Abstract: Image segmentation plays an important role in
medical imaging applications. Therefore, accurate methods are
needed for the successful segmentation of medical images for
diagnosis and detection of various diseases. In this paper, we have
used maximum entropy to achieve image segmentation. Maximum
entropy has been calculated using Shannon, Renyi and Tsallis
entropies. This work has novelty based on the detection of skin lesion
caused by the bite of a parasite called Sand Fly causing the disease is
called Cutaneous Leishmaniasis.
Abstract: An artificial neural network is a mathematical model
inspired by biological neural networks. There are several kinds of
neural networks and they are widely used in many areas, such as:
prediction, detection, and classification. Meanwhile, in day to day life,
people always have to make many difficult decisions. For example,
the coach of a soccer club has to decide which offensive player
to be selected to play in a certain game. This work describes a
novel Neural Network using a combination of the General Regression
Neural Network and the Probabilistic Neural Networks to help a
soccer coach make an informed decision.
Abstract: The effects of hypertension are often lethal thus its
early detection and prevention is very important for everybody. In
this paper, a neural network (NN) model was developed and trained
based on a dataset of hypertension causative parameters in order to
forecast the likelihood of occurrence of hypertension in patients. Our
research goal was to analyze the potential of the presented NN to
predict, for a period of time, the risk of hypertension or the risk of
developing this disease for patients that are or not currently
hypertensive. The results of the analysis for a given patient can
support doctors in taking pro-active measures for averting the
occurrence of hypertension such as recommendations regarding the
patient behavior in order to lower his hypertension risk. Moreover,
the paper envisages a set of three example scenarios in order to
determine the age when the patient becomes hypertensive, i.e.
determine the threshold for hypertensive age, to analyze what
happens if the threshold hypertensive age is set to a certain age and
the weight of the patient if being varied, and, to set the ideal weight
for the patient and analyze what happens with the threshold of
hypertensive age.
Abstract: In this paper, the problem of fault detection and
isolation in the attitude control subsystem of spacecraft formation
flying is considered. In order to design the fault detection method, an
extended Kalman filter is utilized which is a nonlinear stochastic state
estimation method. Three fault detection architectures, namely,
centralized, decentralized, and semi-decentralized are designed based
on the extended Kalman filters. Moreover, the residual generation
and threshold selection techniques are proposed for these
architectures.
Abstract: Over the past four decades, the fatigue behavior of
nickel-based alloys has been widely studied. However, in recent
years, significant advances in the fabrication process leading to grain
size reduction have been made in order to improve fatigue properties
of aircraft turbine discs. Indeed, a change in particle size affects the
initiation mode of fatigue cracks as well as the fatigue life of the
material. The present study aims to investigate the fatigue behavior of
a newly developed nickel-based superalloy under biaxial-planar
loading. Low Cycle Fatigue (LCF) tests are performed at different
stress ratios so as to study the influence of the multiaxial stress state
on the fatigue life of the material. Full-field displacement and strain
measurements as well as crack initiation detection are obtained using
Digital Image Correlation (DIC) techniques. The aim of this
presentation is first to provide an in-depth description of both the
experimental set-up and protocol: the multiaxial testing machine, the
specific design of the cruciform specimen and performances of the
DIC code are introduced. Second, results for sixteen specimens
related to different load ratios are presented. Crack detection, strain
amplitude and number of cycles to crack initiation vs. triaxial stress
ratio for each loading case are given. Third, from fractographic
investigations by scanning electron microscopy it is found that the
mechanism of fatigue crack initiation does not depend on the triaxial
stress ratio and that most fatigue cracks initiate from subsurface
carbides.
Abstract: The detection of moving objects from a video image
sequences is very important for object tracking, activity recognition,
and behavior understanding in video surveillance.
The most used approach for moving objects detection / tracking is
background subtraction algorithms. Many approaches have been
suggested for background subtraction. But, these are illumination
change sensitive and the solutions proposed to bypass this problem
are time consuming.
