Abstract: This paper presents the doping profile measurement
and characterization technique for the pocket implanted nano scale
n-MOSFET. Scanning capacitance microscopy and atomic force
microscopy have been used to image the extent of lateral dopant
diffusion in MOS structures. The data are capacitance vs. voltage
measurements made on a nano scale device. The technique is nondestructive
when imaging uncleaved samples. Experimental data from
the published literature are presented here on actual, cleaved device
structures which clearly indicate the two-dimensional dopant profile
in terms of a spatially varying modulated capacitance signal. Firstorder
deconvolution indicates the technique has much promise for
the quantitative characterization of lateral dopant profiles. The pocket
profile is modeled assuming the linear pocket profiles at the source
and drain edges. From the model, the effective doping concentration
is found to use in modeling and simulation results of the various
parameters of the pocket implanted nano scale n-MOSFET. The
potential of the technique to characterize important device related
phenomena on a local scale is also discussed.
Abstract: Today, money laundering (ML) poses a serious threat
not only to financial institutions but also to the nation. This criminal
activity is becoming more and more sophisticated and seems to have
moved from the cliché of drug trafficking to financing terrorism and
surely not forgetting personal gain. Most international financial
institutions have been implementing anti-money laundering solutions
(AML) to fight investment fraud. However, traditional investigative
techniques consume numerous man-hours. Recently, data mining
approaches have been developed and are considered as well-suited
techniques for detecting ML activities. Within the scope of a
collaboration project for the purpose of developing a new solution for
the AML Units in an international investment bank, we proposed a
data mining-based solution for AML. In this paper, we present a
heuristics approach to improve the performance for this solution. We
also show some preliminary results associated with this method on
analysing transaction datasets.
Abstract: In this paper, using (G/G )-expansion method and modified F-expansion method, we give some explicit formulas of exact traveling wave solutions for the (3+1)-dimensional breaking soliton equation. A modified F-expansion method is proposed by taking full advantages of F-expansion method and Riccati equation in seeking exact solutions of the equation.
Abstract: This paper presents a new technique for detection of
human faces within color images. The approach relies on image
segmentation based on skin color, features extracted from the two-dimensional
discrete cosine transform (DCT), and self-organizing
maps (SOM). After candidate skin regions are extracted, feature
vectors are constructed using DCT coefficients computed from those
regions. A supervised SOM training session is used to cluster feature
vectors into groups, and to assign “face" or “non-face" labels to those
clusters. Evaluation was performed using a new image database of
286 images, containing 1027 faces. After training, our detection
technique achieved a detection rate of 77.94% during subsequent
tests, with a false positive rate of 5.14%. To our knowledge, the
proposed technique is the first to combine DCT-based feature
extraction with a SOM for detecting human faces within color
images. It is also one of a few attempts to combine a feature-invariant
approach, such as color-based skin segmentation, together with
appearance-based face detection. The main advantage of the new
technique is its low computational requirements, in terms of both
processing speed and memory utilization.
Abstract: To understand working features of a micro combustor,
a computer code has been developed to study combustion of
hydrogen–air mixture in a series of chambers with same shape aspect
ratio but various dimensions from millimeter to micrometer level.
The prepared algorithm and the computer code are capable of
modeling mixture effects in different fluid flows including chemical
reactions, viscous and mass diffusion effects. The effect of various
heat transfer conditions at chamber wall, e.g. adiabatic wall, with
heat loss and heat conduction within the wall, on the combustion is
analyzed. These thermal conditions have strong effects on the
combustion especially when the chamber dimension goes smaller and
the ratio of surface area to volume becomes larger.
Both factors, such as larger heat loss through the chamber wall
and smaller chamber dimension size, may lead to the thermal
quenching of micro-scale combustion. Through such systematic
numerical analysis, a proper operation space for the micro-combustor
is suggested, which may be used as the guideline for microcombustor
design. In addition, the results reported in this paper
illustrate that the numerical simulation can be one of the most
powerful and beneficial tools for the micro-combustor design,
optimization and performance analysis.
Abstract: This paper presents a new adaptive DMC controller
that improves the controller performance in case of plant-model
mismatch. The new controller monitors the plant measured output,
compares it with the model output and calculates weights applied to
the controller move. Simulations show that the new controller can
help improve control performance and avoid instability in case of
severe model mismatches.
