Artificial self-powered and self-healable neuromorphic vision skin utilizing silver nanoparticle-doped ionogel photosynaptic heterostructure

Artificial skin should embody a softly functional film that is capable of self-powering,healing and sensing with neuromorphic processing.However,the pursuit of a bionic skin that combines high flexibility,self-healability,and zero-powered photosynaptic functionality remains elusive.In this study,we report a self-powered and self-healable neuromorphic vision skin,featuring silver nanoparticle-doped ionogel heterostructure as photoacceptor.The localized surface plasmon resonance induced by light in the nanoparticles triggers temperature fluctuations within the heterojunction,facilitating ion migration for visual sensing with synaptic behaviors.The abundant reversible hydrogen bonds in the ionogel endow the skin with remarkable mechanical flexibility and self-healing properties.We assembled a neuromorphic visual skin equipped with a 5×5 photosynapse array,capable of sensing and memorizing diverse light patterns.

Vision Transformer for Extracting Tropical Cyclone Intensity from Satellite Images

Tropical cyclone(TC)intensity estimation is a fundamental aspect of TC monitoring and forecasting.Deep learning models have recently been employed to estimate TC intensity from satellite images and yield precise results.This work proposes the ViT-TC model based on the Vision Transformer(ViT)architecture.Satellite images of TCs,including infrared(IR),water vapor(WV),and passive microwave(PMW),are used as inputs for intensity estimation.Experiments indicate that combining IR,WV,and PMW as inputs yields more accurate estimations than other channel combinations.The ensemble mean technique is applied to enhance the model's estimations,reducing the root-mean-square error to 9.32 kt(knots,1 kt≈0.51 m s^(-1))and the mean absolute error to 6.49 kt,which outperforms traditional methods and is comparable to existing deep learning models.The model assigns high attention weights to areas with high PMW,indicating that PMW magnitude is essential information for the model's estimation.The model also allocates significance to the cloud-cover region,suggesting that the model utilizes the whole TC cloud structure and TC eye to determine TC intensity.

抗CV2/CRMP5抗体阳性副肿瘤性脑脊髓炎合并神经精神狼疮一例

1病例报告,患者女,55岁,小学文化水平。因“反应迟钝5 d,双下肢无力1 d”于2021-3-19入院。5 d前无诱因出现反应迟钝,表现为记忆力下降,偶有答非所问但能正常交流,病程中伴有吞咽困难、头晕、视物旋转、言语笨拙,可疑抽搐发作(全面强直发作)1次。

基于Vision Transformer的虹膜——人脸多特征融合识别研究

为了提高生物特征识别系统的准确性和鲁棒性,本文研究基于计算机视觉的虹膜—人脸多特征融合识别方法。本文对面部图像中虹膜区域进行提取以及预处理,采用对比度增强和归一化操作,加强了特征提取的一致性,提升了图像质量。为了获取丰富的深度特征,本文使用Vision Transformer模型对预处理后的虹膜和面部图像进行特征提取。利用多头注意力机制将虹膜和面部的多模态特征信息进行融合,再利用全连接层进行分类识别。试验结果表明,该方法识别性能优秀,识别准确性显著提升。

Dual-Path Vision Transformer用于急性缺血性脑卒中辅助诊断

急性缺血性脑卒中是由于脑组织血液供应障碍导致的脑功能障碍,数字减影脑血管造影(DSA)是诊断脑血管疾病的金标准。基于患者的正面和侧面DSA图像,对急性缺血性脑卒中的治疗效果进行分级评估,构建基于Vision Transformer的双路径图像分类智能模型DPVF。为了提高辅助诊断速度,基于EdgeViT的轻量化设计思想进行了模型的构建;为了使模型保持轻量化的同时具有较高的精度,提出空间-通道自注意力模块,促进Transformer模型捕获更全面的特征信息,提高模型的表达能力;此外,对于DPVF的两分支的特征融合,构建交叉注意力模块对两分支输出进行交叉融合,促使模型提取更丰富的特征,从而提高模型表现。实验结果显示DPVF在测试集上的准确率达98.5%,满足实际需求。

