Policy-Based Group Signature Scheme from Lattice

Although the existing group signature schemes from lattice have been optimized for efficiency,the signing abilities of eachmember in the group are relatively single.It may not be suitable for complex applications.Inspired by the pioneering work of Bellare and Fuchsbauer,we present a primitive called policy-based group signature.In policy-based group signatures,group members can on behalf of the group to sign documents that meet their own policies,and the generated signatures will not leak the identity and policies of the signer.Moreover,the group administrator is allowed to reveal the identity of signer when a controversy occurs.Through the analysis of application scenarios,we concluded that the policy-based group signature needs to meet two essential security properties:simulatability and traceability.And we construct a scheme of policy-based group signature from lattice through techniques such as commitment,zero-knowledge proof,rejection sampling.The security of our scheme is proved to be reduced to the module short integer solution(MSIS)and module learning with errors(MLWE)hard assumptions.Furthermore,we make a performance comparison between our scheme and three lattice-based group signature schemes.The result shows that our scheme has more advantages in storage overhead and the sizes of key and signature are decreased roughly by 83.13%,46.01%,respectively,compared with other schemes.

基于机器视觉的马铃薯图像特征参数获取方法n

目前马铃薯的分级方法主要还是以人工为主,而人工分级普遍存在不稳定性,费时成本高,依赖人的主观经验等.近些年来,机器视觉技术在农业领域取得了广泛的应用,尤其是农产品分级领域取得了较多的成果.机器视觉技术具有无损害、经济性好、精确率高等优点.对于马铃薯分类,可以从外观尺寸上判断马铃薯的形状类型和重量范围.人工分级时,通过观察不同马铃薯样本的不同横截面大小,就可以主观上判断出马铃薯的形状类型,大致评估质量范围.基于人工分级的特点,通过机器视觉进行分级,只需获得不同横截面的的投影,通过提取图像特征参数的方式来对这些横截面进行定量分析,就可以模拟人工分级的模式对马铃薯进行基于机器视觉的自动分级.本文设计了一套机器视觉系统,分别获取马铃薯平放时的图片与竖放时的图片,以获取最大横截面与最小横截面的投影.采集图片并对得到的照片进行图像处理,得到只有目标区域和背景的二值化图像.从一副图像中获得9个特征,然后通过100个马铃薯样本得到了基于这些特征的数据集.利用软件Unscramble对这些数据集进行分析,以验证方法的合理性.

Classification method of cultivated land based on UAV visible light remote sensing

The accurate acquisition of the grain crop planting area is a necessary condition for realizing precision agriculture.UAV remote sensing has the advantages of low cost use,simple operation,real-time acquisition of remote sensor images and high ground resolution.It is difficult to separate cultivated land from other terrain by using only a single feature,making it necessary to extract cultivated land by combining various features and hierarchical classification.In this study,the UAV platform was used to collect visible light remote sensing images of farmland to monitor and extract the area information,shape information and position information of farmland.Based on the vegetation index,texture information and shape information in the visible light band,the object-oriented method was used to study the best scheme for extracting cultivated land area.After repeated experiments,it has been determined that the segmentation scale 50 and the consolidation scale 90 are the most suitable segmentation parameters.Uncultivated crops and other features are separated by using the band information and texture information.The overall accuracy of this method is 86.40%and the Kappa coefficient is 0.80.The experimental results show that the UAV visible light remote sensing data can be used to classify and extract cultivated land with high precision.However,there are some cases where the finely divided plots are misleading,so further optimization and improvement are needed.

Modeling effectiveness and identification of multi-scale objects in farmland soils with improved Yee-FDTD methods

