Analysis on Genetic Diversity of 40 Flowering Cherry Cultivars and Construction of Molecular ID Based on SSR Markers

Studying on the genetic diversity and genetic relationship of flowering cherry cultivars is extremely important for germplasm conservation, cultivar identification and breeding. Flowering cherry is widely cultivated as an important woody ornamental plant in worldwide, especially Japan, China. However, owning to the morphological similarity, many cultivars are distinguished hardly in non-flowering season. Here, we evaluated the genetic diversity and genetic relationship of 40 flowering cherry cultivars, which are mainly cultivated in China. We selected 13 polymorphicprimers to amplify to allele fragments with fluorescent-labeled capillary electrophoresis technology. The population structure analysis results show that these cultivars could be divided into 4 subpopulations. At the population level, Na and Ne were 6.062, 4.326, respectively. Ho and He were 0.458 and 0.670, respectively. The Shannon’s information index (I) was 1.417. The Pop3, which originated from P. serrulata, had the highest Ho, He, and I among the 4 subpopulations. AMOVA showed that only 4% of genetic variation came from populations, the 39% variation came from individuals and 57% (p < 0.05) came from intra-individuals. 5 polymorphic SSR primers were selected to construct molecular ID code system of these cultivars. This analysis on the genetic diversity and relationship of the 40 flowering cherry cultivars will help to insight into the genetic background, relationship of these flowering cherry cultivars and promote to identify similar cultivars.

A Novel Method in Wood Identification Based on Anatomical Image Using Hybrid Model

Nowadays,wood identification is made by experts using hand lenses,wood atlases,and field manuals which take a lot of cost and time for the training process.The quantity and species must be strictly set up,and accurate identification of the wood species must be made during exploitation to monitor trade and enforce regulations to stop illegal logging.With the development of science,wood identification should be supported with technology to enhance the perception of fairness of trade.An automatic wood identification system and a dataset of 50 commercial wood species from Asia are established,namely,wood anatomical images collected and used to train for the proposed model.In the convolutional neural network(CNN),the last layers are usually soft-max functions with dense layers.These layers contain the most parameters that affect the speed model.To reduce the number of parameters in the last layers of the CNN model and enhance the accuracy,the structure of the model should be optimized and developed.Therefore,a hybrid of convolutional neural network and random forest model(CNN-RF model)is introduced to wood identification.The accuracy’s hybrid model is more than 98%,and the processing speed is 3 times higher than the CNN model.The highest accuracy is 1.00 in some species,and the lowest is 0.92.These results show the excellent adaptability of the hybrid model in wood identification based on anatomical images.It also facilitates further investigations of wood cells and has implications for wood science.

基于Android平台的身份证号码识别

日常生活中很多场合都需要对身份证进行检查,并需要人工输入身份证号码。而身份证号码位数偏多,人工输入难免会出错而且速度慢。本文针对此问题提出了一种有效的基于Android平台的身份证号码识别算法。该算法将基于Android操作系统的智能手机的视频流捕捉功能而获得的图片作为输入图像,经过图像预处理、身份证号码区域定位、字符分割等前期步骤,然后进行字符识别。除此以外,考虑了由人工操作的随意性而带来的特殊情况,并对此作出相应的处理,提出了进一步改进的,更有效的识别算法。实验结果表明,本文的整个身份证号码识别的处理流程简单有效且识别率高,获得了满意的效果。

IDentifier DNA分型盒(炎黄34)的法医学验证及应用评估

目的 测试IDentifier DNA分型盒(炎黄34)的技术性能指标,评估其法医学应用价值。方法 根据《法庭科学人类荧光标记STR复合扩增检测试剂质量基本要求》(GB/T 37226—2018),从种属特异性、分型准确性、灵敏度、适应性、耐受性、一致性、均衡性、反应条件验证、混合样本、稳定性、批间差11个方面对IDentifier DNA分型盒(炎黄34)进行测试。比较IDentifier DNA分型盒(炎黄34)与Power Plex~?Fusion 6C系统、Versa Plex~?27PY系统、Veri Filer~(TM) Plus PCR扩增试剂盒的系统效能。使用IDentifier DNA分型盒(炎黄34)检测日常案件中的拭子类生物检材,观察其STR检验结果 。结果 IDentifier DNA分型盒(炎黄34)具有良好的种属特异性、分型准确性、适应性、耐受性和均衡性,灵敏度可达0.062 5 ng,能检测案件中不同类型的检材、降解检材及混有抑制剂的检材,对混合比例为4∶1以内的样本均能获得完整分型。该试剂盒的系统效能非常高,TDP可达1-1.08×10~(-37),CPE_(trio)和CPE_(duo)分别可达1-5.47×10~(-14)和1-6.43×10~(-9)。针对案件中的接触类生物检材,其有效检出率可达21.05%。针对亲缘关系鉴定,单亲鉴定和全同胞鉴定的系统效能均得到了有效提高。结论 IDentifier DNA分型盒(炎黄34)的上述性能指标符合要求,可用于个体识别及亲权鉴定,适合在法庭科学领域应用。

