Abstract: Recurrent event data is a special type of multivariate
survival data. Dynamic and frailty models are one of the approaches
that dealt with this kind of data. A comparison between these two
models is studied using the empirical standard deviation of the
standardized martingale residual processes as a way of assessing the
fit of the two models based on the Aalen additive regression model.
Here we found both approaches took heterogeneity into account and
produce residual standard deviations close to each other both in the
simulation study and in the real data set.
Abstract: Although many studies on the assembly technology of
the bridge construction have dealt mostly with on the pier, girder or the
deck of the bridge, studies on the prefabricated barrier have rarely been
performed. For understanding structural characteristics and
application of the concrete barrier in the modular bridge, which is an
assembly of structure members, static loading test was performed.
Structural performances as a road barrier of the three methods,
conventional cast-in-place(ST), vertical bolt connection(BVC) and
horizontal bolt connection(BHC) were evaluated and compared
through the analyses of load-displacement curves, strain curves of the
steel, concrete strain curves and the visual appearances of crack
patterns. The vertical bolt connection(BVC) method demonstrated
comparable performance as an alternative to conventional
cast-in-place(ST) while providing all the advantages of prefabricated
technology. Necessities for the future improvement in nuts
enforcement as well as legal standard and regulation are also
addressed.
Abstract: The present paper considers the steady free convection
boundary layer flow of a viscoelastic fluid on solid sphere with
Newtonian heating. The boundary layer equations are an order higher
than those for the Newtonian (viscous) fluid and the adherence
boundary conditions are insufficient to determine the solution of
these equations completely. Thus, the augmentation an extra
boundary condition is needed to perform the numerical
computational. The governing boundary layer equations are first
transformed into non-dimensional form by using special
dimensionless group and then solved by using an implicit finite
difference scheme. The results are displayed graphically to illustrate
the influence of viscoelastic K and Prandtl Number Pr parameters on
skin friction, heat transfer, velocity profiles and temperature profiles.
Present results are compared with the published papers and are found
to concur very well.
Abstract: This paper discuss the separation of the miscible
liquids by means of fractional distillation. For complete separation of
liquids, the process of heating, condensation, separation and storage
is done automatically to achieve the objective. PIC micro-controller
has been used to control each and every process of the work. The
controller also controls the storage process by activating and deactivating
the conveyors. The liquids are heated which on reaching
their respective boiling points evaporate and enter the condensation
chamber where they convert into liquid. The liquids are then directed
to their respective tanks by means of stepper motor which moves in
three directions, each movement into different tank. The tank on
filling sends the signal to controller which then opens the solenoid
valves. The tank is emptied into the beakers below the nozzle. As the
beaker filled, the nozzle closes and the conveyors come into
operation. The filled beaker is replaced by an empty beaker from
behind. The work can be used in oil industries, chemical industries
and paint industries.
Abstract: This study was conducted to explore the effects of two
countries model comparison program in Taiwan and Singapore in
TIMSS database. The researchers used Multi-Group Hierarchical
Linear Modeling techniques to compare the effects of two different
country models and we tested our hypotheses on 4,046 Taiwan
students and 4,599 Singapore students in 2007 at two levels: the class
level and student (individual) level. Design quality is a class level
variable. Student level variables are achievement and self-confidence.
The results challenge the widely held view that retention has a positive
impact on self-confidence. Suggestions for future research are
discussed.
Abstract: To compare Healing Effects of an
Ayurvedic Preparation and Silver Sulfadiazine on burn wounds in
Albino Rats.
Methods: Albino rats– 30 male / female rats weighing between
150-200 g were used in the study. They were individually housed and
maintained on normal diet and water ad libitum. Partial thickness
burn wounds were inflicted, on overnight-starved animals under
pentobarbitone (30mg/kg, i.p.) anaesthesia, by pouring hot molten
wax at 80oC into a plastic cylinder of 300 mm2 circular openings
placed on the shaven back of the animal. Apart from the drugs under
investigation no local/ systemic chemotherapeutic cover will be
provided to animals. All the animals were assessed for the percentage
of wound contraction, signs of infection, scab formation and
histopathological examination.
