Abstract: Advances in spatial and spectral resolution of satellite
images have led to tremendous growth in large image databases. The
data we acquire through satellites, radars, and sensors consists of
important geographical information that can be used for remote
sensing applications such as region planning, disaster management.
Spatial data classification and object recognition are important tasks
for many applications. However, classifying objects and identifying
them manually from images is a difficult task. Object recognition is
often considered as a classification problem, this task can be
performed using machine-learning techniques. Despite of many
machine-learning algorithms, the classification is done using
supervised classifiers such as Support Vector Machines (SVM) as the
area of interest is known. We proposed a classification method,
which considers neighboring pixels in a region for feature extraction
and it evaluates classifications precisely according to neighboring
classes for semantic interpretation of region of interest (ROI). A
dataset has been created for training and testing purpose; we
generated the attributes by considering pixel intensity values and
mean values of reflectance. We demonstrated the benefits of using
knowledge discovery and data-mining techniques, which can be on
image data for accurate information extraction and classification from
high spatial resolution remote sensing imagery.
Abstract: Small-size and low-power sensors with sensing, signal
processing and wireless communication capabilities is suitable for the
wireless sensor networks. Due to the limited resources and battery
constraints, complex routing algorithms used for the ad-hoc networks
cannot be employed in sensor networks. In this paper, we propose
node-disjoint multi-path hexagon-based routing algorithms in wireless
sensor networks. We suggest the details of the algorithm and compare
it with other works. Simulation results show that the proposed scheme
achieves better performance in terms of efficiency and message
delivery ratio.
Abstract: Online measurement of the product quality is a
challenging task in cement production, especially in the production of
Celitement, a novel environmentally friendly hydraulic binder. The
mineralogy and chemical composition of clinker in ordinary Portland
cement production is measured by X-ray diffraction (XRD) and
X-ray fluorescence (XRF), where only crystalline constituents can be
detected. But only a small part of the Celitement components can be
measured via XRD, because most constituents have an amorphous
structure. This paper describes the development of algorithms
suitable for an on-line monitoring of the final processing step of
Celitement based on NIR-data. For calibration intermediate products
were dried at different temperatures and ground for variable
durations. The products were analyzed using XRD and
thermogravimetric analyses together with NIR-spectroscopy to
investigate the dependency between the drying and the milling
processes on one and the NIR-signal on the other side. As a result,
different characteristic parameters have been defined. A short
overview of the Celitement process and the challenging tasks of the
online measurement and evaluation of the product quality will be
presented. Subsequently, methods for systematic development of
near-infrared calibration models and the determination of the final
calibration model will be introduced. The application of the model on
experimental data illustrates that NIR-spectroscopy allows for a quick
and sufficiently exact determination of crucial process parameters.
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: Bezier curves have useful properties for path
generation problem, for instance, it can generate the reference
trajectory for vehicles to satisfy the path constraints. Both algorithms
join cubic Bezier curve segment smoothly to generate the path. Some
of the useful properties of Bezier are curvature. In mathematics,
curvature is the amount by which a geometric object deviates from
being flat, or straight in the case of a line. Another extrinsic example
of curvature is a circle, where the curvature is equal to the reciprocal
of its radius at any point on the circle. The smaller the radius, the
higher the curvature thus the vehicle needs to bend sharply. In this
study, we use Bezier curve to fit highway-like curve. We use
different approach to find the best approximation for the curve so that
it will resembles highway-like curve. We compute curvature value by
analytical differentiation of the Bezier Curve. We will then compute
the maximum speed for driving using the curvature information
obtained. Our research works on some assumptions; first, the Bezier
curve estimates the real shape of the curve which can be verified
visually. Even though, fitting process of Bezier curve does not
interpolate exactly on the curve of interest, we believe that the
estimation of speed are acceptable. We verified our result with the
manual calculation of the curvature from the map.
