A model-based approach to automotive three-way catalyst on-board monitoring

A model-based three-way automotive catalyst monitoring and fault detection strategy is presented in this work. The performance of the catalyst is inferred from the error between the post-catalyst exhaust gas oxygen sensor air fuel ratio measurement and the model predicted value. A simplified catalyst oxygen storage and reversible deactivation model is employed to predict the post-catalyst air fuel ratio. The model-based strategy is based on the use of a test statistic that is computed from a window of post-catalyst air fuel ratio prediction error data updated in real-time. A fault is assumed to be present when the value of this statistic exceeds a threshold determined from some specified confidence level. We illustrate several test statistics using engine operating data and conclude with an evaluation of this strategy.     

Reversible fragile watermarking for 2-D vector map authentication with localization

A 2-D vector map is very important data in the surveying and mapping fields. It is considered to be content for which verification of integrity and authenticity are urgently required. Several reversible fragile watermarking schemes based on lossless compression presented in the literature guarantee neither exact recovery of the original content nor good tamper localization performance. Using a novel marking technique and a reversible data-hiding method, we propose a scheme that detects and locates malicious attacks with high accuracy while ensuring exact recovery of the original content. In particular, we propose calculating watermarks for each spatial feature group, embedding the watermark in a reversible manner and marking the original location of each feature using interpolated vertices. While the mark of each feature ensures superior accuracy of tamper localization, the reversible data-hiding method provides exact recovery of the original content. Moreover, this paper discusses selecting appropriate embedding parameters to achieve good performance in terms of the tamper localization ability, invisibility, authentication power and security. Experimental results show that the proposed scheme could detect and locate malicious attacks such as vertex/feature modification, vertex addition/deletion, and feature addition/deletion.     

Multiple Sequence Alignment with Multiobjective Metaheuristics. A Comparative Study

Multiple sequence alignment (MSA) plays a core role in most bioinformatics studies and provides a framework for the analysis of evolution in biological systems. The MSA problem consists in finding an optimal alignment of three or more sequences of nucleotides or amino acids. Different scores have been defined to assess the quality of MSA solutions, so the problem can be formulated as a multiobjective optimization problem. The number of proposals focused on this approach in the literature is scarce, and most of the works take as base algorithm the NSGA‐II metaheuristic. So, there is a lack of a study involving a set of representative multiobjective metaheuristics to deal with this complex problem. Our main goal in this paper is to carry out such study. We propose a biobjective formulation for the MSA and perform an exhaustive comparative study of six multiobjective algorithms. We have considered a number of problems taken from the benchmark BAliBASE (v3.0). Our experiments reveal that the classic NSGA‐II algorithm and MOCell, a cellular metaheuristic, provide the best overall performance.     

Designing a parallel cloud based comparative genomics workflow to improve phylogenetic analyses

Over the last years, comparative genomics analyses have become more compute-intensive due to the explosive number of available genome sequences. Comparative genomics analysis is an important a prioristep for experiments in various bioinformatics domains. This analysis can be used to enhance the performance and quality of experiments in areas such as evolution and phylogeny. A common phylogenetic analysis makes extensive use of Multiple Sequence Alignment (MSA) in the construction of phylogenetic trees, which are used to infer evolutionary relationships between homologous genes. Each phylogenetic analysis aims at exploring several different MSA methods to verify which execution produces trees with the best quality. This phylogenetic exploration may run during weeks, even when executed in High Performance Computing (HPC) environments. Although there are many approaches that model and parallelize phylogenetic analysis as scientific workflows, exploring all MSA methods becomes a complex and expensive task to be performed. If scientists determine a priorithe most adequate MSA method to use in the phylogenetic analysis, it would save time, and, in some cases, financial resources. Comparative genomics analyses play an important role in optimizing phylogenetic analysis workflows. In this paper, we extend the SciHmm scientific workflow, aimed at determining the most suitable MSA method, to use it in a phylogenetic analysis. SciHmm uses SciCumulus, a cloud workflow execution engine, for parallel execution. Experimental results show that using SciHmm considerably reduces the total execution time of the phylogenetic analysis (up to 80%). Experiments also show that trees built with the MSA program elected by using SciHmm presented more quality than the remaining, as expected. In addition, the parallel execution of SciHmm shows that this kind of bioinformatics workflow has an excellent cost/benefit when executed in cloud environments.     

