Abstract: Access control is a critical security service in Wire- less
Sensor Networks (WSNs). To prevent malicious nodes from joining
the sensor network, access control is required. On one hand, WSN
must be able to authorize and grant users the right to access to the
network. On the other hand, WSN must organize data collected by
sensors in such a way that an unauthorized entity (the adversary)
cannot make arbitrary queries. This restricts the network access only
to eligible users and sensor nodes, while queries from outsiders will
not be answered or forwarded by nodes. In this paper we presentee
different access control schemes so as to ?nd out their objectives,
provision, communication complexity, limits, etc. Using the node
density parameter, we also provide a comparison of these proposed
access control algorithms based on the network topology which can
be flat or hierarchical.
Abstract: The main goal of the present work is to decrease the
computational burden for optimum design of steel frames with
frequency constraints using a new type of neural networks called
Wavelet Neural Network. It is contested to train a suitable neural
network for frequency approximation work as the analysis program.
The combination of wavelet theory and Neural Networks (NN)
has lead to the development of wavelet neural networks.
Wavelet neural networks are feed-forward networks using
wavelet as activation function. Wavelets are mathematical
functions within suitable inner parameters, which help them to
approximate arbitrary functions. WNN was used to predict the
frequency of the structures. In WNN a RAtional function with
Second order Poles (RASP) wavelet was used as a transfer
function. It is shown that the convergence speed was faster
than other neural networks. Also comparisons of WNN with
the embedded Artificial Neural Network (ANN) and with
approximate techniques and also with analytical solutions are
available in the literature.
Abstract: In this paper the reference current for Voltage Source
Converter (VSC) of the Shunt Active Power Filter (SAPF) is
generated using Synchronous Reference Frame method,
incorporating the PI controller with anti-windup scheme. The
proposed method improves the harmonic filtering by compensating
the winding up phenomenon caused by the integral term of the PI
controller.
Using Reference Frame Transformation, the current is transformed
from om a - b - c stationery frame to rotating 0 - d - q frame. Using
the PI controller, the current in the 0 - d - q frame is controlled to
get the desired reference signal. A controller with integral action
combined with an actuator that becomes saturated can give some
undesirable effects. If the control error is so large that the integrator
saturates the actuator, the feedback path becomes ineffective because
the actuator will remain saturated even if the process output changes.
The integrator being an unstable system may then integrate to a very
large value, the phenomenon known as integrator windup.
Implementing the integrator anti-windup circuit turns off the
integrator action when the actuator saturates, hence improving the
performance of the SAPF and dynamically compensating harmonics
in the power network. In this paper the system performance is
examined with Shunt Active Power Filter simulation model.
Abstract: Most routing protocols (DSR, AODV etc.) that have
been designed for wireless adhoc networks incorporate the broadcasting
operation in their route discovery scheme. Probabilistic broadcasting
techniques have been developed to optimize the broadcast operation
which is otherwise very expensive in terms of the redundancy
and the traffic it generates. In this paper we have explored percolation
theory to gain a different perspective on probabilistic broadcasting
schemes which have been actively researched in the recent years.
This theory has helped us estimate the value of broadcast probability
in a wireless adhoc network as a function of the size of the network.
We also show that, operating at those optimal values of broadcast
probability there is at least 25-30% reduction in packet regeneration
during successful broadcasting.
Abstract: Happening of Ferroresonance phenomenon is one of the reasons of consuming and ruining transformers, so recognition of Ferroresonance phenomenon has a special importance. A novel method for classification of Ferroresonance presented in this paper. Using this method Ferroresonance can be discriminate from other transients such as capacitor switching, load switching, transformer switching. Wavelet transform is used for decomposition of signals and Competitive Neural Network used for classification. Ferroresonance data and other transients was obtained by simulation using EMTP program. Using Daubechies wavelet transform signals has been decomposed till six levels. The energy of six detailed signals that obtained by wavelet transform are used for training and trailing Competitive Neural Network. Results show that the proposed procedure is efficient in identifying Ferroresonance from other events.