In this paper, we propose a robust yet computationally efficient
background subtraction approach and, mainly, focus on the ability to
detect moving objects on dynamic scenes, for possible applications in
complex and restricted access areas monitoring, where moving and
motionless persons must be reliably detected. It consists of three
main phases, establishing illumination changes invariance,
background/foreground modeling and morphological analysis for
noise removing.
We handle illumination changes using Contrast Limited Histogram
Equalization (CLAHE), which limits the intensity of each pixel to
user determined maximum. Thus, it mitigates the degradation due to
scene illumination changes and improves the visibility of the video
signal. Initially, the background and foreground images are extracted
from the video sequence. Then, the background and foreground
images are separately enhanced by applying CLAHE.
In order to form multi-modal backgrounds we model each channel
of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture
Model (GMM). Finally, we post process the resulting binary
foreground mask using morphological erosion and dilation
transformations to remove possible noise.
For experimental test, we used a standard dataset to challenge the
efficiency and accuracy of the proposed method on a diverse set of
dynamic scenes.
Abstract: Natural hydrocarbon seepage has helped petroleum
exploration as a direct indicator of gas and/or oil subsurface
accumulations. Surface macro-seeps are generally an indication of a
fault in an active Petroleum Seepage System belonging to a Total
Petroleum System. This paper describes a case study in which
multiple analytical techniques were used to identify and characterize
trace petroleum-related hydrocarbons and other volatile organic
compounds in groundwater samples collected from Sousse aquifer
(Central Tunisia). The analytical techniques used for analyses of
water samples included gas chromatography-mass spectrometry (GCMS),
capillary GC with flame-ionization detection, Compound
Specific Isotope Analysis, Rock Eval Pyrolysis. The objective of the
study was to confirm the presence of gasoline and other petroleum
products or other volatile organic pollutants in those samples in order
to assess the respective implication of each of the potentially
responsible parties to the contamination of the aquifer. In addition,
the degree of contamination at different depths in the aquifer was also
of interest. The oil and gas seeps have been investigated using
biomarker and stable carbon isotope analyses to perform oil-oil and
oil-source rock correlations. The seepage gases are characterized by
high CH4 content, very low δ13CCH4 values (-71,9 ‰) and high
C1/C1–5 ratios (0.95–1.0), light deuterium–hydrogen isotope ratios (-
198 ‰) and light δ13CC2 and δ13CCO2 values (-23,8‰ and-23,8‰
respectively) indicating a thermogenic origin with the contribution of
the biogenic gas. An organic geochemistry study was carried out on
the more ten oil seep samples. This study includes light hydrocarbon
and biomarkers analyses (hopanes, steranes, n-alkanes, acyclic
isoprenoids, and aromatic steroids) using GC and GC-MS. The
studied samples show at least two distinct families, suggesting two
different types of crude oil origins: the first oil seeps appears to be
highly mature, showing evidence of chemical and/or biological
degradation and was derived from a clay-rich source rock deposited
in suboxic conditions. It has been sourced mainly by the lower
Fahdene (Albian) source rocks. The second oil seeps was derived
from a carbonate-rich source rock deposited in anoxic conditions,
well correlated with the Bahloul (Cenomanian-Turonian) source rock.
Abstract: Content Based Image Retrieval (CBIR) coupled with
Case Based Reasoning (CBR) is a paradigm that is becoming
increasingly popular in the diagnosis and therapy planning of medical
ailments utilizing the digital content of medical images. This paper
presents a survey of some of the promising approaches used in the
detection of abnormalities in retina images as well in
mammographic screening and detection of regions of interest
in MRI scans of the brain. We also describe our proposed
algorithm to detect hard exudates in fundus images of the
retina of Diabetic Retinopathy patients.