Effective deep learning based multimodal sentiment analysis from unstructured big data

More recently, as images, memes and graphics interchange formats have dominated social feeds, typographic/infographic visual content has emerged as an important social media component. This multimodal text combines text and image, defining a novel visual language that must be analysed because it has the potential to modify, confirm or grade the sentiment's polarity. The problem is how to effectively use information from the visual and textual content in image-text posts. This article presents a new deep learning-based multimodal sentiment analysis (MSA) model using multimodal data such as images, text and multimodal text (image with embedded text). The text analytic unit, the discretization control unit, the picture analytic component and the decision-making component are all included in this system. The discretization unit separates the text from the picture using the variant and channel augmented maximally stable extremal regions (VCA-MSERs) technique, which are then analysed as discrete elements and fed into the appropriate image and text analytics units. The text analytics system utilizes a stacked recurrent neural network with multilevel attention and feedback module (SRNN-MAFM) to detect the sentiment of the text. A deep convolutional neural network (CNN) structure with parallel-dilated convolution and self-attention module (PDC-SAM) is developed to forecast the emotional response to visual content. Finally, the decision component employs a Boolean framework including an OR function to evaluate and classify the output into three fine-grained sentiment classes: positive, neutral and negative. The proposed work is simulated in the python platform using the STS-Gold, Flickr 8k and B-T4SA datasets for sentiment analysis of text and visual and multimodal text. Simulation outcomes proved that the suggested method achieved better accuracy of 97.8%, 97.7% and 90% for text, visual and MSA individually compared to other methods.     

基于最小生成树的多序列联配算法

多序列联配(MAS)是现代生物信息学中的重要工具之一,MAS问题是NP-难的,因此需要一些启发式方法在合理的时间内联配大的数据集。本文提出了一个基于最小生成树的多序列联配算法,并使用BALiBASE标准数据集合,对我们的算法进行了性能评价,结果表明算法较之ClustalX类的算法其精确度更高。     

A secure mutual authenticated key agreement of user with multiple servers for critical systems

Recent technological advances in almost all critical systems’ domains have led to an explosive growth of multimedia big data. Those advances encompass the ever increasing innovative digital and remote mobile devices being operated on the users’ end. Due to the openness of critical system, the service providers in such networks are facing security challenges to authenticate those mobile devices on the field, and delivering services. In this scenario, the Multi-server authentication (MSA) framework seems to be a promising solution that enables its subscribers to avail services from different servers without getting registered to each server individually. In last few years many MSA protocols depending on RC-Offline authentication during mutual authentication, have been presented. However, to date, there is no efficient MSA scheme to our knowledge that is free of all three weaknesses, simultaneously. That is, 1) free from storage of server-based parameters (public keys or other values) in smart card by registration authority, 2) free from the assumption of publishing of server-based public keys publicly and 3) free from a single secret sharing with all servers so that it could avoid server masquerading (insider) attack. Considering these limitations, we present a multi-server authentication protocol that withstands above drawbacks using lightweight cryptographic operations. The rationale of the proposed work was to present an efficient RC-Offline MSA scheme. Our scheme is also backed by formal security analysis based on GNY logic and automated security verification using ProVerif tool.

     

量子逻辑电路中控制点失效错误的定位

确保可逆电路的正确性与可靠性,错误检测必不可少,错误定位难度更高.通过分析发现当可逆电路中规模为k的可逆门发生控制点失效时仅对2<'n-k>个输入向量的输出产生影响,据此给出了一种把当前错误集分成若干个子集的方法生成控制点失效错误定位树.传统的错误定位方法都是通过生成真值表和错误表来产生错误定位树;该方法不需要生成和存储真值表以及错误表就能够有效定位电路中控制点失效错误.与Rfault算法相比,空间复杂度和时间复杂度更小,算法效率更高,能应用于更大规模的电路.     

一种优化的数据流驱动的微服务化拆分方法

近年来,微服务架构已经成为软件工程领域比较流行的架构风格,其天然支持DevOps和持续交付以及可伸缩性、可扩展性好等特性,驱动着业界实践者纷纷向微服务架构迁移.然而,采用微服务架构也面临诸多挑战,其中最关键的是缺乏自动化、一体化的解决方案来高效支持面向微服务的拆分设计以及候选微服务架构的评估.为了应对该挑战,对已有的数...     

