacoustic imaging(MTAI)has advantages including the large imaging depth,high imaging resolution,high imaging contrast,and fast imaging speed.The thermoacoustic(TA)group of South China Normal University has dedicated to developing TA imaging for more than a decade and has made many breakthroughs.This review introduces these breakthroughs from two aspects including the improvement in techniques and the exploration of applications.On the technological level,there are ultrashort microwave pulse(USMP)-inducedTA imaging that can improve the imaging resolution,nonlinear thermoacoustic imaging(NTAI)that can improve the imaging contrast,polarized microwave-inducedthermoacoustic imaging(P-MTAI)that can obtain cellular-level alignment information,and more convenient and accurate handheld and multimodal probes.On the application side,the optimization and expansion have been carried out,mainly concentrating on breast and myocardial imaging.Finally,several current research directions are introduced,including the application of P-MTAI on joint imaging and research on whole-body imaging of small animals.

Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging,Laboratory VNIR Spectroscopy and Their Combination

Pedo-spectroscopy has the potential to provide valuable information about soil physical,chemical,and biological properties.Nowadays,wemay predict soil properties usingVNIRfield imaging spectra(IS)such as Prisma satellite data or laboratory spectra(LS).The primary goal of this study is to investigate machine learning models namely Partial Least Squares Regression(PLSR)and Support Vector Regression(SVR)for the prediction of several soil properties,including clay,sand,silt,organic matter,nitrate NO3-,and calcium carbonate CaCO_(3),using five VNIR spectra dataset combinations(%IS,%LS)as follows:C1(0%IS,100%LS),C2(20%IS,80%LS),C3(50%IS,50%LS),C4(80%IS,20%LS)and C5(100%IS,0%LS).Soil samples were collected at bare soils and at the upper(0–30 cm)layer.The data set has been split into a training dataset 80%of the collected data(n=248)and a validation dataset 20%of the collected data(n=61).The proposed PLSR and SVR models were trained then tested for each dataset combination.According to our results,SVR outperforms PLSR for both:C1(0%IS,100%LS)and C5(100%IS,0%LS).For Soil Organic Matter(SOM)prediction,it achieves(R^(2)=0.79%,RMSE=1.42%)and(R^(2)=0.76%,RMSE=1.3%),respectively.The data fusion has improved the soil property prediction.The highest improvement was obtained for the SOM property(R^(2)=0.80%,RMSE=1.39)when using the SVR model and applying the second Combination C2(20% of IS and 80%LS).

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。

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。

煤液化调节阀流场演变特性的测试与分析

利用粒子图像测速技术(particle image velocimetry,简称PIV)和涡量分析原理对调节阀不同工况下的流场信息进行测量,研究了进口压力对液压调节阀速度场、涡量场及湍动能的影响。结果表明:调节阀节流口处有对冲射流,其在阀芯头部下游汇合后形成向下游的整体喷射;节流口下游的油液轴向速度先减小后增大,在喉部末尾处附近趋于稳定;在靠近壁面区域油液径向流动速度都较低,在流道中心区域流动速度较高;阀芯头部和下游流道存在由速度梯度引起的介质回流旋涡,高涡量区域主要分布在阀芯头部和壁面处,强的正涡与负涡呈2条斜形宽带分布;阀芯头部为高涡量区且具有贴壁特征,壁面附近高涡量区向下游延展;随着调节阀进口压力的增大,阀口流量、流场高速分布区域、旋涡的强度和尺度以及湍动能均随之增大。

基于光流卷积网络的粒子图像测速自动掩模及速度场计算

粒子图像测速(PIV)技术是一种定量的非接触式全局速度场测量技术。在船舶与海洋工程领域,PIV实验中拍摄的粒子图像常出现结构物遮挡或自由液面等干扰现象,需要对其进行掩模后计算液相区域速度场。因此,实现PIV图像中干扰区域自动掩模及液相区域速度场高精度计算具有重要的意义。本文基于光流卷积神经网络LiteFlowNet,设计了一种可实现自动掩模及速度场计算的深度学习模型Mask-PIV-LiteFlowNet,并使用基于物体入水PIV实验图像掩模数据集和PIV速度场计算数据集制作的数据集对其进行训练和测试。测试结果表明,该模型能够有效减少临近掩模边界区域的速度场计算错误并能够精细地提取流场小尺度流动信息,相比于当前先进的PIV深度学习模型PIV-LiteFlowNet-en,本文提出的模型在对带结构物的合成粒子图像进行流场计算时精度获得了至少14.5%的提升,计算速度上获得了5.7%的提升。最后,使用楔形体入水PIV图像对提出的模型进行了测试,验证了模型的泛化能力。

