Abstract: Wavelength Division Multiplexing (WDM)
technology is the most promising technology for the proper
utilization of huge raw bandwidth provided by an optical fiber. One
of the key problems in implementing the all-optical WDM network is
the packet contention. This problem can be solved by several
different techniques. In time domain approach the packet contention
can be reduced by incorporating Fiber Delay Lines (FDLs) as optical
buffer in the switch architecture. Different types of buffering
architectures are reported in literatures. In the present paper a
comparative performance analysis of three most popular FDL
architectures are presented in order to obtain the best contention
resolution performance. The analysis is further extended to consider
the effect of different fiber non-linearities on the network
performance.
Abstract: In this paper, a prototype PEM fuel cell vehicle
integrated with a 1 kW air-blowing proton exchange membrane fuel
cell (PEMFC) stack as a main power sources has been developed for
a lightweight cruising vehicle. The test vehicle is equipped with a
PEM fuel cell system that provides electric power to a brushed DC
motor. This vehicle was designed to compete with industrial
lightweight vehicle with the target of consuming least amount of
energy and high performance. Individual variations in driving style
have a significant impact on vehicle energy efficiency and it is well
established from the literature. The primary aim of this study was to
assesses the power and fuel consumption of a hydrogen fuel cell
vehicle operating at three difference driving technique (i.e. 25 km/h
constant speed, 22-28 km/h speed range, 20-30 km/h speed range).
The goal is to develop the best driving strategy to maximize
performance and minimize fuel consumption for the vehicle system.
The relationship between power demand and hydrogen consumption
has also been discussed. All the techniques can be evaluated and
compared on broadly similar terms. Automatic intelligent controller
for driving prototype fuel cell vehicle on different obstacle while
maintaining all systems at maximum efficiency was used. The result
showed that 25 km/h constant speed was identified for optimal
driving with less fuel consumption.
Abstract: Brushless DC motors (BLDC) are widely used in
industrial areas. The BLDC motors are driven either by indirect ACAC
converters or by direct AC-AC converters. Direct AC-AC
converters i.e. matrix converters are used in this paper to drive the
three phase BLDC motor and it eliminates the bulky DC link energy
storage element. A matrix converter converts the AC power supply to
an AC voltage of variable amplitude and variable frequency. A
control technique is designed to generate the switching pulses for the
three phase matrix converter. For the control of speed of the BLDC
motor a separate PI controller and Fuzzy Logic Controller (FLC) are
designed and a hysteresis current controller is also designed for the
control of motor torque. The control schemes are designed and tested
separately. The simulation results of both the schemes are compared
and contrasted in this paper. The results show that the fuzzy logic
control scheme outperforms the PI control scheme in terms of
dynamic performance of the BLDC motor. Simulation results are
validated with the experimental results.
Abstract: Average temperatures worldwide are expected to
continue to rise. At the same time, major cities in developing
countries are becoming increasingly populated and polluted.
Governments are tasked with the problem of overheating and air
quality in residential buildings. This paper presents the development
of a model, which is able to estimate the occupant exposure
to extreme temperatures and high air pollution within domestic
buildings. Building physics simulations were performed using the
EnergyPlus building physics software. An accurate metamodel is
then formed by randomly sampling building input parameters and
training on the outputs of EnergyPlus simulations. Metamodels are
used to vastly reduce the amount of computation time required when
performing optimisation and sensitivity analyses. Neural Networks
(NNs) have been compared to a Radial Basis Function (RBF)
algorithm when forming a metamodel. These techniques were
implemented using the PyBrain and scikit-learn python libraries,
respectively. NNs are shown to perform around 15% better than RBFs
when estimating overheating and air pollution metrics modelled by
EnergyPlus.
Abstract: Social networking sites such as Twitter and Facebook
attracts over 500 million users across the world, for those users, their
social life, even their practical life, has become interrelated. Their
interaction with social networking has affected their life forever.
Accordingly, social networking sites have become among the main
channels that are responsible for vast dissemination of different kinds
of information during real time events. This popularity in Social
networking has led to different problems including the possibility of
exposing incorrect information to their users through fake accounts
which results to the spread of malicious content during life events.