Results: Percentage of wound healing was significantly better in
the test ointment group compared to the standard. Signs of infection
were observed in more animals in the test ointment group compared
to the standard. Scab formation also took place earlier in the test
ointment group compared to standard. Epithelial regeneration and
healing profile was better in the test ointment compared to the
standard. Moreover the test ointment group did not show any raised
margins in the wound or blackish discoloration as was observed in
silver sulfadiazine group.
Conclusion: The burn wound healing effect of the ayurvedic
ointment under study is better in comparison to standard therapy of
silver sulfadiazine. The problem of infection encountered with the
test ointment can be overcome by changing the concentrations and
proportions of the ingredients in the test ointment which constitutes
the further plan of the study.
Abstract: Dengue disease is an infectious vector-borne viral
disease that is commonly found in tropical and sub-tropical regions,
especially in urban and semi-urban areas, around the world and
including Malaysia. There is no currently available vaccine or
chemotherapy for the prevention or treatment of dengue disease.
Therefore prevention and treatment of the disease depend on vector
surveillance and control measures. Disease risk mapping has been
recognized as an important tool in the prevention and control
strategies for diseases. The choice of statistical model used for
relative risk estimation is important as a good model will
subsequently produce a good disease risk map. Therefore, the aim of
this study is to estimate the relative risk for dengue disease based
initially on the most common statistic used in disease mapping called
Standardized Morbidity Ratio (SMR) and one of the earliest
applications of Bayesian methodology called Poisson-gamma model.
This paper begins by providing a review of the SMR method, which
we then apply to dengue data of Perak, Malaysia. We then fit an
extension of the SMR method, which is the Poisson-gamma model.
Both results are displayed and compared using graph, tables and
maps. Results of the analysis shows that the latter method gives a
better relative risk estimates compared with using the SMR. The
Poisson-gamma model has been demonstrated can overcome the
problem of SMR when there is no observed dengue cases in certain
regions. However, covariate adjustment in this model is difficult and
there is no possibility for allowing spatial correlation between risks in
adjacent areas. The drawbacks of this model have motivated many
researchers to propose other alternative methods for estimating the
risk.
Abstract: This paper presents an effective traffic lights
recognition method at the daytime. First, Potential Traffic Lights
Detector (PTLD) use whole color source of YCbCr channel image and
make each binary image of green and red traffic lights. After PTLD
step, Shape Filter (SF) use to remove noise such as traffic sign, street
tree, vehicle, and building. At this time, noise removal properties
consist of information of blobs of binary image; length, area, area of
boundary box, etc. Finally, after an intermediate association step witch
goal is to define relevant candidates region from the previously
detected traffic lights, Adaptive Multi-class Classifier (AMC) is
executed. The classification method uses Haar-like feature and
Adaboost algorithm. For simulation, we are implemented through Intel
Core CPU with 2.80 GHz and 4 GB RAM and tested in the urban and
rural roads. Through the test, we are compared with our method and
standard object-recognition learning processes and proved that it
reached up to 94 % of detection rate which is better than the results
achieved with cascade classifiers. Computation time of our proposed
method is 15 ms.
Abstract: In Multiple Sclerosis, pathological changes in the
brain results in deviations in signal intensity on Magnetic Resonance
Images (MRI). Quantitative analysis of these changes and their
correlation with clinical finding provides important information for
diagnosis. This constitutes the objective of our work. A new approach
is developed. After the enhancement of images contrast and the brain
extraction by mathematical morphology algorithm, we proceed to the
brain segmentation. Our approach is based on building statistical
model from data itself, for normal brain MRI and including clustering
tissue type. Then we detect signal abnormalities (MS lesions) as a
rejection class containing voxels that are not explained by the built
model. We validate the method on MR images of Multiple Sclerosis
patients by comparing its results with those of human expert
segmentation.
