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: This paper investigates the characteristics of wall
pressure fluctuations in naturally developing boundary layer flows
on axisymmetric bodies experimentally. The axisymmetric body has
a modified ellipsoidal blunt nose. Flush-mounted microphones are
used to measure the wall pressure fluctuations in the boundary layer
flow over the body. The measurements are performed in a low noise
wind tunnel. It is found that the correlation between the flow regime
and the characteristics of the pressure fluctuations is distinct. The
process from small fluctuation in laminar flow to large fluctuation in
turbulent flow is investigated. Tollmien-Schlichting wave (T-S wave)
is found to generate and develop in transition. Because of the T-S
wave, the wall pressure fluctuations in the transition region are higher
than those in the turbulent boundary layer.
Abstract: Segmentation of left ventricle (LV) from cardiac
ultrasound images provides a quantitative functional analysis of the
heart to diagnose disease. Active Shape Model (ASM) is widely used
for LV segmentation, but it suffers from the drawback that
initialization of the shape model is not sufficiently close to the target,
especially when dealing with abnormal shapes in disease. In this work,
a two-step framework is improved to achieve a fast and efficient LV
segmentation. First, a robust and efficient detection based on Hough
forest localizes cardiac feature points. Such feature points are used to
predict the initial fitting of the LV shape model. Second, ASM is
applied to further fit the LV shape model to the cardiac ultrasound
image. With the robust initialization, ASM is able to achieve more
accurate segmentation. The performance of the proposed method is
evaluated on a dataset of 810 cardiac ultrasound images that are mostly
abnormal shapes. This proposed method is compared with several
combinations of ASM and existing initialization methods. Our
experiment results demonstrate that accuracy of the proposed method
for feature point detection for initialization was 40% higher than the
existing methods. Moreover, the proposed method significantly
reduces the number of necessary ASM fitting loops and thus speeds up
the whole segmentation process. Therefore, the proposed method is
able to achieve more accurate and efficient segmentation results and is
applicable to unusual shapes of heart with cardiac diseases, such as left
atrial enlargement.
Abstract: Digital images are widely used in computer
applications. To store or transmit the uncompressed images
requires considerable storage capacity and transmission bandwidth.
Image compression is a means to perform transmission or storage of
visual data in the most economical way. This paper explains about
how images can be encoded to be transmitted in a multiplexing
time-frequency domain channel. Multiplexing involves packing
signals together whose representations are compact in the working
domain. In order to optimize transmission resources each 4 × 4
pixel block of the image is transformed by a suitable polynomial
approximation, into a minimal number of coefficients. Less than
4 × 4 coefficients in one block spares a significant amount of
transmitted information, but some information is lost. Different
approximations for image transformation have been evaluated as
polynomial representation (Vandermonde matrix), least squares +
gradient descent, 1-D Chebyshev polynomials, 2-D Chebyshev
polynomials or singular value decomposition (SVD). Results have
been compared in terms of nominal compression rate (NCR),
compression ratio (CR) and peak signal-to-noise ratio (PSNR)
in order to minimize the error function defined as the difference
between the original pixel gray levels and the approximated
polynomial output. Polynomial coefficients have been later encoded
and handled for generating chirps in a target rate of about two
chirps per 4 × 4 pixel block and then submitted to a transmission
multiplexing operation in the time-frequency domain.
