基于符号迁移图的互模拟验证算法

符号迁移图是传值进程的一种直观而简洁的语义表示模型，该模型由Hennessy和Lin首先提出，随后又被Lin推广至带赋值的符号迁移图，本文不但定义了符号迁移图各种版本（基／符号）的强操作语义和强互模拟，提出了相互的强互模拟算法，而且通过引入符号观察图和符号同余图，给出了其弱互模拟等价和观察同余的验证算法，给出并证明了了τ－循环和τ－边消去定理，在应用任何弱互模拟观察同余验证算法之前，均可利用这些定理对所给符号迁移图进行化简。     

在Word中巧妙输入数学符号

一，利用“符号栏” 选中“视图→工具栏→符号栏”（符号栏前有√标记），此时“符号栏”会显示在Word窗口的下方，单击相应的数学符号，便可快捷录入数学符号，并可连续、重复录入。如果符号栏上没有你需要的符号，可以通过下列方法重新设定符号栏：     

符号模拟

符号模拟将模拟验证从布尔值扩展到符号领域，是集成电路验证行业中的重要分支。符号方法将符号值加到待验证电路的基本输入端，依次计算每个结点的布尔函数，直到在电路的输出端得到由这些初始符号变量组成的布尔表达式，并判断其是否具有所期望的特征。该文介绍了符号模拟的发展过程、基本技术及其所面临的BDD爆炸问题，并对消除该问题的两种技术：近似值法和参数方法，进行了讨论。介绍了具有一定自动机处理能力的符号轨迹评价方法。     

自动生成华光（方正）符号——内码对应表

由于华光（方正）电子排版系统扩充了国标的符号，因此“符号－内码对应表”不仅对操作人员一目了然，而且对程序员也极为有用。本文简要地介绍了华光系统符号编码规则及Ｓ２文件与表格有关的简单命令，并给出了自动生成符号－内码对应表的实用程序     

巧妙输入数学符号

一、利用“符号栏” 选中“视图→工具栏→符号栏”（符号栏前有√标记），此时“符号栏”会显示在Word窗口的下方．单击相应的数学符号，便可快捷录入数学符号，并可连续、重复录入。如果符号栏上没有你需要的符号，可以通过下列方法重新设定符号栏：     

基于MapX复杂符号标绘的一种新方法

在一些需要使用复杂符号甚至动态符号基于MapX的GIS系统里，Mapx提供的符号标绘功能存在明显的不足，不能满足一些系统的实际需要。鉴于此，本文基于对MapX的内部实现机制的分析，提出一种新的实现复杂符号标绘的技术方法，同时给出了在VC＋＋环境下的具体实现过程。实验证明，该方法可以使我们在MapX上标绘任意复杂的动静态符号。     

符号网络分析系统

本文介绍了作者用符号演算法，在微型计算机上实现了一套符号网络分析系统，使用这套系统作网络辅助分析与设计，可得精确的符号解，还可清楚地了解每个元件在网络中的作用和贡献。     

一种动态消减时间自动机可达性搜索空间的方法

时间自动机的可达性分析算法通常采用对符号状态的枚举来遍历其状态空间。符号状态由位置与时间区域组成，时间区域用形如x-y≤（〈）n的原子公式的合取式来表示。在对时间自动机进行可达性分析的过程中，分析算法将生成大量的符号状态，往往导致对计算机内存的需求超出了可行的范围。本文给出了一个消减符号状态个数的方法。该方法通过对符号状态间的依赖关系进行分析，在不影响分析结果的前提下消去某些时间区域的原子公式，从而扩展符号状态。扩展后的符号状态包含有更加多的其它的状态，通过删除掉那些被包含的符号状态可以减少算法存储的状态个数，节省存储空间。本文最后给出了相关的案例分析，结果表明这个算法有效地减少了某些时间自动机可达性分析过程中所需的存储空间。     

