Abstract: Speech Segmentation is the measure of the change
point detection for partitioning an input speech signal into regions
each of which accords to only one speaker. In this paper, we apply
two features based on multi-scale product (MP) of the clean speech,
namely the spectral centroid of MP, and the zero crossings rate of
MP. We focus on multi-scale product analysis as an important tool
for segmentation extraction. The MP is based on making the product
of the speech wavelet transform coefficients (WTC). We have
estimated our method on the Keele database. The results show the
effectiveness of our method. It indicates that the two features can find
word boundaries, and extracted the segments of the clean speech.
Abstract: Fiber Bragg optic sensor is embedded in composite
material to detect and monitor the damage that occurs in composite
structures. In this paper, we deal with the mode-Ι delamination to
determine the material strength to crack propagation, using the
coupling mode theory and T-matrix method to simulate the FBGs
spectrum for both uniform and non-uniform strain distribution. The
double cantilever beam test is modeled in FEM to determine the
longitudinal strain. Two models are implemented, the first is the
global half model, and the second is the sub-model to represent the
FBGs with higher refined mesh. This method can simulate damage in
composite structures and converting strain to a wavelength shifting in
the FBG spectrum.
Abstract: Early detection of breast cancer saves many thousands
of lives each year via application of mammography and genetic
screening and many more lives could be saved if nurses are involved
in breast care screening practices. So, the aim of the study was to
identify nurse's role in early detection of breast cancer through
mammography and genetic screening and its impact on patient's
outcome. In order to achieve this aim, 400 women above 40 years,
asymptomatic were recruited for mammography and genetic
screening. In addition, 50 nurses and 6 technologists were involved in
the study. A descriptive analytical design was used. Five tools were
utilized: sociodemographic, mammographic examination and risk
factors, women's before, during and after mammography, items
relaying to technologists, and items related to nurses were also
obtained. The study finding revealed that 3% of women detected for
malignancy and 7.25% for fibroadenoma. Statistically significant
differences were found between mammography results and age,
family history, genetic screening, exposure to smoke, and using
contraceptive pills. Nurses have insufficient knowledge about
screening tests. Based on these findings the present study
recommended involvement of nurses in breast care which is very
important to in force population about screening practices.
Abstract: Water contamination by toxic compound is one of the serious environmental problems today. These toxic compounds mostly originated from industrial effluents, agriculture, natural sources and human waste. These studies focus on modification of multiwalled carbon nanotube (MWCNTs) with nanoparticle of calixarene and explore the possibility of using this modification for the remediation of cadmium in water. The nanocomposites were prepared by dissolving calixarene in chloroform solution as solvent, followed by additional multiwalled carbon nanotube (MWCNTs) then sonication process for 3 hour and fabricated the nanocomposites on substrate by spin coating method. Finally, the nanocomposites were tested on cadmium ion (10 mg/ml). The morphology of nanocomposites was investigated by FESEM showing the formation of calixarene on the outer walls of carbon nanotube and cadmium ion also clearly seen from the micrograph. This formation was supported by using energy dispersive x-ray (EDX). The presence of cadmium ions in the films, leads to some changes in the surface potential and Fourier Transform Infrared spectroscopy (FTIR).The nanocomposites MWCNTs-calixarene have potential for development of sensor for pollutant monitoring and nanoelectronics devices applications.
Abstract: In this paper, we considered and applied parametric
modeling for some experimental data of dynamical system. In this
study, we investigated the different distribution of output
measurement from some dynamical systems. Also, with variance
processing in experimental data we obtained the region of
nonlinearity in experimental data and then identification of output
section is applied in different situation and data distribution. Finally,
the effect of the spanning the measurement such as variance to
identification and limitation of this approach is explained.
Abstract: Motion Tracking and Stereo Vision are complicated,
albeit well-understood problems in computer vision. Existing
softwares that combine the two approaches to perform stereo motion
tracking typically employ complicated and computationally expensive
procedures. The purpose of this study is to create a simple and
effective solution capable of combining the two approaches. The
study aims to explore a strategy to combine the two techniques
of two-dimensional motion tracking using Kalman Filter; and depth
detection of object using Stereo Vision. In conventional approaches
objects in the scene of interest are observed using a single camera.
