Graphene synthesis,fabrication, characterization based on bottom-up and top-down approaches: An overview

This study presents an overview on graphene synthesis, fabrication and different characterization techniques utilized in the production. Since its discovery in 2004 by Andre Geim and Kostya Novoselov several research articles have been published globally to this effect, owing to graphene’s extraordinary, and exclusive characteristics which include optical transparency, excellent thermal, and mechanical properties. The properties and applications of this two-dimensional carbon crystal composed of single-layered material have created new avenues for the development of high-performance future electronics and technologies in energy storage and conversion for the sustainable energy. However, despite its potential and current status globally the difficulty in the production of monolayer graphene sheet still persists. Therefore, this review highlighted two approaches in the synthesis of graphene, which are the top-down and bottom-up approaches and examined the advantages and failings of the methods involved. In addition, the prospects and failings of these methods are investigated, as they are essential in optimizing the production method of graphene vital for expanding the yield, and producing high-quality graphene.     

The long-range saliency of edge- and corner-based salient points.

A major goal of salient-point (SP) detection is increasing computational efficiency. Therefore, methods which can detect saliency of a large region by evaluation of only a small local patch are of particular interest. This paper checks for well-known detectors whether saliency outreaches the actual SPs.     

Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers

Low cost RGB-D cameras such as the Microsoft’s Kinect or the Asus’s Xtion Pro are completely changing the computer vision world, as they are being successfully used in several applications and research areas. Depth data are particularly attractive and suitable for applications based on moving objects detection through foreground/background segmentation approaches; the RGB-D applications proposed in literature employ, in general, state of the art foreground/background segmentation techniques based on the depth information without taking into account the color information. The novel approach that we propose is based on a combination of classifiers that allows improving background subtraction accuracy with respect to state of the art algorithms by jointly considering color and depth data. In particular, the combination of classifiers is based on a weighted average that allows to adaptively modifying the support of each classifier in the ensemble by considering foreground detections in the previous frames and the depth and color edges. In this way, it is possible to reduce false detections due to critical issues that can not be tackled by the individual classifiers such as: shadows and illumination changes, color and depth camouflage, moved background objects and noisy depth measurements. Moreover, we propose, for the best of the author’s knowledge, the first publicly available RGB-D benchmark dataset with hand-labeled ground truth of several challenging scenarios to test background/foreground segmentation algorithms.     

Learning of perceptual grouping for object segmentation on RGB-D data

Object segmentation of unknown objects with arbitrary shape in cluttered scenes is an ambitious goal in computer vision and became a great impulse with the introduction of cheap and powerful RGB-D sensors. We introduce a framework for segmenting RGB-D images where data is processed in a hierarchical fashion. After pre-clustering on pixel level parametric surface patches are estimated. Different relations between patch-pairs are calculated, which we derive from perceptual grouping principles, and support vector machine classification is employed to learn Perceptual Grouping. Finally, we show that object hypotheses generation with Graph-Cut finds a globally optimal solution and prevents wrong grouping. Our framework is able to segment objects, even if they are stacked or jumbled in cluttered scenes. We also tackle the problem of segmenting objects when they are partially occluded. The work is evaluated on publicly available object segmentation databases and also compared with state-of-the-art work of object segmentation.     

Incremental object learning and robust tracking of multiple objects from RGB-D point set data

In this paper, we propose a novel model-free approach for tracking multiple objects from RGB-D point set data. This study aims to achieve the robust tracking of arbitrary objects against dynamic interaction cases in real-time. In order to represent an object without prior knowledge, the probability density of each object is represented by Gaussian mixture models (GMM) with a tempo-spatial topological graph (TSTG). A flexible object model is incrementally updated in the pro-posed tracking framework, where each RGB-D point is identified to be involved in each object at each time step. Furthermore, the proposed method allows the creation of robust temporal associations among multiple updated objects during split, complete occlusion, partial occlusion, and multiple contacts dynamic interaction cases. The performance of the method was examined in terms of the tracking accuracy and computational efficiency by various experiments, achieving over 97% accuracy with five frames per second computation time. The limitations of the method were also empirically investigated in terms of the size of the points and the movement speed of objects.     

Biological data integration: wrapping data and tools

Scientific data is inevitably digital and stored in a wide variety of formats in heterogeneous systems. Scientists need to access an integrated view of remote or local heterogeneous data sources with advanced data access, analysis, and visualization tools. Building a digital library for scientific data requires accessing and manipulating data extracted from flat files or databases, documents retrieved from the Web as well as data generated by software. We present an approach to wrapping web data sources, databases, flat files, or data generated by tools through a database view mechanism. Generally, a wrapper has two tasks: it first sends a query to the source to retrieve data and, second builds the expected output with respect to the virtual structure. Our wrappers are composed of a retrieval component based on an intermediate object view mechanism called search views mapping the source capabilities to attributes, and an Extensible Markup Language (XML) engine, respectively, to perform these two tasks. The originality of the approach consists of: 1) a generic view mechanism to access seamlessly data sources with limited capabilities and 2) the ability to wrap data sources as well as the useful specific tools they may provide. Our approach has been developed and demonstrated as part of the multidatabase system supporting queries via uniform object protocol model (OPM) interfaces     

Geometric integration of NOAA AVHRR and SPOT data: low resolutioneffective parameters from high resolution data