Abstract: The aim of this work is to build a model based on
tissue characterization that is able to discriminate pathological and
non-pathological regions from three-phasic CT images. With our
research and based on a feature selection in different phases, we are
trying to design a neural network system with an optimal neuron
number in a hidden layer. Our approach consists of three steps:
feature selection, feature reduction, and classification. For each
region of interest (ROI), 6 distinct sets of texture features are
extracted such as: first order histogram parameters, absolute gradient,
run-length matrix, co-occurrence matrix, autoregressive model, and
wavelet, for a total of 270 texture features. When analyzing more
phases, we show that the injection of liquid cause changes to the high
relevant features in each region. Our results demonstrate that for
detecting HCC tumor phase 3 is the best one in most of the features
that we apply to the classification algorithm. The percentage of
detection between pathology and healthy classes, according to our
method, relates to first order histogram parameters with accuracy of
85% in phase 1, 95% in phase 2, and 95% in phase 3.
Abstract: The edges of low contrast images are not clearly
distinguishable to human eye. It is difficult to find the edges and
boundaries in it. The present work encompasses a new approach for
low contrast images. The Chebyshev polynomial based fractional
order filter has been used for filtering operation on an image. The
preprocessing has been performed by this filter on the input image.
Laplacian of Gaussian method has been applied on preprocessed
image for edge detection. The algorithm has been tested on two test
images.
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: The detection of the polymer melt state during
manufacture process is regarded as an efficient way to control the
molded part quality in advance. Online monitoring rheological
property of polymer melt during processing procedure provides an
approach to understand the melt state immediately. Rheological
property reflects the polymer melt state at different processing
parameters and is very important in injection molding process
especially. An approach that demonstrates how to calculate
rheological property of polymer melt through in-process
measurement, using injection molding as an example, is proposed in
this study. The system consists of two sensors and a data acquisition
module can process the measured data, which are used for the
calculation of rheological properties of polymer melt. The rheological
properties of polymer melt discussed in this study include shear rate
and viscosity which are investigated with respect to injection speed
and melt temperature. The results show that the effect of injection
speed on the rheological properties is apparent, especially for high
melt temperature and should be considered for precision molding
process.
Abstract: In this paper, we regard as a coded transmission over a
frequency-selective channel. We plan to study analytically the
convergence of the turbo-detector using a maximum a posteriori
(MAP) equalizer and a MAP decoder. We demonstrate that the
densities of the maximum likelihood (ML) exchanged during the
iterations are e-symmetric and output-symmetric. Under the Gaussian
approximation, this property allows to execute a one-dimensional
scrutiny of the turbo-detector. By deriving the analytical terminology
of the ML distributions under the Gaussian approximation, we confirm
that the bit error rate (BER) performance of the turbo-detector
converges to the BER performance of the coded additive white
Gaussian noise (AWGN) channel at high signal to noise ratio (SNR),
for any frequency selective channel.
Abstract: A quartz crystal microbalance (QCM) nanosensor was developed to detect lysozyme enzyme by functionalizing its gold surface with the attachment of poly(methacroyl-L-phenylalanine) (PMAPA) nanoparticles. PMAPA was chosen as a hydrophobic matrix. The hydrophobic nanoparticles were synthesized by micro-emulsion polymerization method. Hydrophobic QCM nanosensor was tested for real time detection of lysozyme enzyme from aqueous solution. The kinetic and affinity studies were determined by using lysozyme solutions with different concentrations. The responses related with mass (Δm) and frequency (Δf) shifts were used to evaluate adsorption properties.
Abstract: Operations, maintenance and reliability of wind
turbines have received much attention over the years due to the rapid
expansion of wind farms. This paper explores early fault diagnosis
technique for a 5MW wind turbine system subjected to multiple
faults, where genetic optimization algorithm is employed to make the
residual sensitive to the faults, but robust against disturbances. The
proposed technique has a potential to reduce the downtime mostly
caused by the breakdown of components and exploit the productivity
consistency by providing timely fault alarms. Simulation results show
the effectiveness of the robust fault detection methods used under
Matlab/Simulink/Gatool environment.
Abstract: In order to detect and quantify the phenolic contents
of a wastewater with biosensors, two working electrodes based on
modified Poly(Pyrrole) films were fabricated. Enzyme horseradish
peroxidase was used as biomolecule of the prepared electrodes.
Various phenolics were tested at the biosensor. Phenol detection was
realized by electrochemical reduction of quinones produced by
enzymatic activity. Analytical parameters were calculated and the
results were compared with each other.