Abstract: In the present study, the incorporation of graphene
into blends of acrylonitrile-butadiene-styrene terpolymer with
polypropylene (ABS/PP) was investigated focusing on the
improvement of their thermomechanical characteristics and the effect
on their rheological behavior. The blends were prepared by melt
mixing in a twin-screw extruder and were characterized by measuring
the MFI as well as by performing DSC, TGA and mechanical tests.
The addition of graphene to ABS/PP blends tends to increase their
melt viscosity, due to the confinement of polymer chains motion.
Also, graphene causes an increment of the crystallization temperature
(Tc), especially in blends with higher PP content, because of the
reduction of surface energy of PP nucleation, which is a consequence
of the attachment of PP chains to the surface of graphene through the
intermolecular CH-π interaction. Moreover, the above nanofiller
improves the thermal stability of PP and increases the residue of
thermal degradation at all the investigated compositions of blends,
due to the thermal isolation effect and the mass transport barrier
effect. Regarding the mechanical properties, the addition of graphene
improves the elastic modulus, because of its intrinsic mechanical
characteristics and its rigidity, and this effect is particularly strong in
the case of pure PP.
Abstract: In this report we present a rule-based approach to
detect anomalous telephone calls. The method described here uses
subscriber usage CDR (call detail record) data sampled over two
observation periods: study period and test period. The study period
contains call records of customers- non-anomalous behaviour.
Customers are first grouped according to their similar usage
behaviour (like, average number of local calls per week, etc). For
customers in each group, we develop a probabilistic model to describe
their usage. Next, we use maximum likelihood estimation (MLE) to
estimate the parameters of the calling behaviour. Then we determine
thresholds by calculating acceptable change within a group. MLE is
used on the data in the test period to estimate the parameters of the
calling behaviour. These parameters are compared against thresholds.
Any deviation beyond the threshold is used to raise an alarm. This
method has the advantage of identifying local anomalies as compared
to techniques which identify global anomalies. The method is tested
for 90 days of study data and 10 days of test data of telecom
customers. For medium to large deviations in the data in test window,
the method is able to identify 90% of anomalous usage with less than
1% false alarm rate.
Abstract: This paper deals with the application of artificial
neural network (ANN) and fuzzy based Adaptive Neuro Fuzzy
Inference System(ANFIS) approach to Load Frequency Control
(LFC) of multi unequal area hydro-thermal interconnected power
system. The proposed ANFIS controller combines the advantages of
fuzzy controller as well as quick response and adaptability nature of
ANN. Area-1 and area-2 consists of thermal reheat power plant
whereas area-3 and area-4 consists of hydro power plant with electric
governor. Performance evaluation is carried out by using intelligent
controller like ANFIS, ANN and Fuzzy controllers and conventional
PI and PID control approaches. To enhance the performance of
intelligent and conventional controller sliding surface is included.
The performances of the controllers are simulated using
MATLAB/SIMULINK package. A comparison of ANFIS, ANN,
Fuzzy, PI and PID based approaches shows the superiority of
proposed ANFIS over ANN & fuzzy, PI and PID controller for 1%
step load variation.
Abstract: This paper presents an integrated model that
automatically measures the change of rivers, damage area of bridge
surroundings, and change of vegetation. The proposed model is on the
basis of a neurofuzzy mechanism enhanced by SOM optimization
algorithm, and also includes three functions to deal with river imagery.
High resolution imagery from FORMOSAT-2 satellite taken before
and after the invasion period is adopted. By randomly selecting a
bridge out of 129 destroyed bridges, the recognition results show that
the average width has increased 66%. The ruined segment of the
bridge is located exactly at the most scour region. The vegetation
coverage has also reduced to nearly 90% of the original. The results
yielded from the proposed model demonstrate a pinpoint accuracy rate
at 99.94%. This study brings up a successful tool not only for
large-scale damage assessment but for precise measurement to
disasters.
Abstract: This paper presents an experimental investigation on
the machinability of laser-sintered material using small ball end mill focusing on wear mechanisms. Laser-sintered material was produced
by irradiating a laser beam on a layer of loose fine SCM-Ni-Cu powder. Bulk carbon steel JIS S55C was selected as a reference steel.
The effects of powder consolidation mechanisms and unsintered
powder on the tool life and wear mechanisms were carried out. Results indicated that tool life in cutting laser-sintered material is
lower than that in cutting JIS S55C. Adhesion of the work material and chipping were the main wear mechanisms of the ball end mill in
cutting laser-sintered material. Cutting with the unsintered powder
surrounding the tool and laser-sintered material had caused major fracture on the cutting edge.
