McCTRP:多信道跨层采集树路由协议

无线传感器网络中,单一信道传输模式下数据吞吐率的受限和易受到干扰的缺陷影响了数据传输的可靠性,而多信道传输技术能有效地解决吞吐量受限和单一信道干扰问题。但是,多信道传输的特点使得多数单一信道路由协议在多信道的情况下无法获得满意的性能。本文采取三项措施,设计实现了多信道采集树路由协议:一是采用自适应的信标发送机制,二是采用Extra Expected Number of Transmission(eetx)作链路质量估计,三是通过跨层设计,同步路由层和MAC层邻居表信息的插入与删除等操作。实验室初步实验的结果表明,该协议具有网络的聚敛速度快、拓扑结构稳定等优点,可实现数据的可靠传输。     

ZFS文件系统中双容错编码性能的研究

ZFS是Sun推出的一款革新性的文件系统,它支持用户构建镜像以及单容错/双容错的软Raid。其双容错编码方案采用的是Reed-Solomon编码(RS码)。由于RS码是基于有限域运算,编码/解码时间复杂性差是其根本性的缺陷。ZFS对写操作的处理采用的是聚合后追加的方式而非传统的覆盖方式,每次写操作都会进行一次编码计算。因此,编码计算性能是影响文件系统整体性能的重要因素之一。本文的工作是将RDP这一基于奇偶校验的双容错编码与ZFS相结合,替代Reed-Solomon编码,以优化文件系统写操作的性能。我们设计了Cache优化的RDP编码算法,在ZFS中进行了实现,并通过实验验证了这一方法的有效性。     

基于第三层交换技术的网络实验室建设

网络实验是计算机网络学科重要的教学环节。随着社会对具有较强实践能力的计算机网络专业的毕业生的需求日益增加,网络实验室的规划建设就显得非常重要。本文结合中原工学院广播影视学院的网络实验室的组建实例讨论了其规划及实施方案。     

一种DDOS在线检测法

由DDoS攻击时流量样本与Hurst指数的关系,得到检测DDoS的方法.以提升小波估计Hurst指数法为基础,设计出小波算法的能谱在线计算方法.与传统检测法相比,本法可在线观察业务流特征,能及时发现DDOS入侵。     

实时极限思想的提出和应用

实时任务调度算法之间很难进行比较,而且缺少比较标准。提出具体环境下实时极限的思想,为任务调度的比较研究提供了一个尺度。并可在此基础之上进行算法自身消耗的测量。还可扩展可通行判定理论的应用范围。     

支持装备维修训练的虚拟样机模型研究

按照层次化建模和按需求剪裁的思路,建立了一种新的虚拟样机模型框架,包括层次模型、外观模型、状态模型、功能模型、交互模型、关联关系模型和信息模型七部分。这种虚拟样机模型框架可以支持装备虚拟维修训练的各个环节,简化虚拟样机建模工作的复杂性和工作量。     

内嵌GPS和GSM功能的野外测量终端模型

本文介绍所设计的一种带GPS定位和GSM通讯功能的野外测量终端模型系统。系统采用μC/OS-II嵌入式实时操作系统,以基于ARM并辅以GSM和GPS模块为硬件平台,提供给野外测量终端用户基本的通话功能和个人定位信息,为实时采集地理信息作基础。     

An application of MODIS data to snow cover monitoring in a pastoral area: A case study in Northern Xinjiang, China

Snow is an important land cover on the earth's surface. It is characterized by its changing nature. Monitoring snow cover extent plays a significant role in dynamic studies and prevention of snow-caused disasters in pastoral areas. Using NASA EOS Terra/MODIS snow cover products and in situ observation data during the four snow seasons from November 1 to March 31 of year 2001 to 2005 in northern Xinjiang area, the accuracy of MODIS snow cover mapping algorithm under varied snow depth and land cover types was analyzed. The overall accuracy of MODIS daily snow cover mapping algorithm in clear sky condition is high at 98.5%; snow agreement reaches 98.2%, and ranges from 77.8% to 100% over the 4-year period for individual sites. Snow depth (SD) is one of the major factors affecting the accuracy of MODIS snow cover maps. MODIS does not identify any snow for SD less than 0.5 cm. The overall accuracy increases with snow depth if SD is equal to or greater than 3 cm, and decreases for SD below 3 cm. Land cover has an important influence in the accuracy of MODIS snow cover maps. The use of MOD10A1 snow cover products is severely affected by cloud cover. The 8-day composite products of MOD10A2 can effectively minimize the effect of cloud cover in most cases. Cloud cover in excess of 10% occurs on 99% of the MOD10A1 products and 14.7% of the MOD10A2 products analyzed during the four snow seasons. User-defined multiple day composite images based on MOD10A1, with flexibilities of selecting composite period, starting and ending date and composite sequence of MOD10A1 products, have an advantage in effectively monitoring snow cover extent for regional snow-caused disasters in pastoral areas.     

Wavelet-based image registration technique for high-resolution remote sensing images

Image registration is the process of geometrically aligning one image to another image of the same scene taken from different viewpoints at different times or by different sensors. It is an important image processing procedure in remote sensing and has been studied by remote sensing image processing professionals for several decades. Nevertheless, it is still difficult to find an accurate, robust, and automatic image registration method, and most existing image registration methods are designed for a particular application. High-resolution remote sensing images have made it more convenient for professionals to study the Earth; however, they also create new challenges when traditional processing methods are used. In terms of image registration, a number of problems exist in the registration of high-resolution images: (1) the increased relief displacements, introduced by increasing the spatial resolution and lowering the altitude of the sensors, cause obvious geometric distortion in local areas where elevation variation exists; (2) precisely locating control points in high-resolution images is not as simple as in moderate-resolution images; (3) a large number of control points are required for a precise registration, which is a tedious and time-consuming process; and (4) high data volume often affects the processing speed in the image registration. Thus, the demand for an image registration approach that can reduce the above problems is growing. This study proposes a new image registration technique, which is based on the combination of feature-based matching (FBM) and area-based matching (ABM). A wavelet-based feature extraction technique and a normalized cross-correlation matching and relaxation-based image matching techniques are employed in this new method. Two pairs of data sets, one pair of IKONOS panchromatic images from different times and the other pair of images consisting of an IKONOS panchromatic image and a QuickBird multispectral image, are used to evaluate the proposed image registration algorithm. The experimental results show that the proposed algorithm can select sufficient control points semi-automatically to reduce the local distortions caused by local height variation, resulting in improved image registration results.