Abstract: The heuristic decision rules used for project
scheduling will vary depending upon the project-s size, complexity,
duration, personnel, and owner requirements. The concept of project
complexity has received little detailed attention. The need to
differentiate between easy and hard problem instances and the
interest in isolating the fundamental factors that determine the
computing effort required by these procedures inspired a number of
researchers to develop various complexity measures.
In this study, the most common measures of project complexity are
presented. A new measure of project complexity is developed. The
main privilege of the proposed measure is that, it considers size,
shape and logic characteristics, time characteristics, resource
demands and availability characteristics as well as number of critical
activities and critical paths. The degree of sensitivity of the proposed
measure for complexity of project networks has been tested and
evaluated against the other measures of complexity of the considered
fifty project networks under consideration in the current study. The
developed measure showed more sensitivity to the changes in the
network data and gives accurate quantified results when comparing
the complexities of networks.
Abstract: Next generation networks with the idea of convergence of service and control layer in existing networks (fixed, mobile and data) and with the intention of providing services in an integrated network, has opened new horizon for telecom operators. On the other hand, economic problems have caused operators to look for new source of income including consider new services, subscription of more users and their promotion in using morenetwork resources and easy participation of service providers or 3rd party operators in utilizing networks. With this requirement, an architecture based on next generation objectives for service layer is necessary. In this paper, a new architecture based on IMS model explains participation of 3rd party operators in creation and implementation of services on an integrated telecom network.
Abstract: The previous proposed evacuation routing approaches usually divide the space into multiple interlinked zones. However, it may be harder to clearly and objectively define the margins of each zone. This paper proposes an approach that connects locations of necessary guidance into a spatial network. In doing so, evacuation routes can be constructed based on the links between starting points, turning nodes, and terminal points. This approach more conforms to the real-life evacuation behavior. The feasibility of the proposed approach is evaluated through a case of one floor in a hospital building. Results indicate that the proposed approach provides valuable suggestions for evacuation planning.
Abstract: Today-s manufacturing companies are facing multiple and dynamic customer-supplier-relationships embedded in nonhierarchical production networks. This complex environment leads to problems with delivery reliability and wasteful turbulences throughout the entire network. This paper describes an operational model based on a theoretical framework which improves delivery reliability of each individual customer-supplier-relationship within non-hierarchical production networks of the European machinery and equipment industry. By developing a non-centralized coordination mechanism based on determining the value of delivery reliability and derivation of an incentive system for suppliers the number of in time deliveries can be increased and thus the turbulences in the production network smoothened. Comparable to an electronic stock exchange the coordination mechanism will transform the manual and nontransparent process of determining penalties for delivery delays into an automated and transparent market mechanism creating delivery reliability.
Abstract: In this paper, we present the video quality measure
estimation via a neural network. This latter predicts MOS (mean
opinion score) by providing height parameters extracted from
original and coded videos. The eight parameters that are used are: the
average of DFT differences, the standard deviation of DFT
differences, the average of DCT differences, the standard deviation
of DCT differences, the variance of energy of color, the luminance
Y, the chrominance U and the chrominance V. We chose Euclidean
Distance to make comparison between the calculated and estimated
output.
Abstract: This paper uses the radial basis function neural
network (RBFNN) for system identification of nonlinear systems.
Five nonlinear systems are used to examine the activity of RBFNN in
system modeling of nonlinear systems; the five nonlinear systems are
dual tank system, single tank system, DC motor system, and two
academic models. The feed forward method is considered in this
work for modelling the non-linear dynamic models, where the KMeans
clustering algorithm used in this paper to select the centers of
radial basis function network, because it is reliable, offers fast
convergence and can handle large data sets. The least mean square
method is used to adjust the weights to the output layer, and
Euclidean distance method used to measure the width of the Gaussian
function.
