Abstract: This paper presents the results of thermo-mechanical
characterization of Glass/Epoxy composite specimens using Infrared
Thermography technique. The specimens used for the study were
fabricated in-house with three different lay-up sequences and tested
on a servo hydraulic machine under uni-axial loading. Infrared
Camera was used for on-line monitoring surface temperature changes
of composite specimens during tensile deformation.
Experimental results showed that thermomechanical
characteristics of each type of specimens were distinct. Temperature
was found to be decreasing linearly with increasing tensile stress in
the elastic region due to thermo-elastic effect. Yield point could be
observed by monitoring the change in temperature profile during
tensile testing and this value could be correlated with the results
obtained from stress-strain response. The extent of prior plastic
deformation in the post-yield region influenced the slopes of
temperature response during tensile loading. Partial unloading and
reloading of specimens post-yield results in change in slope in elastic
and plastic regions of composite specimens.
Abstract: In this paper, we propose a new robust and secure
system that is based on the combination between two different
transforms Discrete wavelet Transform (DWT) and Contourlet
Transform (CT). The combined transforms will compensate the
drawback of using each transform separately. The proposed
algorithm has been designed, implemented and tested successfully.
The experimental results showed that selecting the best sub-band for
embedding from both transforms will improve the imperceptibility
and robustness of the new combined algorithm. The evaluated
imperceptibility of the combined DWT-CT algorithm which gave a
PSNR value 88.11 and the combination DWT-CT algorithm
improves robustness since it produced better robust against Gaussian
noise attack. In addition to that, the implemented system shored a
successful extraction method to extract watermark efficiently.
Abstract: The purpose of this study was to find out the
effectiveness of neurological impress method and repeated reading
technique on reading fluency of children with learning disabilities.
Thirty primary four pupils in three public primary schools
participated in the study. There were two experimental groups and a
control. This research employed a 3 by 2 factorial matrix and the
participants were taught for one session. Two hypotheses were
formulated to guide the research. T-test was used to analyse the data
gathered, and data analysis revealed that pupils exposed to the two
treatment strategies had improvement in their reading fluency. It was
recommended that the two strategies used in the study can be used to
intervene in reading fluency problems in children with learning
disabilities.
Abstract: Wireless sensor network has recently emerged as enablers
of several areas. Real applications of WSN are being explored
and some of them are yet to come. While the potential of sensor
networks has been only beginning to be realized, several challenges
still remain. One of them is the experimental evaluation of WSN.
Therefore, deploying and operating a testbed to study the real
behavior of WSN become more and more important. The main
contribution of this work is to analysis the RF link budget behavior
of wireless sensor networks in underground mine gallery.
Abstract: Research has suggested that implicit learning tasks
may rely on episodic processing to generate above chance
performance on the standard classification tasks. The current
research examines the invariant features task (McGeorge and Burton,
1990) and argues that such episodic processing is indeed important.
The results of the experiment suggest that both rejection and
similarity strategies are used by participants in this task to
simultaneously reject unfamiliar items and to accept (falsely) familiar
items. Primarily these decisions are based on the presence of low or
high frequency goal based features of the stimuli presented in the
incidental learning phase. It is proposed that a goal based analysis of
the incidental learning task provides a simple step in understanding
which features of the episodic processing are most important for
explaining the match between incidental, implicit learning and test
performance.
Abstract: The research study was based on an evaluation of the
ability of glued test samples to pass the criterion of sufficient
bondline adhesion under the exposure conditions defined in EN 302-
1. Additionally, an infrared spectroscopic analysis of the evaluated
adhesives (phenol-resorcinol-formaldehyde PRF and melamine-ureaformaldehyde
MUF) with different mix ratios was carried out to
evaluate the possible effects of a faulty technological process.
Abstract: This paper systematically investigates the timedependent
health outcomes for office staff during computer work
using the developed mathematical model. The model describes timedependent
health outcomes in multiple body regions associated with
computer usage. The association is explicitly presented with a doseresponse
relationship which is parametrized by body region
parameters. Using the developed model we perform extensive
investigations of the health outcomes statically and dynamically. We
compare the risk body regions and provide various severity rankings
of the discomfort rate changes with respect to computer-related
workload dynamically for the study population. Application of the
developed model reveals a wide range of findings. Such broad
spectrum of investigations in a single report literature is lacking.
Based upon the model analysis, it is discovered that the highest
average severity level of the discomfort exists in neck, shoulder, eyes,
shoulder joint/upper arm, upper back, low back and head etc. The
biggest weekly changes of discomfort rates are in eyes, neck, head,
shoulder, shoulder joint/upper arm and upper back etc. The fastest
discomfort rate is found in neck, followed by shoulder, eyes, head,
shoulder joint/upper arm and upper back etc. Most of our findings are
consistent with the literature, which demonstrates that the developed
model and results are applicable and valuable and can be utilized to
assess correlation between the amount of computer-related workload
and health risk.