细粒度图像分类上Vision Transformer的发展综述

细粒度图像分类(fine-grained image classification,FGIC)一直是计算机视觉领域中的重要问题。与传统图像分类任务相比,FGIC的挑战在于类间对象极其相似,使任务难度进一步增加。随着深度学习的发展,Vision Transformer(ViT)模型在视觉领域掀起热潮,并被引入到FGIC任务中。介绍了FGIC任务所面临的挑战,分析了ViT模型及其特性。主要根据模型结构全面综述了基于ViT的FGIC算法,包括特征提取、特征关系构建、特征注意和特征增强四方面内容,对每种算法进行了总结,并分析了它们的优缺点。通过对不同ViT模型在相同公用数据集上进行模型性能比较,以验证它们在FGIC任务上的有效性。最后指出了目前研究的不足,并提出未来研究方向,以进一步探索ViT在FGIC中的潜力。

基于Vision Transformer的小麦病害图像识别算法

小麦白粉病、赤霉病和锈病是危害小麦产量的三大病害。为提高小麦病害图像的识别准确率,构建一种基于Vision Transformer的小麦病害图像识别算法。首先,通过田间拍摄的方式收集包含小麦白粉病、赤霉病和锈病3种病害在内的小麦病害图像,并对原始图像进行预处理,建立小麦病害图像识别数据集;然后,基于改进的Vision Transformer构建小麦病害图像识别算法,分析不同迁移学习方式和数据增强对模型识别效果的影响。试验可知,全参数迁移学习和数据增强能明显提高Vision Transformer模型的收敛速度和识别精度。最后,在相同时间条件下,对比Vision Transformer、AlexNet和VGG16算法在相同数据集上的表现。试验结果表明,Vision Transformer模型对3种小麦病害图像的平均识别准确率为96.81%,相较于AlexNet和VGG16模型识别准确率分别提高6.68%和4.94%。

基于Vision Transformer与迁移学习的裤装廓形识别与分类

针对裤装廓形识别与分类模型的分类不准确问题,文章采用带有自注意力机制的Vision Transformer模型实现裤装廓形图像的分类,对于图片背景等无关信息对廓形识别的干扰,添加自注意力机制,增强有用特征通道。为防止因裤型样本数据集较少产生过拟合问题,可通过迁移学习方法对阔腿裤、喇叭裤、紧身裤、哈伦裤4种裤装廓形进行训练和验证,将改进的Vision Transformer模型与传统CNN模型进行对比实验,验证模型效果。实验结果表明:使用Vision Transformer模型在4种裤装廓形分类上的分类准确率达到97.72%,与ResNet-50和MobileNetV2模型相比均有提升,可为服装廓形的图像分类识别提供有力支撑,在实际服装领域中有较高的使用价值。

Artificial hawk-eye camera for foveated, tetrachromatic, and dynamic vision

With the rapid development of drones and autonomous vehicles, miniaturized and lightweight vision sensors that can track targets are of great interests. Limited by the flat structure, conventional image sensors apply a large number of lenses to achieve corresponding functions, increasing the overall volume and weight of the system.

Collaborative positioning for swarms:A brief survey of vision,LiDAR and wireless sensors based methods

As positioning sensors,edge computation power,and communication technologies continue to develop,a moving agent can now sense its surroundings and communicate with other agents.By receiving spatial information from both its environment and other agents,an agent can use various methods and sensor types to localize itself.With its high flexibility and robustness,collaborative positioning has become a widely used method in both military and civilian applications.This paper introduces the basic fundamental concepts and applications of collaborative positioning,and reviews recent progress in the field based on camera,LiDAR(Light Detection and Ranging),wireless sensor,and their integration.The paper compares the current methods with respect to their sensor type,summarizes their main paradigms,and analyzes their evaluation experiments.Finally,the paper discusses the main challenges and open issues that require further research.

基于Vision Transformer的阿尔茨海默病分类研究

为了有效地提升对阿尔茨海默病(AD)的磁共振成像(MRI)图像分类准确率,提出一种LC(Layer-Cut)-ViT方法。该方法通过引入Vision Transformer(ViT)的自注意力机制对MRI图像进行层切分,使模型能更好地理解图像的全局信息,同时突出切片间的特征关系。此外,通过配准、颅骨分离算法提取MRI图像的脑部组织部分,进一步提升模型的性能。实验结果显示,所提方法对阿尔茨海默病的MRI图像具有较好的分类能力。

Frequency and associated factors of accommodation and non-strabismic binocular vision dysfunction among medical university students