However for Stereo Motion Tracking; the scene of interest is
observed using video feeds from two calibrated cameras. Using two
simultaneous measurements from the two cameras a calculation for
the depth of the object from the plane containing the cameras is made.
The approach attempts to capture the entire three-dimensional spatial
information of each object at the scene and represent it through a
software estimator object. In discrete intervals, the estimator tracks
object motion in the plane parallel to plane containing cameras and
updates the perpendicular distance value of the object from the plane
containing the cameras as depth. The ability to efficiently track
the motion of objects in three-dimensional space using a simplified
approach could prove to be an indispensable tool in a variety of
surveillance scenarios. The approach may find application from high
security surveillance scenes such as premises of bank vaults, prisons
or other detention facilities; to low cost applications in supermarkets
and car parking lots.
Abstract: Compositions of different molar ratios of
polymethylmethacrylate-co-methacrylic acid (PMMA-co-MAA)
were synthesized via free-radical polymerization. Polymer coated
surfaces have been produced on silicon wafers. Coated samples were
analyzed by atomic force microscopy (AFM). The results have shown
that the roughness of the surfaces have increased by increasing the
molar ratio of monomer methacrylic acid (MAA). This study reveals
that the gradual increase in surface roughness is due to the fact that
carboxylic functional groups have been generated by MAA segments.
Such surfaces can be desirable platforms for fabrication of the
biosensors for detection of the viruses and diseases.
Abstract: The present study was done to evaluate the presence
of tetracycline resistance genes in Lactococcus garvieae isolated
from cultured rainbow trout, West Iran. The isolates were examined
for antimicrobial resistance using disc diffusion method. Of the 49
strains tested, 19 were resistant to tetracycline (38.7%), 32 to
enrofloxacin (65.3%), 21 to erythromycin (42.8%), 20 to
chloramphenicol and trimetoprim-sulfamethoxazole (40.8%). The
strains were then characterized for their genotypic resistance profiles.
The results revealed that all 49 isolates contained at least one of the
tetracycline resistance genes. Tet (A) was found in 89.4% of
tetracycline resistant isolates and the frequency of other gene were as
follows: tet (E) 42.1%, tet (B) 47.3%, tet (D) 15.7%, tet (L) 26.3%,
tet (K) 52.6%, tet (G) 36.8%, tet (34) 21%, tet (S) 63.1%, tet (C)
57.8%, tet (M) 73.6%, tet (O) 42.1%. The results revealed high levels
of antibiotic resistance in L. garvieae strains which is a potential
danger for trout culture as well as for public health.
Abstract: A Distributed Denial of Service (DDoS) attack is a
major threat to cyber security. It originates from the network layer or
the application layer of compromised/attacker systems which are
connected to the network. The impact of this attack ranges from the
simple inconvenience to use a particular service to causing major
failures at the targeted server. When there is heavy traffic flow to a
target server, it is necessary to classify the legitimate access and
attacks. In this paper, a novel method is proposed to detect DDoS
attacks from the traces of traffic flow. An access matrix is created
from the traces. As the access matrix is multi dimensional, Principle
Component Analysis (PCA) is used to reduce the attributes used for
detection. Two classifiers Naive Bayes and K-Nearest neighborhood
are used to classify the traffic as normal or abnormal. The
performance of the classifier with PCA selected attributes and actual
attributes of access matrix is compared by the detection rate and
False Positive Rate (FPR).
Abstract: In this study, data loss tolerance of Support Vector Machines (SVM) based activity recognition model and multi activity classification performance when data are received over a lossy wireless sensor network is examined. Initially, the classification algorithm we use is evaluated in terms of resilience to random data loss with 3D acceleration sensor data for sitting, lying, walking and standing actions. The results show that the proposed classification method can recognize these activities successfully despite high data loss. Secondly, the effect of differentiated quality of service performance on activity recognition success is measured with activity data acquired from a multi hop wireless sensor network, which introduces high data loss. The effect of number of nodes on the reliability and multi activity classification success is demonstrated in simulation environment. To the best of our knowledge, the effect of data loss in a wireless sensor network on activity detection success rate of an SVM based classification algorithm has not been studied before.
