Abstract: For the last decade, researchers have started to focus
their interest on Multicast Group Key Management Framework. The
central research challenge is secure and efficient group key
distribution. The present paper is based on the Bit model based
Secure Multicast Group key distribution scheme using the most
popular absolute encoder output type code named Gray Code. The
focus is of two folds. The first fold deals with the reduction of
computation complexity which is achieved in our scheme by
performing fewer multiplication operations during the key updating
process. To optimize the number of multiplication operations, an
O(1) time algorithm to multiply two N-bit binary numbers which
could be used in an N x N bit-model of reconfigurable mesh is used
in this proposed work. The second fold aims at reducing the amount
of information stored in the Group Center and group members while
performing the update operation in the key content. Comparative
analysis to illustrate the performance of various key distribution
schemes is shown in this paper and it has been observed that this
proposed algorithm reduces the computation and storage complexity
significantly. Our proposed algorithm is suitable for high
performance computing environment.
Abstract: Multiprocessor task scheduling problem for dependent
and independent tasks is computationally complex problem. Many
methods are proposed to achieve optimal running time. As the
multiprocessor task scheduling is NP hard in nature, therefore, many
heuristics are proposed which have improved the makespan of the
problem. But due to problem specific nature, the heuristic method
which provide best results for one problem, might not provide good
results for another problem. So, Simulated Annealing which is meta
heuristic approach is considered. It can be applied on all types of
problems. However, due to many runs, meta heuristic approach takes
large computation time. Hence, the hybrid approach is proposed by
combining the Duplication Scheduling Heuristic and Simulated
Annealing (SA) and the makespan results of Simple Simulated
Annealing and Hybrid approach are analyzed.
Abstract: Lateral Geniculate Nucleus (LGN) is the relay center
in the visual pathway as it receives most of the input information
from retinal ganglion cells (RGC) and sends to visual cortex. Low
threshold calcium currents (IT) at the membrane are the unique
indicator to characterize this firing functionality of the LGN neurons
gained by the RGC input. According to the LGN functional
requirements such as functional mapping of RGC to LGN, the
morphologies of the LGN neurons were developed. During the
neurological disorders like glaucoma, the mapping between RGC and
LGN is disconnected and hence stimulating LGN electrically using
deep brain electrodes can restore the functionalities of LGN. A
computational model was developed for simulating the LGN neurons
with three predominant morphologies each representing different
functional mapping of RGC to LGN. The firings of action potentials
at LGN neuron due to IT were characterized by varying the
stimulation parameters, morphological parameters and orientation. A
wide range of stimulation parameters (stimulus amplitude, duration
and frequency) represents the various strengths of the electrical
stimulation with different morphological parameters (soma size,
dendrites size and structure). The orientation (0-1800) of LGN
neuron with respect to the stimulating electrode represents the angle
at which the extracellular deep brain stimulation towards LGN
neuron is performed. A reduced dendrite structure was used in the
model using Bush–Sejnowski algorithm to decrease the
computational time while conserving its input resistance and total
surface area. The major finding is that an input potential of 0.4 V is
required to produce the action potential in the LGN neuron which is
placed at 100 μm distance from the electrode. From this study, it can
be concluded that the neuroprostheses under design would need to
consider the capability of inducing at least 0.4V to produce action
potentials in LGN.
Abstract: Routing in adhoc networks is a challenge as nodes are
mobile, and links are constantly created and broken. Present ondemand
adhoc routing algorithms initiate route discovery after a path
breaks, incurring significant cost to detect disconnection and
establish a new route. Specifically, when a path is about to be broken,
the source is warned of the likelihood of a disconnection. The source
then initiates path discovery early, avoiding disconnection totally. A
path is considered about to break when link availability decreases.
This study modifies Adhoc On-demand Multipath Distance Vector
routing (AOMDV) so that route handoff occurs through link
availability estimation.
Abstract: In this paper, the unstable angle of attack of a
FOXTROT aircraft is controlled by using Genetic Algorithm based
flight controller and the result is compared with the conventional
techniques like Tyreus-Luyben (TL), Ziegler-Nichols (ZN) and
Interpolation Rule (IR) for tuning the PID controller. In addition, the
performance indices like Mean Square Error (MSE), Integral Square
Error (ISE), and Integral Absolute Time Error (IATE) etc. are
improved by using Genetic Algorithm. It was established that the
error by using GA is very less as compared to the conventional
techniques thereby improving the performance indices of the
dynamic system.
Abstract: Given the increase in the number of e-commerce sites,
the number of competitors has become very important. This means
that companies have to take appropriate decisions in order to meet the
expectations of their customers and satisfy their needs. In this paper,
we present a case study of applying LRFM (length, recency,
frequency and monetary) model and clustering techniques in the
sector of electronic commerce with a view to evaluating customers’
values of the Moroccan e-commerce websites and then developing
effective marketing strategies. To achieve these objectives, we adopt
LRFM model by applying a two-stage clustering method. In the first
stage, the self-organizing maps method is used to determine the best
number of clusters and the initial centroid. In the second stage, kmeans
method is applied to segment 730 customers into nine clusters
according to their L, R, F and M values. The results show that the
cluster 6 is the most important cluster because the average values of
L, R, F and M are higher than the overall average value. In addition,
this study has considered another variable that describes the mode of
payment used by customers to improve and strengthen clusters’
analysis. The clusters’ analysis demonstrates that the payment method is
one of the key indicators of a new index which allows to assess the
level of customers’ confidence in the company's Website.