Abstract: The paper compares different channel models used for
modeling Broadband Power-Line Communication (BPLC) system.
The models compared are Zimmermann and Dostert, Philipps,
Anatory et al and Anatory et al generalized Transmission Line (TL)
model. The validity of each model was compared in time domain
with ATP-EMTP software which uses transmission line approach. It
is found that for a power-line network with minimum number of
branches all the models give similar signal/pulse time responses
compared with ATP-EMTP software; however, Zimmermann and
Dostert model indicates the same amplitude but different time delay.
It is observed that when the numbers of branches are increased only
generalized TL theory approach results are comparable with ATPEMTP
results. Also the Multi-Carrier Spread Spectrum (MC-SS)
system was applied to check the implication of such behavior on the
modulation schemes. It is observed that using Philipps on the
underground cable can predict the performance up to 25dB better
than other channel models which can misread the actual performance
of the system. Also modified Zimmermann and Dostert under
multipath can predict a better performance of about 5dB better than
the actual predicted by Generalized TL theory. It is therefore
suggested for a realistic BPLC system design and analyses the model
based on generalized TL theory be used.
Abstract: A complex valued neural network is a neural network
which consists of complex valued input and/or weights and/or thresholds
and/or activation functions. Complex-valued neural networks
have been widening the scope of applications not only in electronics
and informatics, but also in social systems. One of the most important
applications of the complex valued neural network is in signal
processing. In Neural networks, generalized mean neuron model
(GMN) is often discussed and studied. The GMN includes a new
aggregation function based on the concept of generalized mean of all
the inputs to the neuron. This paper aims to present exhaustive results
of using Generalized Mean Neuron model in a complex-valued neural
network model that uses the back-propagation algorithm (called
-Complex-BP-) for learning. Our experiments results demonstrate the
effectiveness of a Generalized Mean Neuron Model in a complex
plane for signal processing over a real valued neural network. We
have studied and stated various observations like effect of learning
rates, ranges of the initial weights randomly selected, error functions
used and number of iterations for the convergence of error required on
a Generalized Mean neural network model. Some inherent properties
of this complex back propagation algorithm are also studied and
discussed.
Abstract: Recently global concerns for the energy security have
steadily been on the increase and are expected to become a major
issue over the next few decades. Energy security refers to a resilient
energy system. This resilient system would be capable of
withstanding threats through a combination of active, direct security
measures and passive or more indirect measures such as redundancy,
duplication of critical equipment, diversity in fuel, other sources of
energy, and reliance on less vulnerable infrastructure. Threats and
disruptions (disturbances) to one part of the energy system affect
another. The paper presents methodology in theoretical background
about energy system as an interconnected network and energy supply
disturbances impact to the network. The proposed methodology uses
a network flow approach to develop mathematical model of the
energy system network as the system of nodes and arcs with energy
flowing from node to node along paths in the network.
Abstract: Grid networks provide the ability to perform higher throughput computing by taking advantage of many networked computer-s resources to solve large-scale computation problems. As the popularity of the Grid networks has increased, there is a need to efficiently distribute the load among the resources accessible on the network. In this paper, we present a stochastic network system that gives a distributed load-balancing scheme by generating almost regular networks. This network system is self-organized and depends only on local information for load distribution and resource discovery. The in-degree of each node is refers to its free resources, and job assignment and resource discovery processes required for load balancing is accomplished by using fitted random sampling. Simulation results show that the generated network system provides an effective, scalable, and reliable load-balancing scheme for the distributed resources accessible on Grid networks.
