Robust zero-watermarking algorithm based on discrete wavelet transform and daisy descriptors for encrypted medical image

In the intricate network environment,the secure transmission of medical images faces challenges such as information leakage and malicious tampering,significantly impacting the accuracy of disease diagnoses by medical professionals.To address this problem,the authors propose a robust feature watermarking algorithm for encrypted medical images based on multi-stage discrete wavelet transform(DWT),Daisy descriptor,and discrete cosine transform(DCT).The algorithm initially encrypts the original medical image through DWT-DCT and Logistic mapping.Subsequently,a 3-stage DWT transformation is applied to the encrypted medical image,with the centre point of the LL3 sub-band within its low-frequency component serving as the sampling point.The Daisy descriptor matrix for this point is then computed.Finally,a DCT transformation is performed on the Daisy descriptor matrix,and the low-frequency portion is processed using the perceptual hashing algorithm to generate a 32-bit binary feature vector for the medical image.This scheme utilises cryptographic knowledge and zero-watermarking technique to embed watermarks without modifying medical images and can extract the watermark from test images without the original image,which meets the basic re-quirements of medical image watermarking.The embedding and extraction of water-marks are accomplished in a mere 0.160 and 0.411s,respectively,with minimal computational overhead.Simulation results demonstrate the robustness of the algorithm against both conventional attacks and geometric attacks,with a notable performance in resisting rotation attacks.

Two-dimensional C_(2)N-based single-atom catalyst with complex microenvironment for enhanced electrochemical nitrogen reduction:A descriptor-based design

The catalytic descriptor with operational feasibility is highly desired towards rational design of high-performance catalyst especially the electrode/electrolyte solution interface working under mild conditions.Herein,we demonstrate that the descriptorΩparameterized by readily accessible intrinsic properties of metal center and coordination is highly operational and efficient in rational design of single-atom catalyst(SAC)for driving electrochemical nitrogen reduction(NRR).Using twodimensional metal(M)-B_(x)P_(y)S_(z)N_m@C_(2)N as prototype SAC models,we reveal that^(*)N_(2)+(H~++e~-)→^(*)N_(2)H acts predominantly as the potential-limiting step(PLS)of NRR on M-B_(2)P_(2)S_(2)@C_(2)N and M-B_(1)P_(1)S_(1)N_(3)@C_(2)N regardless of the distinction in coordination microenvironment.Among the 28 screened M active sites,withΩvalues close to the optimal 4,M-B_(2)P_(2)S_(2)@C_(2)N(M=V(Ω=3.53),Mo(Ω=5.12),and W(Ω=3.92))and M-B_(1)P_(1)S_(1)N_(3)@C_(2)N(M=V(Ω=3.00),Mo(Ω=4.34),and W(Ω=3.32))yield the lowered limiting potential(U_(L))as-0.45,-0.54.-0.36,-0.58,-0.25,and-0.24 V,respectively,thus making them the promising NRR catalysts.More importantly,these SACs are located around the top of volcano-shape plot of U_(L) versusΩ,re-validatingΩas an effective descriptor for accurately predicting the high-activity NRR SACs even with complex coordination.Our study unravels the relationship between active-site structure and NRR performance via the descriptorΩ,which can be applied to other important sustainable electrocatalytic reactions involving activation of small molecules viaσ-donation andπ^(*)-backdonation mechanism.

Building Facade Point Clouds Segmentation Based on Optimal Dual-Scale Feature Descriptors

To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.

Degree-Based Entropy Descriptors of Graphenylene Using Topological Indices

Graph theory plays a significant role in the applications of chemistry,pharmacy,communication,maps,and aeronautical fields.The molecules of chemical compounds are modelled as a graph to study the properties of the compounds.The geometric structure of the compound relates to a few physical properties such as boiling point,enthalpy,π-electron energy,andmolecular weight.The article aims to determine the practical application of graph theory by solving one of the interdisciplinary problems describing the structures of benzenoid hydrocarbons and graphenylene.The topological index is an invariant of a molecular graph associated with the chemical structure,which shows the correlation of chemical structures using many physical,chemical properties and biological activities.This study aims to introduce some novel degree-based entropy descriptors such as ENTSO,ENTGH,ENTHG,ENTSS,ENTNSO,ENTNReZ1,ENTNReZ2 and ENTNSS using the respective topological indices.Also,the above-mentioned entropy measures and physico-chemical properties of benzenoid hydrocarbons are fitted using linear regression models and calculated for graphenylene structure.

