Abstract: Vehicular Adhoc Network (VANET) is a new
technology which aims to ensure intelligent inter-vehicle
communications, seamless internet connectivity leading to improved
road safety, essential alerts, and access to comfort and entertainment.
VANET operations are hindered by mobile node’s (vehicles)
uncertain mobility. Routing algorithms use metrics to evaluate which
path is best for packets to travel. Metrics like path length (hop count),
delay, reliability, bandwidth, and load determine optimal route. The
proposed scheme exploits link quality, traffic density, and
intersections as routing metrics to determine next hop. This study
enhances Geographical Routing Protocol (GRP) using fuzzy
controllers while rules are optimized with Bee Swarm Optimization
(BSO). Simulations results are compared to conventional GRP.
Abstract: In this paper, we propose an automatic verification
technology of software patches for user virtual environments on IaaS
Cloud to decrease verification costs of patches. In these days, IaaS
services have been spread and many users can customize virtual
machines on IaaS Cloud like their own private servers. Regarding to
software patches of OS or middleware installed on virtual machines,
users need to adopt and verify these patches by themselves. This task
increases operation costs of users. Our proposed method replicates
user virtual environments, extracts verification test cases for user
virtual environments from test case DB, distributes patches to virtual
machines on replicated environments and conducts those test cases
automatically on replicated environments. We have implemented the
proposed method on OpenStack using Jenkins and confirmed the
feasibility. Using the implementation, we confirmed the effectiveness
of test case creation efforts by our proposed idea of 2-tier abstraction
of software functions and test cases. We also evaluated the automatic
verification performance of environment replications, test cases
extractions and test cases conductions.
Abstract: This paper describes the tradeoffs and the design from
scratch of a self-contained, easy-to-use health dashboard software
system that provides customizable data tracking for patients in smart
homes. The system is made up of different software modules and
comprises a front-end and a back-end component. Built with HTML,
CSS, and JavaScript, the front-end allows adding users, logging into
the system, selecting metrics, and specifying health goals. The backend
consists of a NoSQL Mongo database, a Python script, and a
SimpleHTTPServer written in Python. The database stores user
profiles and health data in JSON format. The Python script makes use
of the PyMongo driver library to query the database and displays
formatted data as a daily snapshot of user health metrics against
target goals. Any number of standard and custom metrics can be
added to the system, and corresponding health data can be fed
automatically, via sensor APIs or manually, as text or picture data
files. A real-time METAR request API permits correlating weather
data with patient health, and an advanced query system is
implemented to allow trend analysis of selected health metrics over
custom time intervals. Available on the GitHub repository system,
the project is free to use for academic purposes of learning and
experimenting, or practical purposes by building on it.
Abstract: Nature is a great source of inspiration for solving
complex problems in networks. It helps to find the optimal solution.
Metaheuristic algorithm is one of the nature-inspired algorithm which
helps in solving routing problem in networks. The dynamic features,
changing of topology frequently and limited bandwidth make the
routing, challenging in MANET. Implementation of appropriate
routing algorithms leads to the efficient transmission of data in
mobile ad hoc networks. The algorithms that are inspired by the
principles of naturally-distributed/collective behavior of social
colonies have shown excellence in dealing with complex
optimization problems. Thus some of the bio-inspired metaheuristic
algorithms help to increase the efficiency of routing in ad hoc
networks. This survey work presents the overview of bio-inspired
metaheuristic algorithms which support the efficiency of routing in
mobile ad hoc networks.
Abstract: This paper presents two types of microstrip bandpass
filter (BPF) at microwave frequencies. The first one is a tunable BPF
using planar patch resonators based on a varactor diode. The filter is
formed by a triple mode circular patch resonator with two pairs of
slots, in which the varactor diodes are connected. Indeed, this filter is
initially centered at 2.4 GHz; the center frequency of the tunable
patch filter could be tuned up to 1.8 GHz simultaneously with the
bandwidth, reaching high tuning ranges. Lossless simulations were
compared to those considering the substrate dielectric, conductor
losses and the equivalent electrical circuit model of the tuning
element in order to assess their effects. Within these variations,
simulation results showed insertion loss better than 2 dB and return
loss better than 10 dB over the passband. The second structure is a
BPF for ultra-wideband (UWB) applications based on multiple-mode
resonator (MMR) and rectangular-shaped defected ground structure
(DGS). This filter, which is compact size of 25.2 x 3.8 mm2, provides
in the pass band an insertion loss of 0.57 dB and a return loss greater
than 12 dB. The proposed filters presents good performances and the
simulation results are in satisfactory agreement with the
experimentation ones reported elsewhere.
Abstract: The goal of image segmentation is to cluster pixels
into salient image regions. Segmentation could be used for object
recognition, occlusion boundary estimation within motion or stereo
systems, image compression, image editing, or image database lookup.