AIM:To investigate the frequency and associated factors of accommodation and non-strabismic binocular vision dysfunction among medical university students.METHODS:Totally 158 student volunteers underwent routine vision examination in the optometry clinic of Guangxi Medical University.Their data were used to identify the different types of accommodation and nonstrabismic binocular vision dysfunction and to determine their frequency.Correlation analysis and logistic regression were used to examine the factors associated with these abnormalities.RESULTS:The results showed that 36.71%of the subjects had accommodation and non-strabismic binocular vision issues,with 8.86%being attributed to accommodation dysfunction and 27.85%to binocular abnormalities.Convergence insufficiency(CI)was the most common abnormality,accounting for 13.29%.Those with these abnormalities experienced higher levels of eyestrain(χ2=69.518,P<0.001).The linear correlations were observed between the difference of binocular spherical equivalent(SE)and the index of horizontal esotropia at a distance(r=0.231,P=0.004)and the asthenopia survey scale(ASS)score(r=0.346,P<0.001).Furthermore,the right eye's SE was inversely correlated with the convergence of positive and negative fusion images at close range(r=-0.321,P<0.001),the convergence of negative fusion images at close range(r=-0.294,P<0.001),the vergence facility(VF;r=-0.234,P=0.003),and the set of negative fusion images at far range(r=-0.237,P=0.003).Logistic regression analysis indicated that gender,age,and the difference in right and binocular SE did not influence the emergence of these abnormalities.CONCLUSION:Binocular vision abnormalities are more prevalent than accommodation dysfunction,with CI being the most frequent type.Greater binocular refractive disparity leads to more severe eyestrain symptoms.

基于Vision Transformer和迁移学习的家庭领域哭声识别

针对SVM等传统机器学习算法准确率低和当前使用CNN处理家庭领域哭声识别在不同婴儿间出现泛化能力差的问题,提出了一种基于Vision Transformer和迁移学习的婴儿哭声音频分类算法。首先,为实现数据集样本的扩增,采用了包括梅尔频谱转换和数据增强的数据预处理技术,进而达到了增强模型鲁棒性的目的。而后,在微调后的Vision Transformer模型上进行迁移学习训练,同时,训练过程中利用了LookAhead优化器来不断调整模型参数以避免过拟合,最终实验实现了对婴儿哭声音频的自动分类。实验结果表明,本实验模型相比其他深度学习模型具有更高的精确率和更快的收敛速度,同时还能有效地学习到婴儿哭声中更具区分性的特征。可以在新生儿监护、听力筛查和异常检测等领域中发挥重要作用。

基于CV法和D-ANP法的国际物流供应链韧性风险评价及其应用

随着世界局势的复杂多变,提高国际物流供应链韧性风险应对能力、增强国际物流供应链韧性,是各行各业开展国际经营的必然策略。基于以上背景,首先,本文按照科学性、综合性和可比性的原则,构建国际物流供应链韧性风险指标体系;其次,利用CV法和D—ANP法确定指标权重,且使用组合赋权法得出最终综合权重;最后,选取安徽省汽车行业作为国际物流供应链韧性风险评价体系的应用对象,使用专家打分法获取国际物流供应链韧性三级指标风险评分,并结合综合权重得出国际物流供应链韧性风险等级,进而从企业和政府两方面提出加速企业数字化转型、构建风险预警体系、完善金融政策支持体系等应对策略,以提高应对国际供应链风险的能力,更好地护航国际物流供应链高质量发展。

Association of age at diagnosis of diabetes with subsequent risk of age-related ocular diseases and vision acuity