Finite-Difference Time-Domain(FDTD)is the most popular time-domain approach in computational electromagnetics.Due to the Courant-Friedrich-Levy(CFL)condition and the perfect match layer(PML)boundary precision,FDTD cannot simulate soil medium whose surface is connected by multiple straight lines or curves(multi-scale)accurately and efficiently,which greatly limits the application of FDTD method to simulate buried objects in soils.Firstly,this study proposed the absorption boundary and adopted two typical perfect matching layers(UPML and CPML)to compare their absorption effects,and then using the three forms of improved Yee-FDTD algorithm,alternating-direction implicit(ADI-FDTD),unconditionally stable(US-FDTD)and hybrid implicit explicit finite time-domain(HIE-FDTD)to divide and contrast the boundary model effects.It showed that the HIE-FDTD was suitable for inversion of multi-scale structure object modeling,while ADI-FDTD and US-FDTD were ideal for single-boundary objects in both uniaxial perfectly matched layer(UMPL)and convolution perfectly matched layer(CPML)finite element space.After that,all the models were tested by computer performance for their simulated efficiency.When simulating single boundary objects,UPML-US-FDTD and ADI-FDTD could achieve the ideal results,and in the boundary inversion of multi-scale objects,HIE-FDTD modeling results and efficiency were the best.Test modeling speeds of CPML-HIE-FDTD were compared with three kinds of waveform sources,Ricker,Blackman-Harris and Gaussian.Finally,under the computer condition in which the CPU was i5-8250,the HIE-FDTD model still had better performance than the traditional Yee-FDTD forward modeling algorithm.For modeling multi-scale objects in farmland soils,the methods used CPML combined with the HIE-FDTD were the most efficient and accurate ways.This study can solve the problem that the traditional FDTD algorithm cannot construct non-mesh objects by utilizing the diversity characteristics of Yee cell elements.

Detection of the foreign object positions in agricultural soils using Mask-RCNN

Objects in agricultural soils will seriously affect the farming operations of agricultural machinery.At present,it still relies on human experience to judge abnormal Gounrd-penetrting Radar(GPR)signals.It is difficult for traditional image processing technology to form a general positioning method for the randomness and diversity characteristics of GPR signals in soil.Although many scholars had researched a variety of image-processing techniques,most methods lack robustness.In this study,the deep learning algorithm Mask Region-based Convolutional Neural Network(Mask-RCNN)and a geometric model were combined to improve the GPR positioning accuracy.First,a soil stratification experiment was set to classify the physical parameters of the soil and study the attenuation law of electromagnetic waves.Secondly,a SOIL-GPR geometric model was proposed,which can be combined with Mask-RCNN's MASK geometric size to predict object sizes.The results proved the effectiveness and accuracy of the model for position detection and evaluation of objects in soils;then,the improved Mask RCNN method was used to compare the feature extraction accuracy of U-Net and Fully Convolutional Networks(FCN);Finally,the operating speed of agricultural machinery was simulated and designed the A-B survey line experiment.The detection accuracy was evaluated by several indicators,such as the survey line direction,soil depth false alarm rate,Mean Average Precision(mAP),and Intersection over Union(IoU).The results showed that pixel-level segmentation and positioning based on Mask RCNN can improve the accuracy of the position detection of objects in agricultural soil effectively,and the average error of depth prediction is 2.87 cm.The results showed that the detection technology proposed in this study integrates the advantage of soil environmental parameters,geometric models,and artificial intelligence algorithms to provide a high-precision and technical solution for the GPR non-destructive detection of soils.

Point Cloud Completion of Plant Leaves under Occlusion Conditions Based on Deep Learning

The utilization of 3-dimensional point cloud technology for non-invasive measurement of plant phenotypic parameters can furnish important data for plant breeding,agricultural production,and diverse research applications.Nevertheless,the utilization of depth sensors and other tools for capturing plant point clouds often results in missing and incomplete data due to the limitations of 2.5D imaging features and leaf occlusion.This drawback obstructed the accurate extraction of phenotypic parameters.Hence,this study presented a solution for incomplete flowering Chinese Cabbage point clouds using Point Fractal Network-based techniques.The study performed experiments on flowering Chinese Cabbage by constructing a point cloud dataset of their leaves and training the network.The findings demonstrated that our network is stable and robust,as it can effectively complete diverse leaf point cloud morphologies,missing ratios,and multi-missing scenarios.A novel framework is presented for 3D plant reconstruction using a single-view RGB-D(Red,Green,Blue and Depth)image.This method leveraged deep learning to complete localized incomplete leaf point clouds acquired by RGB-D cameras under occlusion conditions.Additionally,the extracted leaf area parameters,based on triangular mesh,were compared with the measured values.The outcomes revealed that prior to the point cloud completion,the R^(2)value of the flowering Chinese Cabbage's estimated leaf area(in comparison to the standard reference value)was 0.9162.The root mean square error(RMSE)was 15.88 cm^(2),and the average relative error was 22.11%.However,post-completion,the estimated value of leaf area witnessed a significant improvement,with an R^(2)of 0.9637,an RMSE of 6.79 cm^(2),and average relative error of 8.82%.The accuracy of estimating the phenotypic parameters has been enhanced significantly,enabling efficient retrieval of such parameters.This development offers a fresh perspective for non-destructive identification of plant phenotypes.

