基于开关3-D中值滤波的视频序列去噪算法

为提高含噪视频序列的质量和效果,提出一种基于开关噪声检测与三维中值滤波相结合的开关3-D中值滤波算法。该算法通过判断视频序列的每一个像素点,对噪声点用3-D中值滤波处理,再对其进行二次判断,如果判为噪声点,则采用变型3-D中值滤波处理,直到去除所有噪声点为止。仿真实验表明,该算法对“雪花”噪声有较好的滤波效果,当处理加入25%的“雪花”噪声时,3-D开关中值滤波比三维中值滤波的信噪比平均提高了10 dB左右。     

基于C5.0决策树算法的西北干旱区土地覆盖分类研究——以甘肃省武威市为例

西北干旱区面积广阔,由于土地利用类型多样,成因复杂,对环境变化敏感、变化过程快、幅度大、景观差异明显等特点,在影像上表现出的“同物异谱”现象明显 |利用常规目视解译、监督非监督分类、人工参与的决策树分类等方法在效率或精度等方面各有其缺陷。采用机器学习C5.0决策树算法,综合利用地物波谱、NDVI、TC、纹理等信息,根据样本数据自动挖掘分类规则并对整个研究区进行地物分类。机器学习的决策树可以挖掘出更多的分类规则,C5.0算法对采样数据的分布没有要求,可以处理离散和连续数据,生成的规则易于理解,分类精度高,可以满足西北干旱区大面积的土地利用/覆被变化制图的需要。     

Set Cover和Hitting Set问题的研究进展

Set Cover和Hitting Set问题是两个重要的W[2]完全问题。Set Cover问题在大规模集成电路设备的测试和人员调度等领域有着广泛的应用,Hitting Set问题在生物计算等领域有着重要的应用。在引入参数计算和复杂性理论后,Set Cover和Hitting Set问题再次成为研究的热点。首先介绍Set Cover和Hitting Set的各种分类问题及其定义,并对各种分类问题的计算复杂性和相关算法的研究进展加以分析总结,给出(k,h)-Set Cover和(k,d)-Set Cover问题的复杂性证明。最后总结全文并提出进一步研究的方向。     

钢架网+玻璃幕墙=米斯·凡德罗

本文通过对现代主义建筑大师米斯·凡德罗,在现代主义顶峰时期,代表时代建筑特色的建筑和建筑思想文脉的分析,找到确立米斯·凡德罗钢架网+玻璃幕墙建筑形式的来源思想,使我们清晰地了解现代主义的来源和发展,及现代主义在建筑中的体现方式。     

基于粒子系统和Vega的实时雨雪模拟

在分析粒子系统实现原理的基础上,基于Vega提出了一种在大型场景漫游系统中实时模拟雨、雪的方法.基本思想是基于Vega特效模块粒子属性的设置,采用一种始终面向视点的多边形来表示雨、雪粒子模型.试验结果表明,该方法交互性强,能够有效地再现三维场景中的降雨、降雪景象,在实时交互漫游的前提下表现出较强的真实感.     

基于CHI值特征选取和覆盖的文本分类方法

利用CHI值特征选取和前向神经网络的覆盖算法,通过对文本进行分词的预处理后,实现文本的自动分类。该方法利用CHI值进行特征选取即特征降维,应用覆盖算法进行文本分类。该方法将CHI值特征选取和覆盖算法充分结合,在提高了分类速度的同时还保证了分类的准确度。应用该方法对标准数据集中的文本进行实验,并在不同的维数上与SVM算法、朴素贝叶斯方法的实验结果进行了比较。结果表明,与SVM算法和朴素贝叶斯方法相比较,覆盖算法在准确度上更好。并且,维数的选择对分类的精确度影响很大。     

终端近场通信天线研究北大核心CSCD

近场通信(NFC)已成为无线终端的标配功能,工作范围小、抗干扰性和安全性较高的特性使其被广泛应用于终端的移动支付和虚拟卡片等领域。由于近场天线产生的感应磁场会在临近的金属上耦合出反向涡流,并且金属屏蔽物覆盖会降低收发天线间的磁场耦合,紧凑终端中的电池、主板、外壳等金属元件将大幅影响NFC天线性能。本文从近场通信的历史、标准、系统结构出发,以终端NFC天线工作环境中的金属覆盖情况作为分类依据,对终端近场通信天线的研究进展进行了简要介绍。     

面皮快餐食品工业化生产的调查及工艺研究

通过对面皮传统手工制作工艺的调查分析,提出了面皮工业化生产的工艺和方法,进而对面皮作为方便快餐食品的开发进行了论述。利用先前开发的面皮机和小麦淀粉厂的有关设备,进行了中试试验,在此基础上提出了有关工艺和经济技术指标。     

Development and evaluation of a cloud-gap-filled MODIS daily snow-cover product

The utility of the Moderate Resolution Imaging Spectroradiometer (MODIS) snow-cover products is limited by cloud cover which causes gaps in the daily snow-cover map products. We describe a cloud-gap-filled (CGF) daily snow-cover map using a simple algorithm to track cloud persistence, to account for the uncertainty created by the age of the snow observation. Developed from the 0.05° resolution climate-modeling grid daily snow-cover product, MOD10C1, each grid cell of the CGF map provides a cloud-persistence count (CPC) that tells whether the current or a prior day was used to make the snow decision. Percentage of grid cells “observable” is shown to increase dramatically when prior days are considered. The effectiveness of the CGF product is evaluated by conducting a suite of data assimilation experiments using the community Noah land surface model in the NASA Land Information System (LIS) framework. The Noah model forecasts of snow conditions, such as snow-water equivalent (SWE), are updated based on the observations of snow cover which are obtained either from the MOD10C1 standard product or the new CGF product. The assimilation integrations using the CGF maps provide domain-averaged bias improvement of ~11%, whereas such improvement using the standard MOD10C1 maps is ~3%. These improvements suggest that the Noah model underestimates SWE and snow depth fields, and that the assimilation integrations contribute to correcting this systematic error. We conclude that the gap-filling strategy is an effective approach for increasing cloud-free observations of snow cover.     

MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets

Information related to land cover is immensely important to global change science. In the past decade, data sources and methodologies for creating global land cover maps from remote sensing have evolved rapidly. Here we describe the datasets and algorithms used to create the Collection 5 MODIS Global Land Cover Type product, which is substantially changed relative to Collection 4. In addition to using updated input data, the algorithm and ancillary datasets used to produce the product have been refined. Most importantly, the Collection 5 product is generated at 500-m spatial resolution, providing a four-fold increase in spatial resolution relative to the previous version. In addition, many components of the classification algorithm have been changed. The training site database has been revised, land surface temperature is now included as an input feature, and ancillary datasets used in post-processing of ensemble decision tree results have been updated. Further, methods used to correct classifier results for bias imposed by training data properties have been refined, techniques used to fuse ancillary data based on spatially varying prior probabilities have been revised, and a variety of methods have been developed to address limitations of the algorithm for the urban, wetland, and deciduous needleleaf classes. Finally, techniques used to stabilize classification results across years have been developed and implemented to reduce year-to-year variation in land cover labels not associated with land cover change. Results from a cross-validation analysis indicate that the overall accuracy of the product is about 75% correctly classified, but that the range in class-specific accuracies is large. Comparison of Collection 5 maps with Collection 4 results show substantial differences arising from increased spatial resolution and changes in the input data and classification algorithm.