Combining descriptor-based analyses and mean-field modeling of the electrochemical interface to comprehend trends of catalytic processes at the solid/liquid interface

Electrocatalysis is undergoing a renaissance due to its central importance for a sustainable energy economy,relying on green(electro-)chemical processes to harvest,convert,and store energy.Theoretical considerations by electronic structure methods are key to identify potential material motifs for electrocatalytic processes at the solid/liquid interface.Most commonly,heuristic concepts in the realm of materials screening by the compilation of volcano plots are used,which rely on a plethora of simplifications and approximations of the complex electrochemical interface.While the investigation of the catalytic processes at the solid/liquid interface mainly relies on descriptor-based approaches,in the present future article it is discussed that the inclusion of the liquid part of the interface by mean-field models is crucial to elevate screening approaches to the next level.

T_(M)-Eigenvalues of Odd-Order Tensors

In this paper,we propose a definition for eigenvalues of odd-order tensors based on some operators.Also,we define the Schur form and the Jordan canonical form of such tensors,and discuss commuting families of tensors.Furthermore,we prove some eigenvalue ine-qualities for Hermitian tensors.Finally,we introduce characteristic polynomials of odd-order tensors.

Novel 3D local feature descriptor of point clouds based on spatial voxel homogenization for feature matching

Obtaining a 3D feature description with high descriptiveness and robustness under complicated nuisances is a significant and challenging task in 3D feature matching.This paper proposes a novel feature description consisting of a stable local reference frame(LRF)and a feature descriptor based on local spatial voxels.First,an improved LRF was designed by incorporating distance weights into Z-and X-axis calculations.Subsequently,based on the LRF and voxel segmentation,a feature descriptor based on voxel homogenization was proposed.Moreover,uniform segmentation of cube voxels was performed,considering the eigenvalues of each voxel and its neighboring voxels,thereby enhancing the stability of the description.The performance of the descriptor was strictly tested and evaluated on three public datasets,which exhibited high descriptiveness,robustness,and superior performance compared with other current methods.Furthermore,the descriptor was applied to a 3D registration trial,and the results demonstrated the reliability of our approach.

Design of novel transition-metal-doped C_(4)N_(4) as highly effective electrocatalysts for nitrogen fixation with a new intrinsic descriptor

Electrocatalytic nitrogen reduction reaction(NRR) is an efficient and green way to produce ammonia,which offers an alternative option to the conventional Haber-Bosch process.Unfortunately,the large-scale industrial application of NRR processes is still hindered by poor Faraday efficiency and high overpotential,which need to be overcome urgently.Herein,combined with density functional theory and particle swarm optimization algorithm for the nitrogen carbide monolayer structural search(C_mN_(8-m),m=1-7),the surprising discovery is that single transition metal-atom-doped C_(4)N_(4) monolayers(TM@C_(4)N_(4)) could effectively accelerate nitrogen reduction reaction.TM@C_(4)N_(4)(TM=29 transition metals) as single-atom catalysts are evaluated via traditional multi-step screening method,and their structures,NRR activity,selectivity and solvation effect are investigated to evaluate their NRR performance,Through the screening steps,W@C_(4)N_(4) possesses the highest activity for NRR with a very low limiting potential of-0.29 V.Moreover,an intrinsic descriptor φ is proposed with machine learning,which shortens the screening process and provides a new idea for finding efficient SACs.This work not only offers promising catalysts W@C_(4)N_(4) for NRR process but also offers a new intrinsic and universal descriptor φ.

