一种对光照变化鲁棒的移动目标前景提取方法

运动目标前景提取是对其进一步分析如特征提取、行为分析等的基础。RPCA(鲁棒主成分分析)分解可以得到较为完整的目标前景,但该方法对光照变化敏感,容易导致误检。利用Lab颜色空间中a,b通道对光照变化不敏感的特点,可以提高基于RPCA分解的前景提取方法对光照变化的鲁棒性,首先对图像L,a,b通道分别进行RPCA分解得到稀疏前景,然后利用大津阈值分割各通道二值化前景并采用种子点填充技术融合不同前景,最后利用形态学滤波优化融合结果提取准确的运动目标前景。实验结果表明,该方法可以在复杂背景下准确提取运动目标前景,且能有效克服光照变化的影响。     

A Galerkin finite element scheme for time–space fractional diffusion equation

In this paper, a Galerkin finite element scheme to approximate the time–space fractional diffusion equation is studied. Firstly, the fractional diffusion equation is transformed into a fractional Volterra integro-differential equation. And a second-order fractional trapezoidal formula is used to approach the time fractional integral. Then a Galerkin finite element method is introduced in space direction, where the semi-discretization scheme and fully discrete scheme are given separately. The stability analysis of semi-discretization scheme is discussed in detail. Furthermore, convergence analysis of semi-discretization scheme and fully discrete scheme are given in details. Finally, two numerical examples are displayed to demonstrate the effectiveness of the proposed method.     

Adaptive neural control for an uncertain robotic manipulator with joint space constraints

In this paper, adaptive neural tracking control is proposed for a robotic manipulator with uncertainties in both manipulator dynamics and joint actuator dynamics. The manipulator joints are subject to inequality constraints, i.e., the joint angles are required to remain in some compact sets. Integral barrier Lyapunov functionals (iBLFs) are employed to address the joint space constraints directly without performing an additional mapping to the error space. Neural networks (NNs) are utilised to compensate for the unknown robot dynamics and external force. Adapting parameters are developed to estimate the unknown bounds on NN approximations. By the Lyapunov synthesis, the proposed control can guarantee the semi-global uniform ultimate boundedness of the closed-loop system, and the practical tracking of joint reference trajectory is achieved without the violation of predefined joint space constraints. Simulation results are given to validate the effectiveness of the proposed control scheme.     

Fuzzy logic based energy and throughput aware design space exploration for MPSoCs

Multicore architectures were introduced to mitigate the issue of increase in power dissipation with clock frequency. Introduction of deeper pipelines, speculative threading etc. for single core systems were not able to bring much increase in performance as compared to their associated power overhead. However for multicore architectures performance scaling with number of cores has always been a challenge. The Amdahl’s law shows that the theoretical maximum speedup of a multicore architecture is not even close to the multiple of number of cores. With less amount of code in parallel having more number of cores for an application might just contribute in greater power dissipation instead of bringing some performance advantage. Therefore there is a need of an adaptive multicore architecture that can be tailored for the application in use for higher energy efficiency. In this paper a fuzzy logic based design space exploration technique is presented that is targeted to optimize a multicore architecture according to the workload requirements in order to achieve optimum balance between throughput and energy of the system.     

基于表面肌电的运动意图识别方法研究及应用综述

表面肌电信号 (Surface electromyography, sEMG) 是人体自身的资源, 蕴含着关联人体运动的丰富信息, 用它作为交互媒介以构建人机交互 (Human-robot interaction, HRI) 系统有天然的优势.通过肌电信号实现人机自然交互的关键是由肌电信号识别出人体运动意图, 通常包括离散动作模态分类、关节连续运动量估计及关节刚度/阻抗估计等三方面内容.本文详细归纳基于表面肌电的运动识别方法研究成果, 总结当前研究的特点; 随后, 介绍基于表面肌电的运动识别技术的应用现状, 并探讨制约其推广的主要问题; 最后, 展望该技术的未来发展.     

Information Ordering with an Event‐Enriched Vector Space Model for Multi‐Document News Summarization

Information ordering is a nontrivial task in multi‐document summarization (MDS), which typically relies on the traditional vector space model (VSM) notorious for semantic deficiency. In this article, we propose a novel event‐enriched VSM to alleviate the problem by building event semantics into sentence representations. The mediation of event information between sentence and term, especially in the news domain, has an intuitive appeal as well as technical advantage in common sentence‐level operations such as sentence similarity computation. Inspired by the block‐style writing by humans, we base the sentence ordering algorithm on sentence clustering. To accommodate the complexity introduced by event information, we adopt a soft‐to‐hard clustering strategy on the event and sentence levels, using expectation–maximization clustering and K‐means, respectively. For the purpose of cluster‐based sentence ordering, the event‐enriched VSM enables us to design an ordering algorithm to enhance event coherence computed between sentence and sentence–context pairs. Drawing on the findings of earlier research, we also incorporate topic continuity measures and time information into the scheme. We evaluate the performance of the model and its variants automatically and manually, with experimental results showing clear advantage of the event‐based model over baseline and non‐event‐based models in information ordering for multi‐document news summarization. We are confident that the event‐enriched VSM has even greater potential in summarization and beyond, which awaits further research. © 2014 Wiley Periodicals, Inc.     

基于视觉颜色对比敏感度模型的动态视频压缩预处理算法

针对彩色视频压缩过程中压缩率不高或者视频质量损失过大的问题,提出一种基于视觉感知模型与色差的自适应视频压缩预处理算法。首先,在颜色对比敏感度模型中,人类视觉对高色度区域的色差敏感度较低,因此为不同色度区域分配不同的压缩权重;然后,基于帧之间的偏差图建立运动预测帧的空间运动模型,降低了高运动区域的残差;最终,使用动态的色调映射函数来控制视频的压缩等级,从而保证视频的视觉质量。基于多组视频进行实验,本算法成功地将标准的压缩软件提高了35%的压缩率,但同时可看出,本算法的性能依赖于具体的视频上下文。     

基于高斯词长特征的中文分词方法

中文分词是中文信息处理的基础,在语音合成、中外文翻译、中文检索、文本摘要等方面均有重要应用。在中文分词的任务中,存在的主要问题在于可用有效特征较少,分词准确率较低,如何有效的获取和使用分词特征是关键。该文从中文文本生成的过程出发,基于词长噪声的高斯分布特性,提出利用上下文的词长特征作为分词特征。实验表明,在封闭测试中,采用条件随机场模型,使用该特征对现有的实验结果有提高作用。
     

自由空间MIMO信道测量系统

介绍了MIMO系统无线信道研究的现状,对现阶段的MIMO信道建模方法进行了分析和比较,提出了自由真实空间内的MIMO信道实时测量系统方案,该系统采用USRP平台,能实时地测量真实空间通信信道传输性能,并对信道数据进行存储分析,该系统成本低,为真实MIMO信道研究提供了基础.     

柔性车间调度的解空间距离聚类和变邻域搜索粒子群算法

根据柔性车间调度问题提出基于解空间距离聚类和变邻域搜索的粒子群算法.在粒子群算法基础上采用贪婪策略引入变邻域搜索方式,即调整关键路径上最大关键工序的机器位置,调整关键路径上工序相对位置变化,加强局部搜索能力;根据机器加工工序的空间距离,采用K-means聚类得到机器加工工序“优良个体”,加大局部搜索性能.同时对于粒子群算法速度更新采用局部停滞策略,保留局部片段相对位置不变特性.通过实验仿真,优化算法取得了较好的效果,与一般的粒子群算法相比较收敛速度迅速且性能良好.