数字电路抗单粒子瞬态效应的最小尺寸设计

随着体硅CMOS电路工艺的不断缩小,数字电路在空间中使用时受到的单粒子效应越发严重。特别是高频电路,单粒子瞬态效应会使电路功能完全失效。提出了一种基于电路尺寸计算的抗单粒子瞬态效应的设计方法,主要思想是通过辐射对电路造成的最坏特性,设计电路中MOS管的尺寸,使电路在系统开销和降低软错误率之间达到一个平衡。从单粒子效应电流模型入手,计算出单粒子效应在电路中产生的电荷数,得出为抵消单粒子效应产生的电荷需要多大的电容,再折算到器件电容上,最终得到器件的尺寸。此工作为以后研制抗辐射数字电路奠定了基础,提供了良好的借鉴。     

FPGA三模冗余工具的关键技术与发展

SRAM型现场可编程门阵列(FPGA)在空间辐射环境中容易受到单粒子效应的影响,从而发生软错误,三模冗余技术(TMR)是目前使用最广泛的缓解FPGA软错误的电路加固技术。该文首先介绍了三模冗余技术研究现状,然后总结了三模冗余工具常用的细粒度TMR技术、系统分级技术、配置刷新技术、状态同步技术4项关键技术及其实现原理。随着FPGA的高层次综合技术愈发成熟,基于高层次综合的三模冗余工具逐渐成为新的研究分支,该文分类介绍了当前主流的基于寄存器传输级的三模冗余工具,基于重要软核资源的三模冗余工具,以及新兴的基于高层次综合的三模冗余工具,最后对FPGA三模冗余工具的未来发展趋势进行了总结与展望。     

High performance energy efficient radiation hardened latch for low voltage applications

Energy efficiency is considered to be the most critical design parameter for IoT and other ultra low power applications. However, energy efficient circuits show a lesser immunity against soft error, because of the smaller device node capacitances in nanoscale technologies and near-threshold voltage operation. Due to these reasons, the tolerance of the sequential circuits to SEUs is an important consideration in nanoscale near threshold CMOS design. This paper presents an energy efficient SEU tolerant latch. The proposed latch improves the SEU tolerance by using a clocked Muller- C and memory elements based restorer circuit. The parasitic extracted simulations using STMicroelectronics 65 nm CMOS technology show that by employing the proposed latch, an average improvement of ∼40% in energy delay product (EDP), is obtained over the recently reported latch. Moreover, the proposed latch is also validated in a TCAD calibrated PTM 32 nm framework and PTM 22 nm CMOS technology nodes. In 32 nm and 22 nm technologies, the proposed latch improves the EDP ∼12% and 59% over existing latches respectively.     

OW‐SVM: Ontology and whale optimization‐based support vector machine for privacy‐preserved medical data classification in cloud

Cloud is a multitenant architecture that allows the cloud users to share the resources via servers and is used in various applications, including data classification. Data classification is a widely used data mining technique for big data analysis. It helps the learners to discover hidden data patterns by training massive data collected from the real world. Because this trained model is the private asset of an entity, it should be protected from all other noncollaborative entities. Therefore, it is essential to take effective measures to preserve the confidential data. The objective of this paper is to preserve the privacy of the confidential data in the cloud environment by introducing the medical data classification method. In view of that, this paper presents a method for medical data classification using a novel ontology and whale optimization‐based support vector machine (OW‐SVM) approach. Initially, privacy‐preserved data are developed adopting Kronecker product bat approach, and then, ontology is built for the feature selection process. Ontology and whale optimization‐based support vector machine is then proposed by integrating ontology and whale optimization algorithm into SVM, in which ontology and whale optimization algorithm is used for the feasible selection of kernel parameters. The experiment is done using 3 heart disease datasets, such as Cleveland, Switzerland, and Hungarian. In a comparative analysis, the performance of the OW‐SVM approach is compared with that of K‐nearest neighbor, Naive Bayes, decision tree, SVM, and OW‐SVM, using accuracy, sensitivity, specificity, and fitness, as the evaluation metrics. The OW‐SVM approach could achieve maximum performance with accuracy of 83.21%, the sensitivity of 91.49%, specificity of 73%, and fitness of 81.955, outperforming existing comparative techniques.     

