基于断点恢复技术的DRM打包器

针对目前数字版权管理(DRM)系统中关于打包器的研究只限于功能论述和技术概括的现象,提出了一种基于断点恢复技术的通用DRM打包器。打包器通过断点集实时记录其状态信息,在发生异常中断重新启动时自动恢复中断前的打包任务。并采用自适应加密方案对不同大小的文件自动采取不同的加密策略,在保证安全性的前提下提高了加密效率。与普遍采用的商用DRM打包器的对比测试结果表明,该打包器具有更高的效率和健壮性,可为同类项目开发提供参考的解决方案。     

Automatic Cage Building with Quadric Error Metrics

Modern computer graphics applications usually require high resolution object models for realistic rendering.However,it is expensive and difficult to deform such models in real time.In order to reduce the computational cost during deformations,a dense model is often manipulated through a simplified structure,called cage,which envelops the model.However,cages are usually built interactively by users,which is tedious and time-consuming.In this paper,we introduce a novel method that can build cages automatically for both 2D polygons and 3D triangular meshes.The method consists of two steps:1) simplifying the input model with quadric error metrics and quadratic programming to build a coarse cage;2) removing the self-intersections of the coarse cage with Delaunay partitions.With this new method,a user can build a cage to envelop an input model either entirely or partially with the approximate vertex number the user specifies.Experimental results show that,compared to other cage building methods with the same number of vertex,cages built by our method are more similar to the input models.Thus,the dense models can be manipulated with higher accuracy through our cages.     

A comment on “Using locally estimated geodesic distance to optimize neighborhood graph for isometric data embedding”

A geodesic distance-based approach to build the neighborhood graph for isometric embedding is proposed to deal with the highly twisted and folded manifold by Wen et al. [Using locally estimated geodesic distance to optimize neighborhood graph for isometric data embedding, Pattern Recognition 41 (2008) 2226-2236]. This comment is to identify the error in their example and the ineffectiveness of their algorithm.     

Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity

Accurate cropland information is of paramount importance for crop monitoring. This study compares five existing cropland mapping methodologies over five contrasting Joint Experiment for Crop Assessment and Monitoring (JECAM) sites of medium to large average field size using the time series of 7-day 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) mean composites (red and near-infrared channels). Different strategies were devised to assess the accuracy of the classification methods: confusion matrices and derived accuracy indicators with and without equalizing class proportions, assessing the pairwise difference error rates and accounting for the spatial resolution bias. The robustness of the accuracy with respect to a reduction of the quantity of calibration data available was also assessed by a bootstrap approach in which the amount of training data was systematically reduced. Methods reached overall accuracies ranging from 85% to 95%, which demonstrates the ability of 250 m imagery to resolve fields down to 20 ha. Despite significantly different error rates, the site effect was found to persistently dominate the method effect. This was confirmed even after removing the share of the classification due to the spatial resolution of the satellite data (from 10% to 30%). This underlines the effect of other agrosystems characteristics such as cloudiness, crop diversity, and calendar on the ability to perform accurately. All methods have potential for large area cropland mapping as they provided accurate results with 20% of the calibration data, e.g. 2% of the study area in Ukraine. To better address the global cropland diversity, results advocate movement towards a set of cropland classification methods that could be applied regionally according to their respective performance in specific landscapes.     

A modular approach to learning manipulation strategies from human demonstration

Object manipulation is a challenging task for robotics, as the physics involved in object interaction is complex and hard to express analytically. Here we introduce a modular approach for learning a manipulation strategy from human demonstration. Firstly we record a human performing a task that requires an adaptive control strategy in different conditions, i.e. different task contexts. We then perform modular decomposition of the control strategy, using phases of the recorded actions to guide segmentation. Each module represents a part of the strategy, encoded as a pair of forward and inverse models. All modules contribute to the final control policy; their recommendations are integrated via a system of weighting based on their own estimated error in the current task context. We validate our approach by demonstrating it, both in a simulation for clarity, and on a real robot platform to demonstrate robustness and capacity to generalise. The robot task is opening bottle caps. We show that our approach can modularize an adaptive control strategy and generate appropriate motor commands for the robot to accomplish the complete task, even for novel bottles.     

医院信息系统综合评价体系模型

为推动医院信息化建设工作的规范化,加强医院信息化建设的管理、监督、指导和评审工作,构建出一个具有广泛代表性的医院信息系统评价体系模型,使得我区医院信息化进程能够持续健康的发展. 我们运用一种改进的层次分析法原理,对指标体系中的每一个单一指标进行权重求解,从而得出一个可以求解出系统性能测试和功能测试总得分的指标体系,对医院信息系统进行综合评价. 通过该模型对新疆医科大学两家附属医院信息系统进行计算评分,最终科学有效地评价了该两家医院的信息系统,找出医院信息化工作的不足之处并给出了科学的改进建议.     

Towards online and personalized daily activity recognition,habit modeling,and anomaly detection for the solitary elderly through unobtrusive sensing

Multimedia Tools and Applications - Rapid population aging and advances in sensing technologies motivate the development of unobtrusive healthcare systems, designed to unobtrusively collect an...     

Externalizing cognitive maps via map reconstruction and verbal description

Universal Access in the Information Society - Helping blind people to build cognitive maps of an environment is one of the aims of several assistive systems. In order to evaluate such assistive...     

基于行人运动模态辨识的室内外无缝导航航向算法研究

目前行人导航航向解算算法均基于导航传感器在行人身体上的固定安装模式,或者依赖其他射频信息辅助修正陀螺航向,这极大约束了导航传感器的适用条件.为此,利用陀螺对低频噪声的敏感性及加速度计低频的稳定性,提出了解决行人手持手机稳态查看与非稳态摇摆的运动模态辨识算法和基于时域互补滤波器实现姿态的最优融合方法;研究了改进型互补滤波以消除行人的运动加速度对姿态解算的干扰误差,提高了载体姿态的测量精度;此外,利用磁传感器标定后的数据设计了自适应卡尔曼滤波算法,抑制了航向角的误差发散.经实际数据测试验证,室内外行人手持稳态与非稳态下的航向角测量精度提高了80%,同时大大提高了导航传感器的适用性与便携性,满足实际工程的使用需求.     

基于格林函数表示的近似等距网格之间稀疏对应

基于全局点签名(GPS)和格林函数表示,提出了一种由粗到细的近似等距网格曲面模型间的稀疏点对应算法.针对构建点GPS表示的对应基向量间的符号不定问题,利用Morse理论和修改的层次聚类算法,提取源网格和近似等距目标网格上的关键点作为锚点,并结合符号的组合搜索策略,提出了一种基于GPS的锚点对应鲁棒算法;针对由于网格分辨率不同导致的高维GPS坐标不一致问题,结合前面确定的锚点对,定义了一种点的格林函数表示,并在此基础上提出一种增量式稀疏点对应算法.实验结果表明,与已有网格点对应算法相比,文中算法具有更高的计算效率和准确度,可应用于刚体和非刚体对齐以及三维变形、形状匹配等.