2015 Federated Conference on Computer Science and Information Systems (FedCSIS)最新文献_第6页

Simulation of mobile wireless ad hoc networks for emergency situation awareness 面向应急态势感知的移动无线自组网仿真

2015 Federated Conference on Computer Science and Information Systems (FedCSIS) Pub Date : 2015-11-09 DOI: 10.15439/2015F52

Andrzej Sikora, E. Niewiadomska-Szynkiewicz, Mateusz Krzyszton

{"title":"Simulation of mobile wireless ad hoc networks for emergency situation awareness","authors":"Andrzej Sikora, E. Niewiadomska-Szynkiewicz, Mateusz Krzyszton","doi":"10.15439/2015F52","DOIUrl":"https://doi.org/10.15439/2015F52","url":null,"abstract":"Mobile self-organizing ad hoc networks (MANETs) can significantly enhance the capability to coordinate the emergency actions as well as monitor contaminated areas, explore unmanned space, inspect and control working environments. The management of networks that can dynamically and freely organize into temporary topologies raises interesting problems, that are particularly challenging for networks formed by mobile devices. Due to the inherent complexity of these systems, the development of applications relying on mobile network nodes and wireless communication protocols would be greatly simplified by the use of specific tools for supporting testing and performance evaluation. Modeling and simulation are widely used in the design and development of wireless ad hoc networks. In this paper we model mobile ad hoc network (MANET) using discrete event systems methodology (DEVS) and describe the functionality and performance of the Java-based simulation tool for the performance evaluation of self-organizing and cooperative networks for emergency situation awareness. The simulator can provide a useful support for the verification of the design of a network system, employed communication protocols, control and coordination algorithms, allowing the user to display a step-by-step evolution of the network in a suitable graphical interface. The practical case studies are provided to illustrate the operation and performance of the presented software.","PeriodicalId":276884,"journal":{"name":"2015 Federated Conference on Computer Science and Information Systems (FedCSIS)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121243983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Pawlak's flow graph extensions for video surveillance systems Pawlak的流图扩展视频监控系统

2015 Federated Conference on Computer Science and Information Systems (FedCSIS) Pub Date : 2015-11-09 DOI: 10.15439/2015F384

Karol Lisowski, A. Czyżewski

{"title":"Pawlak's flow graph extensions for video surveillance systems","authors":"Karol Lisowski, A. Czyżewski","doi":"10.15439/2015F384","DOIUrl":"https://doi.org/10.15439/2015F384","url":null,"abstract":"The idea of the Pawlak's flow graphs is applicable to many problems in various fields related to decision algorithms or data mining. The flow graphs can be used also in the video surveillance systems. Especially in distributed multi-camera systems which are problematic to be handled by human operators because of their limited perception. In such systems automated video analysis needs to be implemented. Important part of this analysis is tracking object within a single camera and between cameras' fields of vision. One of element needed to re-identify the single real object besides object's visual features and spatiotemporal dependencies between cameras is a behaviour model. The flow graph after some modifications, is a suitable data structure, which concept is based on the rough set theory, to contained as a behaviour model in it. Additionally, the flow graph can be used to predict the future movement of given object. In this paper a survey of authors research works related to employing flowgraphs in video surveillance systems is contained. The flow graph creation based on the paths of objects inside supervised area will presented. Moreover, a method of building a probability tree on the basis of the flow graph and a method for adapting the flowgraph to the changing topology of the camera network are also discussed.","PeriodicalId":276884,"journal":{"name":"2015 Federated Conference on Computer Science and Information Systems (FedCSIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130559310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Application of Artificial Neural Network and Support Vector Regression in cognitive radio networks for RF power prediction using compact differential evolution algorithm 人工神经网络和支持向量回归在认知无线电网络中的应用，采用紧凑差分进化算法进行射频功率预测

2015 Federated Conference on Computer Science and Information Systems (FedCSIS) Pub Date : 2015-11-09 DOI: 10.15439/2015F14

Sunday Iliya, E. Goodyer, J. Gow, J. Shell, M. Gongora

{"title":"Application of Artificial Neural Network and Support Vector Regression in cognitive radio networks for RF power prediction using compact differential evolution algorithm","authors":"Sunday Iliya, E. Goodyer, J. Gow, J. Shell, M. Gongora","doi":"10.15439/2015F14","DOIUrl":"https://doi.org/10.15439/2015F14","url":null,"abstract":"Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. To enhance the selection of channel with less noise among the white spaces (idle channels), the a priory knowledge of Radio Frequency (RF) power is very important. Computational Intelligence (CI) techniques cans be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) and Support Vector Regression (SVR) models for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) FM and TV bands. Sensitivity analysis was used to reduce the input vector of the prediction models. The inputs of the ANN and SVR consist of only time domain data and past RF power without using any RF power related parameters, thus forming a nonlinear time series prediction model. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters such as signal to noise ratio, bandwidth and bit error rate. Since CR are embedded communication devices with memory constrain limitation, the models used, implemented a novel and innovative initial weight optimization of the ANN's through the use of compact differential evolutionary (cDE) algorithm variants which are memory efficient. This was found to enhance the accuracy and generalization of the ANN model.","PeriodicalId":276884,"journal":{"name":"2015 Federated Conference on Computer Science and Information Systems (FedCSIS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115378707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

Rule quality measures settings in a sequential covering rule induction algorithm - an empirical approach 规则质量测量设置在顺序覆盖规则归纳算法-经验的方法

2015 Federated Conference on Computer Science and Information Systems (FedCSIS) Pub Date : 2015-11-09 DOI: 10.15439/2015F388

M. Michalak, M. Sikora, Lukasz Wróbel

引用次数: 8

Evaluation of methods to combine different speech recognizers 不同语音识别器组合方法的评价

2015 Federated Conference on Computer Science and Information Systems (FedCSIS) Pub Date : 2015-11-09 DOI: 10.15439/2015F62

Tomas Rasymas, V. Rudzionis

引用次数: 9

DISESOR - decision support system for mining industry DISESOR -采矿业决策支持系统

2015 Federated Conference on Computer Science and Information Systems (FedCSIS) Pub Date : 2015-11-09 DOI: 10.15439/2015F168

M. Kozielski, M. Sikora, Lukasz Wróbel

引用次数: 19

Strategies of parallelizing nested loops on the multicore architectures on the example of the WZ factorization for the dense matrices 以密集矩阵的WZ分解为例，研究了多核架构下嵌套循环的并行化策略

2015 Federated Conference on Computer Science and Information Systems (FedCSIS) Pub Date : 2015-11-09 DOI: 10.15439/2015F354

B. Bylina, J. Bylina

引用次数: 5

2015 Federated Conference on Computer Science and Information Systems (FedCSIS) Pub Date : 2015-11-09 DOI: 10.15439/2015F174

A. A. Younes, Frédéric Blanchard, M. Herbin

引用次数: 2

Behavior-preserving abstraction of ESTEREL programs ESTEREL程序的行为保持抽象

2015 Federated Conference on Computer Science and Information Systems (FedCSIS) Pub Date : 2015-11-09 DOI: 10.15439/2015F190

Nir Koblenc, S. Tyszberowicz

引用次数: 0

Real-time schedule for mobile robotics and WSN aplications 移动机器人和无线传感器网络应用的实时调度

2015 Federated Conference on Computer Science and Information Systems (FedCSIS) Pub Date : 2015-11-09 DOI: 10.15439/2015F146

M. Chovanec, P. Sarafín

引用次数: 6