基于新的阈值化方法的背景减法改进

首先将一种新的基于颜色空间模型的阈值化方法用于背景减法中. 该阈值化方法利用每个像素的颜色畸变和亮度畸变检测出场景中所有的运动, 其中像素的颜色畸变检测考虑了颜色向量所处的空间位置; 同时该阈值化方法在一定程度上抑制了运动阴影的影响. 其次, 将一种双阈值化方法用于背景减法中, 实现了复杂场景下前景目标的提取. 通过VSSN 05和PETS 2006测试视频的实验, 验证了本文提出算法的有效性.     

非均匀采样系统的一种递推辨识方法

在非均匀采样系统辨识方法中, 通常利用重采样、数值积分等方法来处理非均匀采样数据, 所用模型多为连续有理分式传递函数, 在递推形式下非均匀采样对象又常局限于``数据缺失'的情况. 本文研究更为一般的异步非均匀采样的多变量系统, 采用连续时间状态空间模型描述, 推导了模型参数、参数梯度和系统状态之间的相互递推关系, 构成一种可变迭代间隔的递推辨识算法, 在每次输出采样点上仅更新模型中受当前采样数据影响的参数. 这种辨识方法可以适用于任意非均匀采样系统, 多采样率系统也可作为一种特例适用于本算法. 仿真结果表明, 所提的方法是可行有效的.     

概念空间中上下位关系的意义识别研究

针对上下位关系在分类层级结构建立阶段遇到的多义性问题,给出一种概念空间中上下位关系意义识别的方法.单个概念的意义识别问题被转换为概念空间中上下位关系的意义识别.首先利用并列语境解决语境稀疏问题,获取上下位关系意义的语境.然后利用<同义词词林>对每个语境进行词义修正,以三种特征计算特征词权重,构建"关系一词'的高维向量空间,然后通过潜在语义分析降维,获取上下位关系意义的潜在语义,最后组平均聚类后得到关系的意义划分.在实验中,给出了聚类阈值自动调整函数,分析了词林和潜在语义分析的作用,实验结果证实了方法的有效性.     

两种级联空时格码方案的EXIT分析

级联空时格码方案可以有效地提高空时格码系统的性能。研究了两种级联空时格码方案:PC-STTC和ST-Turbo-TC。由于两种级联方案都使用了迭代译码方法,而外信息转移(EXIT)图是分析迭代译码性能的有利工具,主要分析比较了PC-STTC和ST-Turbo-TC的EXIT性能,比较了不同信噪比和编码多项式对于译码外信息转移特性的影响。研究结果显示,PC-STTC方案比ST-Turbo-TC方案有着更好的迭代收敛性能,同时给出了BER仿真图证明了EXIT图的分析结论。     

Passivity based adaptive Jacobian tracking for free-floating space manipulators without using spacecraft acceleration

Most research so far on trajectory tracking of free-floating space manipulators has assumed that the kinematics of the space manipulator is exactly known. However, when a space manipulator picks up different tools of unknown lengths or unknown gripping points, its kinematics and dynamics change and are difficult to derive exactly. Thus, in this paper, we have proposed a passivity based adaptive Jacobian controller for free-floating space manipulators. The proposed controller consists of a transposed Jacobian feedback and a dynamic compensation term, and the parameter adaptation laws are derived by Lyapunov-like stability analysis tools. It is shown that the end-effector motion tracking errors converge asymptotically. To avoid using spacecraft acceleration, we define a new reference velocity, which is called spacecraft reference velocity. In addition, we have also conducted passivity interpretation of the proposed controller to obtain some physical insight into its properties. Simulation results are presented to show the performance of the proposed controller.     

Stable local dimensionality reduction approaches

Dimensionality reduction is a big challenge in many areas. A large number of local approaches, stemming from statistics or geometry, have been developed. However, in practice these local approaches are often in lack of robustness, since in contrast to maximum variance unfolding (MVU), which explicitly unfolds the manifold, they merely characterize local geometry structure. Moreover, the eigenproblems that they encounter, are hard to solve. We propose a unified framework that explicitly unfolds the manifold and reformulate local approaches as the semi-definite programs instead of the above-mentioned eigenproblems. Three well-known algorithms, locally linear embedding (LLE), laplacian eigenmaps (LE) and local tangent space alignment (LTSA) are reinterpreted and improved within this framework. Several experiments are presented to demonstrate the potential of our framework and the improvements of these local algorithms.     

A compiler for parsing schemata

We present a compiler that can be used to automatically obtain efficient Java implementations of parsing algorithms from formal specifications expressed as parsing schemata. The system performs an analysis of the inference rules in the input schemata in order to determine the best data structures and indexes to use, and to ensure that the generated implementations are efficient. The system described is general enough to be able to handle all kinds of schemata for different grammar formalisms, such as context‐free grammars and tree‐adjoining grammars, and it provides an extensibility mechanism allowing the user to define custom notational elements. This compiler has proven very useful for analyzing, prototyping and comparing natural‐language parsers in real domains, as can be seen in the empirical examples provided at the end of the paper. Copyright © 2008 John Wiley & Sons, Ltd.     

Testing a MODIS Global Disturbance Index across North America

Large-scale ecosystem disturbances (LSEDs) have major impacts on the global carbon cycle as large pulses of CO₂ and other trace gases from terrestrial biomass loss are emitted to the atmosphere during disturbance events. The high temporal and spatial variability of the atmospheric emissions combined with the lack of a proven methodology to monitor LSEDs at the global scale make the timing, location and extent of vegetation disturbance a significant uncertainty in understanding the global carbon cycle. The MODIS Global Disturbance Index (MGDI) algorithm is designed for large-scale, regular, disturbance mapping using Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and Aqua/MODIS Enhanced Vegetation Index (EVI) data. The MGDI uses annual maximum composite LST data to detect fundamental changes in land-surface energy partitioning, while avoiding the high natural variability associated with tracking LST at daily, weekly, or seasonal time frames. Here we apply the full Aqua/MODIS dataset through 2006 to the improved MGDI algorithm across the woody ecosystems of North America and test the algorithm by comparison with confirmed, historical wildfire events and the windfall areas of documented major hurricanes. The MGDI accurately detects the location and extent of wildfire throughout North America and detects high and moderate severity impacts in the windfall area of major hurricanes. We also find detections associated with clear-cut logging and land-clearing on the forest-agricultural interface. The MGDI indicates that 1.5% (195,580 km²) of the woody ecosystems within North America was disturbed in 2005 and 0.5% (67,451 km²) was disturbed in 2006. The interannual variability is supported by wildfire detections and official burned area statistics.