This situation can result to a huge damage in the real world to the
society in general including citizens, business entities, and others. In this paper, we present a classification method for detecting the
fake accounts on Twitter. The study determines the minimized set of
the main factors that influence the detection of the fake accounts on
Twitter, and then the determined factors are applied using different
classification techniques. A comparison of the results of these
techniques has been performed and the most accurate algorithm is
selected according to the accuracy of the results. The study has been
compared with different recent researches in the same area; this
comparison has proved the accuracy of the proposed study. We claim
that this study can be continuously applied on Twitter social network
to automatically detect the fake accounts; moreover, the study can be
applied on different social network sites such as Facebook with minor
changes according to the nature of the social network which are
discussed in this paper.
Abstract: Recently, Job Recommender Systems have gained
much attention in industries since they solve the problem of
information overload on the recruiting website. Therefore, we
proposed Extended Personalized Job System that has the capability of
providing the appropriate jobs for job seeker and recommending
some suitable information for them using Data Mining Techniques
and Dynamic User Profile. On the other hands, company can also
interact to the system for publishing and updating job information.
This system have emerged and supported various platforms such as
web application and android mobile application. In this paper, User
profiles, Implicit User Action, User Feedback, and Clustering
Techniques in WEKA libraries were applied and implemented. In
additions, open source tools like Yii Web Application Framework,
Bootstrap Front End Framework and Android Mobile Technology
were also applied.
Abstract: Prior literature in the field of adaptive and
personalized learning sequence in e-learning have proposed and
implemented various mechanisms to improve the learning process
such as individualization and personalization, but complex to
implement due to expensive algorithmic programming and need of
extensive and prior data. The main objective of personalizing
learning sequence is to maximize learning by dynamically selecting
the closest teaching operation in order to achieve the learning
competency of learner. In this paper, a revolutionary technique has
been proposed and tested to perform individualization and
personalization using modified reversed roulette wheel selection
algorithm that runs at O(n). The technique is simpler to implement
and is algorithmically less expensive compared to other revolutionary
algorithms since it collects the dynamic real time performance matrix
such as examinations, reviews, and study to form the RWSA single
numerical fitness value. Results show that the implemented system is
capable of recommending new learning sequences that lessens time
of study based on student's prior knowledge and real performance
matrix.
Abstract: Since the advances in digital imaging technologies have led to
development of high quality digital devices, there are a lot of illegal copies
of copyrighted video content on the Internet. Also, unauthorized editing is
occurred frequently. Thus, we propose an editing prevention technique for
high-quality (HQ) video that can prevent these illegally edited copies from
spreading out. The proposed technique is applied spatial and temporal gradient
methods to improve the fidelity and detection performance. Also, the scheme
duplicates the embedding signal temporally to alleviate the signal reduction
caused by geometric and signal-processing distortions. Experimental results
show that the proposed scheme achieves better performance than previously
proposed schemes and it has high fidelity. The proposed scheme can be used
in unauthorized access prevention method of visual communication or traitor
tracking applications which need fast detection process to prevent illegally
edited video content from spreading out.
Abstract: In order to utilize results from global climate models,
dynamical and statistical downscaling techniques have been
developed. For dynamical downscaling, usually a limited area
numerical model is used, with associated high computational cost.
This research proposes dynamic equation for specific space-time
regional climate downscaling from the Educational Global Climate
Model (EdGCM) for Southeast Asia. The equation is for surface air
temperature. This equation provides downscaling values of surface
air temperature at any specific location and time without running a
regional climate model. In the proposed equations, surface air
temperature is approximated from ground temperature, sensible heat
flux and 2m wind speed. Results from the application of the equation
show that the errors from the proposed equations are less than the
errors for direct interpolation from EdGCM.