Abstract: Prediction of highly non linear behavior of suspended
sediment flow in rivers has prime importance in the field of water
resources engineering. In this study the predictive performance of
two Artificial Neural Networks (ANNs) namely, the Radial Basis
Function (RBF) Network and the Multi Layer Feed Forward (MLFF)
Network have been compared. Time series data of daily suspended
sediment discharge and water discharge at Pari River was used for
training and testing the networks. A number of statistical parameters
i.e. root mean square error (RMSE), mean absolute error (MAE),
coefficient of efficiency (CE) and coefficient of determination (R2)
were used for performance evaluation of the models. Both the models
produced satisfactory results and showed a good agreement between
the predicted and observed data. The RBF network model provided
slightly better results than the MLFF network model in predicting
suspended sediment discharge.
Abstract: Software security testing is an important means to ensure software security and trustiness. This paper first mainly discusses the definition and classification of software security testing, and investigates methods and tools of software security testing widely. Then it analyzes and concludes the advantages and disadvantages of various methods and the scope of application, presents a taxonomy of security testing tools. Finally, the paper points out future focus and development directions of software security testing technology.
Abstract: We suggest a novel method to incorporate longterm
redundancy (LTR) in signal time domain compression
methods. The proposition is based on block-sorting and curve
simplification. The proposition is illustrated on the ECG
signal as a post-processor for the FAN method. Test
applications on the new so-obtained FAN+ method using the
MIT-BIH database show substantial improvement of the
compression ratio-distortion behavior for a higher quality
reconstructed signal.
Abstract: Medical image registration is the key technology in image guided radiation therapy (IGRT) systems. On the basis of the previous work on our IGRT prototype with a biorthogonal x-ray imaging system, we described a method focused on the 2D/2D rigid-body registration using multiresolution pyramid based mutual information in this paper. Three key steps were involved in the method ： firstly, four 2D images were obtained including two x-ray projection images and two digital reconstructed radiographies（DRRs ） as the input for the registration ； Secondly, each pair of the corresponding x-ray image and DRR image were matched using multiresolution pyramid based mutual information under the ITK registration framework ； Thirdly, we got the final couch offset through a coordinate transformation by calculating the translations acquired from the two pairs of the images. A simulation example of a parotid gland tumor case and a clinical example of an anthropomorphic head phantom were employed in the verification tests. In addition, the influence of different CT slice thickness were tested. The simulation results showed that the positioning errors were 0.068±0.070, 0.072±0.098, 0.154±0.176mm along three axes which were lateral, longitudinal and vertical. The clinical test indicated that the positioning errors of the planned isocenter were 0.066, 0.07, 2.06mm on average with a CT slice thickness of 2.5mm. It can be concluded that our method with its verified accuracy and robustness can be effectively used in IGRT systems for patient setup.
Abstract: This paper describes the authorization system
architecture for Pervasive Grid environment. It discusses the
characteristics of classical authorization system and requirements of
the authorization system in pervasive grid environment as well.
Based on our analysis of current systems and taking into account the
main requirements of such pervasive environment, we propose new
authorization system architecture as an extension of the existing grid
authorization mechanisms. This architecture not only supports user
attributes but also context attributes which act as a key concept for
context-awareness thought. The architecture allows authorization of
users dynamically when there are changes in the pervasive grid
environment. For this, we opt for hybrid authorization method that
integrates push and pull mechanisms to combine the existing grid
authorization attributes with dynamic context assertions. We will
investigate the proposed architecture using a real testing environment
that includes heterogeneous pervasive grid infrastructures mapped
over multiple virtual organizations. Various scenarios are described
in the last section of the article to strengthen the proposed mechanism
with different facilities for the authorization procedure.
Abstract: The current research paper is an implementation of
Eigen Faces and Karhunen-Loeve Algorithm for face recognition.
The designed program works in a manner where a unique
identification number is given to each face under trial. These faces
are kept in a database from where any particular face can be matched
and found out of the available test faces. The Karhunen –Loeve
Algorithm has been implemented to find out the appropriate right
face (with same features) with respect to given input image as test
data image having unique identification number. The procedure
involves usage of Eigen faces for the recognition of faces.