Abstract: This study investigated the relationship between
exercise imagery use and level of physical activity within a wide
range of exercisers in Klang valley, Malaysia. One hundred and
twenty four respondents (Mage = 28.92, SD = 9.34) completed two
sets of questionnaires (Exercise Imagery Inventory and Leisure-Time
Exercise Questionnaire) that measure the use of imagery and exercise
frequency of participants. From the result obtained, exercise imagery
is found to be significantly correlated to level of physical activity.
Besides that, variables such as gender, age and ethnicity that may
affect the use of imagery and exercise frequency were also being
assessed in this study. Among all variables, only ethnicity showed
significant difference in level of physical activity (p < 0.05). Findings
in this study suggest that further investigation should be done on
other variables such as socioeconomic, educational level, and selfefficacy
that may affect the imagery use and frequency of physical
activity among exercisers.
Abstract: In recent years, a number of works proposing the
combination of multiple classifiers to produce a single
classification have been reported in remote sensing literature. The
resulting classifier, referred to as an ensemble classifier, is
generally found to be more accurate than any of the individual
classifiers making up the ensemble. As accuracy is the primary
concern, much of the research in the field of land cover
classification is focused on improving classification accuracy. This
study compares the performance of four ensemble approaches
(boosting, bagging, DECORATE and random subspace) with a
univariate decision tree as base classifier. Two training datasets,
one without ant noise and other with 20 percent noise was used to
judge the performance of different ensemble approaches. Results
with noise free data set suggest an improvement of about 4% in
classification accuracy with all ensemble approaches in
comparison to the results provided by univariate decision tree
classifier. Highest classification accuracy of 87.43% was achieved
by boosted decision tree. A comparison of results with noisy data
set suggests that bagging, DECORATE and random subspace
approaches works well with this data whereas the performance of
boosted decision tree degrades and a classification accuracy of
79.7% is achieved which is even lower than that is achieved (i.e.
80.02%) by using unboosted decision tree classifier.
Abstract: This paper presents a numerical analysis of the
performance of a five-bladed Darrieus vertical-axis water turbine,
based on the NACA 0025 blade profile, for both bare and shrouded
configurations. A complete campaign of 2-D simulations, performed
for several values of tip speed ratio and based on RANS unsteady
calculations, has been performed to obtain the rotor torque and power
curves. Also the effect of a NACA-shaped central hydrofoil has been
investigated, with the aim of evaluating the impact of a solid
blockage on the performance of the shrouded rotor configuration.
The beneficial effect of the shroud on rotor overall performances
has clearly been evidenced, while the adoption of the central
hydrofoil has proved to be detrimental, being the resulting flow slow
down (due to the presence of the obstacle) much higher with respect
to the flow acceleration (due to the solid blockage effect).
Abstract: In this paper, different approaches to solve the
forward kinematics of a three DOF actuator redundant hydraulic
parallel manipulator are presented. On the contrary to series
manipulators, the forward kinematic map of parallel manipulators
involves highly coupled nonlinear equations, which are almost
impossible to solve analytically. The proposed methods are using
neural networks identification with different structures to solve the
problem. The accuracy of the results of each method is analyzed in
detail and the advantages and the disadvantages of them in
computing the forward kinematic map of the given mechanism is
discussed in detail. It is concluded that ANFIS presents the best
performance compared to MLP, RBF and PNN networks in this
particular application.
Abstract: Heart failure is the most common reason of death
nowadays, but if the medical help is given directly, the patient-s life
may be saved in many cases. Numerous heart diseases can be
detected by means of analyzing electrocardiograms (ECG). Artificial
Neural Networks (ANN) are computer-based expert systems that
have proved to be useful in pattern recognition tasks. ANN can be
used in different phases of the decision-making process, from
classification to diagnostic procedures. This work concentrates on a
review followed by a novel method.
The purpose of the review is to assess the evidence of healthcare
benefits involving the application of artificial neural networks to the
clinical functions of diagnosis, prognosis and survival analysis, in
ECG signals. The developed method is based on a compound neural
network (CNN), to classify ECGs as normal or carrying an
AtrioVentricular heart Block (AVB). This method uses three
different feed forward multilayer neural networks. A single output
unit encodes the probability of AVB occurrences. A value between 0
and 0.1 is the desired output for a normal ECG; a value between 0.1
and 1 would infer an occurrence of an AVB. The results show that
this compound network has a good performance in detecting AVBs,
with a sensitivity of 90.7% and a specificity of 86.05%. The accuracy
value is 87.9%.
