Search results for: cycle composition networks
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 3161

Search results for: cycle composition networks

2561 Modeling of Nitrogen Solubility in Stainless Steel

Authors: Saeed Ghali, Hoda El-Faramawy, Mamdouh Eissa, Michael Mishreky

Abstract:

Scale-resistant austenitic stainless steel, X45CrNiW 18-9, has been developed, and modified steels produced through partial and total nickel replacement by nitrogen. These modified steels were produced in a 10 kg induction furnace under different nitrogen pressures and were cast into ingots. The produced modified stainless steels were forged, followed by air cooling. The phases of modified stainless steels have been investigated using the Schaeffler diagram, dilatometer, and microstructure observations. Both partial and total replacements of nickel using 0.33-0.50% nitrogen are effective in producing fully austenitic stainless steels. The nitrogen contents were determined and compared with those calculated using the Institute of Metal Science (IMS) equation. The results showed great deviations between the actual nitrogen contents and predicted values through IMS equation. So, an equation has been derived based on chemical composition, pressure, and temperature at 1600 oC: [N%] = 0.0078 + 0.0406*X, where X is a function of chemical composition and nitrogen pressure. The derived equation has been used to calculate the nitrogen content of different steels using published data. The results reveal the difficulty of deriving a general equation for the prediction of nitrogen content covering different steel compositions. So, it is necessary to use a narrow composition range.

Keywords: Solubility, nitrogen, stainless steel, Schaeffler.

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2560 Target Tracking in Sensor Networks: A Distributed Constraint Satisfaction Approach

Authors: R.Mostafaei, A.Habiboghli, M.R.Meybodi

Abstract:

In distributed resource allocation a set of agents must assign their resources to a set of tasks. This problem arises in many real-world domains such as distributed sensor networks, disaster rescue, hospital scheduling and others. Despite the variety of approaches proposed for distributed resource allocation, a systematic formalization of the problem, explaining the different sources of difficulties, and a formal explanation of the strengths and limitations of key approaches is missing. We take a step towards this goal by using a formalization of distributed resource allocation that represents both dynamic and distributed aspects of the problem. In this paper we present a new idea for target tracking in sensor networks and compare it with previous approaches. The central contribution of the paper is a generalized mapping from distributed resource allocation to DDCSP. This mapping is proven to correctly perform resource allocation problems of specific difficulty. This theoretical result is verified in practice by a simulation on a realworld distributed sensor network.

Keywords: Distributed CSP, Target Tracking, Sensor Network

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2559 Networks with Unreliable Nodes and Edges: Monte Carlo Lifetime Estimation

Authors: Y. Shpungin

Abstract:

Estimating the lifetime distribution of computer networks in which nodes and links exist in time and are bound for failure is very useful in various applications. This problem is known to be NP-hard. In this paper we present efficient combinatorial approaches to Monte Carlo estimation of network lifetime distribution. We also present some simulation results.

Keywords: Combinatorial spectrum, Monte Carlo, Networklifetime, Unreliable nodes and edges.

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2558 Mobile Robot Navigation Using Local Model Networks

Authors: Hamdi. A. Awad, Mohamed A. Al-Zorkany

Abstract:

Developing techniques for mobile robot navigation constitutes one of the major trends in the current research on mobile robotics. This paper develops a local model network (LMN) for mobile robot navigation. The LMN represents the mobile robot by a set of locally valid submodels that are Multi-Layer Perceptrons (MLPs). Training these submodels employs Back Propagation (BP) algorithm. The paper proposes the fuzzy C-means (FCM) in this scheme to divide the input space to sub regions, and then a submodel (MLP) is identified to represent a particular region. The submodels then are combined in a unified structure. In run time phase, Radial Basis Functions (RBFs) are employed as windows for the activated submodels. This proposed structure overcomes the problem of changing operating regions of mobile robots. Read data are used in all experiments. Results for mobile robot navigation using the proposed LMN reflect the soundness of the proposed scheme.