Abstract: This research aimed to modify pineapple leaf paper
(PALP) for using as wet media in the evaporation cooling system by
improving wet mechanical property (tensile strength) without
compromising water absorption property. Polyamideamineepichorohydrin
resin (PAE) and carboxymethylcellulose (CMC)
were used to strengthen the paper, and the PAE and CMC ratio of
80:20 showed the optimum wet and dry tensile index values, which
were higher than those of the commercial cooling pad (CCP).
Compared with CCP, PALP itself and all the PAE/CMC modified
PALP possessed better water absorption. The PAE/CMC modified
PALP had potential to become a new type of wet media.
Abstract: This paper presents a new method for estimating the mean curve of impulse voltage waveforms that are recorded during impulse tests. In practice, these waveforms are distorted by noise, oscillations and overshoot. The problem is formulated as an estimation problem. Estimation of the current signal parameters is achieved using a fast and accurate technique. The method is based on discrete dynamic filtering algorithm (DDF). The main advantage of the proposed technique is its ability in producing the estimates in a very short time and at a very high degree of accuracy. The algorithm uses sets of digital samples of the recorded impulse waveform. The proposed technique has been tested using simulated data of practical waveforms. Effects of number of samples and data window size are studied. Results are reported and discussed.
Abstract: Human genome is not only the evolutionary
summation of all advantageous events, but also houses lesions of
deleterious foot prints. A single gene mutation sometimes may
express multiple consequences in numerous tissues and a linear
relationship of the genotype and the phenotype may often be obscure.
ß Thalassemia minor, a transfusion independent mild anaemia,
coupled with environment among other factors may articulate into
phenotypic pleotropy with Hypocholesterolemia, Vitamin D
deficiency, Tissue hypoxia, Hyper-parathyroidism and Psychological
alterations. Occurrence of Pancreatic insufficiency, resultant
steatorrhoea, Vitamin-D (25-OH) deficiency (13.86 ngm/ml) with
Hypocholesterolemia (85mg/dl) in a 30 years old male ß Thal-minor
patient (Hemoglobin 11mg/dl with Fetal Hemoglobin 2.10%, Hb A2
4.60% and Hb Adult 84.80% and altered Hemogram) with increased
Para thyroid hormone (62 pg/ml) & moderate Serum Ca+2
(9.5mg/ml) indicate towards a cascade of phenotypic pleotropy
where the ß Thalassemia mutation ,be it in the 5’ cap site of the
mRNA , differential splicing etc in heterozygous state is effecting
several metabolic pathways. Compensatory extramedulary
hematopoiesis may not coped up well with the stressful life style of
the young individual and increased erythropoietic stress with high
demand for cholesterol for RBC membrane synthesis may have
resulted in Hypocholesterolemia.Oxidative stress and tissue hypoxia
may have caused the pancreatic insufficiency, leading to Vitamin D
deficiency. This may in turn have caused the secondary
hyperparathyroidism to sustain serum Calcium level. Irritability and
stress intolerance of the patient was a cumulative effect of the vicious
cycle of metabolic compromises. From these findings we propose
that the metabolic deficiencies in the ß Thalassemia mutations may
be considered as the phenotypic display of the pleotropy to explain
the genetic epidemiology.
According to the recommendations from the NIH Workshop on
Gene-Environment Interplay in Common Complex Diseases: Forging
an Integrative Model, study design of observations should be
informed by gene-environment hypotheses and results of a study
(genetic diseases) should be published to inform future hypotheses.
Variety of approaches is needed to capture data on all possible
aspects, each of which is likely to contribute to the etiology of
disease. Speakers also agreed that there is a need for development of
new statistical methods and measurement tools to appraise
information that may be missed out by conventional method where
large sample size is needed to segregate considerable effect.
A meta analytic cohort study in future may bring about significant
insight on to the title comment.
Abstract: This paper aims to study decomposition behavior in
pyrolytic environment of four lignocellulosic biomass (oil palm shell,
oil palm frond, rice husk and paddy straw), and two commercial
components of biomass (pure cellulose and lignin), performed in a
thermogravimetry analyzer (TGA). The unit which consists of a
microbalance and a furnace flowed with 100 cc (STP) min-1 Nitrogen,
N2 as inert. Heating rate was set at 20⁰C min-1 and temperature
started from 50 to 900⁰C. Hydrogen gas production during the
pyrolysis was observed using Agilent Gas Chromatography Analyzer
7890A. Oil palm shell, oil palm frond, paddy straw and rice husk
were found to be reactive enough in a pyrolytic environment of up to
900°C since pyrolysis of these biomass starts at temperature as low as
200°C and maximum value of weight loss is achieved at about
500°C. Since there was not much different in the cellulose,
hemicelluloses and lignin fractions between oil palm shell, oil palm
frond, paddy straw and rice husk, the T-50 and R-50 values obtained
are almost similar. H2 productions started rapidly at this temperature
as well due to the decompositions of biomass inside the TGA.