基于GA-SVM的钢轨廓形类型在线识别算法研究

针对轨道交通日常运维中钢轨廓形自动化检测识别率不高的情况,提出了一种基于几何描述符和支持向量机(SVM)的高精度钢轨廓形在线识别算法。利用结构光传感器对钢轨廓形数据进行采集,采用几何去噪算法对廓形进行离群点剔除和重采样预处理。通过廓形几何描述符对不同类别钢轨廓形进行特征提取,制作廓形特征数据集用于训练SVM。采用遗传算法(GA)对SVM模型参数进行优化选取。将优化训练后的SVM模型用于钢轨廓形检测并和传统廓形识别方法进行对比。结果表明:提出的采用几何描述符的GA-SVM模型平均准确率达到99.62%,单帧廓形识别用时6.43 ms,能有效提升廓形识别准确率与高速性,满足轨道车辆在线检测的需求,并为轨道自动化检测提供了理论和技术支撑。

基于直线段检测和LT描述符的矿井图像线特征匹配算法

图像匹配是同步定位与地图构建(SLAM)技术中极为重要的一环,用于根据图像之间的变换关系确定相机位姿。基于线特征的图像匹配方法具有较强的鲁棒性和抗噪能力,更加适用于井下图像匹配,基于深度学习的线描述符对线段遮挡等场景具有较高的鲁棒性,性能优于传统描述符,但卷积神经网络架构的描述符将可变长度线段抽象为固定维进行描述,不利于线段长度及视差变化较大图像的匹配。针对上述问题,提出一种基于直线段检测和线描述符的矿井图像线特征匹配算法。在频域利用单参数同态滤波降低图像的照射分量,并增强反射分量,提升亮度及对比度;在YUV空间利用对比度受限的自适应直方图均衡化(CLAHE)算法对亮度分量进行均衡,使亮度分布更加均匀;变换至RGB空间提取直线段检测(LSD)线,引入一种基于Transformer架构的LT描述符构建LSD线的特征向量,最后完成线特征匹配。实验结果表明:该算法结合了同态滤波和CLAHE算法的优点,增强后图像的亮度适中,对比度良好,灰度分布均匀,增强效果优于单参数同态滤波算法、EnlightenGAN算法;该算法提取的线特征数较原图平均提升了32.92%,在不同相似纹理占比、不同程度旋转与平移变化的井下图像匹配中鲁棒性好,平均正确匹配数为61.75对,平均精度为86.83%,优于线二进制描述符(LBD)算法、LBD_NNDR算法、LT算法,能够满足矿井图像稳健匹配的需求。

基于非线性各向异性滤波的图像特征匹配算法

图像特征匹配是增强现实系统中的关键技术,匹配精度是提升特征匹配性能的关键。提出了一种多尺度特征匹配加强算法(I-AKAZE),通过对非线性各向异性滤波过程中传导函数的改进,减缓图像梯度值大的区域非线性扩散速度,极大程度地保留了匹配图像的边缘特征;同时,结合改进的非线性量化加速稳健特征描述符(NLG-SURF),提高了描述符的识别率。实验结果表明I-AKAZE算法在Mikolajczyk数据集上的可重复性得分相比目前先进的AKAZE算法有着大幅度提升,对应的特征描述符的平均识别率提升8.4%,并且运行速度比经典的SIFT算法快约19%,算法整体在检测和描述阶段上的性能都有提升。

