Preparation and characterization of GaN films grown on Ga-diffused Si （111） substrates

Hexagonal GaN films were prepared by nitriding Ga2O3 films with flowing ammonia. Ga2O3 films were deposited on Ga-diffused Si (111) substrates by radio frequency (r.f.) magnetron sputtering. This paper have investigated the change of structural properties of GaN films nitrided in NH3 atmosphere at the temperatures of 850, 900, and 950℃ for 15 min and nitrided at the temperature of 900℃ for 10, 15, and 20 rain, respectively. X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS) were used to analyze the structure, surface morphology and composition of synthesized samples. The results reveal that the as-grown films are polycrystalline GaN with hexagonal wurtzite structure and GaN films with the highest crystal quality can be obtained when nitrided at 900℃ for 15 min.     

灰铸铁组织对弹性模量的影响

研究了CE、Si/C、石墨形态、基体等对灰铸铁弹性模量的影响。     

Camouflage texture evaluation using a saliency map

Multimedia Systems - Camouflage effect evaluation and examination is an important procedure in digital camouflage pattern design as it is helpful in improving the objectivity and effectiveness...     

遥感网格的数据压缩与任务分配方法研究

网格计算作为分布式计算在科学计算领域的发展方向,可以为对地观测数据的处理提供强大的计算力。在分析遥感信息服务网格节点(Remote Sensing Information Service Grid Nodes,RSSN)中网络数据传输和负载均衡两个关键问题的基础上,提出了一种有效的基于游程编码和Huffman编码的数据压缩方法和基于"计算端元"的任务分配策略,该方法针对遥感影像特点进行有效数据压缩,具有较好的压缩比,达到了17%,且能实现任务负载均衡。并在遥感信息服务网格节点计算平台上,以中国范围内1km分辨率气溶胶光学厚度(Aerosol Optical Depth,AOD)反演计算为例,从压缩率和计算时间效率方面验证和分析了上述方法的有效性。     

改进生成树算法的多机器人在线地图覆盖

研究在线多机器人地图覆盖。在单机器人生成树STC算法基础上做出改进,融入了市场拍卖算法,使机器人团体扩散地生成树,并沿各自生成树完成地图覆盖。通过两种不同环境地图覆盖仿真,结果验证改进后的IMPSTC算法,能够使机器人团队在更少的时间和重复覆盖区域情况下完成在线覆盖地图任务。     

基于邻域关系模糊粗糙集的医学图像分类研究

对医学图像进行分类时,特征选择是影响分类准确率的非常重要的因素。针对医学图像的特殊性,以及目前提出的特征选择算法在应用于医学图像分类时效果不够理想等问题,提出一种基于邻域关系的模糊粗糙集模型,基于该模型给出特征选择算法,并将其应用于乳腺X光图像。实验结果表明,同已有的算法相比,该方法能有效选择特征,分类精度有较大的提升。     

Power of the Swipe: Why Mobile Websites Should Add Horizontal Swiping to Tapping,Clicking, and Scrolling Interaction Techniques

Horizontal swiping has become a common interaction technique on smartphones, yet many mobile websites do not incorporate this functionality, instead requiring users to scroll down or click on buttons, arrow keys, and other hotspots. This is perhaps because user studies comparing swiping with other interaction techniques are scarce. An experiment was conducted (N = 252) to inform this issue by investigating whether and how adding the swiping functionality to a tap-only mobile website influences users’ intentions to use the website. The data revealed that the addition of swiping technique positively affected behavioral intentions to use the website. Perceived enjoyment was a key variable that explained this effect. These findings have design implications for mobile websites.     

Multi-label classification via learning a unified object-label graph with sparse representation

Automatic annotation is an essential technique for effectively handling and organizing Web objects (e.g., Web pages), which have experienced an unprecedented growth over the last few years. Automatic annotation is usually formulated as a multi-label classification problem. Unfortunately, labeled data are often time-consuming and expensive to obtain. Web data also accommodate much richer feature space. This calls for new semi-supervised approaches that are less demanding on labeled data to be effective in classification. In this paper, we propose a graph-based semi-supervised learning approach that leverages random walks and ? ₁ sparse reconstruction on a mixed object-label graph with both attribute and structure information for effective multi-label classification. The mixed graph contains an object-affinity subgraph, a label-correlation subgraph, and object-label edges with adaptive weight assignments indicating the assignment relationships. The object-affinity subgraph is constructed using ? ₁ sparse graph reconstruction with extracted structural meta-text, while the label-correlation subgraph captures pairwise correlations among labels via linear combination of their co-occurrence similarity and kernel-based similarity. A random walk with adaptive weight assignment is then performed on the constructed mixed graph to infer probabilistic assignment relationships between labels and objects. Extensive experiments on real Yahoo! Web datasets demonstrate the effectiveness of our approach.