Keywords: Mobile Robot Navigation, Neural Networks, Local Model Networks

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2557 Simultaneous Optimization of Design and Maintenance through a Hybrid Process Using Genetic Algorithms

Authors: O. Adjoul, A. Feugier, K. Benfriha, A. Aoussat

Abstract:

In general, issues related to design and maintenance are considered in an independent manner. However, the decisions made in these two sets influence each other. The design for maintenance is considered an opportunity to optimize the life cycle cost of a product, particularly in the nuclear or aeronautical field, where maintenance expenses represent more than 60% of life cycle costs. The design of large-scale systems starts with product architecture, a choice of components in terms of cost, reliability, weight and other attributes, corresponding to the specifications. On the other hand, the design must take into account maintenance by improving, in particular, real-time monitoring of equipment through the integration of new technologies such as connected sensors and intelligent actuators. We noticed that different approaches used in the Design For Maintenance (DFM) methods are limited to the simultaneous characterization of the reliability and maintainability of a multi-component system. This article proposes a method of DFM that assists designers to propose dynamic maintenance for multi-component industrial systems. The term "dynamic" refers to the ability to integrate available monitoring data to adapt the maintenance decision in real time. The goal is to maximize the availability of the system at a given life cycle cost. This paper presents an approach for simultaneous optimization of the design and maintenance of multi-component systems. Here the design is characterized by four decision variables for each component (reliability level, maintainability level, redundancy level, and level of monitoring data). The maintenance is characterized by two decision variables (the dates of the maintenance stops and the maintenance operations to be performed on the system during these stops). The DFM model helps the designers choose technical solutions for the large-scale industrial products. Large-scale refers to the complex multi-component industrial systems and long life-cycle, such as trains, aircraft, etc. The method is based on a two-level hybrid algorithm for simultaneous optimization of design and maintenance, using genetic algorithms. The first level is to select a design solution for a given system that considers the life cycle cost and the reliability. The second level consists of determining a dynamic and optimal maintenance plan to be deployed for a design solution. This level is based on the Maintenance Free Operating Period (MFOP) concept, which takes into account the decision criteria such as, total reliability, maintenance cost and maintenance time. Depending on the life cycle duration, the desired availability, and the desired business model (sales or rental), this tool provides visibility of overall costs and optimal product architecture.

Keywords: Availability, design for maintenance, DFM, dynamic maintenance, life cycle cost, LCC, maintenance free operating period, MFOP, simultaneous optimization.

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2556 Intelligent Neural Network Based STLF

Authors: H. Shayeghi, H. A. Shayanfar, G. Azimi

Abstract:

Short-Term Load Forecasting (STLF) plays an important role for the economic and secure operation of power systems. In this paper, Continuous Genetic Algorithm (CGA) is employed to evolve the optimum large neural networks structure and connecting weights for one-day ahead electric load forecasting problem. This study describes the process of developing three layer feed-forward large neural networks for load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. We find good performance for the large neural networks. The proposed methodology gives lower percent errors all the time. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.

Keywords: Feed-forward Large Neural Network, Short-TermLoad Forecasting, Continuous Genetic Algorithm.

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2555 A New Scheme for Improving the Quality of Service in Heterogeneous Wireless Network for Data Stream Sending

Authors: Ebadollah Zohrevandi, Rasoul Roustaei, Omid Moradtalab

Abstract:

In this paper, we first consider the quality of service problems in heterogeneous wireless networks for sending the video data, which their problem of being real-time is pronounced. At last, we present a method for ensuring the end-to-end quality of service at application layer level for adaptable sending of the video data at heterogeneous wireless networks. To do this, mechanism in different layers has been used. We have used the stop mechanism, the adaptation mechanism and the graceful degrade at the application layer, the multi-level congestion feedback mechanism in the network layer and connection cutting off decision mechanism in the link layer. At the end, the presented method and the achieved improvement is simulated and presented in the NS-2 software.

Keywords: Congestion, Handoff, Heterogeneous wireless networks, Adaptation mechanism, Stop mechanism, Graceful degrade.

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2554 Tree-on-DAG for Data Aggregation in Sensor Networks

Authors: Prakash G L, Thejaswini M, S H Manjula, K R Venugopal, L M Patnaik

Abstract:

Computing and maintaining network structures for efficient data aggregation incurs high overhead for dynamic events where the set of nodes sensing an event changes with time. Moreover, structured approaches are sensitive to the waiting time that is used by nodes to wait for packets from their children before forwarding the packet to the sink. An optimal routing and data aggregation scheme for wireless sensor networks is proposed in this paper. We propose Tree on DAG (ToD), a semistructured approach that uses Dynamic Forwarding on an implicitly constructed structure composed of multiple shortest path trees to support network scalability. The key principle behind ToD is that adjacent nodes in a graph will have low stretch in one of these trees in ToD, thus resulting in early aggregation of packets. Based on simulations on a 2,000-node Mica2- based network, we conclude that efficient aggregation in large-scale networks can be achieved by our semistructured approach.