ML-QSPR方法预测煤基液体的燃料性能

煤基液体混合物如煤焦油、煤直接液化油的分子结构描述和性质预测是开发煤基液体产品高值化工艺和技术的重要基础。由于煤基液体主要由C、H、O、N、S元素构成数量庞杂、芳环结构各异的混合物,因此,使用Python中的RDKit工具包,利用简化分子线性输入规范(Simplified Molecular Input Line Entry System,SMILES)语言构建煤基液体中物质分子描述符,描述符包含样品元素信息、环数与环结构信息、原子数及分子量信息等共计115个分子描述符。对比人工信息提取方法,将所构建的分子描述符能够体现煤基液体分子结构碎片、分子量及原子个数信息等作为机器学习的特征输入变量,用于建立预测煤基液体的燃料性能的分子机器学习-定量结构性质关系方法 (ML-QSPR),实现对燃料低位热值(LHV)、液体密度(ρ)、闪点(FP)、十六烷值(CN)4个关键燃料性能参数的快速预测。模型验证分析表明LHV、ρ、FP模型的R^(2)分别为0.996、0.988、0.987;CN预测中加入混合物数据进行预测,R^(2)=0.959。与已公开报道的预测LHV、ρ、FP、CN性质方法对比,笔者提出ML-QSPR方法在预测4个关键燃料性能参数准确度方面有提升,在获取结果速度方面有显著优势。利用ML-QSPR模型预测得到的煤基液体制特种燃料性能参数数据库中的信息,分析增加不同族组分物质的碳原子数量时4个燃料性能参数的演变趋势,发现LHV、ρ、FP、CN四个燃料性能参数均受碳数(n)影响显著。由于LHV主要由n决定,不同族组分物质的LHV差距小;而不同族组分物质的ρ、FP和CN性质差距明显。此外,本研究训练好的模型可用于预测新的分子,为新型燃料分子设计提供参考;ML-QSPR方法作为迁移学习模型可在今后用于煤基液体其他场景相关理化性质的分析。

基于形状描述符的滞回曲线相似性指标的形成

为了形成全面的相似度指标以评价滞回曲线的拟合程度,引入了已广泛用于图像特征提取的形状描述符,并提出了基于这些描述符形成计算数值模拟曲线相对于实验曲线的相似度指标的具体流程.根据该流程,形成了一个基于形状上下文的相似度指标.先计算两曲线滞回圈之间的相似度,再对这些相似度进行加权组合,形成整个滞回环的相似度.为了验证该指标,采用Bouc-Wen模型进行数值模拟研究.改变Bouc-Wen模型的5个参数,并形成51条数值模拟曲线,计算这些曲线相对于中值曲线的相似度与力峰值点、耗能、刚度误差.结果表明,相似度与误差之间的斯皮尔曼相关系数绝对值均大于0.78,有强烈的相关性,从而验证了该指标的可行性和指标形成流程的有效性.

面向产业人才需求识别的多层职技网络构建研究

[目的/意义]传统职业分类体系作为观察劳动力市场的重要工具,难以及时反映产业人才需求的变化。为化解这一难题,文章聚焦探究如何利用数字化手段描绘职业与技能要求间的关系,建构多层职技网络的方法与实施路径。[方法/过程]首先,设计一套中文语境下描述不同岗位的通用语言——跨工作描述符,并据此建立起面向招聘数据资源开发利用的岗位描述框架;其次,建立基于岗位描述的职业智能分类技术,进而形成一套体现技能要求的多层职技网络构建方法与模式;最后,以上海集成电路产业为例,对该方法进行验证。[结果/结论]源自企业实际用工活动的多层职技网络能够辅助细粒度、灵活地观测多样化的产业人才需求。

一种基于点云语义图描述符的回环检测方法研究

随着计算机水平和机器人传感器技术的不断进步,移动机器人已逐渐向智能化发展。其中,定位与建图(SLAM)是其定位的关键技术,回环检测是SLAM系统的重要模块。为解决传统回环检测耗时长和效率低等问题,本文提出一种基于点云语义图描述符的回环检测方法,在KITTI里程计数据集00和05序列中进行回环检测性能测试,并与Scan Context、LEGO-LOAM和VFH三种算法进行对比。结果表明:该方法在100%准确率下的召回率和50%召回率下的准确率最高,回环检测性能最好。

