Abstract: This paper discusses the undesirable charge transfer
through the parasitic capacitances of the input transistors in a
multi-inputs voltage sense amplifier. Its intrinsic rail-to-rail voltage
transitions at the output nodes inevitably disturb the input sides
through the capacitive coupling between the outputs and inputs. Then,
it can possible degrade the stabilities of the reference voltage levels.
Moreover, it becomes more serious in multi-channel systems by
altering them for other channels, and so degrades the linearity of the
overall systems. In order to alleviate the internal node voltage
transition, the internal node stabilization techniques are proposed. It
achieves 45% and 40% improvements for node stabilization and input
referred disturbance, respectively.
Abstract: Non-linear FEM calculations are indispensable when
important technical information like operating performance of a
rubber component is desired. For example rubber bumpers built into
air-spring structures may undergo large deformations under load,
which in itself shows non-linear behavior. The changing contact
range between the parts and the incompressibility of the rubber
increases this non-linear behavior further. The material
characterization of an elastomeric component is also a demanding
engineering task.
The shape optimization problem of rubber parts led to the study of
FEM based calculation processes. This type of problems was posed
and investigated by several authors. In this paper the time demand of
certain calculation methods are studied and the possibilities of time
reduction is presented.
Abstract: Human motion capture has become one of the major
area of interest in the field of computer vision. Some of the major
application areas that have been rapidly evolving include the
advanced human interfaces, virtual reality and security/surveillance
systems. This study provides a brief overview of the techniques and
applications used for the markerless human motion capture, which
deals with analyzing the human motion in the form of mathematical
formulations. The major contribution of this research is that it
classifies the computer vision based techniques of human motion
capture based on the taxonomy, and then breaks its down into four
systematically different categories of tracking, initialization, pose
estimation and recognition. The detailed descriptions and the
relationships descriptions are given for the techniques of tracking and
pose estimation. The subcategories of each process are further
described. Various hypotheses have been used by the researchers in
this domain are surveyed and the evolution of these techniques have
been explained. It has been concluded in the survey that most
researchers have focused on using the mathematical body models for
the markerless motion capture.
Abstract: Environmental impact assessment techniques have
been developed as a result of the worldwide efforts to reduce the
environmental impact of global warming. By using the quantification
method in the construction industry, it is now possible to manage the
greenhouse gas is to systematically evaluate the impact on the
environment over the entire construction process. In particular, the
proportion of greenhouse gas emissions at the production stage of
construction material occupied is high, and efforts are needed in
particular in the construction field.
In this research, intended for concrete products for the construction
materials, by using the LCA method, we compared the results of
environmental impact assessment and carbon emissions of developing
products that have been applied low-carbon technologies compared to
existing products. As a results, by introducing a raw material of
industrial waste, showed carbon reduction. Through a comparison of
the carbon emission reduction effect of low carbon technologies, it is
intended to provide academic data for the evaluation of greenhouse
gases in the construction sector and the development of low carbon
technologies of the future.
Abstract: Large-scale data stream analysis has become one of
the important business and research priorities lately. Social networks
like Twitter and other micro-blogging platforms hold an enormous
amount of data that is large in volume, velocity and variety.
Extracting valuable information and trends out of these data would
aid in a better understanding and decision-making. Multiple analysis
techniques are deployed for English content. Moreover, one of the
languages that produce a large amount of data over social networks
and is least analyzed is the Arabic language. The proposed paper is a
survey on the research efforts to analyze the Arabic content in
Twitter focusing on the tools and methods used to extract the
sentiments for the Arabic content on Twitter.
Abstract: In this paper, an analysis of some model order
reduction techniques is presented. A new hybrid algorithm for model
order reduction of linear time invariant systems is compared with the
conventional techniques namely Balanced Truncation, Hankel Norm
reduction and Dominant Pole Algorithm (DPA). The proposed hybrid
algorithm is known as Clustering Dominant Pole Algorithm (CDPA),
is able to compute the full set of dominant poles and its cluster center
efficiently. The dominant poles of a transfer function are specific
eigenvalues of the state space matrix of the corresponding dynamical
system. The effectiveness of this novel technique is shown through
the simulation results.
