基于动态Radio Map的粒子滤波室内无线定位算法

针对目前大多数基于射频信号强度匹配定位算法在定位精度及鲁棒性方面不足,提出了一种基于动态Radio Map的粒子滤波室内无线定位算法.该算法利用参考节点构建基于空间关联性的动态Radio Map模型,以反映信号环境的实时变化,并将移动目标定位由分类问题转化为回归问题,打破了传统网格式Radio Map模型的限制,降低了算法的时空复杂度.实验结果表明,相对于静态Radio Map模型,动态Radio Map模型将定位精度平均提高了约20%,表现出良好的环境动态自适应能力.     

增强现实中的光照一致性研究

增强现实中的光照一致性是确保虚拟物体和真实场景很好融合的关键,它能使计算机模拟出的虚拟物体与真实场景相融合后显得更加真实。因此需要根据真实场景中光照分布情况,对虚拟物体做阴影处理,从而消除虚拟物体的飘浮感。针对动态场景中的高实时性要求,对现有的光照估计算法进行改进,并应用聚类算法对光源进行筛选,较好地解决了动态环境下虚实场景的光照一致性问题。实验结果表明,采用所提出的改进算法,能够得到逼真的阴影视觉效果,同时系统具有良好的实时性。     

利用球谐方法分散计算场景的全局光照

作为一种特殊的光照模型,辐射度在图形学中有着重要的应用.然而,由于其计算的复杂性,辐射度方法一直得不到广泛的应用,特别是在动态场景中.提出一种基于辐射度算法的全局光照实时加速算法,在原有基于点采样进行场景的全局光照近似的基础上,提出了不同的点采样计算方法,以提高辐射度方法对动画场景的适应性.算法对场景中的物体建立六面体包围盒,并在包围盒各顶点设置采样点,这样就可以在环境变化对其光照影响不大的情况下采用原有的计算结果,实现辐射度算法在动态场景中的计算加速.同时,对场景中的每个物体的光照,利用立方体映射计算直接光照;而对物体间的能量交换采用点采样方法进行近似计算并利用附近采样点的光照插值作为物体的光照.实验结果表明,该算法可以减少大量的间接光照计算,提高了辐射度算法的效率.     

聚类立即辐射度及在增强现实中的应用

提出一种聚类立即辐射度方法,以实现增强现实等领域需要高度真实感的全局光照算法来实现实时交互的绘制效果要求。为此,改进了传统的立即辐射度方法,将大量的用于表达间接光照的虚拟点光源聚类到少量的虚拟面光源中,并使用实时软阴影算法快速计算可见性。同时,借助图形硬件GPU加速场景绘制。实验结果表明,算法在增强现实环境等领域中支持完全动态场景,且在保证良好视觉效果的前提下获得了实时绘制帧率。     

一种云场景的实时渲染方法

探讨了真实感云场景的模拟技术,设计了一种云场景的实时渲染方法。基于Perlin噪声建模生成云浓度图,并采用考虑浓度的Phong光照模型与单向散射光照模型,分别计算反射光和透射光,改善了传统方法无法真实反映不同角度太阳光照的问题,实现了不同时段动态云场景的绘制。通过引入基于GPU的Bumping纹理算法与Render-to-Texture技术,极大提高了云场景的渲染速度。实验结果进一步表明该方法能够同时满足真实感与实时性两个方面的要求。     

利用形状因子采样的实时全局光照绘制

为了对物体表面材质进行实时编辑,提出一种动态光照和任意视点条件下的实时全局光照算法.该算法预计算各面片的形状因子,并存储其中较大的形状因子值和相应的面片号,这些面片是光照贡献最大的面片.一次间接光照利用这些面片计算,并用亮度补偿策略增加计算精度,而二次及多次间接光照则用近似公式估算,整个光照计算过程在GPU中完成.实验结果表明,文中算法在视点改变、光照改变和材质改变情况下,对静态场景能获得逼真的实时全局光照绘制效果.     

基于环境映照自动对齐的高质量虚实融合技术

要实现高品质的增强现实效果需要解决虚拟物体与现实场景的光照一致性问题.虽然采用HDR技术能获取场景的环境映照,但需要解决所获取的光照环境信息与真实场景的对齐问题.为此提出一种基于特征自动匹配的环境映照对齐方法.首先采用Affine-SIFT算法和随机抽样一致性算法对环境映照和拍摄场景进行特征匹配并优化匹配结果,然后利用基于运动推断结构的摄像机定标算法求得匹配对的三维位置,从而计算出环境映照与真实场景的对应关系,实现了二者的自动对齐.基于该技术搭建的高真实感实时虚实融合系统采用基于关键帧的相机跟踪技术,可以实时地将虚拟物体注册到拍摄的视频场景里,并允许对其进行实时的交互编辑.在渲染时有效地利用了自动对齐后的光照环境信息,采用重要性采样算法和阴影映射技术实现了实时的高质量渲染.实验结果表明,所搭建的增强现实系统很好地解决了实时虚实融合中的几何一致性和光照一致性问题.     