新的广义时滞系统容许性条件

针对广义时滞系统,基于李雅普诺夫第二方法和广义系统的受限等价变换,结合积分不等式技术,给出一个线性矩阵不等式(linear matrix inequality,LMI)形式的容许性条件。首先,利用广义系统的受限等价变换得出广义时滞系统是正则且无脉冲的;然后,通过选取增广型Lyapunov-Krasovskii泛函(L-K泛函)和多重积分型L-K泛函,引入松弛型L-K泛函构建新的L-K泛函,利用Jensen积分不等式和Wirtinger积分不等式对L-K泛函求导后产生的积分项进行处理,得出广义时滞系统的稳定性条件,进而得到广义时滞系统的容许性条件;最后,利用MATLAB中的LMI工具箱,通过数值算例验证所用方法的可行性和有效性。

酱香型白酒感官品质描述形成、发展与趋势

酱香型白酒因其深厚文化底蕴、传统固态酿造工艺、独特风味与感官体验,深受广大消费者的喜爱。文章于中国知网、全国标准信息公共服务平台及食品伙伴网数据库检索“酱香”“酱酒”相关文献121篇、标准96项,得到酱香型白酒感官描述词537个,通过对文献材料梳理分析,简述酱香型白酒感官品质描述形成、发展、趋势及其标准化历程,总结酱香型白酒风味物质与气味特征关系,分析现有描述词相关问题,并提出构建酱香型白酒感官品质描述体系及结合现代感官评价技术表征酱香型白酒感官品质的相关思路与建议。

基于傅里叶描述子的手势识别方法

手势识别是计算机视觉人机交互应用领域关键技术,手势轮廓携带有手势重要特征,准确捕获手势轮廓对提高手势识别具有重要意义。针对手掌轮廓特征提取困难和手势识别率低问题,提出了基于傅里叶描述子的手势识别方法,按照候选窗口最大轮廓傅里叶描述子匹配度和置信度分割出手掌区域;跟踪手掌轮廓计算其傅里叶描述子得到手势轮廓特征值;将16个手势轮廓特征值作为BP人工神经网络的输入,利用BP人工神经网络识别手势。实验表明,该方法能有效捕获手势轮廓和识别19种手势,具有识别率高、性能优良和鲁棒性好等优点。

食醋主要感官特性描述词筛选及地域消费偏好性分析

食醋风味是影响消费者喜好和选购的主要因素,为建立较为通用的食醋感官评价标准,该研究通过感官定量描述分析的方法对市售食醋感官特性进行分析,建立了22个感官描述词,并定义了感官描述词的风味描述和标准参比物,其中包括3个口感描述词,13个香气描述词,6个口味描述词,筛选出食醋关键感官属性为酸味、甜味、焦糖味、协调度及持久度。研究人员召集了不同地域的250名消费者对陈醋和香醋进行偏好性分析,利用多元统计分析、皮尔森相关性分析对所得数据进行研究。结果表明消费者整体倾向于口感持久度、柔和度及协调度高,香气上带有明显谷物香、花香、果香及焦糖味,口味上甜味、鲜味高的食醋。不同地域消费者对产品偏好有一定差异,华北地区与华东地区的消费者整体口味较为相似,偏向于酸味中等,略有甜味的产品,西部地区的消费者偏向于酸味较重的产品,华中地区消费者则更偏向于甜味较高且酸味较低的产品。

基于局部描述子的小样本轴承故障诊断方法

深度学习由于其在特征表示方面的优势,近年来已被应用于很多领域,但是在故障诊断领域很难获取大量的故障样本来训练模型。针对这一问题,提出了一种基于局部描述子的小样本轴承故障诊断方法,利用改进的深度最近邻神经网络(Deep Nearest Neighbor Neural Network,DN4)进行故障诊断。首先通过短时傅里叶变换将振动信号转换为二维时频图像,特征提取主干网络采用ResNet-12网络,引入高效多尺度注意力机制(Efficient Multi-scale Attention,EMA)来更好地提取故障特征,得到轴承故障的局部描述子,最后利用K近邻算法来得到故障类别。为验证所提方法的有效性,进行了不同样本数量、跨工况条件下的轴承故障诊断实验,实验结果表明,文章所提方法在小样本条件下具有较好的故障识别效果和泛化性能,具有一定的工程应用价值。