Keywords: Aggregation, Packet Merging, Query Processing.

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2553 Minimizing Energy Consumption in Wireless Sensor Networks using Binary Integer Linear Programming

Authors: Chompunut Jantarasorn, Chutima Prommak

Abstract:

The important issue considered in the widespread deployment of Wireless Sensor Networks (WSNs) is an efficiency of the energy consumption. In this paper, we present a study of the optimal relay station planning problems using Binary Integer Linear Programming (BILP) model to minimize the energy consumption in WSNs. Our key contribution is that the proposed model not only ensures the required network lifetime but also guarantees the radio connectivity at high level of communication quality. Specially, we take into account effects of noise, signal quality limitation and bit error rate characteristics. Numerical experiments were conducted in various network scenarios. We analyzed the effects of different sensor node densities and distribution on the energy consumption.

Keywords: Binary Integer Linear Programming, BILP, Energy consumption, Optimal node placement and Wireless sensor networks.

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2552 Continuity Planning in Supply Chain Networks: Degrees of Freedom and Application in the Risk Management Process

Authors: Marco Bötel, Tobias Gelau, Wendelin Gross

Abstract:

Supply chain networks are frequently hit by unplanned events which lead to disruptions and cause operational and financial consequences. It is neither possible to avoid disruption risk entirely, nor are network members able to prepare for every possible disruptive event. Therefore a continuity planning should be set up which supports effective operational responses in supply chain networks in times of emergencies. In this research network related degrees of freedom which determine the options for responsive actions are derived from interview data. The findings are further embedded into a common risk management process. The paper provides support for researchers and practitioners to identify the network related options for responsive actions and to determine the need for improving the reaction capabilities.

Keywords: Supply Chain Risk Management, Business Continuity Planning, Degrees of Freedom, Risk Management Process, Mitigation Measures.

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2551 Avoiding Catastrophic Forgetting by a Dual-Network Memory Model Using a Chaotic Neural Network

Authors: Motonobu Hattori

Abstract:

In neural networks, when new patterns are learned by a network, the new information radically interferes with previously stored patterns. This drawback is called catastrophic forgetting or catastrophic interference. In this paper, we propose a biologically inspired neural network model which overcomes this problem. The proposed model consists of two distinct networks: one is a Hopfield type of chaotic associative memory and the other is a multilayer neural network. We consider that these networks correspond to the hippocampus and the neocortex of the brain, respectively. Information given is firstly stored in the hippocampal network with fast learning algorithm. Then the stored information is recalled by chaotic behavior of each neuron in the hippocampal network. Finally, it is consolidated in the neocortical network by using pseudopatterns. Computer simulation results show that the proposed model has much better ability to avoid catastrophic forgetting in comparison with conventional models.

Keywords: catastrophic forgetting, chaotic neural network, complementary learning systems, dual-network

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2550 Visualisation and Navigation in Large Scale P2P Service Networks

Authors: H. Unger, H. Coltzau

Abstract:

In Peer-to-Peer service networks, where peers offer any kind of publicly available services or applications, intuitive navigation through all services in the network becomes more difficult as the number of services increases. In this article, a concept is discussed that enables users to intuitively browse and use large scale P2P service networks. The concept extends the idea of creating virtual 3D-environments solely based on Peer-to-Peer technologies. Aside from browsing, users shall have the possibility to emphasize services of interest using their own semantic criteria. The appearance of the virtual world shall intuitively reflect network properties that may be of interest for the user. Additionally, the concept comprises options for load- and traffic-balancing. In this article, the requirements concerning the underlying infrastructure and the graphical user interface are defined. First impressions of the appearance of future systems are presented and the next steps towards a prototypical implementation are discussed.

