基于what和where信息的目标检测方法

根据视觉系统两条通路理论,提出了一种基于what和where信息的目标检测方法.采用以环境为中心的where信息进行自顶向下的注意控制,指导what信息驱动的自底向上的注意.自顶向下的注意包括预注意和集中注意两个阶段,预注意依据where信息为特定目标出现与否提供先验,做出是否继续搜索的判定.集中注意的结果与what信息相结合,将注意指向目标最有可能出现的图像区域,并得到一系列样本显著区域.应用于多幅自然图像的实验结果证明了算法的有效性.     

"数字化校园"向"智慧校园"发展的探索

随着科学技术的发展,目前很多发展迅速的高校都已经开始进行智慧校园研究,以期给学生营造良好的学习环境,满足各项信息服务需求.从"智慧校园"发展特点分析,智慧校园实际上是"数字化校园"发展的结果,必须在数字化技术基础上发展.本文主要介绍了智慧校园的特点及组成部分,希望可以促进"数字化校园"向"智慧校园"Z的发展.     

双射弹的擦肩发射原理探索

美国双射程导弹代表着下一代空空导弹的发展方向,它的一个重要性能就是擦肩发射,即在本机和目标擦肩而过时发射导弹。本文主要研究了在未来空战中将发挥十分重要作用的擦肩发射的原理,认为擦肩发射是越肩发射的一种新形式,它可以通过缩短导弹的最小发射距离来增大本机的攻击机会,在近距格斗中由于擦肩发射放宽对本机指向的要求,从而降低了对本机敏捷性的要求。     

Staying Protected from "Social Engineering"

"Your Internet Connection" we provided wealth of information on different protection tools. Although these tools are indeed necessary for anyone brave enough to surf the Internet today, there is no substitute for sound judgement. "Your Internet Connection" describing Internet security resources, the Anti-Phishing Working Group's (APWG) and the Phishing Incident Reporting and Termination squad (http://wiki.castlecops.com/PIRT) were listed among other important security resources.Many criminals generating their garbage from the "sewers of the Internet" employ so called "social engineering" techniques. Just to make sure we are on the same page, here is the definition for the term "social engineering" from SearchSecurity.com: "In computer security, social engineering is a term that describes a non-technical kind of intrusion that relies heavily on human interaction and often involves tricking other people to break normal security procedures.     

基于数字媒体技术的艺术教育的“技”与“艺”

数字媒体技术是伴随现代计算机数字技术及网络媒体环境等兴起而发展的新生事物,同时也是对传统视觉、听觉等各类感官的艺术形式的继承和拓展。那么,对于艺术教育而言,基于数字媒体技术的创新与应用,能够有效实现科学技术与艺术创意思维及品质的高度结合,而这种“技”与“艺”的结合,不仅仅是创新艺术教育理念及方式的内在要求,而且是提升与增强艺术教育成效,实现内涵式发展的有效途径。文章结合游戏艺术教育来探讨基于数字媒体技术的艺术教育的“技”与“艺”问题,着重提出几点发展建议。包括从传统艺术中汲取营养、充分发挥其技术的优势、要学会技术与艺术并重、能够深入生活感知体验以及基于融合生成艺术理念五大方面入手,实现“技”与“艺”融合。     

"Safe-approach" vehicle-follower control

A "safe-approach" control policy is presented, which simultaneously handles close-headway vehicle following and various maneuver situations. The policy is illustrated with an example controller design and simulation evaluation.     

Comments on "The quasi-linear intensity interferometer"

It is pointed out that the sense in which "linearity" obtains in the proposed "quasi-line" intensity interferometer" is not that its response is linear with respect to the observed source flux; in fact, it is quadratic in this sense, as is the conventional intensity interferometer. Also, a reinterpretation of the operation of the quasi-linear intensity interferometer is given, and a mode of operation equivalent to a current very-long-baseline technique is suggested.     

From "macro" to "micro" manipulation: models and experiments

This paper addresses various problems related to manipulation in the micro domain, a field which is increasingly important for research and application. Grasping and manipulating parts with size ranging between a few micrometers and about 1 millimeter (defined in this paper as "micro parts") are required for an increasing number of applications: the assembly of micro systems and micro machines; and the operation in tiny and unpredictable environments, such as for inspection and interventions in pipes and for micro surgery. The aim of this work is to find out similarities and differences between traditional manipulation and micro manipulation, by investigating which requirements are still valid and which must be redefined when the object size scales down. The similarities between the two application domains "macro" and "micro" are pointed out along with the differences, and both are taken into account for the evaluation of different grasping typologies. Dedicated models for the adhesion forces arising at the micro level are presented, preliminarily tested, and discussed.     

排队"梯度"浮点数编码遗传算法

文章主要论述了浮点数编码遗传算法优化的问题。充分利用个体及其适应度函数值的已知信息,依据一定算法得到“梯度”信息,从而加快算法的收敛速度。但如果个别适应度函数值相对较大(例如是全体适应度函数值平均值的100倍以上),则上述利用“梯度”信息的优势尽失。为了解决此问题,提出排队“梯度”浮点数编码遗传算法。最后用典型的四个常用测试函数分别对简单浮点数编码遗传算法、利用“梯度”信息的浮点数编码遗传算法和排队“梯度”浮点数编码遗传算法的性能进行了测试和比较。