基于改进SwiftNet的堆场图像实时分割网络

在堆场环境下,实时图像语义分割可以提供直观的场景类别信息。为节约工控机等边缘设备的硬件资源以及为多源信息融合提供图像语义类别信息,提出一种轻量化的实时语义分割网络模型。首先提出基于空间注意力引导的上采样融合模块,通过引入空间注意力和残差注意力结构设计一种轻量化的解码器,在上采样过程中还原空间细节,抑制冗余信息,进而融合不同来源的特征图;其次提出一种轻量化的级联空洞空间金字塔模块,利用级联的空洞卷积单元增大网络感受野,有效提取多尺度特征;最后使用通道分离、通道混洗、通道池化等操作,降低多尺度聚合过程中的计算开销。在公开数据集Camvid上,该模型的平均交并比(MIoU)为70.1%,推理速度为146.3帧/s,分割精度和推理速度优于ENet、ICNet等模型,消融实验结果也证明了所提各模块的有效性;在实际堆场图像数据集上,该模型的MIoU为93.5%,推理速度为123.8帧/s,证明模型结构具有良好的泛化性能。

基于不同维度低场核磁共振技术的大豆含油率检测与判别

大豆含油率的高低直接影响榨油与育种结果。为探究大豆含油率的最佳检测方法与构建含油率高低判别模型,该研究基于不同维度低场核磁共振(low field nuclear magnetic resonance,LF-NMR)技术,以国标法为对照,利用LF-NMR波谱和LF-NMR含油含水率软件检测大豆含油率;核磁共振成像(magnetic resonance imaging,MRI)结合深度学习,建立大豆含油率高低判别模型。引入低场二维核磁共振(low field two-dimensional nuclear magnetic resonance,LF-2D-NMR)技术,定性分析一维波谱中信号重叠无法区分组分的问题。试验结果表明,LF-NMR含油含水率软件能快速准确检测大豆含油率,T1-T2二维核磁图谱成功解决了自由水和油信号重叠问题。利用U-net++深度学习模型对MRI成像的矢状面、冠状面、横截面以及三面混合数据集进行训练,其中横截面评价指标与其他数据集相比更优,语义分割部分中平均交并比(mean intersection over union,mIoU)约0.9058,全局准确率0.9980,训练后的模型能够将MRI图像识别并分割,快速判别大豆含油率高低。试验证明,LF-NMR及MRI能够快速无损掌握大豆含油率信息,为大豆的高油育种提供了新思路和技术支持。

5.0T MRI临床应用价值与研究进展

5.0T MR成像平台通过创新性设计解决了传统超高场强设备存在的图像信号不均匀、全身成像难等技术问题,使图像质量得以显著提高。目前5.0T MRI已在神经系统、心血管系统、骨骼肌肉系统及腹部成像中展现出巨大潜力,并已衍生出多种相关创新性技术。本文就5.0T MRI的临床应用价值及其研究进展进行述评。

面向车载相机采集图像的智能汽车测试场景关键性量化模型

车载相机图像是构建智能汽车测试场景库的主要数据来源,但其中关键测试场景发生概率低,大部分场景的测试价值小,若将其直接应用于智能汽车测试会浪费大量测试资源。本文提出一种面向车载相机采集图像的智能汽车测试场景关键性量化模型。首先,基于实车相机参数对实车采集的图像进行处理,输出对行车安全具有影响的参数;其次,基于风险场理论将参数整合,输出测试场景关键性量化结果;最后,对实车采集的图像进行测试场景关键性量化验证,结果表明本文模型可以精确输出测试场景关键性的具体数值,进而对比不同场景的测试价值,证明本文提出的模型可以有效筛选智能汽车关键测试场景。

基于声全息法和单目视觉技术的柴油机噪声可视化实现

柴油机属于多噪声耦合动力装置,其噪声的识别和控制是内燃机研究领域的难点。为实现柴油机噪声的可视化,本研究结合了近场声全息法与单目视觉技术,开发了基于Labview的声像匹配模块,测试系统人机界面友好,并通过已知声源试验验证了声像匹配模块的正确性。将验证后的测试系统应用于高压共轨柴油机对主/次推力侧声源进行识别,结果表明:在大气压力为80kPa、转速为1800r/min的最大扭矩工况下,主推力侧出现较大辐射噪声的位置为中冷器进气管、曲轴定时齿形带轮、排气管、脚架和涡轮增压器等;次推力侧辐射噪声峰值出现在起动机位置。