浅谈如何控制条码符号印刷质量

控制好条码符号的印刷及出厂前的检测，是保证条码符号质量的重要条件之一，达不到质量要求的条码符号不但不能方便用户，反而会给管理造成混乱。保证条码符号质量，杜绝不合格条码符号进入应用领域，需要从以下几方面入手：     

多级映射原理在模糊传感器中的应用

在介绍多级映射原理基础上，以模糊温度传感器为例讨论了多级符号表示方法，符号测量结果显示方法等问题。该方法可以推广到需要符号表示的应用研究领域。     

线状图形矢量化中的行段分析与后处理

针对目前工程图纸识别中存在的几项难点技术,提出基于行程编码的矢量化方法, 对小弯度直线的纠正问题、特征线段的延伸与合并算法进行了研究.同时使用图像局部形态 分析的方法,直接在原图像上进行识别,保证了图像信息的完整性和可检测性.该项研究结果 在建筑工程图纸的自动识别及自动计算中得到了较好的应用.     

工程图纸识别与智能化光栅矢量编辑系统

该文描述了工程图纸矢量化、识别的处理与实现过程,对当前矢量化中存在的问题进行了分析研究。提出智能化光栅矢量化的方法,采用自动和交互搜索的方法实现光栅信息的提取、编辑和矢量化,并结合知识引导的联想修正技术使识别的准确性有了明显提高。文中结合建筑结构图的自动识别和计算进行了研究。     

图纸扫描消蓝技术评价与改进算法

描述工程图纸识别技术中的扫描消蓝技术，对几种二值化方法进行了分析和评估，提出了非线性适应算法，微分函数的区域分割算法和基于逻辑分级技术的二值化算法，同时结合建筑结构图的自动识别进行了试验研究，未给出最佳处理方法。而是指出对应不同类型退化的图纸。采用不同的方法或组合形式实现二值化。     

工程图中线状图形的自动分类与识别算法

描述了工程图中图形分类、识别和自动计算的方法及其理论依据。对线状图形的几何属性及其表示作了理论分析,为计算机自动识别工程图形提供了一种依据。提出采用模型逻辑的方法对工程图中常见的闭合图形进行分析、归类和识别,并结合建筑结构图中钢筋的分类、识别和自动计算进行了实验。     

A novel architecture: Using convolutional neural networks for Kansei attributes automatic evaluation and labeling

Kansei evaluation is crucial to the process of Kansei engineering. However, traditional methods are subjective and random. In order to eliminate the differences of individual evaluation criteria in product Kansei attributes evaluation, and further improve the evaluation efficiency, a novel automatic evaluation and labeling architecture for product Kansei attributes was proposed in this paper based on Convolutional Neural Networks (CNNs). The architecture consists of two modules: (1) Target detection module (Faster R-CNN was taken as an example), (2) Fine-Grained classification module (DFL-CNN was taken as an example). A case study was provided to validate the proposed architecture. The proposed architecture transformed design evaluation tasks into the recognition and classification tasks. The experiments achieved 98.837%, 96.899%, 86.047%, and 81.008% accuracy in the binary, triple, and two five-classification tasks, respectively. Our results proved the feasibility of using computer vision to mimic human vision for the automatic evaluation of Kansei attributes.     

动态模式识别算法的GPU平台实现

研究动态模式识别算法在GPU并行计算平台的实现。随着GPGPU(通用计算图形处理器)硬件的发展,基于GPU的大规模并行计算技术将有效地处理动态模式识别算法带来的海量计算问题。文中通过介绍动态模式识别算法,对算法中涉及的巨大计算量进行分析,并针对性地对其中密集计算部分进行并行化分解,移除原算法中在执行中存在的依赖关系,最终得到算法在特定的GPU平台———Jacket上的并行计算实现。实例验证表明,相比于原CPU串行程序,在GPU上运行的并行化程序能实现明显加速,因而具有很好的工程应用价值。     