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: Inspired by the Formula-1 competition, IMechE
(Institute of Mechanical Engineers) and Formula SAE (Society of
Mechanical Engineers) organize annual competitions for University
and College students worldwide to compete with a single-seat racecar
they have designed and built. Design of the chassis or the frame is a
key component of the competition because the weight and stiffness
properties are directly related with the performance of the car and the
safety of the driver. In addition, a reduced weight of the chassis has
direct influence on the design of other components in the car. Among
others, it improves the power to weight ratio and the aerodynamic
performance. As the power output of the engine or the battery
installed in the car is limited to 80 kW, increasing the power to
weight ratio demands reduction of the weight of the chassis, which
represents the major part of the weight of the car. In order to reduce
the weight of the car, ION Racing team from University of
Stavanger, Norway, opted for a monocoque design. To ensure
fulfilment of the competition requirements of the chassis, the
monocoque design should provide sufficient torsional stiffness and
absorb the impact energy in case of possible collision. The study reported in this article is based on the requirements for
Formula Student competition. As part of this study, diverse
mechanical tests were conducted to determine the mechanical
properties and performances of the monocoque design. Upon a
comprehensive theoretical study of the mechanical properties of
sandwich composite materials and the requirements of monocoque
design in the competition rules, diverse tests were conducted
including 3-point bending test, perimeter shear test and test for
absorbed energy. The test panels were homemade and prepared with
equivalent size of the side impact zone of the monocoque, i.e. 275
mm x 500 mm, so that the obtained results from the tests can be
representative. Different layups of the test panels with identical core
material and the same number of layers of carbon fibre were tested
and compared. Influence of the core material thickness was also
studied. Furthermore, analytical calculations and numerical analysis
were conducted to check compliance to the stated rules for Structural
Equivalency with steel grade SAE/AISI 1010. The test results were
also compared with calculated results with respect to bending and
torsional stiffness, energy absorption, buckling, etc. The obtained results demonstrate that the material composition
and strength of the composite material selected for the monocoque
design has equivalent structural properties as a welded frame and thus
comply with the competition requirements. The developed analytical
calculation algorithms and relations will be useful for future
monocoque designs with different lay-ups and compositions.
Abstract: One of the global combinatorial optimization
problems in machine learning is feature selection. It concerned with
removing the irrelevant, noisy, and redundant data, along with
keeping the original meaning of the original data. Attribute reduction
in rough set theory is an important feature selection method. Since
attribute reduction is an NP-hard problem, it is necessary to
investigate fast and effective approximate algorithms. In this paper,
we proposed two feature selection mechanisms based on memetic
algorithms (MAs) which combine the genetic algorithm with a fuzzy
record to record travel algorithm and a fuzzy controlled great deluge
algorithm, to identify a good balance between local search and
genetic search. In order to verify the proposed approaches, numerical
experiments are carried out on thirteen datasets. The results show that
the MAs approaches are efficient in solving attribute reduction
problems when compared with other meta-heuristic approaches.
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: The power electronic components within Electric Vehicles (EV) need to operate in several important modes. Some modes directly influence safety, while others influence vehicle performance. Given the variety of functions and operational modes required of the power electronics, it needs to meet efficiency requirements to minimize power losses. Another challenge in the control and construction of such systems is the ability to support bidirectional power flow. This paper considers the construction, operation, and feasibility of available converters for electric vehicles with feasible configurations of electrical buses and loads. This paper describes logic and control signals for the converters for different operations conditions based on the efficiency and energy usage bases.
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: This paper discusses the thematic structure of Yoruba
popular music of Southwest Nigeria. It examines the use of themes
and variations in early and contemporary Juju music. The work is an
outcome of a research developed by the author in his doctoral studies
at the University of Lagos, Nigeria, with the aim of analyzing the
thematic and motivic developments in Yoruba popular genres.
Observations, interviews, live recordings and CDs were used as
methods for eliciting information. Field recordings and CDs of
selected musical samples were also transcribed and notated. The
research established the prevalent use of string of themes by Juju
musicians as a compositional technique in moving from one musical
section to another, as they communicate the verbal messages in their
song. These themes consist of the popular ‘call and response’ form
found in most African music, analogous to the western ‘subject and
answer’ style of the fugue or sonata form, although without the tonic–
dominant relations. Due to the short and repetitive form of African
melodies and rhythms, a theme is restated as a variation, where its
rhythmic and melodic motifs are stylistically developed and repeated,
but still retaining its recognizable core musical structure. The
findings of this study showed that Juju musicians generally often
employ a thematic plan where new themes are used to arrange the
songs into sections, and each theme is developed into variations in
order to further expand the music, eliminate monotony, and create
musical aesthetics, serving as hallmark of its musical identity. The
study established the musical and extra-musical attributes of the
genre, while recommending further research towards analyzing the
various compositional techniques employed in African popular
genres.