Abstract: In this study, the effects and interactions of reaction
time and capping agent assistance during sol-gel synthesis of
magnesium substituted hydroxyapatite nanopowder (MgHA) on
hydroxyapatite (HA) to β-tricalcium phosphate (β-TCP) ratio, Ca/P
ratio and mean crystallite size was examined experimentally as well
as through statistical analysis. MgHA nanopowders were synthesized
by sol-gel technique at room temperature using aqueous solution of
calcium nitrate tetrahydrate, magnesium nitrate hexahydrate and
potassium dihydrogen phosphate as starting materials. The reaction
time for sol-gel synthesis was varied between 15 to 60 minutes. Two
process routes were followed with and without addition of
triethanolamine (TEA) in the solutions. The elemental compositions
of as-synthesized powders were determined using X-ray fluorescence
(XRF) spectroscopy. The functional groups present in the assynthesized
MgHA nanopowders were established through Fourier
Transform Infrared Spectroscopy (FTIR). The amounts of phases
present, Ca/P ratio and mean crystallite sizes of MgHA nanopowders
were determined using X-ray diffraction (XRD). The HA content in
biphasic mixture of HA and β-TCP and Ca/P ratio in as-synthesized
MgHA nanopowders increased effectively with reaction time of sols
(p0.15, two way ANOVA). The MgHA nanopowders
synthesized with TEA assistance exhibited 14 nm lower crystallite
size (p
Abstract: Response Surface Methods (RSM) provide
statistically validated predictive models that can then be manipulated
for finding optimal process configurations. Variation transmitted to
responses from poorly controlled process factors can be accounted
for by the mathematical technique of propagation of error (POE),
which facilitates ‘finding the flats’ on the surfaces generated by
RSM. The dual response approach to RSM captures the standard
deviation of the output as well as the average. It accounts for
unknown sources of variation. Dual response plus propagation of
error (POE) provides a more useful model of overall response
variation. In our case, we implemented this technique in predicting
compressive strength of concrete of 28 days in age. Since 28 days is
quite time consuming, while it is important to ensure the quality
control process. This paper investigates the potential of using design
of experiments (DOE-RSM) to predict the compressive strength of
concrete at 28th day. Data used for this study was carried out from
experiment schemes at university of Benghazi, civil engineering
department. A total of 114 sets of data were implemented. ACI mix
design method was utilized for the mix design. No admixtures were
used, only the main concrete mix constituents such as cement, coarseaggregate,
fine aggregate and water were utilized in all mixes.
Different mix proportions of the ingredients and different water
cement ratio were used. The proposed mathematical models are
capable of predicting the required concrete compressive strength of
concrete from early ages.
Abstract: The aim of this paper is to propose a general
framework for storing, analyzing, and extracting knowledge from
two-dimensional echocardiographic images, color Doppler images,
non-medical images, and general data sets. A number of high
performance data mining algorithms have been used to carry out this
task. Our framework encompasses four layers namely physical
storage, object identification, knowledge discovery, user level.
Techniques such as active contour model to identify the cardiac
chambers, pixel classification to segment the color Doppler echo
image, universal model for image retrieval, Bayesian method for
classification, parallel algorithms for image segmentation, etc., were
employed. Using the feature vector database that have been
efficiently constructed, one can perform various data mining tasks
like clustering, classification, etc. with efficient algorithms along
with image mining given a query image. All these facilities are
included in the framework that is supported by state-of-the-art user
interface (UI). The algorithms were tested with actual patient data
and Coral image database and the results show that their performance
is better than the results reported already.
Abstract: This paper presents the results of a study on the
influence of varying percentages of rock bridges along a basal surface
defining a biplanar failure mode. A pseudo-coupled-hydromechanical
brittle fracture analysis is adopted using the state-of-the-art code
Slope Model. Model results show that rock bridge failure is strongly
influenced by the incorporation of groundwater pressures. The
models show that groundwater pressure can promote total failure of a
5% rock bridge along the basal surface. Once the percentage of the
rock bridges increases to 10 and 15%, although, the rock bridges are
broken, full interconnection of the surface defining the basal surface
of the biplanar mode does not occur. Increased damage is caused
when the rock bridge is located at the daylighting end of the basal
surface in proximity to the blast damage zone. As expected, some
cracking damage is experienced in the blast damage zone, where
properties representing a good quality controlled damage blast
technique were assumed. Model results indicate the potential increase
of permeability towards the blast damage zone.
Abstract: The myocardium is composed of specialized muscle
which relies mainly on fatty acid and sugar metabolism and it is
widely contribute to the heart functioning. The changes of the cardiac
energy-producing system during heart failure have been proved using
autoradiography techniques. This study focused on evaluating sugar
and fatty acid metabolism in myocardium as cardiac energy getting
system using heart-accumulated radiopharmaceuticals. Two sets of
autoradiographs of heart cross sections of Lewis male rats were
analyzed and the time- accumulation curve obtained with use of the
MATLAB image processing software to evaluate fatty acid and sugar
metabolic functions.
Abstract: Job satisfaction and motivation have been given an
important attention in psychology because they are seen as main
instruments in maintaining organizational growth and development;
they are also used to accomplish organizational aims and objectives.