Vertical distribution characteristics of PM in the surface layer of Guangzhou

Measurements of particulate matter （PM）, i.e., PM10, PM2.5, and PM1, have been performed on the Can- ton Tower, a landmark building in Guangzhou, at heights of 121 and 454 m since November 2010, using a GRIMM 180 aerosol particle spectrometer （Germany）. Analyses of data from November 2010 to May 2013 showed that the annual average values of PM10, PM2.5, and PM 1 at the observation height of 121 m above the ground were 44.1, 38,2, and 34.9 μg/m3, respectively, and those at 454m above the ground were 35.7, 30,4, and 27.5 μg/m3, respectively. By considering the values of the secondary concentration limits given in the Ambient Air Quality Standards issued in 2012, it was observed that the annual average values of PM10 at the observation heights of 121 and 454 m, as well as those of PM2.5 at 454 m, reached those standards. Furthermore, the over-standard amplitude of the annual average value of PM2.5 at the observation height of 121 m was 9.1%. During the observation period, the maximum daily average val- ues of PM10, PM2.5, and PMI at the observation height of 121 m were 183.3, 144.8, and 123.8 μg/m3, respectively, and those at 454 m were 102.8, 92.7, and 86.4 μg/m3. The daily average values of PM10 at the observation height of 454 m were not above the standards. The over-standard frequencies of the daily average values of PM10 and PM2.5 at the observation height of 121 m were 0.6% and 10,7% respectively, and the over-standard amplitudes were 9.0% and 24.4%, respectively. The over-standard frequency of the daily average value of PM2.5 at the observation height of 454 m was 2.0%, and the over-standard amplitude was 10.4%. Accordingly, it can be stated that the air at the observation height 454 m above the ground did not reach the secondary limit of the new standards. The pollution was most serious during winter, and the air was relatively cleaner during summer, Overall, the vertical distributions of PM10, PM2.5, and PMI decreased with height. The lapse rates showed the following sequence： PMIO 〉 PM2.5 〉 PM1, which indicates that the vertical distribution of fine particles is more uniform than that of coarse particles; the vertical distribution in summer is more uniform than in other seasons.

Monocular vision and calculation of regular three-dimensional target pose based on Otsu and Haar-feature AdaBoost classifier

Using machine vision to identify and sort scattered regular targets is an urgent problem to be solved in automated production lines.This study proposed a three-dimensional(3D)recognition method combining monocular vision and machine learning algorithms.According to the color characteristics of the targets,to convert the original color picture into YCbCr mode and use the 2D Otsu algorithm to perform gray level image segmentation on the Cb channel.Then the Haar-feature training was carried out.The comparison of feature training and Haar method for Hough transform showed that the recognized time of Haar-feature AdaBoost trainer reached 31.00 ms,while its false recognized rate was 3.91%.The strong classifier was formed by weight combination,and the Hough contour transformation algorithm was set to correct the normal vector between plane coordinate and camera coordinate system.The monocular vision system ensured that the field of camera view had not obstructed while the dots were being struck.It was measured and calculated angles between targets and the horizontal plane which coordinate points of the identified plane feature.The testing results were compared with the Otsu and AdaBoost trainer where the prediction and training set have an error of no more than 0.25 mm.Its correct rate can reach 95%.It shows that the Otsu and Haar-feature based on AdaBoost algorithm is feasible within a certain error ranges and meet the engineering requirements for solving the poses of automated regular three-dimensional targets.