Keywords: Internet Operating System, Peer-To-Peer, Service Exploration

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2549 A Maximum Parsimony Model to Reconstruct Phylogenetic Network in Honey Bee Evolution

Authors: Usha Chouhan, K. R. Pardasani

Abstract:

Phylogenies ; The evolutionary histories of groups of species are one of the most widely used tools throughout the life sciences, as well as objects of research with in systematic, evolutionary biology. In every phylogenetic analysis reconstruction produces trees. These trees represent the evolutionary histories of many groups of organisms, bacteria due to horizontal gene transfer and plants due to process of hybridization. The process of gene transfer in bacteria and hybridization in plants lead to reticulate networks, therefore, the methods of constructing trees fail in constructing reticulate networks. In this paper a model has been employed to reconstruct phylogenetic network in honey bee. This network represents reticulate evolution in honey bee. The maximum parsimony approach has been used to obtain this reticulate network.

Keywords: Hybridization, HGT, Reticulate networks, Recombination, Species, Parsimony.

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2548 Proximate Composition and Textural Properties of Cooked Sausages Formulated from Mechanically Deboned Chicken Meat with Addition of Chicken Offal

Authors: Marija R. Jokanović, Vladimir M. Tomović, Mihajlo T. Jović, Branislav V. Šojić, Snežana B. Škaljac, Tatjana A. Tasić, Predrag M. Ikonić

Abstract:

Proximate composition (moisture, protein, total fat, and total ash) and textural characteristics (hardness, adhesiveness, springiness, cohesiveness, chewiness and firmness and work of shear) of cooked sausages formulated from mechanically deboned chicken meat (MDCM) with addition of chicken offal (heart, gizzard or liver) were investigated. Chicken offal replaced equal weight (15 kg) of MDCM in standard sausage formulation. Regarding proximate composition sausage with heart addition was significantly (P<0.05) lower in moisture content (70.45%) than sausage with liver addition (71.35%), and significantly (P<0.05) the highest in total ash content (2.83%). Sausage with gizzard addition was significantly higher in protein content (9.77%) than sausage with liver addition (9.42%). Total fat content didn’t significantly (P>0.05) differ among all three sausages. The effect of offal addition was more notable in Warner-Bratzler shear test results than in texture profile analysis test. Firmness and work of shear were significantly different (P<0.05) among all three sausages. Sausage with liver addition was significantly (P<0.05) lower in hardness (1672 g) and chewiness (1020 g) and numerically the lowest in springiness (0.90) and adhesiveness (–70 g*s) comparing with other two sausages. Sausage with heart addition was significantly (P<0.05) higher in cohesiveness (0.74) comparing with other two sausages.

Keywords: Cooked sausage, mechanically deboned chicken meat, offal, proximate composition, texture

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2547 A Distributed Topology Control Algorithm to Conserve Energy in Heterogeneous Wireless Mesh Networks

Authors: F. O. Aron, T. O. Olwal, A. Kurien, M. O. Odhiambo

Abstract:

A considerable amount of energy is consumed during transmission and reception of messages in a wireless mesh network (WMN). Reducing per-node transmission power would greatly increase the network lifetime via power conservation in addition to increasing the network capacity via better spatial bandwidth reuse. In this work, the problem of topology control in a hybrid WMN of heterogeneous wireless devices with varying maximum transmission ranges is considered. A localized distributed topology control algorithm is presented which calculates the optimal transmission power so that (1) network connectivity is maintained (2) node transmission power is reduced to cover only the nearest neighbours (3) networks lifetime is extended. Simulations and analysis of results are carried out in the NS-2 environment to demonstrate the correctness and effectiveness of the proposed algorithm.

Keywords: Topology Control, Wireless Mesh Networks, Backbone, Energy Efficiency, Localized Algorithm.

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2546 Chemical Composition of Essential Oil and in vitro Antibacterial and Anticancer Activity of the Hydroalcolic Extract from Coronilla varia

Authors: Dehpour A. A., Eslami B., Rezaie S., Hashemian S. F., Shafie F., Kiaie M.

