巨型多不确定串匹配完全自动机及其快速生成算法

在串匹配搜索中,字符串常常采用U-不确定串、V-不确定串及其结合的U-V-不确定串.如何识别巨量U-不确定字符串、V-不确定字符串和U-V-不确定字符串,以及两个和两个以上U-V-不确定字符串的交错情况的串匹配,是没有遗漏地检测有害信息的关键问题.本文提出一个快速检测巨量U-不确定字符串、巨量V-不确定字符串和巨量U-V-不确定字符串的多串匹配完全自动机及其快速生成方法,包括两个和两个以上不确定字符串相互交错的情况;并且给出V-不确定字符串的完全自动机的最大并行台数,指出通常正则表达式匹配可能出现相似连接和交错情况的两种遗漏,指出如果没有从整体的角度对U-不确定串中的字符子串集进行两两不相交化及无同源后续奇点化的处理,结果就可能出现错误或者增加状态数目.     

视线追踪系统中视线估计方法研究

文中提出一种基于瞳孔—角膜反射(PCCR)的视线估计方法(GEMHSSO).针对现有PCCR存在的主要问题:限制使用者头部运动和个体标定问题,提出了一种单相机单光源条件下头部位置的补偿方法,实现了头部位置变化对瞳孔角膜向量影响的解析补偿,并建立一种个体差异的转化模型,进而简化标定过程为单点标定.以此为基础形成一种新的视线估计方法,本方法使精确视线估计的最小硬件要求降低到单相机(未标定)单光源,既不需要繁杂的系统标定,又实现了自然头动视线估计,并且简化用户标定为单点标定.该方法的各个环节都满足实时性要求,为面向人机交互的视线追踪系统提供了有效的解决方案.     

基于多属性层次分析的信息安全风险评估方法

信息系统安全风险本质上是动态、多目标、多属性的风险共存过程。为了真实、准确地反映信息系统安全的总体风险,降低分析判断中主观因素的影响。文章在AHP的基础上,根据多属性决策的理论与方法,对AHP中因素的非相关性进行修正,省略了规则层与因素层之间的权重关系,利用多属性影响因素之间相关性对信息系统安全进行风险评估分析与计算,实现了从对象族单一评价,关键因素的关联到系统整体的全局评价。     

Orthogonal variant moments features in image analysis

Moments are statistical measures used to obtain relevant information about a certain object under study (e.g., signals, images or waveforms), e.g., to describe the shape of an object to be recognized by a pattern recognition system. Invariant moments (e.g., the Hu invariant set) are a special kind of these statistical measures designed to remain constant after some transformations, such as object rotation, scaling, translation, or image illumination changes, in order to, e.g., improve the reliability of a pattern recognition system. The classical moment invariants methodology is based on the determination of a set of transformations (or perturbations) for which the system must remain unaltered. Although very well established, the classical moment invariants theory has been mainly used for processing single static images (i.e. snapshots) and the use of image moments to analyze images sequences or video, from a dynamic point of view, has not been sufficiently explored and is a subject of much interest nowadays. In this paper, we propose the use of variant moments as an alternative to the classical approach. This approach presents clear differences compared to the classical moment invariants approach, that in specific domains have important advantages. The difference between the classical invariant and the proposed variant approach is mainly (but not solely) conceptual: invariants are sensitive to any image change or perturbation for which they are not invariant, so any unexpected perturbation will affect the measurements (i.e. is subject to uncertainty); on the contrary, a variant moment is designed to be sensitive to a specific perturbation, i.e., to measure a transformation, not to be invariant to it, and thus if the specific perturbation occurs it will be measured; hence any unexpected disturbance will not affect the objective of the measurement confronting thus uncertainty. Furthermore, given the fact that the proposed variant moments are orthogonal (i.e. uncorrelated) it is possible to considerably reduce the total inherent uncertainty. The presented approach has been applied to interesting open problems in computer vision such as shape analysis, image segmentation, tracking object deformations and object motion tracking, obtaining encouraging results and proving the effectiveness of the proposed approach.     

多元智能理论在任职教育中的应用初探

多元智能理论强调人类智能在性质与结构上的多元性。针对新形势下军队任职教育的特点与需求,以提高岗位任职能力和创新实践能力为目标,考虑学员在个体需求和智能结构上的差异,探讨了任职教育在教学方法、教学环境和资源保障需求与应对策略,分析了相应的教学评估手段。     

基于增强现实的头部跟踪系统的设计与实现

该文以提出一种对设备要求较低、较易实现、更符合"以人为中心"理念的交互方式为出发点,以普通摄像头作为输入设备,基于计算机视觉技术,设计了简单的头部跟踪系统,实现上以增强现实技术为核心,结合了ARToolKit和Coin3D两个开源库的优点,大大降低了开发成本.实验中本系统在Vega构建的视景仿真环境中得到了良好的应用,...     

基于SQL的多条件选择智能实现算法

B/S架构的信息系统开发中,一般情况下,根据用户的选择,n个条件的输入组合数是2n个。但是,该文中提出的算法,其组合数仅为n到n(n+1)/2,大大简化了编程。     

应用差分GPS实现小麦精播机器人定位

应用差分GPS（Global Positioning System,全球定位系统）进行小麦精播机器人的定位,结合NI公司研发的Lab—view图形化编程软件开发了小麦精播机器人定位系统。首先分析了相对定位和绝对定位各种方法的优缺点;然后介绍了小麦精播机器人定位系统具体的工作流程,详细叙述了GPS数据的接收和提取、经纬度...     

Dynamical information fusion of heterogeneous sensors for 3D tracking using particle swarm optimization

This paper presents a new method for three dimensional object tracking by fusing information from stereo vision and stereo audio. From the audio data, directional information about an object is extracted by the Generalized Cross Correlation (GCC) and the object’s position in the video data is detected using the Continuously Adaptive Mean shift (CAMshift) method. The obtained localization estimates combined with confidence measurements are then fused to track an object utilizing Particle Swarm Optimization (PSO). In our approach the particles move in the 3D space and iteratively evaluate their current position with regard to the localization estimates of the audio and video module and their confidences, which facilitates the direct determination of the object’s three dimensional position. This technique has low computational complexity and its tracking performance is independent of any kind of model, statistics, or assumptions, contrary to classical methods. The introduction of confidence measurements further increases the robustness and reliability of the entire tracking system and allows an adaptive and dynamical information fusion of heterogenous sensor information.