BACKGROUND The importance of age on the development of ocular conditions has been reported by numerous studies.Diabetes may have different associations with different stages of ocular conditions,and the duration of diabetes may affect the development of diabetic eye disease.While there is a dose-response relationship between the age at diagnosis of diabetes and the risk of cardiovascular disease and mortality,whether the age at diagnosis of diabetes is associated with incident ocular conditions remains to be explored.It is unclear which types of diabetes are more predictive of ocular conditions.AIM To examine associations between the age of diabetes diagnosis and the incidence of cataract,glaucoma,age-related macular degeneration(AMD),and vision acuity.METHODS Our analysis was using the UK Biobank.The cohort included 8709 diabetic participants and 17418 controls for ocular condition analysis,and 6689 diabetic participants and 13378 controls for vision analysis.Ocular diseases were identified using inpatient records until January 2021.Vision acuity was assessed using a chart.RESULTS During a median follow-up of 11.0 years,3874,665,and 616 new cases of cataract,glaucoma,and AMD,respectively,were identified.A stronger association between diabetes and incident ocular conditions was observed where diabetes was diagnosed at a younger age.Individuals with type 2 diabetes(T2D)diagnosed at<45 years[HR(95%CI):2.71(1.49-4.93)],45-49 years[2.57(1.17-5.65)],50-54 years[1.85(1.13-3.04)],or 50-59 years of age[1.53(1.00-2.34)]had a higher risk of AMD independent of glycated haemoglobin.T2D diagnosed<45 years[HR(95%CI):2.18(1.71-2.79)],45-49 years[1.54(1.19-2.01)],50-54 years[1.60(1.31-1.96)],or 55-59 years of age[1.21(1.02-1.43)]was associated with an increased cataract risk.T2D diagnosed<45 years of age only was associated with an increased risk of glaucoma[HR(95%CI):1.76(1.00-3.12)].HRs(95%CIs)for AMD,cataract,and glaucoma associated with type 1 diabetes(T1D)were 4.12(1.99-8.53),2.95(2.17-4.02),and 2.40(1.09-5.31),respectively.In multivariable-adjusted analysis,individuals with T2D diagnosed<45 years of age[β95%CI:0.025(0.009,0.040)]had a larger increase in LogMAR.Theβ(95%CI)for LogMAR associated with T1D was 0.044(0.014,0.073).CONCLUSION The younger age at the diagnosis of diabetes is associated with a larger relative risk of incident ocular diseases and greater vision loss.

基于改进Vision Transformer网络的农作物病害识别方法

基于DCNN模型的农作物病害识别方法在实验室环境下识别准确率高,但面对噪声时缺少鲁棒性.为了兼顾农作物病害识别的精度和鲁棒性,本文在标准ViT模型基础上加入增强分块序列化和掩码多头注意力,解决标准ViT模型缺乏局部归纳偏置和视觉特征序列的自注意力过于关注自身的问题.实验结果表明,本文的EPEMMSA-ViT模型对比标准ViT模型可以更高效的从零学习;当添加预训练权重训练网络时,EPEMMSA-ViT模型在数据增强的PlantVillage番茄子集上能够得到99.63%的分类准确率;在添加椒盐噪声的测试数据集上,对比ResNet50、DenseNet121、MobileNet和ConvNeXt的分类准确率分别提升了6.08%、9.78%、29.78%和12.41%;在添加均值模糊的测试数据集上,对比ResNet50、DenseNet121、MobileNet和ConvNeXt的分类准确率分别提升了18.92%、31.11%、20.37%和19.58%.

基于Vision Transformer和迁移学习的垃圾图像分类研究

为解决垃圾图像分类中分类准确率低及小样本类别性能差的问题,以生活垃圾图像为研究对象,以正确识别生活垃圾类别为研究目标,利用Vision Transformer模型为分类网络架构,使用迁移学习机制实现该模型在华为云垃圾分类数据集上的训练及分类推理。实验结果表明,基于注意力机制的分类模型相较于基于卷积结构的ResNet、DenseNet分类模型具有更高的分类准确率,可达96%,同时测试集的混淆矩阵表明Vision Transformer分类模型在样本不均衡数据集中对于小样本类别也具有较高的准确率,具有实际部署、推理的应用价值。

CVS的替代品——Subvision

目前，有许多商业与开放源码的版本控制系统存在。在开放源码界，最早出现的大概就是SCCS．其后又演变成为了RCS。以后的发展使得CVS几乎成为了开放源代码专门的标准版本控制系统．大多数的开放源代码专门都有提供CVS的档案库让一般使用者来取得专门的源代码。但随着开放源代码社区的快速成长，CVS的瓶颈渐渐显现出来。于是，类似于Aegis、Bitkeeper或是Perforce这样的工具不断涌现。但因为版权问题．都无法动摇CVS在开放源代码社区的地位。

基于融合卷积Vision Transformer的轻量化图像分类方法

在执行图像分类任务时,为了在确保模型具备全面的全局表征能力的同时,进一步增强其对局部特征的识别能力,提出一种融合卷积Vision Transformer(ViT)的图像分类方法,在ViT模型嵌入模块中融入卷积层。该方法在以复杂背景苹果叶部病理图像为主的Apple Leaf 9数据集上的平均准确率高达98.49%,超越主流CNN模型,接近于该数据集上最先进算法的性能水平。通过引入轻量化技术,不仅实现了模型精度的显著提升,同时还达到了将模型体积压缩至原有的四分之一,并有效地适配了INT8类型计算的硬件部署需求。此外,当该轻量化模型部署于我们自主研发的模型推理Web应用上时,推理时间相比之前减少了50%。