Abstract: As technology-based service industries grow
drastically worldwide; companies are recognizing the importance of
market preoccupancy and have made an effort to capture a large
market to gain the upper hand. To this end, a focus on patents can be
used to determine the properties of a technology, as well as to capture
advantages in technical skills, in comparison with the firm’s
competitors. However, technology-based services largely depend not
only on their technological value but also their economic value, due
to the recognized worth that is passed to a plurality of users. Thus, it
is important to determine whether there are any competitors in the
target areas and what services they provide in any field. Despite this
importance, little effort has been made to systematically benchmark
competitors in order to identify business opportunities. Thus, this
study aims to not only identify each position of technology-centered
service companies in complex market dynamics, but also to discover
new business opportunities. For this, we try to consider both
technology and market environments simultaneously by utilizing
patent data as a representative proxy for technology and trademark
dates as an index for a firm’s target goods and services. Theoretically,
this is one of the earliest attempts to combine patent data and
trademark data to analyze corporate strategies. In practice, the
research results are expected to be used as a decision criterion to
diagnose the economic value that companies can obtain by entering
the market, as well as the technological value to be passed onto their
customers. Thus, the proposed approach can be useful to support
effective technology and business strategies in a firm.
Abstract: This paper introduces a video sharing platform based
on WiFi, which consists of camera, mobile phone and PC server. This
platform can receive wireless signal from the camera and show the live
video on the mobile phone captured by camera. In addition, it is able to
send commands to camera and control the camera’s holder to rotate.
The platform can be applied to interactive teaching and dangerous
area’s monitoring and so on. Testing results show that the platform can
share the live video of mobile phone. Furthermore, if the system’s PC
server and the camera and many mobile phones are connected
together, it can transfer photos concurrently.
Abstract: Driver fatigue is an important factor in the increasing
number of road accidents. Dynamic template matching method was
proposed to address the problem of real-time driver fatigue detection
system based on eye-tracking. An effective vision based approach
was used to analyze the driver’s eye state to detect fatigue. The driver
fatigue system consists of Face detection, Eye detection, Eye
tracking, and Fatigue detection. Initially frames are captured from a
color video in a car dashboard and transformed from RGB into YCbCr
color space to detect the driver’s face. Canny edge operator was used
to estimating the eye region and the locations of eyes are extracted.
The extracted eyes were considered as a template matching for eye
tracking. Edge Map Overlapping (EMO) and Edge Pixel Count
(EPC) matching function were used for eye tracking which is used to
improve the matching accuracy. The pixel of eyeball was tracked
from the eye regions which are used to determine the fatigue state of
the driver.
Abstract: This paper presents a novel algorithm for secure,
reliable and flexible transmission of big data in two hop wireless
networks using cooperative jamming scheme. Two hop wireless
networks consist of source, relay and destination nodes. Big data has
to transmit from source to relay and from relay to destination by
deploying security in physical layer. Cooperative jamming scheme
determines transmission of big data in more secure manner by
protecting it from eavesdroppers and malicious nodes of unknown
location. The novel algorithm that ensures secure and energy balance
transmission of big data, includes selection of data transmitting
region, segmenting the selected region, determining probability ratio
for each node (capture node, non-capture and eavesdropper node) in
every segment, evaluating the probability using binary based
evaluation. If it is secure transmission resume with the two- hop
transmission of big data, otherwise prevent the attackers by
cooperative jamming scheme and transmit the data in two-hop
transmission.
Abstract: Quantitative analyses of whisker movements provide a
means to study functional recovery and regeneration of mouse facial
nerve after an injury. However, accurate tracking of the mouse whisker
movement is challenging. Most methods for whisker tracking require
manual intervention, e.g. fixing the head of the mouse during a study.
Here we describe a semi-automated image processing method, which
is applied to high-speed video recordings of free-moving mice to track
the whisker movements. We first track the head movement of a mouse
by delineating the lower head contour frame-by-frame that allows for
detection of the location and orientation of the head. Then, a region of
interest is identified for each frame; the subsequent application of a
mask and the Hough transform detects the selected whiskers on each
side of the head. Our approach is used to examine the functional
recovery of damaged facial nerves in mice over a course of 21 days.