In this paper, we present a color image segmentation using
support vector machine (SVM) pixel classification. Firstly, the pixel
level color and texture features of the image are extracted and they
are used as input to the SVM classifier. These features are extracted
using the homogeneity model and Gabor Filter. With the extracted
pixel level features, the SVM Classifier is trained by using FCM
(Fuzzy C-Means).The image segmentation takes the advantage of
both the pixel level information of the image and also the ability of
the SVM Classifier. The Experiments show that the proposed method
has a very good segmentation result and a better efficiency, increases
the quality of the image segmentation compared with the other
segmentation methods proposed in the literature.
Abstract: Unmanned Aircraft Systems (UAS) become
indispensable parts of modern airpower as force multiplier. One of
the main advantages of UAS is long endurance. UAS have to take
extra payloads to accomplish different missions but these payloads
decrease endurance of aircraft because of increasing drag. There are
continuing researches to increase the capability of UAS. There are
some vertical thermal air currents, which can cause climb and
increase endurance, in nature. Birds and gliders use thermals to gain
altitude with no effort. UAS have wide wings which can use
thermals like birds and gliders. Thermal regions, which is area of
2000-3000 meter (1 NM), exist all around the world. It is natural and
infinite source. This study analyses if thermal regions can be adopted
and implemented as an assistant tool for UAS route planning. First
and second part of study will contain information about the thermal
regions and current applications about UAS in aviation and climbing
performance with a real example. Continuing parts will analyze the
contribution of thermal regions to UAS endurance. Contribution is
important because planning declaration of UAS navigation rules will
be in 2015.
Abstract: The performance and analysis of speech recognition
system is illustrated in this paper. An approach to recognize the
English word corresponding to digit (0-9) spoken by 2 different
speakers is captured in noise free environment. For feature extraction,
speech Mel frequency cepstral coefficients (MFCC) has been used
which gives a set of feature vectors from recorded speech samples.
Neural network model is used to enhance the recognition
performance. Feed forward neural network with back propagation
algorithm model is used. However other speech recognition
techniques such as HMM, DTW exist. All experiments are carried
out on Matlab.
Abstract: The edges of low contrast images are not clearly
distinguishable to human eye. It is difficult to find the edges and
boundaries in it. The present work encompasses a new approach for
low contrast images. The Chebyshev polynomial based fractional
order filter has been used for filtering operation on an image. The
preprocessing has been performed by this filter on the input image.
Laplacian of Gaussian method has been applied on preprocessed
image for edge detection. The algorithm has been tested on two test
images.
Abstract: The wide use of the Internet-based applications bring many challenges to the researchers to guarantee the continuity of the connections needed by the mobile hosts and provide reliable Internet access for them. One of proposed solutions by Internet Engineering Task Force (IETF) is to connect the local, multi-hop, and infrastructure-less Mobile Ad hoc Network (MANET) with Internet structure. This connection is done through multi-interface devices known as Internet Gateways. Many issues are related to this connection like gateway discovery, handoff, address auto-configuration and selecting the optimum gateway when multiple gateways exist. Many studies were done proposing gateway selection schemes with a single selection criterion or weighted multiple criteria. In this research, a review of some of these schemes is done showing the differences, the features, the challenges and the drawbacks of each of them.
Abstract: The aim of this work is to build a model based on
tissue characterization that is able to discriminate pathological and
non-pathological regions from three-phasic CT images. With our
research and based on a feature selection in different phases, we are
trying to design a neural network system with an optimal neuron
number in a hidden layer. Our approach consists of three steps:
feature selection, feature reduction, and classification. For each
region of interest (ROI), 6 distinct sets of texture features are
extracted such as: first order histogram parameters, absolute gradient,
run-length matrix, co-occurrence matrix, autoregressive model, and
wavelet, for a total of 270 texture features. When analyzing more
phases, we show that the injection of liquid cause changes to the high
relevant features in each region. Our results demonstrate that for
detecting HCC tumor phase 3 is the best one in most of the features
that we apply to the classification algorithm. The percentage of
detection between pathology and healthy classes, according to our
method, relates to first order histogram parameters with accuracy of
85% in phase 1, 95% in phase 2, and 95% in phase 3.
Abstract: The implementation of e-assessment as tool to support
the process of teaching and learning in university has become a
popular technological means in universities. E-Assessment provides
many advantages to the users especially the flexibility in teaching and
learning. The e-assessment system has the capability to improve its
quality of delivering education. However, there still exists a
drawback in terms of security which limits the user acceptance of the
online learning system. Even though there are studies providing
solutions for identified security threats in e-learning usage, there is no
particular model which addresses the factors that influences the
acceptance of e-assessment system by lecturers from security
perspective. The aim of this study is to explore security aspects of eassessment
in regard to the acceptance of the technology. As a result
a conceptual model of secure acceptance of e-assessment is proposed.
Both human and security factors are considered in formulation of this
conceptual model. In order to increase understanding of critical issues
related to the subject of this study, interpretive approach involving
convergent mixed method research method is proposed to be used to
execute the research. This study will be useful in providing more
insightful understanding regarding the factors that influence the user
acceptance of e-assessment system from security perspective.
