Abstract: In this paper, genetic algorithm (GA) is proposed for
the design of an optimization algorithm to achieve the bandwidth
allocation of ATM network. In Broadband ISDN, the ATM is a highbandwidth;
fast packet switching and multiplexing technique. Using
ATM it can be flexibly reconfigure the network and reassign the
bandwidth to meet the requirements of all types of services. By
dynamically routing the traffic and adjusting the bandwidth
assignment, the average packet delay of the whole network can be
reduced to a minimum. M/M/1 model can be used to analyze the
performance.
Abstract: Mobile Ad hoc Networks is an autonomous system of
mobile nodes connected by multi-hop wireless links without
centralized infrastructure support. As mobile communication gains
popularity, the need for suitable ad hoc routing protocols will
continue to grow. Efficient dynamic routing is an important research
challenge in such a network. Bandwidth constrained mobile devices
use on-demand approach in their routing protocols because of its
effectiveness and efficiency. Many researchers have conducted
numerous simulations for comparing the performance of these
protocols under varying conditions and constraints. Most of them are
not aware of MAC Protocols, which will impact the relative
performance of routing protocols considered in different network
scenarios. In this paper we investigate the choice of MAC protocols
affects the relative performance of ad hoc routing protocols under
different scenarios. We have evaluated the performance of these
protocols using NS2 simulations. Our results show that the
performance of routing protocols of ad hoc networks will suffer when
run over different MAC Layer protocols.
Abstract: This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW Photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three Radial Basis Function Neural Networks (RBFNN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated RBFNN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and non-linear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network.
Abstract: This paper studies a vital issue in wireless
communications, which is the transmission of images over Wireless
Personal Area Networks (WPANs) through the Bluetooth network. It
presents a simple method to improve the efficiency of error control
code of old Bluetooth versions over mobile WPANs through
Interleaved Error Control Code (IECC) technique. The encoded
packets are interleaved by simple block interleaver. Also, the paper
presents a chaotic interleaving scheme as a tool against bursts of
errors which depends on the chaotic Baker map. Also, the paper
proposes using the chaotic interleaver instead of traditional block
interleaver with Forward Error Control (FEC) scheme. A comparison
study between the proposed and standard techniques for image
transmission over a correlated fading channel is presented.
Simulation results reveal the superiority of the proposed chaotic
interleaving scheme to other schemes. Also, the superiority of FEC
with proposed chaotic interleaver to the conventional interleavers
with enhancing the security level with chaotic interleaving packetby-
packet basis.
Abstract: Transmission network expansion planning (TNEP) is a basic part of power system planning that determines where, when and how many new transmission lines should be added to the network. Up till now, various methods have been presented to solve the static transmission network expansion planning (STNEP) problem. But in all of these methods, lines adequacy rate has not been considered at the end of planning horizon, i.e., expanded network misses adequacy after some times and needs to be expanded again. In this paper, expansion planning has been implemented by merging lines loading parameter in the STNEP and inserting investment cost into the fitness function constraints using genetic algorithm. Expanded network will possess a maximum adequacy to provide load demand and also the transmission lines overloaded later. Finally, adequacy index could be defined and used to compare some designs that have different investment costs and adequacy rates. In this paper, the proposed idea has been tested on the Garvers network. The results show that the network will possess maximum efficiency economically.
Abstract: This paper describes the performance of TCP Vegas
over the wireless IPv6 network. The performance of TCP Vegas is
evaluated using network simulator (ns-2). The simulation experiment
investigates how packet spacing affects the network delay, network
throughput and network efficiency of TCP Vegas. Moreover, we
investigate how the variable FTP packet sizes affect the network
performance. The result of the simulation experiment shows that as
the packet spacing is implements, the network delay is reduces,
network throughput and network efficiency is optimizes. As the FTP
packet sizes increase, the ratio of delay per throughput decreases.
From the result of experiment, we propose the appropriate packet size
in transmitting file transfer protocol application using TCP Vegas
with packet spacing enhancement over wireless IPv6 environment in
ns-2. Additionally, we suggest the appropriate ratio in determining
the appropriate RTT and buffer size in a network.
Abstract: Message Passing Interface is widely used for Parallel
and Distributed Computing. MPICH and LAM are popular open
source MPIs available to the parallel computing community also
there are commercial MPIs, which performs better than MPICH etc.
In this paper, we discuss a commercial Message Passing Interface, CMPI
(C-DAC Message Passing Interface). C-MPI is an optimized
MPI for CLUMPS. It is found to be faster and more robust compared
to MPICH. We have compared performance of C-MPI and MPICH
on Gigabit Ethernet network.
Abstract: The purpose of this article is to introduce an advanced
system for the support of processing of medical image information,
and the terminology related to this system, which can be an important
element to a faster transition to a fully digitalized hospital.
