网格环境下基于P2P的数据集成方法

针对旧有数据集成方法已不适应网格环境下高动态数据集成的现状,提出基于P2P的数据集成方法,给出一个异构的XML数据源的集成框架。通过在各Peer点直接建立模式映射,利用给出的分解再组成算法,有效地完成对各网格节点上动态XML数据源的集成。     

OWL本体到软件代码模型映射的研究

OWL本体在知识工程中有着广泛应用,人们考虑用它来解决软件开发中的问题。该文分析OWL本体和Java面向对象模型问的差别,指出直接映射存在的问题,阐述OWL本体映射到面向方面模型的特点,并提出OWL本体向AspectJ面向方面模型映射的具体方法,较好实现OWL本体向软件代码模型的映射。     

基于调和映射的三维医学图像增强系统

医学成像仪器产生12位的体数据,然而普通显示器只支持8位的灰度图。如果使用普通显示器来显示医学图像,显卡会自动将这些12位数据转化成8位数据,这就造成了数据动态范围被压缩。解决这一问题,在2D领域通常使用昂贵的医学专用灰阶显示器。在3D领域却限制了医生使用三维可视化软件。尝试将一些在2D图像处理中使用的图像增强技术如加权平均模板,不同程度曝光等来实现色调映射算法等应用到三维体数据的显示当中,通过种种复杂的数据变换,使得在普通显示器上显示的8位灰度图呈现出更高的动态范围,得到更为生动、更加丰富的显示效果。这种技术也可以实现任意位数的转化,使得12位显示器的显示效果也可以得到增强。     

公共元素模型数据类型的XML Schema描述

AI-ESTATE标准中采用EXPRESS语言对其诊断知识进行描述,但EXPRESS不是程序设计语言,导致采用EXPRESS语言描述的诊断知识难以用程序设计实现,为诊断知识的共享和重用造成了一定困难;XML语言以其良好的灵活性、可读性和可扩展性,为信息交换带来了很大的方便,将EXPRESS语言映射为XML Schema,对诊断知识的共享意义重大.首先,分析了EXPRESS语言和XML Schema的数据类型;然后,分别研究了EXPRESS中简单数据类型、聚合数据类型和构造数据类型到XML数据类型的映射机制;最后,在分析AI-ESTATE标准公共元素模型数据类型的基础上,研究了将公共元素模型的诊断知识用XML Schema标准化描述的过程,实现了诊断知识的可移植和重用.     

基于Henon映射的加密遥感图像的安全检索方案

遥感图像具有多时相、多语义、多波段等特点,鉴于遥感图像在商业行业及国防军事中的重要性,海量遥感图像密文检索的效率和精度,直接影响了遥感大数据使用的广泛性和实时性.对密文存储的遥感大数据的安全检索是其可用性最重要的标志之一.本文提出了一种基于Henon 映射的遥感图像可搜索加密方案,根据遥感图像的成像原理及多波段特征,采用改进的Henon 映射对每个波段的灰度值进行加密处理;并根据遥感图像的“大数据”特征,通过统计灰度值的区间信息来构造遥感图像的特征向量,并根据相似度匹配算法来检索目标图像.通过对Landsat 8遥感图像进行加密与检索实验,该方案有效地提高了检索密文遥感图像的安全性及准确性,且计算复杂度低、通信成本开销小.     

基于簇的水声传感器网络的安全认证协议

近年来,基于簇的水声传感器网络得到广泛应用。由于水声传感器多用于军事行业,使得提高水声传感器网络的安全性变得十分重要。针对提高水声传感器网络安全性这一问题,提出了结合ECC Diffie-Hellman密钥交换、一元高阶多项式、双线性映射和哈希函数的安全认证协议。经过分析,该协议满足了认证、访问控制、数据保密性、数据完整性和不可否认性等安全需求。结合BAN逻辑进行形式化分析,证明该协议是安全的;同时,将协议的两个阶段分别与同类型认证协议进行对比,实验结果表明,所提协议的性能在时间或者空间上有所提升。     

Comparison of bathymetry and seagrass mapping with hyperspectral imagery and airborne bathymetric lidar in a shallow estuarine environment

