Learning in an Ambient Intelligent World: Enabling Technologies and Practices

The rapid evolution of information and communication technology opens a wide spectrum of opportunities to change our surroundings into an Ambient Intelligent (AmI) world. AmI is a vision of future information society, where people are surrounded by a digital environment that is sensitive to their needs, personalized to their requirements, anticipatory of their behavior, and responsive to their presence. It emphasizes on greater user friendliness, user empowerment, and more effective service support, with an aim to bring information and communication technology to everyone, every home, every business, and every school, thus improving the quality of human life. AmI unprecedentedly enhances learning experiences by endowing the users with the opportunities of learning in context, a breakthrough from the traditional education settings. In this survey paper, we examine some major characteristics of an AmI learning environment. To deliver a feasible and effective solution to ambient learning, we overview a few latest developed enabling technologies in context awareness and interactive learning. Associated practices are meanwhile reported. We also describe our experience in designing and implementing a smart class prototype, which allows teachers to simultaneously instruct both local and remote students in a context-aware and natural way.     

A DR algorithm based on artificial potential field method

Considering player entity’s motion regularity into DR (Dead Reckoning) algorithm can improve its prediction accuracy in MMOG (Massively Multiplayer Online Games), a novel DR algorithm was proposed to solve this problem in this paper. First the artificial potential field model of player entities is created, and then the acceleration of player entities is weighted with the acceleration produced by the potential field force and the acceleration reckoned by the traditional DR algorithm. In order to calculate the weight, Q-Learning algorithm is used. The experiments show that the method can improve prediction accuracy and reduce the network traffic.     

The asymptotic optimization of pre-edited ANN classifier

The generalization problem of an artificial neural network (ANN) classifier with unlimited size of training sample, namely asymptotic optimization in probability, is discussed in this paper. As an improved ANN network model, the pre-edited ANN classifier shows better practical performance than the standard one. However, it has not been widely applied due to the absence of the related theoretical support. To further promote its application in practice, the asymptotic optimization of the pre-edited ANN classifier is studied in this paper. To help study ANN asymptotic optimization in probability, we gives a review of the previous research works on asymptotic optimization in probability of non-parametric classifier, and grouped the main methods into four classes: two-step method, one-step method, generalization method and hypothesis method. In this paper, we adopt generalization/hypothesis mixed method to prove that pre-edited ANN is asymptotically optimal in probability. Furthermore, a simulation is presented to provide an experimental support for our theoretical work.     

Image Retargeting Using Mesh Parametrization

Image retargeting aims to adapt images to displays of small sizes and different aspect ratios. Effective retargeting requires emphasizing the important content while retaining surrounding context with minimal visual distortion. In this paper, we present such an effective image retargeting method using saliency-based mesh parametrization. Our method first constructs a mesh image representation that is consistent with the underlying image structures. Such a mesh representation enables easy preservation of image structures during retargeting since it captures underlying image structures. Based on this mesh representation, we formulate the problem of retargeting an image to a desired size as a constrained image mesh parametrization problem that aims at finding a homomorphous target mesh with desired size. Specifically, to emphasize salient objects and minimize visual distortion, we associate image saliency into the image mesh and regard image structure as constraints for mesh parametrization. Through a stretch-based mesh parametrization process we obtain the homomorphous target mesh, which is then used to render the target image by texture mapping. The effectiveness of our algorithm is demonstrated by experiments.     

基于ART2神经网络的车辆感应波形识别的方法

在智能交通系统中,无论是在交通监控领域还是在不停车收费系统方面,对车辆进行自动分类都十分重要;环形线圈车辆检测器以其良好的适应性、稳定性和高效性在车辆监测方面得到了广泛的应用,同时利用同类型或同种车辆经过环形线圈产生轮廓相似的电磁感应波形这一特点也可以进行车型识别;对波形进行预处理,以波形轮廓的抽样、量化值作为特征向量,将特征向量作为ART2神经网络的输入向量,经ART2神经网络的自动聚类最终实现车辆感应波形的聚类与识别。     

基于时间放大的光盘抖晃测试系统设计

利用TIA(时序分析仪)或其它模拟方法来测量光盘的抖晃存在成本高、灵活性差、稳定性低等不足,而在实际的光盘生产测试过程中,更需要一种升级扩展方便、性价比高的测试设备。在粗糙脉宽检测方法的基础上,引入时间放大电路,成功地设计出一套以FPGA为核心,基于PCI Express总线的抖晃测试系统;实验表明,该系统具有结构简单、精度高、设计灵活、成本低等特点,在光盘测试和抖晃特性研究方面具有较好的应用价值和一定的市场前景。     

嵌入式Linux中SDIO协议无线网络驱动程序的设计和实现

从嵌入式Linux项目SDIO总线接V1的开发设计需求出发,对嵌入式系统的SDIO总线协议栈进行了分析,讨论了其作为一个通用总线协议栈模块对原来WinCE环境BSQUARE接口的代码移植的可行性,并介绍了包含SDIO总线协议栈模块的无线网络驱动程序的实现方法。     

A class of classification and regression methods by multiobjective programming

An extensive review for the recent developments of multiple criteria linear programming data mining models is provided in this paper. These researches, which include classification and regression methods, are introduced in a systematic way. Some applications of these methods to real-world problems are also involved in this paper. This paper is a summary and reference of multiple criteria linear programming methods that might be helpful for researchers and applications in data mining.     

基于多类别支持向量机的城市交通网络模式分类研究

在网络层次上进行区域交通信号控制、交通分配和路径诱导是缓解交通堵塞的有效途径之一。为进一步提高城市交通网络分类检测的准确性,将支持向量机（Support Vector Machine）应用于交通事件的模式分类研究。通过提出一种基于多类别支持向量机的交通模式分类方法,设计了适合该检测系统的网络结构。仿真结果表明：相对于其他算法,城市交通网络的状态可分为数量有限且不同类型的模式,并且这些模式不断重复出现,当系统识别出网络处于某种模式时,就可参照事先确定的优化参数及策略进行交通控制和诱导,以缓解交通拥塞,提高交通系统的运行效率。该网络结构对于小样本数据具有检测率高、误报率低的优点,完全适用于城市交通的模式分类,同时也存在不足之处,指出了今后进一步研究的方向。     

The effect of polyethylene thickness in fixed- and mobile-bearing total knee replacements

In this paper fixed- and mobile-bearing implants were simulated using a multibody dynamic model and a finite element model to investigate the contact pressure distribution in the ultra high molecular weight polyethylene tibial bearing component. The thickness of polyethylene varied from 6.8 to 12.3 mm and the polyethylene was modelled as a non-linear material. It was found that the contact pressure on the polyethylene decreased in the fixed-bearing implant when the thickness of polyethylene increased from 6.8 to 8 and 9.6 mm, but there was little further decrease in pressure with the increase of polyethylene thickness from 9.6 to 11.0 and 12.3 mm. In the mobile-bearing implant, no increase in contact pressure on the superior surface was found with the increase in the thickness of the polyethylene; however, the contact pressures on the inferior contact surface of the thicker designs were higher than those in the 6.8 mm design. The numerical results obtained in this paper are in good agreement with published experimental test results. Moreover, the paper presents a detailed pressure distribution on the tibial bearing component during a full gait cycle.