Abstract: In this work, neural networks methods MLP type were
applied to a database from an array of six sensors for the detection of
three toxic gases. The choice of the number of hidden layers and the
weight values are influential on the convergence of the learning
algorithm. We proposed, in this article, a mathematical formula to
determine the optimal number of hidden layers and good weight
values based on the method of back propagation of errors. The results
of this modeling have improved discrimination of these gases and
optimized the computation time. The model presented here has
proven to be an effective application for the fast identification of
toxic gases.
Abstract: The IEEE 802.22 working group aims to drive the
Digital Video Broadcasting-Terrestrial (DVB-T) bands for data
communication to the rural area without interfering the TV broadcast.
In this paper, we arrive at a closed-form expression for average
detection probability of Fusion center (FC) with multiple antenna
over the κ − μ fading channel model. We consider a centralized
cooperative multiple antenna network for reporting. The DVB-T
samples forwarded by the secondary user (SU) were combined using
Maximum ratio combiner at FC, an energy detection is performed
to make the decision. The fading effects of the channel degrades
the detection probability of the FC, a generalized independent and
identically distributed (IID) κ − μ and an additive white Gaussian
noise (AWGN) channel is considered for reporting and sensing
respectively. The proposed system performance is verified through
simulation results.
Abstract: Real time image and video processing is a demand in
many computer vision applications, e.g. video surveillance, traffic
management and medical imaging. The processing of those video
applications requires high computational power. Thus, the optimal
solution is the collaboration of CPU and hardware accelerators. In
this paper, a Canny edge detection hardware accelerator is proposed.
Edge detection is one of the basic building blocks of video and image
processing applications. It is a common block in the pre-processing
phase of image and video processing pipeline. Our presented
approach targets offloading the Canny edge detection algorithm from
processing system (PS) to programmable logic (PL) taking the
advantage of High Level Synthesis (HLS) tool flow to accelerate the
implementation on Zynq platform. The resulting implementation
enables up to a 100x performance improvement through hardware
acceleration. The CPU utilization drops down and the frame rate
jumps to 60 fps of 1080p full HD input video stream.
Abstract: Software Architecture is the basic structure of
software that states the development and advancement of a software
system. Software architecture is also considered as a significant tool
for the construction of high quality software systems. A clean design
leads to the control, value and beauty of software resulting in its
longer life while a bad design is the cause of architectural erosion
where a software evolution completely fails. This paper discusses the
occurrence of software architecture erosion and presents a set of
methods for the detection, declaration and prevention of architecture
erosion. The causes and symptoms of architecture erosion are
observed with the examples of prescriptive and descriptive
architectures and the practices used to stop this erosion are also
discussed by considering different types of software erosion and their
affects. Consequently finding and devising the most suitable
approach for fighting software architecture erosion and in some way
reducing its affect is evaluated and tested on different scenarios.
Abstract: We present an analytical model for the calculation of
the sensitivity, the spectral current noise and the detective parameter
for an optically illuminated In0.53Ga0.47As n+nn+ diode. The
photocurrent due to the excess carrier is obtained by solving the
continuity equation. Moreover, the current noise level is evaluated at
room temperature and under a constant voltage applied between the
diode terminals. The analytical calculation of the current noise in the
n+nn+ structure is developed by considering the free carries
fluctuations. The responsivity and the detection parameter are
discussed as functions of the doping concentrations and the emitter
layer thickness in one-dimensional homogeneous n+nn+ structure.