Abstract: The nutritional composition and hypoglycaemic effect
of crackers produced from blend of sprouted pigeon pea, unripe
plantain and brewers’ spent grain and fed to Alloxan induced diabetic
rat was investigated. Crackers were produced from different blends of
sprouted pigeon pea, unripe plantain and brewers’ spent grain. The
crackers were evaluated for proximate composition, amino acid
profile and antinutritional factors. Blood glucose levels of normal and
diabetic rats fed with the control sample and different formulations of
cracker were measured. The protein content of the samples were
significantly different (p
Abstract: Journal bearings used in IC engines are prone to premature
failures and are likely to fail earlier than the rated life due to
highly impulsive and unstable operating conditions and frequent
starts/stops. Vibration signature extraction and wear debris analysis
techniques are prevalent in industry for condition monitoring of
rotary machinery. However, both techniques involve a great deal of
technical expertise, time, and cost. Limited literature is available on
the application of these techniques for fault detection in reciprocating
machinery, due to the complex nature of impact forces that
confounds the extraction of fault signals for vibration-based analysis
and wear prediction. In present study, a simulation model was developed to investigate
the bearing wear behaviour, resulting because of different operating
conditions, to complement the vibration analysis. In current
simulation, the dynamics of the engine was established first, based on
which the hydrodynamic journal bearing forces were evaluated by
numerical solution of the Reynold’s equation. In addition, the
essential outputs of interest in this study, critical to determine wear
rates are the tangential velocity and oil film thickness between the
journals and bearing sleeve, which if not maintained appropriately,
have a detrimental effect on the bearing performance. Archard’s wear prediction model was used in the simulation to
calculate the wear rate of bearings with specific location information
as all determinative parameters were obtained with reference to crank
rotation. Oil film thickness obtained from the model was used as a
criterion to determine if the lubrication is sufficient to prevent contact
between the journal and bearing thus causing accelerated wear. A
limiting value of 1 μm was used as the minimum oil film thickness
needed to prevent contact. The increased wear rate with growing
severity of operating conditions is analogous and comparable to the
rise in amplitude of the squared envelope of the referenced vibration
signals. Thus on one hand, the developed model demonstrated its
capability to explain wear behaviour and on the other hand it also
helps to establish a co-relation between wear based and vibration
based analysis. Therefore, the model provides a cost effective and
quick approach to predict the impending wear in IC engine bearings
under various operating conditions.
Abstract: The statistical study has become indispensable for various fields of knowledge. Not any different, in Geotechnics the study of probabilistic and statistical methods has gained power considering its use in characterizing the uncertainties inherent in soil properties. One of the situations where engineers are constantly faced is the definition of a probability distribution that represents significantly the sampled data. To be able to discard bad distributions, goodness-of-fit tests are necessary. In this paper, three non-parametric goodness-of-fit tests are applied to a data set computationally generated to test the goodness-of-fit of them to a series of known distributions. It is shown that the use of normal distribution does not always provide satisfactory results regarding physical and behavioral representation of the modeled parameters.
Abstract: Background modeling and subtraction in video
analysis has been widely used as an effective method for moving
objects detection in many computer vision applications. Recently, a
large number of approaches have been developed to tackle different
types of challenges in this field. However, the dynamic background
and illumination variations are the most frequently occurred problems
in the practical situation. This paper presents a favorable two-layer
model based on codebook algorithm incorporated with local binary
pattern (LBP) texture measure, targeted for handling dynamic
background and illumination variation problems. More specifically,
the first layer is designed by block-based codebook combining with
LBP histogram and mean value of each RGB color channel. Because
of the invariance of the LBP features with respect to monotonic
gray-scale changes, this layer can produce block wise detection results
with considerable tolerance of illumination variations. The pixel-based
codebook is employed to reinforce the precision from the output of the
first layer which is to eliminate false positives further. As a result, the
proposed approach can greatly promote the accuracy under the
circumstances of dynamic background and illumination changes.
Experimental results on several popular background subtraction
datasets demonstrate very competitive performance compared to
previous models.
Abstract: This experimental study consists of a characterization
of epoxy grout where an amount of 2% of graphene nanoplatelets
particles were added to commercial epoxy resin to evaluate their
behavior regarding neat epoxy resin. Compressive tests, tensile tests
and flexural tests were conducted to study the effect of graphene
nanoplatelets on neat epoxy resin. By comparing graphene-based and
neat epoxy grout, there is no significant increase of strength due to
weak interface in the graphene nanoplatelets/epoxy composites.
From this experiment, the tension and flexural strength of graphenebased
epoxy grouts is slightly lower than ones of neat epoxy grout.
Nevertheless, the addition of graphene has produced more consistent
results according to a smaller standard deviation of strength.
Furthermore, the graphene has also improved the ductility of the
grout, hence reducing its brittle behaviour. This shows that the
performance of graphene-based grout is reliably predictable and able
to minimise sudden rupture. This is important since repair design of
damaged pipeline is of deterministic nature.