Abstract: The customers use the best compromise criterion
between price and quality of service (QoS) to select or change
their Service Provider (SP). The SPs share the same market and
are competing to attract more customers to gain more profit. Due
to the divergence of SPs interests, we believe that this situation is a
non-cooperative game of price and QoS. The game converges to an
equilibrium position known Nash Equilibrium (NE). In this work, we
formulate a game theoretic framework for the dynamical behaviors
of SPs. We use Genetic Algorithms (GAs) to find the price and
QoS strategies that maximize the profit for each SP and illustrate
the corresponding strategy in NE. In order to quantify how this NE
point is performant, we perform a detailed analysis of the price of
anarchy induced by the NE solution. Finally, we provide an extensive
numerical study to point out the importance of considering price and
QoS as a joint decision parameter.
Abstract: Direct Torque Control (DTC) is an AC drive control
method especially designed to provide fast and robust responses. In
this paper a progressive algorithm for direct torque control of threephase
induction drive system supplied by photovoltaic arrays using
voltage source inverter to control motor torque and flux with
maximum power point tracking at different level of insolation is
presented. Experimental results of the new DTC method obtained by
an experimental rapid prototype system for drives are presented.
Simulation and experimental results confirm that the proposed system
gives quick, robust torque and speed responses at constant switching
frequencies.
Abstract: This article proposes a hybrid algorithm for spectrum
allocation in cognitive radio networks based on the algorithms
Analytical Hierarchical Process (AHP) and Technique for Order of
Preference by Similarity to Ideal Solution (TOPSIS) to improve the
performance of the spectrum mobility of secondary users in cognitive
radio networks. To calculate the level of performance of the proposed algorithm a
comparative analysis between the proposed AHP-TOPSIS, Grey
Relational Analysis (GRA) and Multiplicative Exponent Weighting
(MEW) algorithm is performed. Four evaluation metrics are used.
These metrics are accumulative average of failed handoffs,
accumulative average of handoffs performed, accumulative average
of transmission bandwidth, and accumulative average of the
transmission delay. The results of the comparison show that AHP-TOPSIS Algorithm
provides 2.4 times better performance compared to a GRA Algorithm
and, 1.5 times better than the MEW Algorithm.
Abstract: Wireless Sensor Network (WSN) routing is complex
due to its dynamic nature, computational overhead, limited battery
life, non-conventional addressing scheme, self-organization, and
sensor nodes limited transmission range. An energy efficient routing
protocol is a major concern in WSN. LEACH is a hierarchical WSN
routing protocol to increase network life. It performs self-organizing
and re-clustering functions for each round. This study proposes a
better sensor networks cluster head selection for efficient data
aggregation. The algorithm is based on Tabu search.
Abstract: The use of solar energy as a source for pumping water
is one of the promising areas in the photovoltaic (PV) application.
The energy of photovoltaic pumping systems (PVPS) can be widely
improved by employing an MPPT algorithm. This will lead
consequently to maximize the electrical motor speed of the system.
This paper presents a modified incremental conductance (IncCond)
MPPT algorithm with direct control method applied to a standalone
PV pumping system. The influence of the algorithm parameters on
system behavior is investigated and compared with the traditional
(INC) method. The studied system consists of a PV panel, a DC-DC
boost converter, and a PMDC motor-pump. The simulation of the
system by MATLAB-SIMULINK is carried out. Simulation results
found are satisfactory.
Abstract: This paper is meant to analyze the ranking of
University of Malaysia Terengganu, UMT’s website in the World
Wide Web. There are only few researches have been done on
comparing the ranking of universities’ websites so this research will
be able to determine whether the existing UMT’s website is serving
its purpose which is to introduce UMT to the world. The ranking is
based on hub and authority values which are accordance to the
structure of the website. These values are computed using two websearching
algorithms, HITS and SALSA. Three other universities’
websites are used as the benchmarks which are UM, Harvard and
Stanford. The result is clearly showing that more work has to be done
on the existing UMT’s website where important pages according to
the benchmarks, do not exist in UMT’s pages. The ranking of UMT’s
website will act as a guideline for the web-developer to develop a
more efficient website.
Abstract: This paper addresses the problem of offline path
planning for Unmanned Aerial Vehicles (UAVs) in complex threedimensional
environment with obstacles, which is modelled by 3D
Cartesian grid system. Path planning for UAVs require the
computational intelligence methods to move aerial vehicles along the
flight path effectively to target while avoiding obstacles. In this paper
Modified Particle Swarm Optimization (MPSO) algorithm is applied
to generate the optimal collision free 3D flight path for UAV. The
simulations results clearly demonstrate effectiveness of the proposed
algorithm in guiding UAV to the final destination by providing
optimal feasible path quickly and effectively.
