通过模式语言研究GIS用户界面

目前国内外已经有学者通过模式语言来研究用户界面设计。但是还没有学者通过模式语言研究GIS用户界面设计的问题。本文建立了一个初步的GIS用户界面模式语言框架,并在用户认知心理学,地图学等理论和寻路,屏幕截图等实验的基础上提出了4个有代表性的模式：中央地图模式、“抬头”地图模式、“用户中心”地图模式和分级圆模式。     

GPS接收机抗干扰自适应阵性能仿真研究

自适应天线技术是提高GPS接收机抗干扰能力的一种重要措施。为了指导自适应天线系统的优化工程设计,以及把握系统的特性和设计中的关键要素,按照工程设计中自适应算法的实际实现方式对自适应天线系统进行了数值仿真研究。采用自适应算法进行仿真,验证了算法实现方案的可行性,并检验了系统的抗干扰效果。在不同的干扰条件下,通过仿真计算得到了直线阵和平面圆阵的稳态权值,进而得出阵列的稳态方向图。比较了直线阵和圆阵的特点,并给出了实际应用中设计天线阵列的一般方法。     

超音速扩压器喷雾冷却数值仿真研究

为了探讨研究喷雾冷却中冷却水流量、喷射速度及喷嘴轴向分布位置等因素对超音速扩压器壁面温度的影响,借助CFD计算软件FLUENT,采用标准k-ε湍流模型,对喷雾冷却下超音速扩压器内流场进行了数值仿真。通过计算,得到了扩压器内压强分布及壁面温度分布。分析了喷雾冷却中冷却水流量、喷射速度及喷嘴轴向位置对超音速扩压器壁面温度的影响。研究表明：随着冷却水流量的增大,扩压器壁面温度降低。同时,扩压器入口处静压升高,对应仿真高度降低;减小冷却水喷射速度,可以有效降低扩压器入口处壁面温度;喷嘴的轴向分布位置对扩压器壁面温度有较大影响,仿真结果证明,喷嘴沿轴向呈对数分布有利于降低扩压器壁面温度。     

基于水印技术的隐蔽通信算法设计

本文研究了在音频信息上实现水印技术的难点与价值,分析和对比了利用音频信号进行信息隐藏的算法,探讨了隐蔽通信的编码策略,提出了一种基于混合模式的语音信号的信息隐藏编码算法。文中首先给出了编码过程中覆盖半径、覆盖编码等相关定义和信息隐藏编码的相关定理,从理论上分析了隐蔽通信编码策略的可行性和步骤;接着详细论证了该编码算法的基本原理、构造方法和算法的实现过程;然后以一段音频信息为例,阐述了隐蔽通信算法的实现方法和编码步骤;最后从算法的频谱分析、隐蔽信息嵌入量和听觉效果三个方面对算法性能进行测试。测试结果表明,算法隐蔽信息嵌入量可达2.1×103bps。     

基于流量特征和载荷特征的P2P流量识别

本文分析了目前的P2P网络流量识别方法及其存在的问题。设计识别P2P流量的数据结构;在流量识别阶段,在传输层捕获TCP和UDP数据包,依据P2P流在传输层表现出来的主要流量特征,进行TCP/UDP流量特征的P2P流量识别;在载荷特征识别阶段,对载荷特征库定期更新,将在流量识别阶段中识别出的P2P流作精确载荷特征识别,并将流量识别阶段中漏掉的流量作载荷特征识别;在模式匹配过程采用比较指印函数值来加快识别速度;进而提出一个可准确识别出新生、加密的P2P流量及其名称的算法。实验结果表明,该算法具有较高识别和分类P2P流量的能力。     

基于多级CS-LBP特征融合的人脸识别方法

通常,采用中心对称局部二值模式CS-LBP对人脸图像只进行一次特征提取,提取的纹理特征不够丰富。因此,本文利用CS-LBP多次提取人脸图像更丰富的纹理特征,提出了多级CS-LBP特征融合的人脸识别算法。首先,用CS-LBP对原始人脸图像进行特征提取;然后,对所得特征图像再进行相同方式的特征提取,这样能够得到原始人脸图像的多级CS-LBP特征图像;最后,将每一级特征图像的分块直方图特征进行融合并用于人脸识别。在ORL、Yale标准人脸库上的实验结果表明,相比人脸图像的一级CS-LBP特征,多级CS-LBP特征融合的方法能够显著提高识别精度。     

术眼止血仪设计

随着现代科学技术的迅猛发展,外科手术经历了革命性变革,外科技术的核心之一是要达到术眼无血的效果。如今,止血技术已由过去单纯的器械止血发展为现代外科条件下的纷繁复杂的技术体系。本文提出了一种基于单片机的肢体手术止血控制系统实现方案,并详细介绍了系统的软硬件电路设计。系统可应用于肢体手术的止血环节,可以达到术眼无血的效果。     

Supervised classification of share price trends

Share price trends can be recognized by using data clustering methods. However, the accuracy of these methods may be rather low. This paper presents a novel supervised classification scheme for the recognition and prediction of share price trends. We first produce a smooth time series using zero-phase filtering and singular spectrum analysis from the original share price data. We train pattern classifiers using the classification results of both original and filtered time series and then use these classifiers to predict the future share price trends. Experiment results obtained from both synthetic data and real share prices show that the proposed method is effective and outperforms the well-known K-means clustering algorithm.     

The scaling problem in the pattern recognition approach to machine translation

Statistical machine translation (SMT) has proven to be an interesting pattern recognition framework for automatically building machine translations systems from available parallel corpora. In the last few years, research in SMT has been characterized by two significant advances. First, the popularization of the so called phrase-based statistical translation models, which allows to incorporate local contextual information to the translation models. Second, the availability of larger and larger parallel corpora, which are composed of millions of sentence pairs, and tens of millions of running words. Since phrase-based models basically consists in statistical dictionaries of phrase pairs, their estimation from very large corpora is a very costly task that yields a huge number of parameters which are to be stored in memory. The handling of millions of model parameters and a similar number of training samples have become a bottleneck in the field of SMT, as well as in other well-known pattern recognition tasks such as speech recognition or handwritten recognition, just to name a few. In this paper, we propose a general framework that deals with the scaling problem in SMT without introducing significant time overhead by means of the combination of different scaling techniques. This new framework is based on the use of counts instead of probabilities, and on the concept of cache memory.     

Fast kriging of large data sets with Gaussian Markov random fields

Spatial data sets are analysed in many scientific disciplines. Kriging, i.e. minimum mean squared error linear prediction, is probably the most widely used method of spatial prediction. Computation time and memory requirement can be an obstacle for kriging for data sets with many observations. Calculations are accelerated and memory requirements decreased by using a Gaussian Markov random field on a lattice as an approximation of a Gaussian field. The algorithms are well suited also for nonlattice data when exploiting a bilinear interpolation at nonlattice locations.