Abstract: This paper proposes a rotational invariant texture
feature based on the roughness property of the image for psoriasis
image analysis. In this work, we have applied this feature for image
classification and segmentation. The fuzzy concept is employed to
overcome the imprecision of roughness. Since the psoriasis lesion is
modeled by a rough surface, the feature is extended for calculating
the Psoriasis Area Severity Index value. For classification and
segmentation, the Nearest Neighbor algorithm is applied. We have
obtained promising results for identifying affected lesions by using
the roughness index and severity level estimation.
Abstract: The goal of this study is to identify success factors
that could influence the ISMS self-implementation in government
sector from qualitative perspective. This study is based on a case
study in one of the Malaysian government agency. Semi-structured
interviews involving five key informants were conducted to examine
factors addressed in the conceptual framework. Subsequently,
thematic analysis was executed to describe the influence of each
factor on the success implementation of ISMS. The result of this
study indicates that management commitment, implementer
commitment and implementer competency are part of the success
factors for ISMS self-implementation in Malaysian Government
Sector.
Abstract: Quantification of cardiac function is performed by
calculating blood volume and ejection fraction in routine clinical
practice. However, these works have been performed by manual
contouring, which requires computational costs and varies on the
observer. In this paper, an automatic left ventricle segmentation
algorithm on cardiac magnetic resonance images (MRI) is presented.
Using knowledge on cardiac MRI, a K-mean clustering technique is
applied to segment blood region on a coil-sensitivity corrected image.
Then, a graph searching technique is used to correct segmentation
errors from coil distortion and noises. Finally, blood volume and
ejection fraction are calculated. Using cardiac MRI from 15 subjects,
the presented algorithm is tested and compared with manual
contouring by experts to show outstanding performance.
Abstract: Due to the rapid increase of Internet, web opinion
sources dynamically emerge which is useful for both potential
customers and product manufacturers for prediction and decision
purposes. These are the user generated contents written in natural
languages and are unstructured-free-texts scheme. Therefore, opinion
mining techniques become popular to automatically process customer
reviews for extracting product features and user opinions expressed
over them. Since customer reviews may contain both opinionated and
factual sentences, a supervised machine learning technique applies
for subjectivity classification to improve the mining performance. In
this paper, we dedicate our work is the task of opinion
summarization. Therefore, product feature and opinion extraction is
critical to opinion summarization, because its effectiveness
significantly affects the identification of semantic relationships. The
polarity and numeric score of all the features are determined by
Senti-WordNet Lexicon. The problem of opinion summarization
refers how to relate the opinion words with respect to a certain
feature. Probabilistic based model of supervised learning will
improve the result that is more flexible and effective.
Abstract: Game theory is the study of how people interact and
make decisions to handle competitive situations. It has mainly been
developed to study decision making in complex situations. Humans
routinely alter their behaviour in response to changes in their social
and physical environment. As a consequence, the outcomes of
decisions that depend on the behaviour of multiple decision makers
are difficult to predict and require highly adaptive decision-making
strategies. In addition to the decision makers may have preferences
regarding consequences to other individuals and choose their actions
to improve or reduce the well-being of others. Nash equilibrium is a
fundamental concept in the theory of games and the most widely used
method of predicting the outcome of a strategic interaction in the
social sciences. A Nash Equilibrium exists when there is no unilateral
profitable deviation from any of the players involved. On the other
hand, no player in the game would take a different action as long as
every other player remains the same.
Abstract: Content Based Image Retrieval (CBIR) coupled with
Case Based Reasoning (CBR) is a paradigm that is becoming
increasingly popular in the diagnosis and therapy planning of medical
ailments utilizing the digital content of medical images. This paper
presents a survey of some of the promising approaches used in the
detection of abnormalities in retina images as well in
mammographic screening and detection of regions of interest
in MRI scans of the brain. We also describe our proposed
algorithm to detect hard exudates in fundus images of the
retina of Diabetic Retinopathy patients.
Abstract: In this paper, performances of shuffled frog leaping
algorithm was investigated on the stealth laser dicing process. Effect
of problem on the performance of the algorithm was based on the
tolerance of meandering data. From the customer specification it
could be less than five microns with the target of zero microns.
Currently, the meandering levels are unsatisfactory when compared
to the customer specification. Firstly, the two-level factorial design
was applied to preliminarily study the statistically significant effects
of five process variables. In this study one influential process variable
is integer. From the experimental results, the new operating condition
from the algorithm was superior when compared to the current
manufacturing condition.
Abstract: Over the past few years, the online multimedia
collection has grown at a fast pace. Several companies showed
interest to study the different ways to organise the amount of audio
information without the need of human intervention to generate
metadata. In the past few years, many applications have emerged on
the market which are capable of identifying a piece of music in a
short time. Different audio effects and degradation make it much
harder to identify the unknown piece. In this paper, an audio
fingerprinting system which makes use of a non-parametric based
algorithm is presented. Parametric analysis is also performed using
Gaussian Mixture Models (GMMs). The feature extraction methods
employed are the Mel Spectrum Coefficients and the MPEG-7 basic
descriptors. Bin numbers replaced the extracted feature coefficients
during the non-parametric modelling. The results show that nonparametric
analysis offer potential results as the ones mentioned in
the literature.