Adaptive Audio-Based Context Recognition

Context recognition is an essential aspect of intelligent systems and environments. In most cases, the recognition of a context of interest cannot be achieved in a single step. Between measuring a physical phenomenon and the estimation or recognition of what this phenomenon represents, there are several intermediate stages which require a significant computation. Understanding the resource requirements of these steps is vital to determine the feasibility of context recognition on a given device. In this paper, we propose an adaptive context-recognition architecture that accommodates uncertain knowledge to deal with sensed data. The architecture consists of an adaptation component that monitors the capability and workload of a device and dynamically adapts recognition accuracy and processing time. The architecture is implemented for an audio-based context recognition. A detail account of the tradeoff between recognition time and recognition accuracy is provided.     

A Dynamic Network Model of the Color Visual Pathways for Attentive Recognition

A neural network architecture for the segmentation and recognition of colored and textured visual stimuli is presented. The architecture is based on the Boundary Contour System and Feature Contour System (BCS/FCS) of S. Grossberg and E. Mingolla. The architecture proposes a biologically-inspired mechanism for color processing based on antagonist interactions. It suggests how information from different modalities (i.e. color or texture) can be fused together to form a coherent segmentation of the visual scene. It identifies two stages of visual pattern recognition, namely, a global preattentive recognition of the visual scene followed by a local attentive recognition within a particular visual context. The global and local classification and recognition of visual stimuli use ART-type models of G. Carpenter and S. Grossberg for pattern learning and recognition based on color and texture. One example is presented corresponding to an figure-figure separation task. The architecture provides a mechanism for segmentation, categorization and recognition of images from different classes based on self-organizing principles of perception and pattern recognition.     

Fingerprint image enhancement and postprocessing based on the directional fields

Fingerprint recognition is based on minutiae matching. The matching correctness of the fingerprints is due to the effect of the accuracy of the minutiae. Fingerprint enhancement and postprocessing are used to reduce the false minutiae. In this paper, we propose methods on fingerprint enhancement and postprocessing, based on the directional fields of a fingerprint. We directly enhance the fingerprint on a gray-scale image and reduce most false minutiae in the postprocessing step. The achieved results are compared with other methods, and the reduction of false minutiae and the recovery of dropped minutiae are improved. The text was submitted by the authors in English. Gwo-Cheng Chao was born in Dasi, Taoyuan, Taiwan, in 1978. He received MS degrees in computer science and information engineering from Taiwan University of Science and Technology, Taiwan, in 2004. He is currently pursuing a PhD degree in networking and multimedia at National Taiwan University, Taipei, Taiwan. His research interests include pattern recognition, image processing, computer vision, biometrics, computer graphics, and multimedia systems. Shung-Shing Lee received BS and MS degrees in electronic engineering and a PhD degree in electrical engineering in 1980, 1987, and 1996, respectively, all from National Taiwan Institute of Technology, Taipei, Taiwan. Currently, he is an associate professor in the Department of Electrical Engineering, Ching Yun University, Jung-Li, Taiwan. His research interests include image processing, biometrics, embedded system design, SOPC, parallel computing, and parallel algorithms. Hung-Chuan Lai received his MS degree in computer science and information engineering from Chung-Hua University, Hsinchu, Taiwan, in 2002. He is currently pursuing a PhD degree at National Taiwan University of Science and Technology, Taipei, Taiwan. His research interests include image processing, VLSI, fault tolerance architecture, embedded system design, data compression, computer architecture and organization, and biometrics.     

基于软件体系结构的用户界面设计方法

为实现软件体系结构指导下的软件工程设计，该文提出了一个在体系结构指导下、支持自动生成的用户界面工程开发方法。该方法把界面的体系结构作为工程模型的直接描述对象，在界面体系结构的引导和约束下，通过可视化用户干预建立界面的展示模型，在代码生成阶段采用了界面体系结构指导下的界面设计模式。研究表明，以体系结构为指导的软件模型化设计是可行的。