Exploiting parallelism on progressive alignment methods

Multiple Sequence Alignment (MSA) constitutes an extremely powerful tool for important biological applications such as phylogenetic analysis, identification of conserved motifs and domains and structure prediction. In spite of the improvement in speed and accuracy introduced by MSA programs, the computational requirements for large-scale alignments requires high-performance computing and parallel applications. In this paper we present an improvement to a parallel implementation of T-Coffee, a widely used MSA package. Our approximation resolves the bottleneck of the progressive alignment stage on MSA. This is achieved by increasing the degree of parallelism by balancing the guide tree that drives the progressive alignment process. The experimental results show improvements in execution time of over 68% while maintaining the biological accuracy.     

Multivariate measurement system analysis in multisite testing: An online technique using principal component analysis

Multisite testing improves manufacturing throughput and reduces costs by applying simultaneous testing to products with multiple measurement instruments in parallel. It is important to perform measurement system analysis (MSA) on a multisite testing system to assess its testing capability. Traditional MSA methods are designed to be either univariate or multivariate in a single-site system. They are not capable of analyzing a complex multisite testing system where there are multivariate measurements and multiple instruments in parallel. We propose an online multivariate MSA approach to detecting faulty test instruments in a multisite testing system. In order to pinpoint a faulty test instrument in a multisite testing system we compare the performance of each test instrument to the overall performance of all the parallel instruments in the system. A modified principal component analysis (PCA) method is proposed to transform multivariate measurement data with dependent variables into those with independent principal components. Assuming that all the instruments have the same measurement accuracy and precision we consider a faulty instrument as one whose principal component values are beyond the three sigma control limits of the principal component values of all instruments. We conduct an experiment to provide empirical evidence that the proposed approach is capable of identifying the faulty instruments in a multisite testing system. This approach can be implemented as an online monitoring technique so that production is not interrupted until a faulty instrument is identified.     

Improving multiple sequence alignment biological accuracy through genetic algorithms

Accuracy on multiple sequence alignments (MSA) is of great significance for such important biological applications as evolution and phylogenetic analysis, homology and domain structure prediction. In such analyses, alignment accuracy is crucial. In this paper, we investigate a combined scoring function capable of obtaining a good approximation to the biological quality of the alignment. The algorithm uses the information obtained by the different quality scores in order to improve the accuracy. The results show that the combined score is able to evaluate alignments better than the isolated scores.     

基于内容的多模态可逆密写方法

为了提高生物认证信息在网络传输过程中的安全性,提出一种基于内容相关性分析的多模态双重可逆密写 方法。与现存大多数方法不同,为了充分利用载体图像丰富的内容和提高方法的隐藏性能,首先采用最小二乘回归方 法分析掌纹图像与人脸图像之间的内容相关性,即用人脸图像表示掌纹图像,未被表示的部分掌纹图像被嵌入到相应 的人脸图像中,另外,重构系数作为密钥存储;然后,为了不引起攻击者的注意,将得到的含密人脸图像嵌入到随机选 取的自然载体图像中;最后,将得到含有掌纹信息和人脸信息的含密图像进行传输。提出的方法实现了生物认证信息 的双重可逆信息隐藏,而且哈希函数和密钥的使用提高了该方法的安全性。大量实验结果表明该,方法具有很好的安 全性、不可见性和很高的嵌入容量。特别地,采用双重隐藏机制进一步增强了生物认证信息的安全性,确保了多模态 生物认证的有效性。     

基于MSA的SMIM实现机制

提出基于元数据存储访问的共享管理信息模型实现机制,以解决异构系统互操作问题,保证数据一致性。该实现机制基于已有异构系统和已有数据,使用分层的逻辑划分,将实现过程划分为数据应用层、数据访问适配层、数据访问代理层、元数据存储层及数据存储层5个层次,并介绍了各个层次的功能和相关算法。     

Cooperative vehicle-infrastructure localization based on the symmetric measurement equation filter

Precise and accurate localization is important for safe autonomous driving. Given a traffic scenario which has multiple vehicles equipped with internal sensors for self-localization, and external sensors from the infrastructure for vehicle localization, vehicle-infrastructure communication can be used to improve the accuracy and precision of localization. However, as the number of vehicles in a scenario increases, associating measurement data with the correct source becomes increasingly challenging. We propose a solution utilizing the symmetric measurement equation filter (SME) for cooperative localization to address data association issue, as it does not require an enumeration of measurement-to-target associations. The principal idea is to define a symmetrical transformation which maps measurements to a homogeneous function, thereby effectively addressing several challenges in vehicle-infrastructure scenarios such as data association, bandwidth limitations and registration/configuration of the external sensor. To the best of our knowledge, the proposed solution is among the first to address all these issues of cooperative localization simultaneously, by utilizing the topology information of the vehicles.     