Abstract: Hard coatings are widely used in cutting and forming
tool industries. Titanium Nitride (TiN) possesses good hardness,
strength, and corrosion resistance. The coating properties are
influenced by many process parameters. The coatings were deposited
on steel substrate by changing the process parameters such as
substrate temperature, nitrogen flow rate and target power in a D.C
planer magnetron sputtering. The structure of coatings were analysed
using XRD. The hardness of coatings was found using Micro
hardness tester. From the experimental data, a regression model was
developed and the optimum response was determined using Response
Surface Methodology (RSM).
Abstract: Rapid developments in technology in the present age
have made it necessary for communities to follow technological
developments and adapt themselves to these developments. One of
the fields that are most rapidly affected by these developments is
undoubtedly education. Determination of the attitudes of preservice
teachers, who live in an age of technology and get ready to raise
future individuals, is of paramount importance both educationally and
professionally. The purpose of this study was to analyze attitudes of
preservice teachers towards technology and some variables that
predict these attitudes (gender, daily duration of internet use, and the
number of technical devices owned). 329 preservice teachers
attending the education faculty of a large university in central Turkey
participated, on a volunteer basis, in this study, where relational
survey model was used as the research method. Research findings
reveal that preservice teachers’ attitudes towards technology are
positive and at the same time, the attitudes of male preservice
teachers towards technology are more positive than their female
counterparts. As a result of the stepwise multiple regression analysis
where factors predicting preservice teachers’ attitudes towards
technology, it was found that duration of daily internet use was the
strongest predictor of attitudes towards technology.
Abstract: Web Usage Mining is the application of data mining
techniques to find usage patterns from web log data, so as to grasp
required patterns and serve the requirements of Web-based
applications. User’s expertise on the internet may be improved by
minimizing user’s web access latency. This may be done by
predicting the future search page earlier and the same may be prefetched
and cached. Therefore, to enhance the standard of web
services, it is needed topic to research the user web navigation
behavior. Analysis of user’s web navigation behavior is achieved
through modeling web navigation history. We propose this technique
which cluster’s the user sessions, based on the K-medoids technique.
Abstract: Context-aware technologies provide system
applications with the awareness of environmental conditions,
customer behaviours, object movements, etc. Further, with such
capability system applications can be smart to intelligently adapt their
responses to the changing conditions. In regard to business
operations, this promises businesses that their business processes can
run more intelligently, adaptively and flexibly, and thereby either
improve customer experience, enhance reliability of service delivery,
or lower operational cost, to make the business more competitive and
sustainable. Aiming at realising such context-aware business process
management, this paper firstly explores its potential benefit, and then
identifies some gaps between the current business process
management support and the expected. In addition, some preliminary
solutions are also discussed in regard to context definition, rule-based
process execution, run-time process evolution, etc. A framework is
also presented to give a conceptual architecture of context-aware
business process management system to guide system
implementation.
Abstract: Fluctuations of Schottky diode parameters in a
structure of the mixer are investigated. These fluctuations are
manifested in two ways. At the first, they lead to fluctuations in the
transfer factor that is lead to the amplitude fluctuations in the signal
of intermediate frequency. On the basis of the measurement data of
1/f noise of the diode at forward current, the estimation of a spectrum
of relative fluctuations in transfer factor of the mixer is executed.
Current dependence of the spectrum of relative fluctuations in
transfer factor of the mixer and dependence of the spectrum of
relative fluctuations in transfer factor of the mixer on the amplitude
of the heterodyne signal are investigated. At the second, fluctuations
in parameters of the diode lead to occurrence of 1/f noise in the
output signal of the mixer. This noise limits the sensitivity of the
mixer to the value of received signal.