基于隐性标识符的零权限Android智能终端识别

针对现有Android智能终端识别直接利用IMEI,Android_ID等显性标识符存在依赖敏感权限和易失效的问题,提出了一种基于隐性标识符组合的零权限设备识别方法.通过调用系统API并执行Linux Shell命令,从物理层、应用层以及用户层3个层次上获取设备型号、屏幕信息、内核编译信息、User Agent、系统语言、字体大小、字体列表、用户安装程序列表等8个隐性标识符,并将其组合形成设备指纹.然后,提出了指纹精确匹配算法和变化指纹关联匹配算法,用于仅1个隐性标识符发生变化且变化前后相似程度大于0.85时的设备关联识别.通过采集真实用户数据进行实验验证,结果表明所提算法的设备识别准确率达到94.52%.

Scalability and Security in Identifier-to-Locator Mapping: Case Study for Locator/ID Separated Networks

The locator/ID separation paradigm has been widely discussed to resolve the serious scalability issue that today's Internet is facing. Many researches have been carried on with this issue to alleviate the routing burden of the Default Free Zone (DFZ), improve the traffic engineering capabilities and support efficient mobility and multi-homing. However, in the locator/ID split networks, a third party is needed to store the identifier-to-locator pairs. How to map identifiers onto locators in a scalable and secure way is a really critical challenge. In this paper, we propose SS-MAP, a scalable and secure locator/ID mapping scheme for future Internet. First, SS-MAP uses a near-optimal DHT to map identifiers onto locators, which is able to achieve the maximal performance of the system with reasonable maintenance overhead relatively. Second, SS-MAP uses a decentralized admission control system to protect the DHT-based identifier-to-locator mapping from Sybil attacks, where a malicious mapping server creates numerous fake identities (called Sybil identifiers) to control a large fraction of the mapping system. This is the first work to discuss the Sybil attack problem in identifier-to-locator mapping mechanisms with the best knowledge of the authors. We evaluate the performance of the proposed approach in terms of scalability and security. The analysis and simulation results show that the scheme is scalable for large size networks and can resistant to Sybil attacks.

Attributes-based person re-identification via CNNs with coupled clusters loss

Person re-identification(re-id)involves matching a person across nonoverlapping views,with different poses,illuminations and conditions.Visual attributes are understandable semantic information to help improve the issues including illumination changes,viewpoint variations and occlusions.This paper proposes an end-to-end framework of deep learning for attribute-based person re-id.In the feature representation stage of framework,the improved convolutional neural network(CNN)model is designed to leverage the information contained in automatically detected attributes and learned low-dimensional CNN features.Moreover,an attribute classifier is trained on separate data and includes its responses into the training process of our person re-id model.The coupled clusters loss function is used in the training stage of the framework,which enhances the discriminability of both types of features.The combined features are mapped into the Euclidean space.The L2 distance can be used to calculate the distance between any two pedestrians to determine whether they are the same.Extensive experiments validate the superiority and advantages of our proposed framework over state-of-the-art competitors on contemporary challenging person re-id datasets.

Application of PRO-ESPRIT algorithm in identification of surface acoustic wave identification-tags

The surface acoustic wave （SAW） identification （ID）-tags have great potential for application in radio frequency identification （RFID） due to their characteristics of wireless sensing and passive operation. In the measurements based on the frequency domain sampling （FDS）, to expand the range of detection and allow the system work in harsh environments, it is necessary to enhance the identification capability at low SNR. In addition, to identify the tags in real time, it is important to reduce identification time. Therefore, estimation of signal parameters based on the Procrustes rotations via the rotational invariance technique （PRO-ESPRIT） is adopted. Experimental results show that good identification capability is achieved with a relatively faster measurement speed.

Parameter Identifiability and Parameter Estimation of a Diesel Engine Combustion Model

In this paper an original method based on the link between a piecewise identifiability analysis and a piecewise numerical estimation is presented for estimating parameters of a phenomenological diesel engine combustion model. This model is used for design, validation and pre-tuning of engine control laws. A cascade algebro-differential elimination method is used for studying identifiability. This investigation is done by using input-output-parameter relationship. Then these relations are transformed by using iterated integration. They are combined with an original numerical derivative estimation based on distribution theory which gives explicit point-wise derivative?estimation formulas for each given order. Then new approximate relations, linking block of parameters and outputs (without derivative) are obtained. These relations are linear relatively to the blocks of parameters and yield a first estimation of parameters which is used as initial guess for a local optimization method (least square method and a local search genetic algorithm).