Abstract: IP multicasting is a key technology for many existing and emerging applications on the Internet. Furthermore, with increasing popularity of wireless devices and mobile equipment, it is necessary to determine the best way to provide this service in a wireless environment. IETF Mobile IP, that provides mobility for hosts in IP networks, proposes two approaches for mobile multicasting, namely, remote subscription (MIP-RS) and bi-directional tunneling (MIP-BT). In MIP-RS, a mobile host re-subscribes to the multicast groups each time it moves to a new foreign network. MIP-RS suffers from serious packet losses while mobile host handoff occurs. In MIP-BT, mobile hosts send and receive multicast packets by way of their home agents (HAs), using Mobile IP tunnels. Therefore, it suffers from inefficient routing and wastage of system resources. In this paper, we propose a protocol called Mobile Multicast support using Old Foreign Agent (MMOFA) for Mobile Hosts. MMOFA is derived from MIP-RS and with the assistance of Mobile host's Old foreign agent, routes the missing datagrams due to handoff in adjacent network via tunneling. Also, we studied the performance of the proposed protocol by simulation under ns-2.27. The results demonstrate that MMOFA has optimal routing efficiency and low delivery cost, as compared to other approaches.
Abstract: Bluetooth is a personal wireless communication
technology and is being applied in many scenarios. It is an emerging
standard for short range, low cost, low power wireless access
technology. Current existing MAC (Medium Access Control)
scheduling schemes only provide best-effort service for all masterslave
connections. It is very challenging to provide QoS (Quality of
Service) support for different connections due to the feature of
Master Driven TDD (Time Division Duplex). However, there is no
solution available to support both delay and bandwidth guarantees
required by real time applications. This paper addresses the issue of
how to enhance QoS support in a Bluetooth piconet. The Bluetooth
specification proposes a Round Robin scheduler as possible solution
for scheduling the transmissions in a Bluetooth Piconet. We propose
an algorithm which will reduce the bandwidth waste and enhance the
efficiency of network. We define token counters to estimate traffic of
real-time slaves. To increase bandwidth utilization, a back-off
mechanism is then presented for best-effort slaves to decrease the
frequency of polling idle slaves. Simulation results demonstrate that
our scheme achieves better performance over the Round Robin
scheduling.
Abstract: In this paper, novel techniques in increasing the accuracy
and speed of convergence of a Feed forward Back propagation
Artificial Neural Network (FFBPNN) with polynomial activation
function reported in literature is presented. These technique was
subsequently used to determine the coefficients of Autoregressive
Moving Average (ARMA) and Autoregressive (AR) system. The
results obtained by introducing sequential and batch method of weight
initialization, batch method of weight and coefficient update, adaptive
momentum and learning rate technique gives more accurate result
and significant reduction in convergence time when compared t the
traditional method of back propagation algorithm, thereby making
FFBPNN an appropriate technique for online ARMA coefficient
determination.
Abstract: Focusing on the environmental issues, including the reduction of scrap and consumer residuals, along with the benefiting from the economic value during the life cycle of goods/products leads the companies to have an important competitive approach. The aim of this paper is to present a new mixed nonlinear facility locationallocation model in recycling collection networks by considering multi-echelon, multi-suppliers, multi-collection centers and multifacilities in the recycling network. To make an appropriate decision in reality, demands, returns, capacities, costs and distances, are regarded uncertain in our model. For this purpose, a fuzzy mathematical programming-based possibilistic approach is introduced as a solution methodology from the recent literature to solve the proposed mixed-nonlinear programming model (MNLP). The computational experiments are provided to illustrate the applicability of the designed model in a supply chain environment and to help the decision makers to facilitate their analysis.
Abstract: Wireless ad hoc nodes are freely and dynamically
self-organize in communicating with others. Each node can act as
host or router. However it actually depends on the capability of
nodes in terms of its current power level, signal strength, number
of hops, routing protocol, interference and others. In this research,
a study was conducted to observe the effect of hops count over
different network topologies that contribute to TCP Congestion
Control performance degradation. To achieve this objective, a
simulation using NS-2 with different topologies have been
evaluated. The comparative analysis has been discussed based on
standard observation metrics: throughput, delay and packet loss
ratio. As a result, there is a relationship between types of topology
and hops counts towards the performance of ad hoc network. In
future, the extension study will be carried out to investigate the
effect of different error rate and background traffic over same
topologies.