We compared hyperspectral imagery and single-wavelength airborne bathymetric light detection and ranging (lidar) for shallow water (<2 m) bathymetry and seagrass mapping. Both the bathymetric results from hyperspectral imagery and airborne bathymetric lidar reveal that the presence of a strongly reflecting benthic layer under seagrass affects the elevation estimates towards the bottom depth instead of the top of seagrass canopy. Full waveform lidar was also investigated for bathymetry and similar performance to discrete lidar was observed. A provisional classification was performed with limited ground reference samples and four supervised classifiers were applied in the study to investigate the capability of airborne bathymetric lidar and hyperspectral imagery to identify seagrass genera. The overall classification accuracy is highly variable and strongly dependent on the classification strategy used. Features from bathymetric lidar alone are not sufficient for substrate classification, while hyperspectral imagery alone showed significant capability for substrate classification with over 95% overall accuracy. The fusion of hyperspectral imagery and bathymetric lidar only marginally improved the overall accuracy of seagrass classification.     

Planar Shape Interpolation Based On Teichmüller Mapping

Shape interpolation is a classical problem in computer graphics and has been widely investigated in the past two decades. Ideal shape interpolation should be natural and smooth which have good properties such as affine and conformal reproduction, bounded distortion, no fold‐overs, etc. In this paper, we present a new approach for planar shape interpolation based on Teichmüller maps ‐ a special type of maps in the class of quasi‐conformal maps. The algorithm consists of two steps. In the first step, a Teichmüller map is computed from the source shape to the target shape, and then the Beltrami coefficient is interpolated such that the conformal distortion is linear with respect to the time variable. In the second step, the intermediate shape is reconstructed by solving the Beltrami equation locally over each triangle and then stitching the mapped triangles by conformal transformations. The new approach preserves all the good properties mentioned above and produces more natural and more uniform intermediate shapes than the start‐of‐the‐art methods. Especially, the conformal distortion changes linearly with respect to the time variable. Experiment results show that our method can produce appealing results regardless of interpolating between the same or different objects.     

Imaging and Doppler parameter estimation for maneuvering target using axis mapping based coherently integrated cubic phase function

In this paper, we propose a novel imaging and Doppler parameter estimation algorithm for ground maneuvering targets. Since the cross-track acceleration will induce the quadratic chirp rate (third-order phase) in the phase history, it may cause the maneuvering target severely smeared in the Doppler domain. To obtain a well-focused target imaging result, the quadratic chirp rate must be estimated accurately. Though cubic phase function (CPF) is efficient in estimating the parameters of a single maneuvering target, it may suffer from the identifiability problem when dealing with multiple maneuvering targets. To address these issues, an axis mapping (AM) based coherently integrated cubic phase function (CICPF) algorithm is proposed. This algorithm consists of two stages. Firstly, the linear chirp rate migration (i.e. quadratic chirp rate) of target in the time and chirp-rate domain is corrected by AM. After that, a dechirping technique is utilized to coherently integrate the auto-terms, and suppress the cross-terms and spurious peaks. Compared with several existing quadratic chirp rate estimation approaches, AM based CICPF (AMCICPF) algorithm can acquire lower signal-to-noise ratio threshold and estimate the centroid frequency, chirp rate and quadratic chirp rate of maneuvering target simultaneously. By compensating the chirp rate and quadratic chirp rate, a finely focused maneuvering target imaging can be obtained. Both simulated and real data processing results show that the AMCICPF algorithm serves as a good candidate for maneuvering target Doppler parameter estimation and imaging.     

3D‐vision based detection,localization, and sizing of broccoli heads in the field

This paper describes a 3D vision system for robotic harvesting of broccoli using low‐cost RGB‐D sensors, which was developed and evaluated using sensory data collected under real‐world field conditions in both the UK and Spain. The presented method addresses the tasks of detecting mature broccoli heads in the field and providing their 3D locations relative to the vehicle. The paper evaluates different 3D features, machine learning, and temporal filtering methods for detection of broccoli heads. Our experiments show that a combination of Viewpoint Feature Histograms, Support Vector Machine classifier, and a temporal filter to track the detected heads results in a system that detects broccoli heads with high precision. We also show that the temporal filtering can be used to generate a 3D map of the broccoli head positions in the field. Additionally, we present methods for automatically estimating the size of the broccoli heads, to determine when a head is ready for harvest. All of the methods were evaluated using ground‐truth data from both the UK and Spain, which we also make available to the research community for subsequent algorithm development and result comparison. Cross‐validation of the system trained on the UK dataset on the Spanish dataset, and vice versa, indicated good generalization capabilities of the system, confirming the strong potential of low‐cost 3D imaging for commercial broccoli harvesting.