Abstract:

The aims of study were investigation on chemical composition essential oil and the effect of extract of Coronilla varia on antimicrobial and cytotoxicity activity. The essential oils of Coronilla varia is obtained by hydrodistillation and analyzed by (GC/MS) for determining their chemical composition and identification of their components. Antibacterial activity of plant extract was determined by disc diffusion method and anticancer activity measured by MTT assay. The major components in essential oil were Caryophyllene Oxide (60.19%), Alphacadinol (4.13%) and Homoadantaneca Robexylic Acid (3.31%). The extracts from Coronilla varia had interesting activity against Proteus mirabilis in the concentration of 700 μg/disc and did not show any activity against Staphylococus aureus, Bacillus subtillis, Klebsiella pneumonia and Entrobacter cloacae. The positive control, Ampicillin, Chloramphenicol and Cenphalothin had shown zone of inhibition resistant all bacteria. The ethanol extract of Corohilla varia inhibited on MCF7 cell lines. IC50 0.6(mg/ml) was the optimum concentration of extract from Coronilla varia inhibition of cell line growth. The MCF7 cancer cell line and Proteus mirabilis were more sensitive to Coronilla varia ethanol extract.

Keywords: Coronilla varia, Essential oil, Antibacterial, Anticancer, HeLa cell line.

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2545 Designing a Novel General Sorting Network Constructor Using Artificial Evolution

Authors: Michal Bidlo, Radek Bidlo, Lukas Sekanina

Abstract:

A method is presented for the construction of arbitrary even-input sorting networks exhibiting better properties than the networks created using a conventional technique of the same type. The method was discovered by means of a genetic algorithm combined with an application-specific development. Similarly to human inventions in the area of theoretical computer science, the evolved invention was analyzed: its generality was proven and area and time complexities were determined.

Keywords: Development, genetic algorithm, program, sorting network.

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2544 Weaknesses and Strengths Analysis over Wireless Network Security Standards

Authors: Daniel Padilla, Edward Guillen

Abstract:

Several wireless networks security standards have been proposed and widely implemented in both business and home environments in order to protect the network from unauthorized access. However, the implementation of such standards is usually achieved by network administrators without even knowing the standards- weaknesses and strengths. The intention of this paper is to evaluate and analyze the impact over the network-s security due to the implementation of the wireless networks security standards WEP, WPA and WLAN 802.1X.

Keywords: 802.1X, vulnerabilities analysis, WEP, wireless security, WPA.

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2543 Conditions for Fault Recovery of Interconnected Asynchronous Sequential Machines with State Feedback

Authors: Jung–Min Yang

Abstract:

In this paper, fault recovery for parallel interconnected asynchronous sequential machines is studied. An adversarial input can infiltrate into one of two submachines comprising parallel composition of the considered asynchronous sequential machine, causing an unauthorized state transition. The control objective is to elucidate the condition for the existence of a corrective controller that makes the closed-loop system immune against any occurrence of adversarial inputs. In particular, an efficient existence condition is presented that does not need the complete modeling of the interconnected asynchronous sequential machine.

Keywords: Asynchronous sequential machines, parallel composition, corrective control, fault tolerance.

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2542 Seismic Alert System based on Artificial Neural Networks

Authors: C. M. A. Robles G., R. A. Hernandez-Becerril

Abstract:

We board the problem of creating a seismic alert system, based upon artificial neural networks, trained by using the well-known back-propagation and genetic algorithms, in order to emit the alarm for the population located into a specific city, about an eminent earthquake greater than 4.5 Richter degrees, and avoiding disasters and human loses. In lieu of using the propagation wave, we employed the magnitude of the earthquake, to establish a correlation between the recorded magnitudes from a controlled area and the city, where we want to emit the alarm. To measure the accuracy of the posed method, we use a database provided by CIRES, which contains the records of 2500 quakes incoming from the State of Guerrero and Mexico City. Particularly, we performed the proposed method to generate an issue warning in Mexico City, employing the magnitudes recorded in the State of Guerrero.

Keywords: Seismic Alert System, Artificial Neural Networks, Genetic Algorithms.

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2541 Investigation of Artificial Neural Networks Performance to Predict Net Heating Value of Crude Oil by Its Properties

Authors: Mousavian, M. Moghimi Mofrad, M. H. Vakili, D. Ashouri, R. Alizadeh

Abstract:

The aim of this research is to use artificial neural networks computing technology for estimating the net heating value (NHV) of crude oil by its Properties. The approach is based on training the neural network simulator uses back-propagation as the learning algorithm for a predefined range of analytically generated well test response. The network with 8 neurons in one hidden layer was selected and prediction of this network has been good agreement with experimental data.