Biomass with more lignin content such as oil palm shell was found to
have longer duration of H2 production compared to materials of high
cellulose and hemicelluloses contents.
Abstract: In wireless communication system, a Decision Feedback Equalizer (DFE) to cancel the intersymbol interference (ISI) is required. In this paper, an exact convergence analysis of the (DFE) adapted by the Least Mean Square (LMS) algorithm during the training phase is derived by taking into account the finite alphabet context of data transmission. This allows us to determine the shortest training sequence that allows to reach a given Mean Square Error (MSE). With the intention of avoiding the problem of ill-convergence, the paper proposes an initialization strategy for the blind decision directed (DD) algorithm. This then yields a semi-blind DFE with high speed and good convergence.
Abstract: A robot simulator was developed to measure and
investigate the performance of a robot navigation system based on
the relative position of the robot with respect to random obstacles in
any two dimensional environment. The presented simulator focuses
on investigating the ability of a fuzzy-neural system for object
avoidance. A navigation algorithm is proposed and used to allow
random navigation of a robot among obstacles when the robot faces
an obstacle in the environment. The main features of this simulator
can be used for evaluating the performance of any system that can
provide the position of the robot with respect to obstacles in the
environment. This allows a robot developer to investigate and
analyze the performance of a robot without implementing the
physical robot.
Abstract: This paper presents the prediction of kidney
dysfunction using different neural network (NN) approaches. Self
organization Maps (SOM), Probabilistic Neural Network (PNN) and
Multi Layer Perceptron Neural Network (MLPNN) trained with Back
Propagation Algorithm (BPA) are used in this study. Six hundred and
sixty three sets of analytical laboratory tests have been collected from
one of the private clinical laboratories in Baghdad. For each subject,
Serum urea and Serum creatinin levels have been analyzed and tested
by using clinical laboratory measurements. The collected urea and
cretinine levels are then used as inputs to the three NN models in
which the training process is done by different neural approaches.
SOM which is a class of unsupervised network whereas PNN and
BPNN are considered as class of supervised networks. These
networks are used as a classifier to predict whether kidney is normal
or it will have a dysfunction. The accuracy of prediction, sensitivity
and specificity were found for each type of the proposed networks
.We conclude that PNN gives faster and more accurate prediction of
kidney dysfunction and it works as promising tool for predicting of
routine kidney dysfunction from the clinical laboratory data.
Abstract: A company CSR commitment, as stated in its Social
Report is, actually, perceived by its stakeholders?And in what
measure? Moreover, are stakeholders satisfied with the company
CSR efforts? Indeed, business returns from Corporate Social
Responsibility (CSR) practices, such as company reputation and
customer loyalty, depend heavily on how stakeholders perceive the
company social conduct. In this paper, we propose a methodology to
assess a company CSR commitment based on Global Reporting
Initiative (GRI) indicators, Content Analysis and a CSR positioning
matrix. We evaluate three aspects of CSR: the company commitment
disclosed through its Social Report; the company commitment
perceived by its stakeholders; the CSR commitment that stakeholders
require to the company. The positioning of the company under study
in the CSR matrix is based on the comparison among the three
commitment aspects (disclosed, perceived, required) and it allows
assessment and development of CSR strategies.
Abstract: Reinforced concrete crash barriers used in road traffic
must meet a number of criteria. Crash barriers are laid lengthwise,
one behind another, and joined using specially designed steel locks.
While developing BSV reinforced concrete crash barriers (type
ŽPSV), experiments and calculations aimed to optimize the shape of
a newly designed lock and the reinforcement quantity and
distribution in a crash barrier were carried out. The tension carrying
capacity of two parallelly joined locks was solved experimentally.
Based on the performed experiments, adjustments of nonlinear
properties of steel were performed in the calculations. The obtained
results served as a basis to optimize the lock design using a
computational model that takes into account the plastic behaviour of
steel and the influence of the surrounding concrete [6]. The response
to the vehicle impact has been analyzed using a specially elaborated
complex computational model, comprising both the nonlinear model
of the damping wall or crash barrier and the detailed model of the
vehicle [7].