一种新的综合安全管理架构的设计与实现

传统的安全管理框架缺少对底层数据的整合分析,不能形成有效的网络安全态势信息和风险评估,也不能满足用户对其扩展性和健壮性的要求。文中利用TMN(电信管理网)管理与受管资源分开的思路启发,提出了一种新的综合安全管理框架。新的框架在J2EE分布式架构平台加以实现,能够满足用户对综合安管系统扩展性、易用性和健壮性的需求。     

用本体论来解决协同编著中的一致性问题

协同编著是一个群体认知和创造的过程。不同背景的人对同一个主题的理解产生的自然差异会导致主题的偏移。目前,国内外协同编著研发还没有很好地解决协同编著过程发散而又要求主题一致性收敛的问题。利用本体论中的领域本体,为实现人人交互提供一种共享的语义基础,从而为解决协同编著中的主题一致性问题提供一种研究思路。另外,可以用协议本体解决系统之间的通信问题并提出了基于本体论的协同编著模型的构想。     

A low power-delay-product and robust Isolated-DICE based SEU-tolerant latch circuit design

Soft-error interference is a crucial design challenge in the advanced CMOS VLSI circuit designs. In this paper, we proposed a SEU Isolating DICE latch (Iso-DICE) design by combing the new proposed soft-error isolating technique and the inter-latching technique used in the DICE (Calin et al., 1996 [1]) design. To further enhance SEU-tolerance of DICE design, we keep the storage node pairs having the ability to recover the SEU fault occurring in each other pair but also avoid the storage node to be affected by each other. To mitigate the interference effect between dual storage node pairs, we use the isolation mechanism to resist high energy particle strikes instead of the original interlocking design method. Through isolating the output nodes and the internal circuit nodes, the Iso-DICE latch can possess more superior SEU-tolerance as compared with the DICE design (Calin et al., 1996 [1]). As compared with the FERST design (Fazeli, 2009 [2]) which performs with the same superior SEU-tolerance, the proposed Iso-DICE latch consumes 50% less power with only 45% of power delay product in TSMC 90 nm CMOS technology. Under 22 nm PTM model, the proposed Iso-DICE latch can also perform with 11% power delay product saving as compared with the FERST design (Fazeli, 2009 [2]) that performs with the same superior SEU-tolerance.     

基于本体的高校知识分析和推理

以高校知识为研究和应用背景,研究基于本体理论的高校知识本体结构化表示和描述,并采用protege 4.0建立了高校知识本体。同时从高校知识中提取出基本公理、类内公理和类间公理,建立公理库,并采用protege的PAL推理引擎对高校知识进行一致性分析和推理。解决了高校知识语义层次上的信息共享和交互的问题,为高校知识的管理提供语义支撑。     

DTMF采用SIP扩展进行带外传输的实现

DTMF信号作为拨号音信号不仅可以实现拨号业务,而且可以通过SIP扩展的方法实现更多的增值业务。文中主要对SIP扩展的INFO方法和事件通告机制进行了阐述,对两者传送DTMF信号的方法进行了分析和比较。实现了DTMF的带外传送。     

基于支持向量机的中文模糊本体映射

Taking into account that fuzzy ontology mapping has wide application and cannot be dealt with in many fields at present, a Chinese fuzzy ontology model and a method for Chinese fuzzy ontology mapping are proposed. The mapping discovery between two ontologies is achieved by computing the similarity between the concepts of two ontologies. Every concept consists of four features of concept name, property, instance and structure. First, the algorithms of calculating four individual similarities corresponding to the four features are given. Secondly, the similarity vectors consisting of four weighted individual similarities are built, and the weights are the linear function of harmony and reliability. The similarity vector is used to represent the similarity relation between two concepts which belong to different fuzzy ontolgoies. Lastly, Support Vector Machine (SVM) is used to get the mapping concept pairs by the similarity vectors. Experiment results are satisfactory.