可变材质的实时全局光照明绘制

现有的基于预计算的全局光照明绘制算法都假设场景中物体的材质固定不变,这样,从入射光照到出射的辐射亮度之间的传输变换就是线性变换.通过对这种线性变换的预计算,可以在动态光源下实现全局光照明的实时绘制.但是,当材质可以改变时,这种线性变换不再成立,因此,现有算法无法直接用于动态材质的场景.提出了一种方法:在修改场景中的物体材质时,可以实时得到场景在直接光照和间接光照下的绘制效果.将最终到达视点的辐射亮度根据其之前经过的反射次数及相应的反射材质分为多个部分,每个部分和先后反射的材质的乘积成正比,从而把该非线性问题转化为线性问题.又将所有可选的材质都表示为一组基的线性组合.将这组基作为材质赋予场景中的物体,就有各种不同的组合方式,预计算每种组合下所有部分的出射辐射亮度.在绘制时,根据各物体材质投影到基上的系数线性组合预计算的数据就能实时得到最终的全局光照明的绘制结果.该方法适用于几何场景、光照和视点都不发生变化的场景.使用双向反射分布函数来表示物体的材质,不考虑折射或者半透明的情况.该实现最多包含两次反射,并可以实时绘制得到一些很有趣的全局光照明效果,比如渗色、焦散等等.     

采用重要性面片采样的实时全局光照

提出了一种实时全局光照的计算方法。该方法支持任意视点下动态光源的一次间接光照计算,并且物体表面材质可实时编辑,该算法预计算了各面片上的形状因子来解决遮挡问题,并记录形状因子较大的重要性面片作为间接光源。渲染时先从光源方向对场景记录了一个扩展的阴影图,包含了光源照射到的面片ID和其光通量,再根据采样好的间接光源来计算间接光照。使用CUDA,整个光照计算过程在GPU中完成,可以对静态场景进行实时渲染,并能达到逼真的渲染效果。     

CAD学报“动态场景实时绘制”专辑征文通知

真实感图形的实时绘制一直是图形学研究的热点和主流·《计算机辅助设计与图形学学报》拟于2008年下半年出版“动态场景实时绘制”专辑·本专辑的主题包括:①大规模、复杂、动态场景的实时绘制算法;②实时阴影算法;③全局光照算法·征文截稿日期2008年4月30日,热诚欢迎相关专家     

基于一种新型粗糙集神经网络的故障诊断

分析了粗糙集与神经网络各自的优缺点,结合粗糙集与神经网络提出了一种基于SOFM网络的新型粗糙集神经网络,给出了该网络的流程图,描述了系统各组成部分的工作原理。应用一实例验证了该网络在故障诊断中的有效性。结果表明,新网络较好地解决了训练样本的大小、样本质量等对人工神经网络的精度以及泛化能力有直接影响的问题,简化了神经网络的结构,缩短了训练时间,并实现了结果可视化,最后得到了故障可视拓扑映射图。     

Redundant Hash Addressing of Feature Sequences Using the Self-Organizing Map

Kohonen's Self-Organizing Map (SOM) is combined with the Redundant Hash Addressing (RHA) principle. The SOM encodes the input feature vector sequence into the sequence of best-matching unit (BMU) indices and the RHA principle is then used to associate the BMU index sequence with the dictionary items. This provides a fast alternative for dynamic programming (DP) based methods for comparing and matching temporal sequences. Experiments include music retrieval and speech recognition. The separation of the classes can be improved by error-corrective learning. Comparisons to DP-based methods are presented.     

Defect spatial pattern recognition using a hybrid SOM–SVM approach in semiconductor manufacturing

As manufacturing geometries continue to shrink and circuit performance increases, fast fault detection and semiconductor yield improvement is of increasing concern. Circuits must be controlled to reduce parametric yield loss, and the resulting circuits tested to guarantee that they meet specifications. In this paper, a hybrid approach that integrates the Self-Organizing Map and Support Vector Machine for wafer bin map classification is proposed. The log odds ratio test is employed as a spatial clustering measurement preprocessor to distinguish between the systematic and random wafer bin map distribution. After the smoothing step is performed on the wafer bin map, features such as co-occurrence matrix and moment invariants are extracted. The wafer bin maps are then clustered with the Self-Organizing Map using the aforementioned features. The Support Vector Machine is then applied to classify the wafer bin maps to identify the manufacturing defects. The proposed method can transform a large number of wafer bin maps into a small group of specific failure patterns and thus shorten the time and scope for troubleshooting to yield improvement. Real data on over 3000 wafers were applied to the proposed approach. The experimental results show that our approach can obtain over 90% classification accuracy and outperform back-propagation neural network.     

Simulated annealing technique for fast learning of SOM networks

The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for multidimensional input datasets. In this paper, we present an application of the simulated annealing procedure to the SOM learning algorithm with the aim to obtain a fast learning and better performances in terms of quantization error. The proposed learning algorithm is called Fast Learning Self-Organized Map, and it does not affect the easiness of the basic learning algorithm of the standard SOM. The proposed learning algorithm also improves the quality of resulting maps by providing better clustering quality and topology preservation of input multi-dimensional data. Several experiments are used to compare the proposed approach with the original algorithm and some of its modification and speed-up techniques.     