Abstract: ESPRIT-TLS method appears a good choice for high
resolution fault detection in induction machines. It has a very high
effectiveness in the frequency and amplitude identification.
Contrariwise, it presents a high computation complexity which
affects its implementation in real time fault diagnosis. To avoid this
problem, a Fast-ESPRIT algorithm that combined the IIR band-pass
filtering technique, the decimation technique and the original
ESPRIT-TLS method was employed to enhance extracting accurately
frequencies and their magnitudes from the wind stator current with
less computation cost. The proposed algorithm has been applied to
verify the wind turbine machine need in the implementation of an online,
fast, and proactive condition monitoring. This type of remote
and periodic maintenance provides an acceptable machine lifetime,
minimize its downtimes and maximize its productivity. The
developed technique has evaluated by computer simulations under
many fault scenarios. Study results prove the performance of Fast-
ESPRIT offering rapid and high resolution harmonics recognizing
with minimum computation time and less memory cost.
Abstract: In this study, we have focused our attention on
combining of molecular imprinting into nanofilms and QCM
nanosensor approaches and producing QCM nanosensor for anti-
CCP, chosen as model protein, using anti-CCP imprinted nanofilms.
The nonimprinted nanosensor was also prepared to evaluate the
selectivity of the imprinted nanosensor. Anti-CCP imprinted QCM
nanosensor was tested for real time detection of anti-CCP from
aqueous solution. The kinetic and affinity studies were determined by
using anti-CCP solutions with different concentrations. The
responses related with mass shifts (%m) and frequency shifts (%f)
were used to evaluate adsorption properties. To show the selectivity
of the anti-CCP imprinted QCM nanosensor, competitive adsorption
of anti-CCP and IgM was investigated. The results indicate that anti-
CCP imprinted QCM nanosensor has higher adsorption capabilities
for anti-CCP than for IgM, due to selective cavities in the polymer
structure.
Abstract: Agriculture is the backbone of economy of Pakistan
and cotton is the major agricultural export and supreme source of raw
fiber for our textile industry. To combat severe problems of insect
and weed, combination of three genes namely Cry1Ac, Cry2A and
EPSPS genes was transferred in locally cultivated cotton variety
MNH-786 with the use of Agrobacterium mediated genetic
transformation. The present study focused on the molecular screening
of transgenic cotton plants at T3 generation in order to confirm
integration and expression of all three genes (Cry1Ac, Cry2A and
EPSP synthase) into the cotton genome. Initially, glyphosate spray
assay was used for screening of transgenic cotton plants containing
EPSP synthase gene at T3 generation. Transgenic cotton plants which
were healthy and showed no damage on leaves were selected after 07
days of spray. For molecular analysis of transgenic cotton plants in
the laboratory, the genomic DNA of these transgenic cotton plants
were isolated and subjected to amplification of the three genes. Thus,
seventeen out of twenty (Cry1Ac gene), ten out of twenty (Cry2A
gene) and all twenty (EPSP synthase gene) were produced positive
amplification. On the base of PCR amplification, ten transgenic plant
samples were subjected to protein expression analysis through
ELISA. The results showed that eight out of ten plants were actively
expressing the three transgenes. Real-time PCR was also done to
quantify the mRNA expression levels of Cry1Ac and EPSP synthase
gene. Finally, eight plants were confirmed for the presence and active
expression of all three genes at T3 generation.
Abstract: This paper presents an efficient fusion algorithm for
iris images to generate stable feature for recognition in unconstrained
environment. Recently, iris recognition systems are focused on real
scenarios in our daily life without the subject’s cooperation. Under
large variation in the environment, the objective of this paper is to
combine information from multiple images of the same iris. The
result of image fusion is a new image which is more stable for further
iris recognition than each original noise iris image. A wavelet-based
approach for multi-resolution image fusion is applied in the fusion
process. The detection of the iris image is based on Adaboost
algorithm and then local binary pattern (LBP) histogram is then
applied to texture classification with the weighting scheme.
Experiment showed that the generated features from the proposed
fusion algorithm can improve the performance for verification system
through iris recognition.