Abstract: Cardiac pulse-related artifacts in the EEG recorded
simultaneously with fMRI are complex and highly variable. Their
effective removal is an unsolved problem. Our aim is to develop an
adaptive removal algorithm based on the matching pursuit (MP)
technique and to compare it to established methods using a visual
evoked potential (VEP). We recorded the VEP inside the static
magnetic field of an MR scanner (with artifacts) as well as in an
electrically shielded room (artifact free). The MP-based artifact
removal outperformed average artifact subtraction (AAS) and
optimal basis set removal (OBS) in terms of restoring the EEG field
map topography of the VEP. Subsequently, a dipole model was fitted
to the VEP under each condition using a realistic boundary element
head model. The source location of the VEP recorded inside the MR
scanner was closest to that of the artifact free VEP after cleaning
with the MP-based algorithm as well as with AAS. While none of the
tested algorithms offered complete removal, MP showed promising
results due to its ability to adapt to variations of latency, frequency
and amplitude of individual artifact occurrences while still utilizing a
common template.
Abstract: In this paper, an ultrasonic technique is proposed to
predict oil content in a fresh palm fruit. This is accomplished by
measuring the attenuation based on ultrasonic transmission mode.
Several palm fruit samples with known oil content by Soxhlet
extraction (ISO9001:2008) were tested with our ultrasonic
measurement. Amplitude attenuation data results for all palm samples
were collected. The Feedforward Neural Networks (FNNs) are
applied to predict the oil content for the samples. The Root Mean
Square Error (RMSE) and Mean Absolute Error (MAE) of the FNN
model for predicting oil content percentage are 7.6186 and 5.2287
with the correlation coefficient (R) of 0.9193.
Abstract: Alcohol and water extracts of Cymbopogon citratus
was investigated for anti-bacterial properties and phytochemical
constituents. The extract was screened against four gram-negative
bacteria Escherichia coli, Klebsiella pneumoniae, Pseudomonas
aeruginosa, Proteus vulgaris) and two grampositive bacteria Bacillus
subtilis and Staphylococcus aureus at four different concentrations
(1:1, 1:5, 1:10 and 1:20) using disc diffusion method. The antibacterial
examination was by disc diffusion techniques, while the
photochemical constituents were investigated using standard
chemical methods. Results showed that the extracts inhibited the
growth of standard and local strains of the organisms used. The
treatments were significantly different (P = 0.05). The minimum
inhibitory concentration of the extracts against the tested
microorganisms ranged between 150mg/ml and 50mg/ml. The
alcohol extracts were found to be generally more effective than the
water extract. The photochemical analysis revealed the presence of
alkaloids and phenol but absence of cardiac and cyanogenic
glycosides. The presence of alkaloid and phenols were inferred as
being responsible for the anti-bacterial properties of the extracts.
Abstract: One of the main trouble in a steel strip manufacturing
line is the breakage of whatever weld carried out between steel coils,
that are used to produce the continuous strip to be processed. A weld
breakage results in a several hours stop of the manufacturing line. In
this process the damages caused by the breakage must be repaired.
After the reparation and in order to go on with the production it will
be necessary a restarting process of the line. For minimizing this
problem, a human operator must inspect visually and manually each
weld in order to avoid its breakage during the manufacturing process.
The work presented in this paper is based on the Bayesian decision
theory and it presents an approach to detect, on real-time, steel strip
defective welds. This approach is based on quantifying the tradeoffs
between various classification decisions using probability and the
costs that accompany such decisions.
Abstract: The present study was designed to test the influence
of confirmed expectations, perceived usefulness and perceived
competence on e-learning satisfaction among university teachers. A
questionnaire was completed by 125 university teachers from 12
different universities in Norway. We found that 51% of the variance
in university teachers- satisfaction with e-learning could be explained
by the three proposed antecedents. Perceived usefulness seems to be
the most important predictor of teachers- satisfaction with e-learning.
Abstract: Clustering is the process of subdividing an input data set into a desired number of subgroups so that members of the same subgroup are similar and members of different subgroups have diverse properties. Many heuristic algorithms have been applied to the clustering problem, which is known to be NP Hard. Genetic algorithms have been used in a wide variety of fields to perform clustering, however, the technique normally has a long running time in terms of input set size. This paper proposes an efficient genetic algorithm for clustering on very large data sets, especially on image data sets. The genetic algorithm uses the most time efficient techniques along with preprocessing of the input data set. We test our algorithm on both artificial and real image data sets, both of which are of large size. The experimental results show that our algorithm outperforms the k-means algorithm in terms of running time as well as the quality of the clustering.
Abstract: Intrusion Detection System is significant in network
security. It detects and identifies intrusion behavior or intrusion
attempts in a computer system by monitoring and analyzing the
network packets in real time. In the recent year, intelligent algorithms
applied in the intrusion detection system (IDS) have been an
increasing concern with the rapid growth of the network security.
IDS data deals with a huge amount of data which contains irrelevant
and redundant features causing slow training and testing process,
higher resource consumption as well as poor detection rate. Since the
amount of audit data that an IDS needs to examine is very large even
for a small network, classification by hand is impossible. Hence, the
primary objective of this review is to review the techniques prior to
classification process suit to IDS data.