Abstract: Hypersonic flows around spatial vehicles during their reentry phase in planetary atmospheres are characterized by intense aerothermodynamics phenomena. The aim of this work is to analyze high temperature flows around an axisymmetric blunt body taking into account chemical and vibrational non-equilibrium for air mixture species and the no slip condition at the wall. For this purpose, the Navier-Stokes equations system is resolved by the finite volume methodology to determine the flow parameters around the axisymmetric blunt body especially at the stagnation point and in the boundary layer along the wall of the blunt body. The code allows the capture of shock wave before a blunt body placed in hypersonic free stream. The numerical technique uses the Flux Vector Splitting method of Van Leer. CFL coefficient and mesh size level are selected to ensure the numerical convergence.
Abstract: This paper presents the development of a robot car
that can track the motion of an object by detecting its color through
an Android device. The employed computer vision algorithm uses the
OpenCV library, which is embedded into an Android application of a
smartphone, for manipulating the captured image of the object. The
captured image of the object is subjected to color conversion and is
transformed to a binary image for further processing after color
filtering. The desired object is clearly determined after removing
pixel noise by applying image morphology operations and contour
definition. Finally, the area and the center of the object are
determined so that object’s motion to be tracked. The smartphone
application has been placed on a robot car and transmits by Bluetooth
to an Arduino assembly the motion directives so that to follow
objects of a specified color. The experimental evaluation of the
proposed algorithm shows reliable color detection and smooth
tracking characteristics.
Abstract: An unconventional composite inorganic ceramic
membrane capable of enhancing carbon dioxide emission decline was
fabricated and tested at laboratory scale in conformism to various
environmental guidelines and also to mitigate the effect of global
warming. A review of the existing membrane technologies for carbon
capture including the relevant gas transport mechanisms is presented.
Single gas permeation experiments using silica modified ceramic
membrane with internal diameter 20mm, outside diameter 25mm and
length of 368mm deposited on a macro porous support was carried
out to investigate individual gas permeation behaviours at different
pressures at room temperature. Membrane fabrication was achieved
using after a dip coating method. Nitrogen, Carbon dioxide, Argon,
Oxygen and Methane pure gases were used to investigate their
individual permeation rates at various pressures. Results show that
the gas flow rate increases with pressure drop. However above a
pressure of 3bar, CO2 permeability ratio to that of the other gases
indicated control of a more selective surface adsorptive transport
mechanism.
Abstract: Scrubbing by a liquid spraying is one of the most
effective processes used for removal of fine particles and soluble
gas pollutants (such as SO2, HCl, HF) from the flue gas. There are
many configurations of scrubbers designed to provide contact
between the liquid and gas stream for effectively capturing
particles or soluble gas pollutants, such as spray plates, packed bed
towers, jet scrubbers, cyclones, vortex and venturi scrubbers. The
primary function of venturi scrubber is the capture of fine particles
as well as HCl, HF or SO2 removal with effect of the flue gas
temperature decrease before input to the absorption column. In this
paper, sulfur dioxide (SO2) from flue gas was captured using new
design replacing venturi scrubber (1st degree of wet scrubbing).
The flue gas was prepared by the combustion of the carbon
disulfide solution in toluene (1:1 vol.) in the flame in the reactor.
Such prepared flue gas with temperature around 150°C was
processed in designed laboratory O-element scrubber. Water was
used as absorbent liquid. The efficiency of SO2 removal, pressure
drop and temperature drop were measured on our experimental
device. The dependence of these variables on liquid-gas ratio was
observed. The average temperature drop was in the range from
150°C to 40°C. The pressure drop was increased with increasing of
a liquid-gas ratio, but no too much as for the common venturi
scrubber designs. The efficiency of SO2 removal was up to 70 %.
The pressure drop of our new designed wet scrubber is similar to
commonly used venturi scrubbers; nevertheless the influence of
amount of the liquid on pressure drop is not so significant.
Abstract: In this article, the antibiogram and heavy metal
resistance profile of the bacteria isolated from total 34 studied
animals (Pelophylax ridibundus = 12; Mauremys rivulata = 14;
Natrix natrix = 8) captured around the Biga Stream, are described.
There was no database information on antibiogram and heavy metal
resistance profile of bacteria from these area’s amphibians and
reptiles.