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: 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: 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: 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 traditional rhythms of the West African country
of Guinea have played a centuries-long role in defining the different
people groups that make up the country. Throughout their history,
before and since colonization by the French, the different ethnicities
have used their traditional music as a distinct part of their historical
identities. That is starting to change. Guinea is an impoverished
nation created in the early twentieth-century with little regard for the
history and cultures of the people who were included. The traditional
rhythms of the different people groups and their heritages have
remained. Fifteen individual traditional Guinean rhythms were
chosen to represent popular rhythms from the four geographical
regions of Guinea. Each rhythm was traced back to its native village
and video recorded on-site by as many different local performing
groups as could be located. The cyclical patterns rhythms were
transcribed via a circular, spatial design and then copied into a box
notation system where sounds happening at the same time could be
studied. These rhythms were analyzed for their consistency-overperformance
in a Fundamental Rhythm Pattern analysis so rhythms
could be compared for how they are changing through different
performances. The analysis showed that the traditional rhythm
performances of the Middle and Forest Guinea regions were the most
cohesive and showed the least evidence of change between
performances. The role of music in each of these regions is both
limited and focused. The Coastal and High Guinea regions have
much in common historically through their ethnic history and
modern-day trade connections, but the rhythm performances seem to
be less consistent and demonstrate more changes in how they are
performed today. In each of these regions the role and usage of music
is much freer and wide-spread. In spite of advances being made as a
country, different ethnic groups still frequently only respond and
participate (dance and sing) to the music of their native ethnicity.
There is some evidence that this self-imposed musical barrier is
beginning to change and evolve, partially through the development of
better roads, more access to electricity and technology, the nationwide
Ebola health crisis, and a growing self-identification as a
unified nation.
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: In this paper, we present an application of Riemannian
geometry for processing non-Euclidean image data. We consider the
image as residing in a Riemannian manifold, for developing a new
method to brain edge detection and brain extraction. Automating this
process is a challenge due to the high diversity in appearance brain
tissue, among different patients and sequences. The main contribution, in this paper, is the use of an edge-based
anisotropic diffusion tensor for the segmentation task by integrating
both image edge geometry and Riemannian manifold (geodesic,
metric tensor) to regularize the convergence contour and extract
complex anatomical structures. We check the accuracy of the
segmentation results on simulated brain MRI scans of single
T1-weighted, T2-weighted and Proton Density sequences. We
validate our approach using two different databases: BrainWeb
database, and MRI Multiple sclerosis Database (MRI MS DB). We
have compared, qualitatively and quantitatively, our approach with
the well-known brain extraction algorithms. We show that using
a Riemannian manifolds to medical image analysis improves the
efficient results to brain extraction, in real time, outperforming the
results of the standard techniques.
Abstract: Evolutionary Algorithms (EAs) have been used
widely through evolution theory to discover acceptable solutions that
corresponds to challenges such as natural resources management.
EAs are also used to solve varied problems in the real world. EAs
have been rapidly identified for its ease in handling multiple
objective problems. Reservoir operations is a vital and researchable
area which has been studied in the last few decades due to the limited
nature of water resources that is found mostly in the semi-arid
regions of the world. The state of some developing economy that
depends on electricity for overall development through hydropower
production, a renewable form of energy, is appalling due to water
scarcity. This paper presents a review of the applications of
evolutionary algorithms to reservoir operation for hydropower
production. This review includes the discussion on areas such as
genetic algorithm, differential evolution, and reservoir operation. It
also identified the research gaps discovered in these areas. The results
of this study will be an eye opener for researchers and decision
makers to think deeply of the adverse effect of water scarcity and
drought towards economic development of a nation. Hence, it
becomes imperative to identify evolutionary algorithms that can
address this issue which can hamper effective hydropower
generation.