A novel voting system for the identification of eukaryotic genome promoters

Motivation: Accurate identification and delineation of promoters/TSSs (transcription start sites) is important for improving genome annotation and devising experiments to study and understand transcriptional regulation. Many promoter identifiers are developed for promoter identification. However, each promoter identifier has its own focuses and limitations, and we introduce an integration scheme to combine some identifiers together to gain a better prediction performance. Result: In this contribution, 8 promoter identifiers (Proscan, TSSG, TSSW, FirstEF, eponine, ProSOM, EP3, FPROM) are chosen for the investigation of integration. A feature selection method, called mRMR (Minimum Redundancy Maximum Relevance), is novelly transferred to promoter identifier selection by choosing a group of robust and complementing promoter identifiers. For comparison, four integration methods (SMV, WMV, SMV_IS, WMV_IS), from simple to complex, are developed to process a training dataset with 1400 se- quences and a testing dataset with 378 sequences. As a result, 5 identifiers (FPROM, FirstEF, TSSG, epo- nine, TSSW) are chosen by mRMR, and the integration of them achieves 70.08% and 67.83% correct prediction rates for a training dataset and a testing dataset respectively, which is better than any single identifier in which the best single one only achieves 59.32% and 61.78% for the training dataset and testing dataset respectively.

MODELING INTRAPERSONAL DEFORMATION SUBSPACE USING GMM FOR PALMPRINT IDENTIFICATION

In this paper, an efficient model of palmprint identification is presented based on subspace density estimation using Gaussian Mixture Model (GMM). While a few training samples are available for each person, we use intrapersonal palmprint deformations to train the global GMM instead of modeling GMMs for every class. To reduce the dimension of such variations while preserving density function of sample space, Principle Component Analysis (PCA) is used to find the principle differences and form the Intrapersonal Deformation Subspace (IDS). After training GMM using Expectation Maximization (EM) algorithm in IDS, a maximum likelihood strategy is carried out to identify a person. Experimental results demonstrate the advantage of our method compared with traditional PCA method and single Gaussian strategy.

Structural-Identification Aspects of Decision-Making in Systems with Bouc-Wen Hysteresis

Considering the structural analysis problem of systems properties with Bouc-Wen hysteresis (BWH), various approaches are proposed for the identification of BWH parameters. The applied methods and algorithms are based on the design of parametric models and consider a priori information and the results of data analysis. Structural changes in the BWH form a priori. Methods for the Bouc-Wen model (BWM) identification and its structure estimation are not considered under uncertainty. The study’s purpose is the analysis the structural problems of the Bouc-Wen hysteresis identification. The analysis base is the application of geometric frameworks (GF) under uncertainty. Methods for adaptive estimation parameters and structural of BWM were proposed. The adaptive system stability is proved based on vector Lyapunov functions. An approach is proposed to estimate the identifiability and structure of the system with BWH. The method for estimating the identifiability degree based on the analysis of GF is considered. BWM modifications are proposed to guarantee the system’s stability and simplify its description.

融合VR-眼动的施工现场隐患识别视觉注意与搜索特征研究

为探究施工现场隐患识别视觉注意与搜索特征,提升识别绩效水平,融合虚拟现实(Virtual Reality,VR)和眼动追踪技术,开展VR施工场景内的隐患识别试验,收集不同隐患识别经验群体的眼动数据;运用统计学方法,对比分析累计注视时间、注视百分比等眼动指标的差异性,揭示隐患识别经验与注意力资源分配方式的内在联系;基于眼动轨迹匹配法,聚类首次视觉搜索轨迹,结合施工现场空间布局,探究隐患识别经验影响下搜索习惯的差异。结果表明:经验知识水平对隐患识别绩效具备驱动作用,但隐患专业度过低则会弱化经验知识的效果;被试的注意资源大多集中于具有视觉显著性特征的无关要素,仅有小部分分配于隐患,然而,具有较高经验知识水平的被试对隐患信息更加敏感,分配于隐患的注意资源占比更高;相较于新手组同一区域多次重复的搜索习惯,专家组大多采用某一区域的详细搜索再切换到下一区域的方式。

多尺度融合与双输出U-Net网络的行人重识别

受行人姿态的多变性和行人被遮挡等因素的影响,行人重识别模型难以提取行人关键特征。为增强模型的特征表达能力,提出一种基于多尺度融合与双输出U-Net网络的行人重识别方法,旨在解决现有方法中难以提取行人关键特征、特征表达能力较低的问题。首先,提出多尺度融合的双输出U-Net网络,并对输出特征进行欧氏距离和散度距离约束;其次,设计联合损失函数,解决生成对抗网络在训练过程中不易收敛的问题,提高训练过程的收敛速度。在3个公共基准数据集上的仿真实验结果表明,相比经典特征提取网络,所提特征提取网络的平均精度均值(mAP)提升超过10%,所提行人重识别方法相比主流方法的mAP提高约2%,该方法能够增强模型的特征表达能力,提高行人重识别的准确率。