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: The purposes of this study were to: 1) investigate
operation conditions of SME automotive part industry in Bangkok
and vicinity and 2) to compare operation problem levels of SME
automotive part industry in Bangkok and vicinity according to the
sizes of the enterprises. Samples in this study included 196
entrepreneurs of SME automotive part industry in Bangkok and
vicinity derived from simple random sampling and calculation from
R. V. Krejcie and D. W. Morgan’s tables. Research statistics included
frequency, percentage, mean, standard deviation, and T-test. The
results revealed that in general the problem levels of SME automotive
part industry in Bangkok and vicinity were high. When considering
in details, it was found that the problem levels were high at every
aspect, i.e. personal, production, export, finance, and marketing
respectively. The comparison of the problem levels according to the
sizes of the enterprises revealed statistically significant differences at
.05. When considering on each aspect, it was found that the aspect
with the statistical difference at .05 included 5 aspects, i.e.
production, marketing, finance, personal, and export. The findings
also showed that small enterprises faced more severe problems than
those of medium enterprises.
Abstract: Value addition to agricultural produce is of possible
potential in reducing poverty, improving food security and
malnutrition, therefore the need to develop small and microenterprises
of sweet potato production. A study was carried out in Nigeria to determine the acceptability
of blends sweet potato (Ipomea batatas) and commodities yellow
maize (Zea mays), millet (Pennisetum glaucum), soybean (Glycine
max), bambara groundnut (Vigna subterranean), guinea corn
(Sorghum vulgare), wheat (Triticum aestivum), and roselle (Hibiscus
sabdariffa) through sensory evaluation. Sweet potato (Ipomea batatas) roots were processed using two
methods: oven and sun drying. The blends were also assessed in
terms of functional, chemical and color properties. Most acceptable blends include BAW (80:20 of sweet
potato/wheat), BBC (80:20 of sweet potato/guinea corn), AAB (60:40
of sweet potato/guinea corn), YTE (100% soybean), TYG (100%
sweet potato), KTN (100% wheat flour), XGP (80:20 of sweet
potato/soybean), XAX (60:40 of sweet potato/wheat), LSS (100%
Roselle), CHK (100% Guinea corn), and ABC (60:40% of sweet
potato/ yellow maize). In addition, carried out chemical analysis
revealed that sweet potato has high percentage of vitamins A and C,
potassium (K), manganese (Mn), calcium (Ca), magnesium (Mg) and
iron (Fe) and fibre content. There is also an increase of vitamin A and
Iron in the blended products.
Abstract: A broadband wire monopole antenna loaded by inhomogeneous stack of annular dielectric ring resonators (DRRs) is proposed. The proposed antenna exhibits a broad impedance bandwidth from 3 to 30 GHz. This is achieved by adding an external step matching network at the antenna feed point. The matching network is comprised of three annular DRRs possessing different permittivity values and sharing the same axial over a finite ground plane. The antenna performance is characterized using full-wave EM simulation. Compared to previous-reported wire antennas with improved bandwidth achieved by DRRs, the proposed topology provides relatively compact realization and superior broadband performance.
Abstract: Dengue is a mosquito-borne viral disease endemic in
many countries in the tropics and sub-tropics. The state of Punjab in
India shows cyclical and seasonal variation in dengue cases. The
Case Fatality Rate of Dengue has ranged from 0.6 to 1.0 in the past
years. The department has initiated review of the cases that have died
due to dengue in order to know the exact cause of the death in a case
of dengue. The study has been undertaken to know the other
associated co-morbidities and factors causing death in a case of
dengue. The study used the predesigned proforma on which the
records (medical and Lab) were recorded and reviewed by the expert
committee of the doctors. This study has revealed that cases of
dengue having co-morbidities have longer stay in hospital. Fluid
overload and co-morbidities have been found as major factors leading
to death, however, in a confirmed case of dengue hepatorenal
shutdown was found to be major cause of mortality. The data
obtained will help in sensitizing the treating physicians in order to
decrease the mortality due to dengue in future.
Abstract: The Internet of Things (IoT) field has been applied in
industries with different purposes. Sensing Enterprise (SE) is an
attribute of an enterprise or a network that allows it to react to
business stimuli originating on the Internet. These fields have come
into focus recently on the enterprises, and there is some evidence of
the use and implications in supply chain management, while
finding it as an interesting aspect to work on. This paper presents a
revision and proposals of IoT applications in supply chain
management.