Keywords: Neural Network, Net Heating Value, Crude Oil, Experimental, Modeling.

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2540 Recognition of Noisy Words Using the Time Delay Neural Networks Approach

Authors: Khenfer-Koummich Fatima, Mesbahi Larbi, Hendel Fatiha

Abstract:

This paper presents a recognition system for isolated words like robot commands. It’s carried out by Time Delay Neural Networks; TDNN. To teleoperate a robot for specific tasks as turn, close, etc… In industrial environment and taking into account the noise coming from the machine. The choice of TDNN is based on its generalization in terms of accuracy, in more it acts as a filter that allows the passage of certain desirable frequency characteristics of speech; the goal is to determine the parameters of this filter for making an adaptable system to the variability of speech signal and to noise especially, for this the back propagation technique was used in learning phase. The approach was applied on commands pronounced in two languages separately: The French and Arabic. The results for two test bases of 300 spoken words for each one are 87%, 97.6% in neutral environment and 77.67%, 92.67% when the white Gaussian noisy was added with a SNR of 35 dB.

Keywords: Neural networks, Noise, Speech Recognition.

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2539 Key Issues and Challenges of Intrusion Detection and Prevention System: Developing Proactive Protection in Wireless Network Environment

Authors: M. Salman, B. Budiardjo, K. Ramli

Abstract:

Nowadays wireless technology plays an important role in public and personal communication. However, the growth of wireless networking has confused the traditional boundaries between trusted and untrusted networks. Wireless networks are subject to a variety of threats and attacks at present. An attacker has the ability to listen to all network traffic which becoming a potential intrusion. Intrusion of any kind may lead to a chaotic condition. In addition, improperly configured access points also contribute the risk to wireless network. To overcome this issue, a security solution that includes an intrusion detection and prevention system need to be implemented. In this paper, first the security drawbacks of wireless network will be analyzed then investigate the characteristics and also the limitations on current wireless intrusion detection and prevention system. Finally, the requirement of next wireless intrusion prevention system will be identified including some key issues which should be focused on in the future to overcomes those limitations.

Keywords: intrusion detection, intrusion prevention, wireless networks, proactive protection

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2538 Design of Non-Blocking and Rearrangeable Modified Banyan Network with Electro-Optic MZI Switching Elements

Authors: Ghanshyam Singh, Tirtha Pratim Bhattacharjee, R. P. Yadav, V. Janyani

Abstract:

Banyan networks are really attractive for serving as the optical switching architectures due to their unique properties of small depth and absolute signal loss uniformity. The fact has been established that the limitations of blocking nature and the nonavailability of proper connections due to non-rearrangeable property can be easily ruled out using electro-optic MZI switches as basic switching elements. Combination of the horizontal expansion and vertical stacking of optical banyan networks is an appropriate scheme for constructing non-blocking banyan-based optical switching networks. The interconnected banyan switching fabrics (IBSF) have been considered and analyzed to best serve the purpose of optical switching with electro-optic MZI basic elements. The cross/bar state interchange for the switches has been facilitated by appropriate voltage switching or the by the switching of operating wavelength. The paper is dedicated to the modification of the basic switching element being used as well as the architecture of the switching network.

Keywords: MZI switch, Banyan network, Reconfigurable switches.

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2537 Kohonen Self-Organizing Maps as a New Method for Determination of Salt Composition of Multi-Component Solutions

Authors: Sergey A. Burikov, Tatiana A. Dolenko, Kirill A. Gushchin, Sergey A. Dolenko

Abstract:

The paper presents the results of clusterization by Kohonen self-organizing maps (SOM) applied for analysis of array of Raman spectra of multi-component solutions of inorganic salts, for determination of types of salts present in the solution. It is demonstrated that use of SOM is a promising method for solution of clusterization and classification problems in spectroscopy of multicomponent objects, as attributing a pattern to some cluster may be used for recognition of component composition of the object.

Keywords: Kohonen self-organizing maps, clusterization, multicomponent solutions, Raman spectroscopy.