Information available from ephemeris data, combined with a few ground control points, is used to carry out the geometric combination of NOAA, AVHRR, and SPOT images by means of orbital models for each satellite. The observation conditions are reconstructed and the areas observed in common determined in the SPOT image pixels and each low-resolution AVHRR pixel. The determination of effective parameters, at low spatial resolution, from high-spatial-resolution data takes into account overlapping effects between adjacent pixels, and pixel size and shape variations with viewing angle for AVHRR data. The method is applied to surface temperature mapping of a selected heterogeneous area by incorporating NOAA AVHRR thermal data in a high-spatial-resolution emissivity map derived from SPOT data     

浅谈财务数据的融合

本文以ACCESS数据库存储的财务数据为例,对财务数据融合中存在的问题进行分析,简述财务数据融合的过程.     

基于KETTLE的高校多源异构数据集成研究及实践

解决数字化校园建设中的多源异构数据集成问题.利用开源ETL工具KETⅡE,对学生考试信息与上网信息的抽取、转换和加载.ETL模型自动生成了以上网行为事实表为中心表,以学生信息、上网信息、时间3个维度表的数据仓库.利用KETTLE能够快速实现高校多源异构数据集成,形成高质量的分析型数据,为决策支持服务.     

基于OpenSpirit平台的数据整合应用

随着越来越多的石油专业软件被应用到油田工作中,如何使软件之间能够轻松的进行数据交换,节省数据转换与加载的时间,已成为用户十分关注的问题。基于OpenSpirit平台进行数据整合解决了软件与软件、软件与数据库之间数据交换困难的问题,在油田的实际应用中取得了很好的使用效果。     

基于VRML和GIS底层数据融合研究框架

针对现阶段VR和GIS结合的发展情况,分析了其中存在的关键问题,提出了基于VRML和GIS底层数据融合的研究框架。     

基于显著性检测的不同视角下红外与可见光图像融合

融合红外图像的热源目标和可见光图像的清晰背景可以实现低照度条件下与场景关联的异常行为识别.现有以特征匹配为主的融合方法,受监控场景下可见光与红外成像的尺度、视角、目标特性等差异影响,配准及融合效率及准确性受限.针对该问题,本文提出了基于显著性检测的不同视角下红外与可见光图像融合方法.通过预设热敏感目标,计算可见光与红外...     

基于XML的电力数据集成平台研究

为了解决电力系统内部异构数据共享和集中管理的问题,提出基于“集成数据库-中间件”的四层电力数据集成平台框架设计方案。该方案结合可扩展标记语言XML的优势定义数据交换格式,参考HSDA视图的组织形式搭建集成数据库,通过查询转换器、数据合法性验证工具以及数据转换器实现对用户请求的中间处理,最终将转换得到的XML文件提供给Web服务器,实现电力数据集成平台系统。实践证明,该方案对异构数据集成问题的解决提供了一个切实可行的模式和方法,具有较好的可扩展性和适应性。     

Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation

Image segmentation refers to the process to divide an image into meaningful non-overlapping regions according to human perception, which has become a classic topic since the early ages of computer vision. A lot of research has been conducted and has resulted in many applications. While many segmentation algorithms exist, there are only a few sparse and outdated summarizations available. Thus, in this paper, we aim to provide a comprehensive review of the recent progress in the field. Covering 190 publications, we give an overview of broad segmentation topics including not only the classic unsupervised methods, but also the recent weakly-/semi-supervised methods and the fully-supervised methods. In addition, we review the existing influential datasets and evaluation metrics. We also suggest some design choices and research directions for future research in image segmentation.     

数据资源整合系统的应用实现

随着信息化程度的提高,各行各业的积累数据量急剧增长,基于各行业对数据资源整合的需求,提出了一种数据资源整合系统的应用设计架构。该系统采用分层框架技术对资源整合系统的架构进行设计与实现,灵活地解决了各类数据资源整合的问题。结合教育考试数据的资源整合系统的功能模块的设计与实现,实现数据资源整合系统的测试,展示了分层架构设计的灵活性和优势。     

基于XML和Web Service的数据集成中间件

主要研究多源异构数据集成的方法,其目标是向具有访问异构数据源要求的用户提供一个统一的查询接口,而不必考虑各局部数据源之间的差异。目前,在多种异构数据源集成方法中,中间件法由于其成本低、容易实现,以及局部数据源加入方便灵活,自治性强等优点,已成为数据集成的主要方法。本文采用中间件法,利用XML技术和Web Service,对多源异构数据的集成问题进行了探讨。     

基于信息仓库的企业信息资源整合与集成

对企业信息资源整合与集成的实现技术及应用进行了研究，在对企业信息系统建设现状、信息资源整合方案等分析的基础上，综合运用数据仓库与数据挖掘、XML、分布式Web应用等技术，提出了一种基于数据仓库的企业信息资源整合与集成设计体系。     

基于XML的音乐数据库异构集成研究与实现

在音乐信息检索领域,不同类型、不同存储位置的音乐信息面临共享的需求,异构数据集成是必须解决的问题。探讨了已有的主流异构数据集成策略,采用基于XML标准格式文档的异构数据集成参考模型,对基于Oracle的音乐检索网站开发过程中的数据集成进行了应用研究。