Distilled Collections from Textual Image Queries

We present a distillation algorithm which operates on a large, unstructured, and noisy collection of internet images returned from an online object query. We introduce the notion of a distilled set, which is a clean, coherent, and structured subset of inlier images. In addition, the object of interest is properly segmented out throughout the distilled set. Our approach is unsupervised, built on a novel clustering scheme, and solves the distillation and object segmentation problems simultaneously. In essence, instead of distilling the collection of images, we distill a collection of loosely cutout foreground “shapes”, which may or may not contain the queried object. Our key observation, which motivated our clustering scheme, is that outlier shapes are expected to be random in nature, whereas, inlier shapes, which do tightly enclose the object of interest, tend to be well supported by similar shapes captured in similar views. We analyze the commonalities among candidate foreground segments, without aiming to analyze their semantics, but simply by clustering similar shapes and considering only the most significant clusters representing non‐trivial shapes. We show that when tuned conservatively, our distillation algorithm is able to extract a near perfect subset of true inliers. Furthermore, we show that our technique scales well in the sense that the precision rate remains high, as the collection grows. We demonstrate the utility of our distillation results with a number of interesting graphics applications.     

渲染农场研究进展综述

作为高性能计算领域的新兴应用,渲染有着计算密集和数据密集的应用特征,如何提高渲染农场的效率进行大规模快速渲染,近年来逐渐成为研究的热点问题.对渲染农场的国内外应用现状作了介绍,阐述了渲染农场的组织结构和工作流程.针对渲染农场在优化作业调度算法、改善负载均衡策略以及完善渲染管理平台等三个方面的研究,进行了全面的分析和比较.探讨和总结了在云计算背景下渲染农场在渲染作业调度策略、异构集群渲染任务分发算法、基于云模式的渲染农场新架构和负载均衡策略这四个方面中有待进一步研究的问题.     

Real-time congestion prediction for urban arterials using adaptive data-driven methods

Multimedia Tools and Applications - Congestion prediction can support traffic departments to take effective traffic management measures, and it aids users to adopt smarter trip strategies,...     

Privacy Preserving Geo-Linkage in the Big Urban Data Era

Big data technologies and a range of Government open data initiatives provide the basis for discovering new insights into cities; how they are planned, how they managed and the day-to-day challenges they face in health, transport and changing population profiles. The Australian Urban Research Infrastructure Network (AURIN – www.aurin.org.au) project is one example of such a big data initiative that is currently running across Australia. AURIN provides a single gateway providing online (live) programmatic access to over 2000 data sets from over 70 major and typically definitive data-driven organizations across federal and State government, across industry and across academia. However whilst open (public) data is useful to bring data-driven intelligence to cities, more often than not, it is the data that is not-publicly accessible that is essential to understand city challenges and needs. Such sensitive (unit-level) data has unique requirements on access and usage to meet the privacy and confidentiality demands of the associated organizations. In this paper we highlight a novel geo-privacy supporting solution implemented as part of the AURIN project that provides seamless and secure access to individual (unit-level) data from the Department of Health in Victoria. We illustrate this solution across a range of typical city challenges in localized contexts around Melbourne. We show how unit level data can be combined with other data in a privacy-protecting manner. Unlike other secure data access and usage solutions that have been developed/deployed, the AURIN solution allows any researcher to access and use the data in a manner that meets all of the associated privacy and confidentiality concerns, without obliging them to obtain ethical approval or any other hurdles that are normally put in place on access to and use of sensitive data. This provides a paradigm shift in secure access to sensitive data with geospatial content.     

基于灰关联混合蛙跳算法的雷达波形设计

提出了一种基于灰关联混合蛙跳算法的雷达波形设计方法,以混合蛙跳算法为主体,在局部更 新算子中引入遗传算法的遗传算子,并改进原始蛙跳算法的分组方法,丰富了种群的多样性,同时引入灰关联综合评价法则对适应度函数值加以关联度分析。文中以设计具有低自相关旁瓣和互相关特性的正交多相编码为例,将该算法用于雷达波形设计中。仿真结果表明使用本文算法产生 的波形具备较好的低自相关特性和互相关特性,表明了该算法是有效和可行的。     

基于细菌觅食算法的多线圈电- 机械转换器双模控制技术研究

针对提升电-机械转换器快速响应特性的技术需求,提出了新型三线圈结构电-机械转换器。通过建立的电-机械转换器数学模型,提出采用细菌觅食算法来寻找时间最优解,并基于此方法设计了砰-砰与比例-积分双模控制器。分析与仿真结果表明,相对于传统单线圈电-机械转换器,采用双模控制器的三线圈电-机械转换器,其响应时间从8.5 ms缩短至2.5 ms,震荡和超调不超过5%,可知该技术能够实现响应特性,前景良好。     

基于变周期技术的自适应数据采集算法

在数据采集系统中,按需采集是平衡数据数量、提高数据质量的有效手段。本文所提出的自适应数据采集算法是基于有限采样点的一元线性回归拟合,通过实时地对采集到的数据进行分析,不断修正采样周期,使得采样周期能够快速、准确地适应被测对象的动态变化。MATLAB软件仿真表明,本方法对于被测量加速变化具有较强的动态捕捉能力,能够在降低平均采样频率的前提下减小采样失真度,且与同类方法相比失真度低,稳定性高,是测控系统中实时数据采集的一种较好的代替方案。