基于差值扩展和纠错编码的可逆图像认证

针对一些敏感数字图像在认证水印嵌入过程中不能引入失真的问题,提出一种能够定位图像篡改块的可逆图像认证方案,利用纠错编码使认证数据能抵抗可能受到的篡改攻击,并用差值扩展的方式将编码后的认证数据嵌入到图像中。仿真实验结果表明,若认证通过,则图像可完全恢复到原始状态,否则,图像中篡改的块可被定位,并完全恢复其他未篡改的区域。     

Multiple sequence alignment using the Hidden Markov Model trained by an improved quantum-behaved particle swarm optimization

Multiple sequence alignment (MSA) is an NP-complete and important problem in bioinformatics. For MSA, Hidden Markov Models (HMMs) are known to be powerful tools. However, the training of HMMs is computationally hard so that metaheuristic methods such as simulated annealing (SA), evolutionary algorithms (EAs) and particle swarm optimization (PSO), have been employed to tackle the training problem. In this paper, quantum-behaved particle swarm optimization (QPSO), a variant of PSO, is analyzed mathematically firstly, and then an improved version is proposed to train the HMMs for MSA. The proposed method, called diversity-maintained QPSO (DMQPO), is based on the analysis of QPSO and integrates a diversity control strategy into QPSO to enhance the global search ability of the particle swarm. To evaluate the performance of the proposed method, we use DMQPSO, QPSO and other algorithms to train the HMMs for MSA on three benchmark datasets. The experiment results show that the HMMs trained with DMQPSO and QPSO yield better alignments for the benchmark datasets than other most commonly used HMM training methods such as Baum–Welch and PSO.     

一种时序型总线硬件木马的植入与检测

RS总线集成电路在航空航天及工业控制领域具有广泛的应用,随着集成电路硬件木马的检测成为研究热点,作为总线硬件木马研究领域的分支,其设计越来越受关注.在常规时序型硬件木马的基础上,针对RS232总线集成电路,设计一种基于可逆计数器的时序型总线硬件木马.采用Xillix公司的ISE软件在RTL层设计相应的RS232总线Ve...     

A modified simulated annealing algorithm and an excessive area model for floorplanning using fixed-outline constraints

Outline-free floorplanning focuses on area and wirelength reductions, which are usually meaningless, since they can hardly satisfy modern design requirements. We concentrate on a more difficult and useful issue, fixed-outline floorplanning. This issue imposes fixed-outline constraints on the outline-free floorplanning, making the physical design more interesting and challenging. The contributions of this paper are primarily twofold. First, a modified simulated annealing (MSA) algorithm is proposed. In the beginning of the evolutionary process, a new attenuation formula is used to decrease the temperature slowly, to enhance MSA’s global searching capacity. After a period of time, the traditional attenuation formula is employed to decrease the temperature rapidly, to maintain MSA’s local searching capacity. Second, an excessive area model is designed to guide MSA to find feasible solutions readily. This can save much time for refining feasible solutions. Additionally, B*-tree representation is known as a very useful method for characterizing floorplanning. Therefore, it is employed to perform a perturbing operation for MSA. Finally, six groups of benchmark instances with different dead spaces and aspect ratios—circuits n10, n30, n50, n100, n200, and n300—are chosen to demonstrate the efficiency of our proposed method on fixed-outline floorplanning. Compared to several existing methods, the proposed method is more efficient in obtaining desirable objective function values associated with the chip area, wirelength, and fixed-outline constraints.     

Reversible fragile watermarking for locating tampered blocks in 2D vector maps

For 2D vector maps, obtaining good tamper localization performance and original content recovery with existing reversible fragile watermarking schemes is a technically challenging problem. Using an improved reversible watermarking method and a fragile watermarking algorithm based on vertex insertion, we propose a reversible fragile watermarking scheme that detects and locates tampered blocks with high accuracy while ensuring recovery of the original content. In particular, we propose dividing the features of the vector map into different blocks, calculating the block authentication watermarks and embedding the watermarks with different watermarking schemes. While the block division ensures superior accuracy of tamper localization, the reversible watermarking method and the fragile watermarking algorithm based on vertex insertion provide recovery of the original content. Experimental results show that the proposed scheme could detect and locate malicious attacks such as vertex/feature modification, vertex/feature addition, and vertex/feature deletion.