Spatial and temporal scaling of nitrous oxide emissions from the field to the regional scale in Scotland

Nitrous oxide (N₂O) is a powerful greenhouse gas. As the UK government is committed to reducing greenhouse gas emissions, it is important to know not only how much of these gases are released but also where and when. Targeted measurements of emissions in relation to crop growth cycles, soil wetness and fertiliser applications were used to derive annual emission rates for specific combinations of soil type, land management and fertiliser practices. These annual emission rates were then spatially scaled to derive regional figures through the development of a Geographic Information System (GIS) based model framework. Digital soil and land use maps at a scale of 1:25000 for two test areas of approximately 200000 ha each (Lothians and the Ayrshire Basin) were overlain with a climate map within the GIS, deriving unique combinations of soil wetness and land use. The calculated annual emission rates (kg N ha^–1 yr^–1) were then applied to these and multiplied by the total area of each soil/land use type to derive annual emission losses for each area. The annual emission of nitrous oxide from the Lothians was determined as approximately 381000 kg N yr^–1, while the emissions from the Ayrshire Basin were predicted to be 794000 kg N yr^–1. This indicates the increased emissions associated with both the wetter soils of Ayrshire and the greater extent of grazed pasture systems in this area. Due to the detailed scale of the input data, localised areas with large emissions were identified. Abatement strategies would be concentrated on areas of high emissions that include a change to crops with lower emission potential, reducing fertiliser and manure inputs, reducing grazing intensity and improving soil drainage.Supplementary material to this paper is available in electronic form at http://dx.doi.org/10.1023/A:1024422604493     

Robust spatial flood vulnerability assessment for Han River using fuzzy TOPSIS with α-cut level set

This study aims to improve the general flood vulnerability approach using fuzzy TOPSIS based on α-cut level sets which can reduce the uncertainty inherent in even fuzzy multi-criteria decision making process. Since fuzzy TOPSIS leads to a crisp closeness for each alternative, it is frequently argued that fuzzy weights and fuzzy ratings should be in fuzzy relative closeness. Therefore, this study used a modified α-cut level set based fuzzy TOPSIS to develop a spatial flood vulnerability approach for Han River in Korea, considering various uncertainties in weights derivation and crisp data aggregation. Two results from fuzzy TOPSIS and modified fuzzy TOPSIS were compared. Some regions which showed no or small ranking changes have their centro-symmetric distributions, while other regions whose rankings varied dynamically, have biased (anti-symmetric) distributions. It can be concluded that α-cut level set based fuzzy TOPSIS produce more robust prioritization since more uncertainties can be considered. This method can be applied to robust spatial vulnerability or decision making in water resources management.     

A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm

Steganography is knowledge and art of hiding secret data into information which is largely used in information security systems. Various methods have been proposed in the literature which most of them are not capable of both preventing visual degradation and providing a large embedding capacity. In this paper, we propose a tunable visual image quality and data lossless method in spatial domain based on a genetic algorithm (GA). The main idea of the proposed technique is modeling the steganography problem as a search and optimization problem. Experimental results, in comparison with other currently popular steganography techniques, demonstrate that the proposed algorithm not only achieves high embedding capacity but also enhances the PSNR of the stego image.     

基于空间加权互信息的非刚性医学图像配准

互信息作为相似性测度标准在医学图像配准领域应用广泛,尤其是在刚性配准研究中,能实现较高配准精度和非常稳健的配准效果。然而由于非线性形变的复杂性,在非刚性配准领域,对基于互信息的配准方法进行深入研究势在必行。提出的方案采用基于B样条的自由变换模型模拟医学图像中解剖结构的非线性形变,同时根据变换模型,考虑空间信息对配准效果的影响,以空间加权的方式对互信息方法进行改进。使用对大规模参数优化效果较好的LBFGS方法对配准参数进行优化,编程实现程序,并通过实验对效果进行验证和分析。实验结果显示,该方案配准精度明显优于传统的互信息方法。     

自动化翻拍图像空间失真复原研究

在自动化翻拍的图像采集过程中,图像中各像素间的空间相互关系会随着采集发生变化,通过对空间失真复原算法的深入研究,利用几何极坐标、中值滤波等方法,提出自动化翻拍图像复原算法。实验结果表明,使用该算法能够消除由图像采集引起的空间畸变失真,提高图像的可识别性,是一种有效的方法。     

Spatial ecology of Bay of Quinte walleye (Sander vitreus): Annual timing,extent, and patterns of migrations in eastern Lake Ontario