The core of the system is a set of DICOM compliant applications
running over a dedicated computer network. The whole integrated
system creates a collaborative platform supporting daily routines in
the radiology community, developing communication channels,
supporting the exchange of information and special consultations
among various medical institutions as well as supporting medical
training for practicing radiologists and medical students. It gives the
users outside of hospitals the tools to work in almost the same
conditions as in the radiology departments.
Abstract: The paper presents a comparative performance of the
models developed to predict 28 days compressive strengths using
neural network techniques for data taken from literature (ANN-I) and
data developed experimentally for SCC containing bottom ash as
partial replacement of fine aggregates (ANN-II). The data used in the
models are arranged in the format of six and eight input parameters
that cover the contents of cement, sand, coarse aggregate, fly ash as
partial replacement of cement, bottom ash as partial replacement of
sand, water and water/powder ratio, superplasticizer dosage and an
output parameter that is 28-days compressive strength and
compressive strengths at 7 days, 28 days, 90 days and 365 days,
respectively for ANN-I and ANN-II. The importance of different
input parameters is also given for predicting the strengths at various
ages using neural network. The model developed from literature data
could be easily extended to the experimental data, with bottom ash as
partial replacement of sand with some modifications.
Abstract: Hospitals in southern Hualien teamed with the
Hypertension Joint Care Network. Working with the network, the
team provided a special designed health education to the individual
who had been identified as a hypertension patient in the outpatient
department. Some metabolism improvements achieved. This is a
retrospective study by purposively taking 106 patients from a hospital
between 2008 and 2010. Records of before and after education
intervention of the objects was collected and analyzed to see the how
the intervention affected the patients- hypertension control via clinical
parameter monitoring. The results showed that the clinical indicators,
the LDL-C, the cholesterol and the systolic blood pressure were
significantly improved. The study provides evidence for the
effectiveness of the network in controlling hypertension.
Abstract: Transmission network expansion planning (TNEP) is an important component of power system planning that its task is to minimize the network construction and operational cost while satisfying the demand increasing, imposed technical and economic conditions. Up till now, various methods have been presented to solve the static transmission network expansion planning (STNEP) problem. But in all of these methods, the lines adequacy rate has not been studied after the planning horizon, i.e. when the expanded network misses its adequacy and needs to be expanded again. In this paper, in order to take transmission lines condition after expansion in to account from the line loading view point, the adequacy of transmission network is considered for solution of STNEP problem. To obtain optimal network arrangement, a decimal codification genetic algorithm (DCGA) is being used for minimizing the network construction and operational cost. The effectiveness of the proposed idea is tested on the Garver's six-bus network. The results evaluation reveals that the annual worth of network adequacy has a considerable effect on the network arrangement. In addition, the obtained network, based on the DCGA, has lower investment cost and higher adequacy rate. Thus, the network satisfies the requirements of delivering electric power more safely and reliably to load centers.
Abstract: A wireless sensor network with a large number of tiny sensor nodes can be used as an effective tool for gathering data in various situations. One of the major issues in wireless sensor networks is developing an energy-efficient routing protocol which has a significant impact on the overall lifetime of the sensor network. In this paper, we propose a novel hierarchical with static clustering routing protocol called Energy-Efficient Protocol with Static Clustering (EEPSC). EEPSC, partitions the network into static clusters, eliminates the overhead of dynamic clustering and utilizes temporary-cluster-heads to distribute the energy load among high-power sensor nodes; thus extends network lifetime. We have conducted simulation-based evaluations to compare the performance of EEPSC against Low-Energy Adaptive Clustering Hierarchy (LEACH). Our experiment results show that EEPSC outperforms LEACH in terms of network lifetime and power consumption minimization.
Abstract: Tool Tracker is a client-server based application. It is essentially a catalogue of various network monitoring and management tools that are available online. There is a database maintained on the server side that contains the information about various tools. Several clients can access this information simultaneously and utilize this information. The various categories of tools considered are packet sniffers, port mappers, port scanners, encryption tools, and vulnerability scanners etc for the development of this application. This application provides a front end through which the user can invoke any tool from a central repository for the purpose of packet sniffing, port scanning, network analysis etc. Apart from the tool, its description and the help files associated with it would also be stored in the central repository. This facility will enable the user to view the documentation pertaining to the tool without having to download and install the tool. The application would update the central repository with the latest versions of the tools. The application would inform the user about the availability of a newer version of the tool currently being used and give the choice of installing the newer version to the user. Thus ToolTracker provides any network administrator that much needed abstraction and ease-ofuse with respect to the tools that he can use to efficiently monitor a network.
Abstract: We have developed a distributed asynchronous Web
based training system. In order to improve the scalability and robustness
of this system, all contents and a function are realized on
mobile agents. These agents are distributed to computers, and they
can use a Peer to Peer network that modified Content-Addressable
Network. In this system, all computers offer the function and exercise
by themselves. However, the system that all computers do the same
behavior is not realistic. In this paper, as a solution of this issue,
we present an e-Learning system that is composed of computers
of different participation types. Enabling the computer of different
participation types will improve the convenience of the system.