A total of 200 bacteria were successfully isolated from cloaca and
oral samples of the aquatic amphibians and reptiles as well as from
the water sample. According to Jaccard’s similarity index, the degree
of similarity in the bacterial flora was quite high among the
amphibian and reptile species under examination, whereas it was
different from the bacterial diversity in the water sample. The most
frequent isolates were A. hydrophila (31.5%), B. pseudomallei
(8.5%), and C. freundii (7%). The total numbers of bacteria obtained
were as follows: 45 in P. ridibundus, 45 in N. natrix 30 in M.
rivulata, and 80 in the water sample. The result showed that
cefmetazole was the most effective antibiotic to control the bacteria
isolated in this study and that approximately 93.33% of the bacterial
isolates were sensitive to this antibiotic. The multiple antibiotic
resistances (MAR) index indicated that P. ridibundus (0.95) > N.
natrix (0.89) > M. rivulata (0.39). Furthermore, all the tested heavy
metals (Pb+2, Cu+2, Cr+3, and Mn+2) inhibit the growth of the bacterial
isolates at different rates. Therefore, it indicated that the water source
of the animals was contaminated with both antibiotic residues and
heavy metals.
Abstract: This paper discusses the value theory in cultural
heritage and the value theory in environmental economics. Two
economic views of the value theory are compared, within the field of
cultural heritage maintenance and within the field of the environment.
The main aims are to find common features in these two differently
structured theories under the layer of differently defined terms as well
as really differing features of these two approaches; to clear the
confusion which stems from different terminology as in fact these
terms capture the same aspects of reality; and to show possible
inspiration these two perspectives can offer one another. Another aim
is to present these two value systems in one value framework. First,
important moments of the value theory from the economic
perspective are presented, leading to the marginal revolution of (not
only) the Austrian School. Then the theory of value within cultural
heritage and environmental economics are explored. Finally,
individual approaches are compared and their potential mutual
inspiration searched for.
Abstract: These days customer satisfaction plays vital role in
any business. When customer searches for a product, significantly a
junk of irrelevant information is what is given, leading to customer
dissatisfaction. To provide exactly relevant information on the
searched product, we are proposing a model of KaaS (Knowledge as
a Service), which pre-processes the information using decision
making paradigm using Multi-agents.
Information obtained from various sources is taken to derive
knowledge and they are linked to Cloud to capture new idea. The
main focus of this work is to acquire relevant information
(knowledge) related to product, then convert this knowledge into a
service for customer satisfaction and deploy on cloud.
For achieving these objectives we are have opted to use multi
agents. They are communicating and interacting with each other,
manipulate information, provide knowledge, to take decisions. The
paper discusses about KaaS as an intelligent approach for Knowledge
acquisition.
Abstract: This paper presents two techniques, local feature
extraction using image spectrum and low frequency spectrum
modelling using GMM to capture the underlying statistical
information to improve the performance of face recognition
system. Local spectrum features are extracted using overlap sub
block window that are mapped on the face image. For each of this
block, spatial domain is transformed to frequency domain using
DFT. A low frequency coefficient is preserved by discarding high
frequency coefficients by applying rectangular mask on the
spectrum of the facial image. Low frequency information is non-
Gaussian in the feature space and by using combination of several
Gaussian functions that has different statistical properties, the best
feature representation can be modelled using probability density
function. The recognition process is performed using maximum
likelihood value computed using pre-calculated GMM components.
The method is tested using FERET datasets and is able to achieved
92% recognition rates.
Abstract: There has been a significant decline in active travel
and a massive increase in the use of car dependent travel in many
countries during the past two decades. Evidential risks for people’s
physical and mental health problems are correlated with this
increased use of motorized travel. These health related problems
range from overweight and obesity to increased air pollution. In
response to these rising concerns health professionals, traffic planers,
local authorities and others have introduced a variety of initiatives to
counterbalance the dominance of cars for daily journeys.
However, the nature of travel behavior change interventions,
which aim to reduce car use, are very complex and challenging
regarding their interactions with human behavior. To change travel
behavior at least two aspects have to be taken into consideration.
First, how to alter attitudes and perceptions toward the sustainable
and healthy modes of travel, in competition with experiences of
private car use. And second, how to make these behavior change
processes irreversible and sustainable. There are no comprehensive
models available to guide policy interventions to increase the level of
success of travel behavior change interventions across both these
dimensions.
A comprehensive theoretical framework is required in the effort to
optimize how to facilitate and guide the processes of data collection
and analysis to achieve the best possible guidelines for policy
makers. Regarding the gaps in the travel behavior change research
literature, this paper attempted to identify and suggest a
multidimensional framework in order to facilitate planning the
implemented travel behavior change interventions. A structured
mixed-method model is suggested to improve the analytic power of
the results according to the complexity of human behavior.
In order to recognize people’s attitudes towards a specific travel
mode, the Theory of Planned Behavior (TPB) was operationalized.
But in order to capture decision making processes the Transtheoretical
model of Behavior Change (TTM) was also used.