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2536 Proposal of Commutation Protocol in Hybrid Sensors and Vehicular Networks for Intelligent Transport Systems

Authors: Taha Bensiradj, Samira Moussaoui

Abstract:

Hybrid Sensors and Vehicular Networks (HSVN), represent a hybrid network, which uses several generations of Ad-Hoc networks. It is used especially in Intelligent Transport Systems (ITS). The HSVN allows making collaboration between the Wireless Sensors Network (WSN) deployed on the border of the road and the Vehicular Network (VANET). This collaboration is defined by messages exchanged between the two networks for the purpose to inform the drivers about the state of the road, provide road safety information and more information about traffic on the road. Moreover, this collaboration created by HSVN, also allows the use of a network and the advantage of improving another network. For example, the dissemination of information between the sensors quickly decreases its energy, and therefore, we can use vehicles that do not have energy constraint to disseminate the information between sensors. On the other hand, to solve the disconnection problem in VANET, the sensors can be used as gateways that allow sending the messages received by one vehicle to another. However, because of the short communication range of the sensor and its low capacity of storage and processing of data, it is difficult to ensure the exchange of road messages between it and the vehicle, which can be moving at high speed at the time of exchange. This represents the time where the vehicle is in communication range with the sensor. This work is the proposition of a communication protocol between the sensors and the vehicle used in HSVN. The latter has as the purpose to ensure the exchange of road messages in the available time of exchange.

Keywords: HSVN, ITS, VANET, WSN.

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2535 Multimode Dynamics of the Beijing Road Traffic System

Authors: Zundong Zhang, Limin Jia, Xiaoliang Sun

Abstract:

The Beijing road traffic system, as a typical huge urban traffic system, provides a platform for analyzing the complex characteristics and the evolving mechanisms of urban traffic systems. Based on dynamic network theory, we construct the dynamic model of the Beijing road traffic system in which the dynamical properties are described completely. Furthermore, we come into the conclusion that urban traffic systems can be viewed as static networks, stochastic networks and complex networks at different system phases by analyzing the structural randomness. As well as, we demonstrate the evolving process of the Beijing road traffic network based on real traffic data, validate the stochastic characteristics and the scale-free property of the network at different phases

Keywords: Dynamic Network Models, Structural Randomness, Scale-free Property, Multi-mode character

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2534 Mobile Ad-Hoc Service Grid – MASGRID

Authors: Imran Ihsan, Muhammad Abdul Qadir, Nadeem Iftikhar

Abstract:

Mobile devices, which are progressively surrounded in our everyday life, have created a new paradigm where they interconnect, interact and collaborate with each other. This network can be used for flexible and secure coordinated sharing. On the other hand Grid computing provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities. In this paper, efforts are made to map the concepts of Grid on Ad-Hoc networks because both exhibit similar kind of characteristics like Scalability, Dynamism and Heterogeneity. In this context we propose “Mobile Ad-Hoc Services Grid – MASGRID".

Keywords: Mobile Ad-Hoc Networks, Grid Computing, Resource Discovery, Routing

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2533 Environmental Interference Cancellation of Speech with the Radial Basis Function Networks: An Experimental Comparison

Authors: Nima Hatami

Abstract:

In this paper, we use Radial Basis Function Networks (RBFN) for solving the problem of environmental interference cancellation of speech signal. We show that the Second Order Thin- Plate Spline (SOTPS) kernel cancels the interferences effectively. For make comparison, we test our experiments on two conventional most used RBFN kernels: the Gaussian and First order TPS (FOTPS) basis functions. The speech signals used here were taken from the OGI Multi-Language Telephone Speech Corpus database and were corrupted with six type of environmental noise from NOISEX-92 database. Experimental results show that the SOTPS kernel can considerably outperform the Gaussian and FOTPS functions on speech interference cancellation problem.

Keywords: Environmental interference, interference cancellation of speech, Radial Basis Function networks, Gaussian and TPS kernels.

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2532 Load Modeling for Power Flow and Transient Stability Computer Studies at BAKHTAR Network

Authors: M. Sedighizadeh, A. Rezazadeh

Abstract:

A method has been developed for preparing load models for power flow and stability. The load modeling (LOADMOD) computer software transforms data on load class mix, composition, and characteristics into the from required for commonly–used power flow and transient stability simulation programs. Typical default data have been developed for load composition and characteristics. This paper defines LOADMOD software and describes the dynamic and static load modeling techniques used in this software and results of initial testing for BAKHTAR power system.

Keywords: Load Modelling, Static, Power Flow.

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