Real-time surface inspection by texture

In this paper a real-time surface inspection method based on texture features is introduced. The proposed approach is based on the Local Binary Pattern (LBP) texture operator and the Self-Organizing Map (SOM). A very fast software implementation of the LBP operator is presented. The SOM is used as a powerful classifier and visualizer. The efficiency of the method is empirically evaluated in two different problems including textures from the Outex database and from a paper inspection problem.     

Probabilistic Road Map sampling strategies for multi-robot motion planning

This paper presents a Probabilistic Road Map (PRM) motion planning algorithm to be queried within Dynamic Robot Networks—a multi-robot coordination platform for robots operating with limited sensing and inter-robot communication.

First, the Dynamic Robot Networks (DRN) coordination platform is introduced that facilitates centralized robot coordination across ad hoc networks, allowing safe navigation in dynamic, unknown environments. As robots move about their environment, they dynamically form communication networks. Within these networks, robots can share local sensing information and coordinate the actions of all robots in the network.

Second, a fast single-query Probabilistic Road Map (PRM) to be called within the DRN platform is presented that has been augmented with new sampling strategies. Traditional PRM strategies have shown success in searching large configuration spaces. Considered here is their application to on-line, centralized, multiple mobile robot planning problems. New sampling strategies that exploit the kinematics of non-holonomic mobile robots have been developed and implemented. First, an appropriate method of selecting milestones in a PRM is identified to enable fast coverage of the configuration space. Second, a new method of generating PRM milestones is described that decreases the planning time over traditional methods. Finally, a new endgame region for multi-robot PRMs is presented that increases the likelihood of finding solutions given difficult goal configurations.

Combining the DRN platform with these new sampling strategies, on-line centralized multi-robot planning is enabled. This allows robots to navigate safely in environments that are both dynamic and unknown. Simulations and real robot experiments are presented that demonstrate: (1) speed improvements accomplished by the sampling strategies, (2) centralized robot coordination across Dynamic Robot Networks, (3) on-the-fly motion planning to avoid moving and previously unknown obstacles and (4) autonomous robot navigation towards individual goal locations.     

自底向上优化神经网络的方法

引荐了一种自动优化神经网络的新方法。这种启迪方法综合采用了相关有效算法,通过快速自底向上构造神经网络算法,可以获得优化结构的神经网络,即时选定参数算法动态优化神经网络的学习参数,并且快速交叉校验算法为解决过度适应问题提供了捷径。实验证明,这种启迪方法能自动有效地优化神经网络,与其它算法相较而言,具有更好的归纳性能、优化的网络结构和更快的学习速度。     

一种基于MapReduce的不确定图上的相似性连接方法

相比于确定图上的相似性连接,不确定图上的相似性连接通常具有更大的实际应用价值以及计算复杂性。文中研究了基于MapReduce分布式编程框架的不确定图上的相似性连接问题,提出了基于概率和的Map方剪枝和Reduce方剪枝的两种剪枝策略。Map方剪枝策略在映射过程中过滤掉了不可能具有相似图的不确定图。Reduce方剪枝策略用于减少约减过程中的候选图对。基于这两种剪枝策略,文中提出了一种基于MapReduce框架的不确定图上的相似性连接算法MUGSJoin。实验结果证明,该算法与同类算法相比具有更好的性能和可扩展性。     

成年树光照资源仿真的快速计算

光照是树木生长需要的重要资源,是树木生长仿真计算中必不可少的因素.但在森林演化的计算机模拟中,由于光照模拟的复杂性,使得光照模型的计算量十分巨大.本文采用了光照指数(Gap Light Index,GLI)为因子的光照模型,并针对该模型开展了快速计算研究.由于该模型计算中存在着大量限制计算效率的几何求交运算,本文根据光照指数与植物暴露面积所具有的共同特点,提出采用基于暴露面积的计算方法来近似拟合GLI的值,并在并行计算架构CUDA上实现了该算法.最后通过不同实验比较,验证了本文提出的方法针对较大的树木规模,在保证较小误差率的前提下,获得了比GPU并行求交方法快数十倍以上的加速比.     

The corridor map method: a general framework for real‐time high‐quality path planning

In many virtual environment applications, paths have to be planned for characters to traverse from a start to a goal position in the virtual world while avoiding obstacles. Contemporary applications require a path planner that is fast (to ensure real‐time interaction with the environment) and flexible (to avoid local hazards such as small and dynamic obstacles). In addition, paths need to be smooth and short to ensure natural looking motions. Current path planning techniques do not obey these criteria simultaneously. For example, A* approaches generate unnatural looking paths, potential field‐based methods are too slow, and sampling‐based path planning techniques are inflexible. We propose a new technique, the Corridor Map Method (CMM), which satisfies all the criteria. In an off‐line construction phase, the CMM creates a system of collision‐free corridors for the static obstacles in an environment. In the query phase, paths can be planned inside the corridors for different types of characters while avoiding dynamic obstacles. Experiments show that high‐quality paths for single characters or groups of characters can be obtained in real‐time. Copyright © 2007 John Wiley & Sons, Ltd.