Abstract: This paper presents a system overview of Mobile to Server Face Recognition, which is a face recognition application developed specifically for mobile phones. Images taken from mobile phone cameras lack of quality due to the low resolution of the cameras. Thus, a prototype is developed to experiment the chosen method. However, this paper shows a result of system backbone without the face recognition functionality. The result demonstrated in this paper indicates that the interaction between mobile phones and server is successfully working. The result shown before the database is completely ready. The system testing is currently going on using real images and a mock-up database to test the functionality of the face recognition algorithm used in this system. An overview of the whole system including screenshots and system flow-chart are presented in this paper. This paper also presents the inspiration or motivation and the justification in developing this system.
Abstract: The study in this paper underlines the importance of
correct joint selection of the spreading codes for uplink of multicarrier
code division multiple access (MC-CDMA) at the transmitter
side and detector at the receiver side in the presence of nonlinear
distortion due to high power amplifier (HPA). The bit error rate
(BER) of system for different spreading sequences (Walsh code, Gold
code, orthogonal Gold code, Golay code and Zadoff-Chu code) and
different kinds of receivers (minimum mean-square error receiver
(MMSE-MUD) and microstatistic multi-user receiver (MSF-MUD))
is compared by means of simulations for MC-CDMA transmission
system. Finally, the results of analysis will show, that the application
of MSF-MUD in combination with Golay codes can outperform
significantly the other tested spreading codes and receivers for all
mostly used models of HPA.
Abstract: In this paper, an adaptive radio resource allocation
(RRA) algorithm applying to multiple traffic OFDMA system is
proposed, which distributes sub-carrier and loading bits among users
according to their different QoS requirements and traffic class. By
classifying and prioritizing the users based on their traffic
characteristic and ensuring resource for higher priority users, the
scheme decreases tremendously the outage probability of the users
requiring a real time transmission without impact on the spectrum
efficiency of system, as well as the outage probability of data users is
not increased compared with the RRA methods published.
Abstract: This paper presents comparative study on recent
integer DCTs and a new method to construct a low sensitive structure
of integer DCT for colored input signals. The method refers to
sensitivity of multiplier coefficients to finite word length as an
indicator of how word length truncation effects on quality of output
signal. The sensitivity is also theoretically evaluated as a function of
auto-correlation and covariance matrix of input signal. The structure of
integer DCT algorithm is optimized by combination of lower sensitive
lifting structure types of IRT. It is evaluated by the sensitivity of
multiplier coefficients to finite word length expression in a function of
covariance matrix of input signal. Effectiveness of the optimum
combination of IRT in integer DCT algorithm is confirmed by quality
improvement comparing with existing case. As a result, the optimum
combination of IRT in each integer DCT algorithm evidently improves
output signal quality and it is still compatible with the existing one.
Abstract: Retrieval image by shape similarity, given a template
shape is particularly challenging, owning to the difficulty to derive a
similarity measurement that closely conforms to the common
perception of similarity by humans. In this paper, a new method for the
representation and comparison of shapes is present which is based on
the shape matrix and snake model. It is scaling, rotation, translation
invariant. And it can retrieve the shape images with some missing or
occluded parts. In the method, the deformation spent by the template
to match the shape images and the matching degree is used to evaluate
the similarity between them.
Abstract: This paper describes an algorithm to estimate realtime vehicle velocity using image processing technique from the known camera calibration parameters. The presented algorithm involves several main steps. First, the moving object is extracted by utilizing frame differencing technique. Second, the object tracking method is applied and the speed is estimated based on the displacement of the object-s centroid. Several assumptions are listed to simplify the transformation of 2D images from 3D real-world images. The results obtained from the experiment have been compared to the estimated ground truth. From this experiment, it exhibits that the proposed algorithm has achieved the velocity accuracy estimation of about ± 1.7 km/h.