基于模式识别技术的光电探测器故障辨识研究

当前光电探测器故障辨识错误率高,为提升光电探测器故障辨识效果,设计了基于模式识别技术的光电探测器故障辨识方法。首先采集光电探测器状态信号,并从光电探测器状态信号中提取特征,然后利用主成分分析算法对特征进行降维处理,得到最优光电探测器状态辨识特征,最后将光电探测器状态特征作为支持向量机的输入,光电探测器状态作为支持向量机输出,通过支持向量机学习设计光电探测器状态辨识器,实验结果表明,本方法可以有效辨识光电探测器辨识故障,光电探测器故障辨识正确率超过了90%,光电探测器故障辨识时间控制在20 ms以内,为光电探测器状态分析提供了理论依据。

RFID技术在智能化工厂中的应用

对现代工厂智能化进行了初步探讨。在现代工厂生产、物流、安防及备件资材的管理中,标识和识别已成为其技术进一步发展的瓶颈。PFID技术的应用,解决了工厂智能化发展中的这一关键技术问题。通过RFID技术与工厂生产、物流、安防及备件资材的管理流程的有机结合,实现企业制造与管理过程中,对象的自动识别,以此实现企业生产、物流、安防及备件资材的数字化、精确化管理。提高企业的管理效率,降低人工劳动强度。所提及的基于RFID的管理方法,已在多个企业得到应用,效果良好。

一种新的RFID标签识别防冲突算法

在RFID网络通信中,当多个标签同时回应阅读器的查询时,如果没有相应的防冲突机制,会导致标签到阅读器的通信冲突,使得从标签返回的数据难以被阅读器正确识别.防冲突算法是阅读器快速、正确获取标签数据的关键.一种被称为基于栈的ID-二进制树防冲突算法(Stack-based ID-binary tree anti-collision algorithm,SIBT)被提出,SIBT算法的新颖性在于它将n个标签的ID号映射为一棵唯一对应的ID-二进制树,标签识别过程转化为在阅读器中创建ID-二进制树的过程.为了提高多标签识别效率,阅读器使用栈保存已经获取的ID-二进制树创建线索,用计数器保存标签在该栈中的深度.理论分析和仿真结果表明SIBT算法的性能优于其他基于树的防冲突算法.

2012年北方春大豆国家区试大豆品种纯度鉴定、分子ID构建及遗传多样性分析

为了鉴定北方春大豆组国家区域试验大豆品种的纯度、构建参试大豆品种的分子ID及品种间遗传关系分析,从而有效地指导中国北方春大豆新品种的选育和推广。通过对参加2012年北方春大豆国家区试的94份大豆材料用分布在大豆基因组8个连锁群的13对SSR标记进行分析。结果表明：94份参试大豆品种纯度分布范围为53.85%-100%,平均纯度为99.02%;利用Satt231、Satt288、Satt160、Satt193和Sat-092这5对引物可以将94份参试大豆品种区分开,并获得唯一的分子ID;94个参试大豆品种间遗传相似系数为0-0.846,平均相似系数为0.2783,说明参试大豆品种间遗传差异性较大。通过SSR标记分析,可以有效地鉴定参试大豆品种的纯度、建立参试大豆品种的分子ID及实现品种间遗传关系分析。

2012年黑龙江垦区大豆参试品系纯度鉴定、分子ID构建及遗传多样性分析

以2012年黑龙江垦区区域试验的121份大豆品系为材料，选择分布在大豆基因组8个连锁群的13对SSR标记进行纯度分析、分子ID构建及遗传多样性分析。结果表明：121份参试大豆品系纯度分布范围为84．62％～100％，平均纯度为99．24％；利用Sat-092等10对引物可以将121个参试大豆品系区分开，并获得唯一的分子ID；品系间遗传相似系数为0～1．00，平均相似系数为0．3402。结果说明黑龙江垦区2012年参试大豆品系遗传同源性较小，差异性较大。

一种基于部分ID的新型RFID安全隐私相互认证协议

在低成本电子标签中实现安全隐私功能是RFID研究领域需要解决的一项关键技术,该文采用部分ID,CRC校验以及ID动态更新的方法,提出一种新型RFID相互认证协议,该协议具有前向安全性,能够防止位置隐私攻击、重传攻击、窃听攻击和拒绝服务攻击,新协议有效地解决了RFID安全隐私问题,并且符合EPC Class1 Gen2标准,它的硬件复杂度较低,适用于低成本电子标签。