Abstract: Brain-Computer Interfaces (BCIs) measure brain
signals activity, intentionally and unintentionally induced by users,
and provides a communication channel without depending on the
brain’s normal peripheral nerves and muscles output pathway.
Feature Selection (FS) is a global optimization machine learning
problem that reduces features, removes irrelevant and noisy data
resulting in acceptable recognition accuracy. It is a vital step
affecting pattern recognition system performance. This study presents
a new Binary Particle Swarm Optimization (BPSO) based feature
selection algorithm. Multi-layer Perceptron Neural Network
(MLPNN) classifier with backpropagation training algorithm and
Levenberg-Marquardt training algorithm classify selected features.
Abstract: Wireless Sensor Networks (WSNs), which sense
environmental data with battery-powered nodes, require multi-hop
communication. This power-demanding task adds an extra workload
that is unfairly distributed across the network. As a result, nodes run
out of battery at different times: this requires an impractical
individual node maintenance scheme. Therefore we investigate a new
Cooperative Sensing approach that extends the WSN operational life
and allows a more practical network maintenance scheme (where all
nodes deplete their batteries almost at the same time). We propose a
novel cooperative algorithm that derives a piecewise representation
of the sensed signal while controlling approximation accuracy.
Simulations show that our algorithm increases WSN operational life
and spreads communication workload evenly. Results convey a
counterintuitive conclusion: distributing workload fairly amongst
nodes may not decrease the network power consumption and yet
extend the WSN operational life. This is achieved as our cooperative
approach decreases the workload of the most burdened cluster in the
network.
Abstract: In this paper genetic based test data compression is
targeted for improving the compression ratio and for reducing the
computation time. The genetic algorithm is based on extended pattern
run-length coding. The test set contains a large number of X value
that can be effectively exploited to improve the test data
compression. In this coding method, a reference pattern is set and its
compatibility is checked. For this process, a genetic algorithm is
proposed to reduce the computation time of encoding algorithm. This
coding technique encodes the 2n compatible pattern or the inversely
compatible pattern into a single test data segment or multiple test data
segment. The experimental result shows that the compression ratio
and computation time is reduced.
Abstract: In this paper, a novel fuzzy approach is developed
while solving the Dynamic Routing and Wavelength Assignment
(DRWA) problem in optical networks with Wavelength Division
Multiplexing (WDM). In this work, the effect of nonlinear and linear
impairments such as Four Wave Mixing (FWM) and amplifier
spontaneous emission (ASE) noise are incorporated respectively. The
novel algorithm incorporates fuzzy logic controller (FLC) to reduce
the effect of FWM noise and ASE noise on a requested lightpath
referred in this work as FWM aware fuzzy dynamic routing and
wavelength assignment algorithm. The FWM crosstalk products and
the static FWM noise power per link are pre computed in order to
reduce the set up time of a requested lightpath, and stored in an
offline database. These are retrieved during the setting up of a
lightpath and evaluated online taking the dynamic parameters like
cost of the links into consideration.
Abstract: In this paper, we propose two algorithms to optimally
solve makespan and total completion time scheduling problems with
learning effect and job dependent delivery times in a single machine
environment. The delivery time is the extra time to eliminate adverse
effect between the main processing and delivery to the customer. In
this paper, we introduce the job dependent delivery times for some
single machine scheduling problems with position dependent learning
effect, which are makespan are total completion. The results with
respect to two algorithms proposed for solving of the each problem
are compared with LINGO solutions for 50-jobs, 100-jobs and 150-
jobs problems. The proposed algorithms can find the same results in
shorter time.
Abstract: The relationship dependence between RSS and distance
in an enclosed environment is an important consideration because it is
a factor that can influence the reliability of any localization algorithm
founded on RSS. Several algorithms effectively reduce the variance of
RSS to improve localization or accuracy performance. Our proposed
algorithm essentially avoids this pitfall and consequently, its high
adaptability in the face of erratic radio signal. Using 3 anchors in
close proximity of each other, we are able to establish that RSS can be
used as reliable indicator for localization with an acceptable degree of
accuracy. Inherent in this concept, is the ability for each prospective
anchor to validate (guarantee) the position or the proximity of the
other 2 anchors involved in the localization and vice versa. This
procedure ensures that the uncertainties of radio signals due to
multipath effects in enclosed environments are minimized. A major
driver of this idea is the implicit topological relationship among
sensors due to raw radio signal strength. The algorithm is an area
based algorithm; however, it does not trade accuracy for precision
(i.e the size of the returned area).
Abstract: Digital cameras to reduce cost, use an image sensor to
capture color images. Color Filter Array (CFA) in digital cameras
permits only one of the three primary (red-green-blue) colors to be
sensed in a pixel and interpolates the two missing components
through a method named demosaicking. Captured data is interpolated
into a full color image and compressed in applications. Color
interpolation before compression leads to data redundancy. This
paper proposes a new Vector Quantization (VQ) technique to
construct a VQ codebook with Differential Evolution (DE)
Algorithm. The new technique is compared to conventional Linde-
Buzo-Gray (LBG) method.