However, it has been observed that some institutions failed in
motivating and stimulating their workers; in contrast, workers may be
motivated but not satisfied with the job and failed to perform
efficiently and effectively. It is hoped that the study of this nature
would be of significance value to all stakeholders in education
specifically, lecturers in higher institutions in Nigeria. Also, it is
hoped that the findings of this study will enhance lecturers’
effectiveness and performance in discharging their duties. In the light
of the above statements, this study investigated whether job
satisfaction and motivation predict lecturers’ effectiveness in Nigeria
Police Academy, Wudil, Kano State. Correlational research method
was adopted for the study, while purposive sampling technique was
used to choose the institution and the sampled lectures (70). Simple
random sampling technique was used to select one hundred cadets
across the academy. Two instruments were used to elicit information
from both lecturers and cadets. These were job satisfaction and
motivation; and lecturers’ effectiveness Questionnaires. The
instruments were subjected to pilot testing and found to have
reliability coefficient of 0.69 and 0.71 respectively. The results of the
study revealed that there was a significance relationship among job
satisfaction, motivation and lecturers effectiveness in Nigeria Police
Academy. There was a significance relationship between job
satisfaction and lecturers’ effectiveness in Nigeria Police Academy
the cal r is 0.21 while the crt r is 0.19. at p
Abstract: Project Portfolio Management (PPM) is an essential
component of an organisation’s strategic procedures, which requires
attention of several factors to envisage a range of long-term outcomes
to support strategic project portfolio decisions. To evaluate overall
efficiency at the portfolio level, it is essential to identify the
functionality of specific projects as well as to aggregate those
findings in a mathematically meaningful manner that indicates the
strategic significance of the associated projects at a number of levels
of abstraction. PPM success is directly associated with the quality of
decisions made and poor judgment increases portfolio costs. Hence,
various Multi-Criteria Decision Making (MCDM) techniques have
been designed and employed to support the decision-making
functions. This paper reviews possible options to enhance the
decision-making outcomes in organisational portfolio management
processes using the Analytic Hierarchy Process (AHP) both from
academic and practical perspectives and will examine the usability,
certainty and quality of the technique. The results of the study will
also provide insight into the technical risk associated with current
decision-making model to underpin initiative tracking and strategic
portfolio management.
Abstract: A seizure prediction method is proposed by extracting
global features using phase correlation between adjacent epochs for
detecting relative changes and local features using fluctuation/
deviation within an epoch for determining fine changes of different
EEG signals. A classifier and a regularization technique are applied
for the reduction of false alarms and improvement of the overall
prediction accuracy. The experiments show that the proposed method
outperforms the state-of-the-art methods and provides high prediction
accuracy (i.e., 97.70%) with low false alarm using EEG signals in
different brain locations from a benchmark data set.
Abstract: The aim of this work is to detect geometrical shape
objects in an image. In this paper, the object is considered to be as a
circle shape. The identification requires find three characteristics,
which are number, size, and location of the object. To achieve the
goal of this work, this paper presents an algorithm that combines
from some of statistical approaches and image analysis techniques.
This algorithm has been implemented to arrive at the major
objectives in this paper. The algorithm has been evaluated by using
simulated data, and yields good results, and then it has been applied
to real data.
Abstract: The underutilization of biomass resources in the
Philippines, combined with its growing population and the rise in
fossil fuel prices confirms demand for alternative energy sources. The
goal of this paper is to provide a comparison of MODIS-based and
Landsat-based agricultural land cover maps when used in the
estimation of rice hull’s available energy potential. Biomass resource
assessment was done using mathematical models and remote sensing
techniques employed in a GIS platform.
Abstract: Rotary entrainment is a phenomenon in which the
interface of two immiscible fluids are subjected to external flux by
means of rotation. Present work reports the experimental study on
rotary motion of a horizontal cylinder between the interface of air and
water to observe the penetration of gas inside the liquid. Experiments
have been performed to establish entrainment of air mass in water
alongside the cylindrical surface. The movement of tracer and seeded
particles has been tracked to calculate the speed and path of the
entrained air inside water. Simplified particle image velocimetry
technique has been used to trace the movement of particles/tracers at
the moment they are injected inside the entrainment zone and
suspended beads have been used to replicate the particle movement
with respect to time in order to determine the flow dynamics of the
fluid along the cylinder. Present paper establishes a thorough experimental analysis of the
rotary entrainment phenomenon between air and water keeping in
interest the extent to which we can intermix the two and also to study
its entrainment trajectories.