Ad Hoc网中的移动数据库应用

传统的移动数据库模型应用到Ad Hoc网中,会带来通信代价增大等问题。为此,对传统的移动数据库模型进行改进,加入本地服务器作为中介,形成由移动主机、本地服务器和主服务器3类结点构成的移动数据库模型,并提出2个算法用来解决模型中本地服务器与主服务器上的数据同步和事务重做问题,从而使移动主机能够高效正确地访问数据库。实验结果证明,该移动数据库模型具有较好的稳定性。

Real-time reliability evaluation based on damaged measurement degradation data

A method was proposed to evaluate the real-time reliability for a single product based on damaged measurement degradation data.Most researches on degradation analysis often assumed that the measurement process did not have any impact on the product's performance.However,in some cases,the measurement process may exert extra stress on products being measured.To obtain trustful results in such a situation,a new degradation model was derived.Then,by fusing the prior information of product and its own on-line degradation data,the real-time reliability was evaluated on the basis of Bayesian formula.To make the proposed method more practical,a procedure based on expectation maximization (EM) algorithm was presented to estimate the unknown parameters.Finally,the performance of the proposed method was illustrated by a simulation study.The results show that ignoring the influence of the damaged measurement process can lead to biased evaluation results,if the damaged measurement process is involved.

Combined GPS/GLONASS Data Processing

To obtain the GLONASS satellite position at an epoch other than reference time,the satellite’s equation of motion has to be integrated with broadcasting ephemerides.The iterative detecting and repairing method of cycle slips based on triple difference residuals for combined GPS/GLONASS positioning and the iterative ambiguity resolution approach suitable for combined post processing positioning are discussed systematically.Experiments show that millimeter accuracy can be achieved in short baselines with a few hours’ dual frequency or even single frequency GPS/GLONASS carrier phase observations,and the precision of dual frequency observations is distinctly higher than that of single frequency observations.

Bitmap lattice index in road networks

A novel technique called the bitmap lattice index(BLI) is proposed, which combines the advantages of a wireless broadcasting environment with a road network. Existing road networks are based on the on-demand method: a server's workload increases as the query request increases when a server sends a client information. To solve this problem, we propose the BLI. The BLI denotes an object and a node as 0 and 1 in the Hilbert curve(HC) map. The BLI can identify the position of a node and an object through bit information; it can also reduce the broadcasting frequency of a server by reducing the size of the index, thereby decreasing the access latency and query processing times. Moreover, the BLI is highly effective for data filtering, as it can identify the positions of both an object and a node. In a road network, if filtering is done via the Euclidean distance, it may result in an error. To prevent this, we add another validation procedure. The experiment is conducted by applying the BLI to kNN query, and the technique is assessed by a performance evaluation experiment.

Studying the Condition of Soil Protection Agrolandscape in Ukraine Using Remote Sensing Methods

The article reviews the scientific approaches to monitoring of soil condition on the soil protection agrolandscape. In 1980s, the contour-meliorative soil protection system was established on the selected fields in Ukraine. The objective of the current research was to determine the capabilities of satellite survey to identify the changes of soil cover that had occurred on these fields during the past 25 years. Soil erosion processes are very dynamic, therefore it is essential to use time-series of operative satellite images to track those changes. Rills on the fields, caused by water erosion, are clearly identified on high-resolution satellite data. Erosion causes the decrease of humus content, which affects soil reflection values. This in turn leads to a corresponding change of color shade on satellite images. The research allowed to determine correlation between remote sensing data and soil organic carbon content and to acquire a mathematical model which describes this correlation. The condition of the agrolandscape soils was assessed using the regression model, which helped to evaluate erosion risk for different areas of the test polygon. The visual interpretation of satellite imagery led to a conclusion about a damaging effect of erosion on protective forest belts and accordingly on fields＇ soil cover and crops. Visual analysis results were approved by field research. Photos taken during the field research indicate an unsatisfactory status of forest belts and a devastating effect of eroding water flows. These are the results of irresponsible land use and constant violation of methodical principles of the contour-meliorative system organization. The article concludes that the use of time-series of high-resolution satellite imagery allows monitoring the condition of soil protection agrolandscape, in particular the forest belts＇ status soil cover conditions and their change over time. The research results can be used as an informational basis for the soil protection agrolandscape monitoring system.

Managing High Volume Data for Network Attack Detection Using Real-Time Flow Filtering

In this paper, we present Real-Time Flow Filter (RTFF) -a system that adopts a middle ground between coarse-grained volume anomaly detection and deep packet inspection. RTFF was designed with the goal of scaling to high volume data feeds that are common in large Tier-1 ISP networks and providing rich, timely information on observed attacks. It is a software solution that is designed to run on off-the-shelf hardware platforms and incorporates a scalable data processing architecture along with lightweight analysis algorithms that make it suitable for deployment in large networks. RTFF also makes use of state of the art machine learning algorithms to construct attack models that can be used to detect as well as predict attacks.

Subspace identification for continuous-time errors-in-variables model from sampled data

We study the subspace identification for the continuous-time errors-in-variables model from sampled data.First,the filtering approach is applied to handle the time-derivative problem inherent in continuous-time identification.The generalized Poisson moment functional is focused.A total least squares equation based on this filtering approach is derived.Inspired by the idea of discrete-time subspace identification based on principal component analysis,we develop two algorithms to deliver consistent estimates for the continuous-time errors-in-variables model by introducing two different instrumental variables.Order determination and other instrumental variables are discussed.The usefulness of the proposed algorithms is illustrated through numerical simulation.