Walleye (Sander vitreus) are the top nearshore predator in the Bay of Quinte and eastern Lake Ontario, where they have strong ecological and socio-economic impacts. The population is known to migrate seasonally; however, the precise timing and extent are not well defined. This study used acoustic telemetry to provide a fisheries-independent measure of timing, extent, and seasonal distribution of migration in Lake Ontario and to examine the influence of sex, spawning river, size, and year. Annual detection histories were used to determine the timing of migrations into and out of the spawning rivers, departure from the Bay of Quinte post-spawn, and the pre-spawn return to the Bay of Quinte. Sequence analysis was subsequently used to examine how fish occupy defined regions of eastern Lake Ontario annually and identify patterns in migration strategy. Spawning site fidelity was high for both rivers (91–97%) and annual residency within the Bay of Quinte was low (9.5%). Females spent less time in spawning rivers, migrated to the main lake earlier, and generally travelled further than males. Larger fish also migrated to the main lake first and travelled further, and differences in timing between spawning rivers were minor. Annual differences in timing were observed and were most likely related to environmental differences between years. Cluster analysis was used to identify groups of fish which utilized unique annual migration strategies and demonstrated sex and size had an important influence on the variability in annual spatial occupancy, but the importance of spawning river and year was minimal.     

一种基于DAG动态重构的认知网络服务迁移方法

针对认知网络高度动态性带来的服务随机失效问题,提出了一种服务迁移方法以保障认知网络的 QoS.首先,采用先迁移、后优化的思想,重新生成关联服务有向无环图(directed acyclic graph,简称DAG),并在此基础上提出 DAG 动态重构算法,将关联服务转化为层次化 DAG 服务;其次,计算关键服务迁移路径,并给出可迁移服务死锁避免理论分析,将迁移服务提前迁移到当前网络空闲资源运行,以缩短服务的执行时间.仿真实验测试了3种故障注入类型下网络服务迁移方案的服务性能.实验结果显示,该方法在弹性网络负载与未知故障情况下具有较好的 QoS保障效果.     

动态受限区域内的单纯型连续近邻链查询方法

受限区域内的单纯型连续近邻链查询在空间数据挖掘、数据的相似分析和推理、空间数据库等方面具有重要的作用。为了弥补已有方法的不足,详细研究了动态受限区域内的单纯型连续近邻链查询方法。基于计算几何中的Voronoi图给出了VOR_IN_CRSCNNC算法、VOR_EX_CRSCNNC算法和VOR_DE_CRSCNNC算法。进一步进行了实验比较和分析。理论研究和实验分析表明,所提出的算法在查询过程中减少了数据逐一筛选和判断的冗余计算,在处理空间数据量较大、初始受限区域数据量较多、受限区域形状较为复杂的单纯型连续近邻链查询方面具有较大的优势。     

一种基于空间滤波的钢轨表面擦伤检测改进算法

钢轨表面擦伤检测是保障铁路运输安全的重要手段之一。应用图像处理和模式识别技术来处理钢轨数字图像,检测并定位擦伤区域是一种可行且发展迅速的研究手段。课题组在前期工作中提出了一种鲁棒实时的钢轨表面擦伤检测算法,该算法首先对钢轨图像进行灰度对比度增强,在此基础上定位可疑擦伤区域并进行判定。算法对于常规擦伤图像具有较高的检测性能,但对于钢轨图像包含多处擦伤且擦伤区域灰度值差异较大的情况,往往造成漏检。针对原算法的不足,提出了一种基于空间滤波的钢轨表面擦伤检测改进算法,该算法对原算法检测到的擦伤区域进行钢轨灰度图均值填充,并对填充后的图像进行二次检测,在重新生成的灰度对比度图中,原检测图像中灰度值不明显的擦伤区域的灰度对比度值得到增强,从而增加了检出的可能性。经实验结果验证,改进算法具有较高的检测性能:在总的时间耗费没有明显增加的情况下,检测的平均准确率为90.8%,平均漏检率为4.0%,较原算法有较大改善。     

基于Fisher字典学习稀疏表示的高光谱图像分类

近年基于稀疏表示的分类框架(Sparse Representation based Classification,SRC)在计算机视觉和模式识别领域取得了巨大成功,高光谱图像解译也逐渐引入稀疏表示方法。针对基于SRC的高光谱图像分类算法随机抽取训练样本构成字典较难捕获相似类别的相对差异性信息问题,提出采用Fisher字典学习方法增强相似类训练样本的可区分性。此外,考虑到高光谱图像具有较强空间相关性的特点,设计一种简单有效的投票策略进行类别判决。大量实验表明:基于Fisher字典学习的联合投票分类方法能够较好地改善高光谱分类精度。