Порівняльний аналіз систем сигналів систем ідентифікації об’єктів на базі фазової та частотної маніпуляції

Приводиться порівняльний аналіз систем сигналів, які реалізуються на базі псевдохаотичних послідовностей при використанні фазової та частотної маніпуляцій. Показано, що завдяки значно меншим бічним пелюсткам функції невизначеності сигналів з фазовою маніпуляцією їх краще використовувати в системах ідентифікації повітряних об'єктів, для підвищення, як перешкодостійкості, так і енергетичної скритності.A signals systems comparative analysis over, which will be realized on the base of pseudo-random sequences at the use of phase and frequency manipulations is brought. It is shown that due to the considerablyless lateral petals signals vagueness function with phase manipulation it is better to use them in the systems of air objects authentication, for an increase, both noise-immunity and power secrecy

Detection of an anomalous cluster in a network

We consider the problem of detecting whether or not, in a given sensor network, there is a cluster of sensors which exhibit an "unusual behavior." Formally, suppose we are given a set of nodes and attach a random variable to each node. We observe a realization of this process and want to decide between the following two hypotheses: under the null, the variables are i.i.d. standard normal; under the alternative, there is a cluster of variables that are i.i.d. normal with positive mean and unit variance, while the rest are i.i.d. standard normal. We also address surveillance settings where each sensor in the network collects information over time. The resulting model is similar, now with a time series attached to each node. We again observe the process over time and want to decide between the null, where all the variables are i.i.d. standard normal, and the alternative, where there is an emerging cluster of i.i.d. normal variables with positive mean and unit variance. The growth models used to represent the emerging cluster are quite general and, in particular, include cellular automata used in modeling epidemics. In both settings, we consider classes of clusters that are quite general, for which we obtain a lower bound on their respective minimax detection rate and show that some form of scan statistic, by far the most popular method in practice, achieves that same rate to within a logarithmic factor. Our results are not limited to the normal location model, but generalize to any one-parameter exponential family when the anomalous clusters are large enough.Comment: Published in at http://dx.doi.org/10.1214/10-AOS839 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

О моделировании сенсорних сетей средствами високого уровня

Развитие полупроводниковых технологий сделало возможным создание беспроводных сенсорных сетей. Для таких сетей необходимо создание систем моделирования, позволяющих выбирать оптимальные протоколы для каждой прикладной задачи. В работе предложен подход к моделированию сенсорных сетей, объединяющий возможности среды визуального моделирования AnyLogic и системы символьных вычислений TermWare. Для демонстрации этого подхода разработан прототип системы моделирования. Рассматривается пример декларативного описания протокола – простой синхронный протокол доступа к каналу.Recent technological advances enabled the creation of Wireless Sensor Networks (WSN). There is a need for simulation systems, which should allow users to choose the best protocols for each applied problem. We present an approach to sensor network simulation, based on AnyLogic – visual simulation environment and TermWare – symbolic computation system. A prototype simulation system implementing this approach is described. As an example of declarative protocol description we use a simple synchronous media access protocol

Data synthesis improves detection of radiation sources in urban environments

Distributed sensors have been proposed to detect nuclear materials if they enter urban areas. Past work on small detector systems has shown that data fusion can improve detection. Here, we show how this could be done for a large detector network using Pearsons Method. We evaluate how a sensor network would perform in New York City using a combination of radiation transport and geographic information systems. We use OpenStreetMap data to construct a grid over the streets and analyze vehicle paths using pickup and drop off data from the New York State Department of Transportation. The results show that data synthesis in a large network not only improves the time to the first detection but reduces the number of missed sources

IMPROVED VIRTUAL CIRCUIT ROUTING ALGORITHM FOR WIRELESS SENSOR NETWORKS UNDER THE ASPECT OF POWER AWARENESS

Routing algorithms have shown their importance in the power aware wireless micro-sensor networks. In this paper first we present virtual circuit algorithm (VCRA), a routing algorithm for wireless sensor networks. We analyze the power utilized by nodes to lengthen the battery life and thus improving the lifetime of wireless sensor network. We discuss VCRA in comparison with the Multihoprouter, an algorithm developed by UC Berkeley. Then we present Improved Virtual Circuit Routing Algorithm (IVCRA) which is an improved form of VCRA. In IVCRA node failure detection and path repairing scheme has been implemented. We also present the energy analysis of IVCRA and prove that IVCRA is the best choice. We first implement our routing algorithms in simulator TOSSIM and then on real hardware of mica2 mote-sensor network platform and prove the reliable routing of the data packets from different nodes to the base station. The motes used as nodes in our mote-sensor network are from Berkeley USA. By using simulator POWERTOSSIM, we estimate and present the energy utilized by different nodes of the network. At the end we present a comparison of our work with the network layer of Zigbee/IEEE 802.15.4, which is an emerging standard for wireless sensor networks and then compare its energy efficiency with the packet size chosen for our algorithm

Bayesian source detection and parameter estimation of a plume model based on sensor network measurements

We consider a network of sensors that measure the intensities of a complex plume composed of multiple absorption–diffusion source components. We address the problem of estimating the plume parameters, including the spatial and temporal source origins and the parameters of the diffusion model for each source, based on a sequence of sensor measurements. The approach not only leads to multiple-source detection, but also the characterization and prediction of the combined plume in space and time. The parameter estimation is formulated as a Bayesian inference problem, and the solution is obtained using a Markov chain Monte Carlo algorithm. The approach is applied to a simulation study, which shows that an accurate parameter estimation is achievable. Copyright © 2010 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78051/1/859_ftp.pd