Abstract: Image spam is a kind of email spam where the spam
text is embedded with an image. It is a new spamming technique
being used by spammers to send their messages to bulk of internet
users. Spam email has become a big problem in the lives of internet
users, causing time consumption and economic losses. The main
objective of this paper is to detect the image spam by using histogram
properties of an image. Though there are many techniques to
automatically detect and avoid this problem, spammers employing
new tricks to bypass those techniques, as a result those techniques are
inefficient to detect the spam mails. In this paper we have proposed a
new method to detect the image spam. Here the image features are
extracted by using RGB histogram, HSV histogram and combination
of both RGB and HSV histogram. Based on the optimized image
feature set classification is done by using k- Nearest Neighbor(k-NN)
algorithm. Experimental result shows that our method has achieved
better accuracy. From the result it is known that combination of RGB
and HSV histogram with k-NN algorithm gives the best accuracy in
spam detection.
Abstract: The method of introducing the proxy interpretation for
sending and receiving requests increase the capability of the server
and our approach UDIV (User-Data Identity Security) to solve the
data and user authentication without extending size of the data makes
better than hybrid IDS (Intrusion Detection System). And at the same
time all the security stages we have framed have to pass through less
through that minimize the response time of the request. Even though
an anomaly detected, before rejecting it the proxy extracts its identity
to prevent it to enter into system. In case of false anomalies, the
request will be reshaped and transformed into legitimate request for
further response. Finally we are holding the normal and abnormal
requests in two different queues with own priorities.
Abstract: Aerated concrete is a load bearing construction
material, which has high heat insulation parameters. Walls can be
erected from aerated concrete masonry constructions and in perfect
circumstances additional heat insulation is not required. The most
common problem in aerated concrete heat insulation properties is the
humidity distribution throughout the cross section of the masonry
elements as well as proper and conducted drying process of the
aerated concrete construction because only dry aerated concrete
masonry constructions can reach high heat insulation parameters.
In order to monitor drying process of the masonry and detect
humidity distribution throughout the cross section of aerated concrete
masonry construction application of electrical impedance
spectrometry is applied. Further test results and methodology of this
non-destructive testing method is described in this paper.
Abstract: Animal fats (camel, sheep, goat, rabbit and chicken)
and vegetable oils (corn, sunflower, palm oil and olive oil) were
substituted with different proportions (1, 5, 10 and 20%) of lard.
Fatty acid composition in TG and 2-MG were determined using
lipase hydrolysis and gas chromatography before and after
adulteration. Results indicated that, genuine lard had a high
proportion (60.97%) of the total palmitic acid at 2-MG. However, it
was 8.70%, 16.40%, 11.38%, 10.57%, 29.97 and 8.97% for camel,
beef, sheep, goat, rabbit and chicken, respectively. It could be noticed
also the position-2-MG is mostly occupied by unsaturated fatty acids
among all tested fats except lard. Vegetable oils (corn, sunflower,
palm oil and olive oil) revealed that the levels of palmitic acid
esterifies at 2-MG position was 6.84, 1.43, 9.86 and 1.70%,
respectively. It could be observed also the studied oils had a higher
level of unsaturated fatty acids in the same position, compared with
animal fats under investigation. Moreover, palmitic acid esterifies at
2-MG and PAEF increased gradually as the substituted levels
increased among all tested fat and oil samples. Statistical analysis
showed that the PAEF correlated well with lard level. The detection
of lard in some commercial processed foods (5 French fries, 4 Butter
fats, 5 processed meat and 6 candy samples) was carried out. Results
revealed that 2 samples of French fries and 4 samples of processed
meat contained lard due to their higher PAEF, while butter fat and
candy were free of lard.
Abstract: Mammography has been one of the most reliable
methods for early detection of breast cancer. There are different
lesions which are breast cancer characteristic such as
microcalcifications, masses, architectural distortions and bilateral
asymmetry. One of the major challenges of analysing digital
mammogram is how to extract efficient features from it for accurate
cancer classification. In this paper we proposed a hybrid feature
extraction method to detect and classify all four signs of breast
cancer. The proposed method is based on multiscale surrounding
region dependence method, Gabor filters, multi fractal analysis,
directional and morphological analysis. The extracted features are
input to self adaptive resource allocation network (SRAN) classifier
for classification. The validity of our approach is extensively
demonstrated using the two benchmark data sets Mammographic
Image Analysis Society (MIAS) and Digital Database for Screening
Mammograph (DDSM) and the results have been proved to be
progressive.