Abstract: Target tracking and localization are important applications
in wireless sensor networks. In these applications, sensor nodes
collectively monitor and track the movement of a target. They have
limited energy supplied by batteries, so energy efficiency is essential
for sensor networks. Most existing target tracking protocols need to
wake up sensors periodically to perform tracking. Some unnecessary
energy waste is thus introduced. In this paper, an energy efficient
protocol for target localization is proposed. In order to preserve
energy, the protocol fixes the number of sensors for target tracking,
but it retains the quality of target localization in an acceptable
level. By selecting a set of sensors for target localization, the other
sensors can sleep rather than periodically wake up to track the target.
Simulation results show that the proposed protocol saves a significant
amount of energy and also prolongs the network lifetime.
Abstract: This paper is a continuation of our daily energy peak load forecasting approach using our modified network which is part of the recurrent networks family and is called feed forward and feed back multi context artificial neural network (FFFB-MCANN). The inputs to the network were exogenous variables such as the previous and current change in the weather components, the previous and current status of the day and endogenous variables such as the past change in the loads. Endogenous variable such as the current change in the loads were used on the network output. Experiment shows that using endogenous and exogenous variables as inputs to the FFFBMCANN rather than either exogenous or endogenous variables as inputs to the same network produces better results. Experiments show that using the change in variables such as weather components and the change in the past load as inputs to the FFFB-MCANN rather than the absolute values for the weather components and past load as inputs to the same network has a dramatic impact and produce better accuracy.
Abstract: Wireless Sensor Networks (WSNs) are wireless
networks consisting of number of tiny, low cost and low power
sensor nodes to monitor various physical phenomena like
temperature, pressure, vibration, landslide detection, presence of any
object, etc. The major limitation in these networks is the use of nonrechargeable
battery having limited power supply. The main cause of
energy consumption WSN is communication subsystem. This paper
presents an efficient grid formation/clustering strategy known as Grid
based level Clustering and Aggregation of Data (GCAD). The
proposed clustering strategy is simple and scalable that uses low duty
cycle approach to keep non-CH nodes into sleep mode thus reducing
energy consumption. Simulation results demonstrate that our
proposed GCAD protocol performs better in various performance
metrics.
Abstract: Using mini modules of Tmotes, it is possible to automate a small personal area network. This idea can be extended to large networks too by implementing multi-hop routing. Linking the various Tmotes using Programming languages like Nesc, Java and having transmitter and receiver sections, a network can be monitored. It is foreseen that, depending on the application, a long range at a low data transfer rate or average throughput may be an acceptable trade-off. To reduce the overall costs involved, an optimum number of Tmotes to be used under various conditions (Indoor/Outdoor) is to be deduced. By analyzing the data rates or throughputs at various locations of Tmotes, it is possible to deduce an optimal number of Tmotes for a specific network. This paper deals with the determination of optimum distances to reduce the cost and increase the reliability of the entire sensor network with Wireless Local Loop (WLL) capability.
Abstract: In this paper we present a method for gene ranking
from DNA microarray data. More precisely, we calculate the correlation
networks, which are unweighted and undirected graphs, from
microarray data of cervical cancer whereas each network represents
a tissue of a certain tumor stage and each node in the network
represents a gene. From these networks we extract one tree for
each gene by a local decomposition of the correlation network. The
interpretation of a tree is that it represents the n-nearest neighbor
genes on the n-th level of a tree, measured by the Dijkstra distance,
and, hence, gives the local embedding of a gene within the correlation
network. For the obtained trees we measure the pairwise similarity
between trees rooted by the same gene from normal to cancerous
tissues. This evaluates the modification of the tree topology due to
progression of the tumor. Finally, we rank the obtained similarity
values from all tissue comparisons and select the top ranked genes.
For these genes the local neighborhood in the correlation networks
changes most between normal and cancerous tissues. As a result
we find that the top ranked genes are candidates suspected to be
involved in tumor growth and, hence, indicates that our method
captures essential information from the underlying DNA microarray
data of cervical cancer.
Abstract: The most common result of analysis of highthroughput
data in molecular biology represents a global list of
genes, ranked accordingly to a certain score. The score can be a
measure of differential expression. Recent work proposed a new
method for selecting a number of genes in a ranked gene list from
microarray gene expression data such that this set forms the
Optimally Functionally Enriched Network (OFTEN), formed by
known physical interactions between genes or their products. Here
we present calculation results of relative connectivity of genes from
META-OFTEN network and tentative biological interpretation of the
most reproducible signal. The relative connectivity and
inbetweenness values of genes from META-OFTEN network were
estimated.