Consequently, the combination of these two theories (TTM and TPB)
has resulted in a synthesis with appropriate concepts to identify and
design an implemented travel behavior change interventions.
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: In this paper, an effective non-destructive, noninvasive
approach for leak detection was proposed. The process relies
on analyzing thermal images collected by an IR viewer device that
captures thermo-grams. In this study a statistical analysis of the
collected thermal images of the ground surface along the expected
leak location followed by a visual inspection of the thermo-grams
was performed in order to locate the leak. In order to verify the
applicability of the proposed approach the predicted leak location
from the developed approach was compared with the real leak
location. The results showed that the expected leak location was
successfully identified with an accuracy of more than 95%.
Abstract: Precise capture of plantar 3D surface of the foot at the
loading gait phases on rigid substrates was found to be valuable for
the assessment of the physiology, health and problems of the feet.
Photogrammetry, a precision 3D spatial data capture technique is
suitable for this type of dynamic application. In this research, the
technique is utilised to study the plantar deformation as a result of
having a strip of kinesiology tape on the plantar surface during the
loading phase of gait. For this pilot study, one healthy adult male
subject was recruited under the University’s human research ethics
guidelines for this preliminary study. The 3D plantar deformation
data with and without applying the tape were analysed. The results
and analyses are presented together with detailed findings.
Abstract: Human motion capture has become one of the major
area of interest in the field of computer vision. Some of the major
application areas that have been rapidly evolving include the
advanced human interfaces, virtual reality and security/surveillance
systems. This study provides a brief overview of the techniques and
applications used for the markerless human motion capture, which
deals with analyzing the human motion in the form of mathematical
formulations. The major contribution of this research is that it
classifies the computer vision based techniques of human motion
capture based on the taxonomy, and then breaks its down into four
systematically different categories of tracking, initialization, pose
estimation and recognition. The detailed descriptions and the
relationships descriptions are given for the techniques of tracking and
pose estimation. The subcategories of each process are further
described. Various hypotheses have been used by the researchers in
this domain are surveyed and the evolution of these techniques have
been explained. It has been concluded in the survey that most
researchers have focused on using the mathematical body models for
the markerless motion capture.
Abstract: A large amount of data is typically stored in relational
databases (DB). The latter can efficiently handle user queries which
intend to elicit the appropriate information from data sources.
However, direct access and use of this data requires the end users to
have an adequate technical background, while they should also cope
with the internal data structure and values presented. Consequently
the information retrieval is a quite difficult process even for IT or DB
experts, taking into account the limited contributions of relational
databases from the conceptual point of view. Ontologies enable users
to formally describe a domain of knowledge in terms of concepts and
relations among them and hence they can be used for unambiguously
specifying the information captured by the relational database.
However, accessing information residing in a database using
ontologies is feasible, provided that the users are keen on using
semantic web technologies. For enabling users form different
disciplines to retrieve the appropriate data, the design of a Graphical
User Interface is necessary. In this work, we will present an
interactive, ontology-based, semantically enable web tool that can be
used for information retrieval purposes. The tool is totally based on
the ontological representation of underlying database schema while it
provides a user friendly environment through which the users can
graphically form and execute their queries.
Abstract: The 3D body movement signals captured during
human-human conversation include clues not only to the content of
people’s communication but also to their culture and personality.
This paper is concerned with automatic extraction of this information
from body movement signals. For the purpose of this research, we
collected a novel corpus from 27 subjects, arranged them into groups
according to their culture. We arranged each group into pairs and
each pair communicated with each other about different topics.
A state-of-art recognition system is applied to the problems of
person, culture, and topic recognition. We borrowed modeling,
classification, and normalization techniques from speech recognition.
We used Gaussian Mixture Modeling (GMM) as the main technique
for building our three systems, obtaining 77.78%, 55.47%, and
39.06% from the person, culture, and topic recognition systems
respectively. In addition, we combined the above GMM systems with
Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and
40.63% accuracy for person, culture, and topic recognition
respectively.
Although direct comparison among these three recognition
systems is difficult, it seems that our person recognition system
performs best for both GMM and GMM-SVM, suggesting that intersubject
differences (i.e. subject’s personality traits) are a major
source of variation. When removing these traits from culture and
topic recognition systems using the Nuisance Attribute Projection
(NAP) and the Intersession Variability Compensation (ISVC)
techniques, we obtained 73.44% and 46.09% accuracy from culture
and topic recognition systems respectively.