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 the simulation results of the
effects of sampling frequency on the total harmonic distortion (THD)
of three-phase inverters using the space vector pulse width
modulation (SVPWM) and space vector control (SVC) algorithms.
The relationship between the variables was studied using curve fitting
techniques, and it has been shown that, for 50 Hz inverters, there is
an exponential relation between the sampling frequency and THD up
to around 8500 Hz, beyond which the performance of the model
becomes irregular, and there is an negative exponential relation
between the sampling frequency and the marginal improvement to
the THD. It has also been found that the performance of SVPWM is
better than that of SVC with the same sampling frequency in most
frequency range, including the range where the performance of the
former is irregular.
Abstract: Load modeling is one of the central functions in
power systems operations. Electricity cannot be stored, which means
that for electric utility, the estimate of the future demand is necessary
in managing the production and purchasing in an economically
reasonable way. A majority of the recently reported approaches are
based on neural network. The attraction of the methods lies in the
assumption that neural networks are able to learn properties of the
load. However, the development of the methods is not finished, and
the lack of comparative results on different model variations is a
problem. This paper presents a new approach in order to predict the
Tunisia daily peak load. The proposed method employs a
computational intelligence scheme based on the Fuzzy neural
network (FNN) and support vector regression (SVR). Experimental
results obtained indicate that our proposed FNN-SVR technique gives
significantly good prediction accuracy compared to some classical
techniques.
Abstract: Macro invertebrates have been used to monitor
organic pollution in rivers and streams. Several biotic indices based
on macro invertebrates have been developed over the years including
the Biological Monitoring Working Party (BMWP). A new biotic
index, the Gammarus:Asellus ratio has been recently proposed as an
index of organic pollution. This study tested the validity of the
Gammarus:Asellus ratio as an index of organic pollution, by
examining the relationship between the Gammarus:Asellus ratio and
physical chemical parameters, and other biotic indices such as
BMWP and, Average Score Per Taxon (ASPT) from lakes and
streams at Markeaton Park, Allestree Park and Kedleston Hall,
Derbyshire. Macro invertebrates were sampled using the standard
five minute kick sampling techniques physical and chemical
environmental variables were obtained based on standard sampling
techniques. Eighteen sites were sampled, six sites from Markeaton
Park (three sites across the stream and three sites across the lake). Six
sites each were also sampled from Allestree Park and Kedleston Hall
lakes. The Gammarus:Asellus ratio showed an opposite significant
positive correlations with parameters indicative of organic pollution
such as the level of nitrates, phosphates, and calcium and also
revealed a negatively significant correlations with other biotic indices
(BMWP/ASPT). The BMWP score correlated positively significantly
with some water quality parameters such as dissolved oxygen and
flow rate, but revealed no correlations with other chemical
environmental variables. The BMWP score was significantly higher
in the stream than the lake in Markeaton Park, also The ASPT scores
appear to be significantly higher in the upper Lakes than the middle
and lower lakes. This study has further strengthened the use of
BMWP/ASPT score as an index of organic pollution. But additional
application is required to validate the use of Gammarus:Asellus as a
rapid bio monitoring tool.
Abstract: The 3D body movement signals captured during
human-human conversation include clues not only to the content of
people’s communication but also to their culture and personality.
This paper is concerned with automatic extraction of this information
from body movement signals. For the purpose of this research, we
collected a novel corpus from 27 subjects, arranged them into groups
according to their culture. We arranged each group into pairs and
each pair communicated with each other about different topics.
A state-of-art recognition system is applied to the problems of
person, culture, and topic recognition. We borrowed modeling,
classification, and normalization techniques from speech recognition.
We used Gaussian Mixture Modeling (GMM) as the main technique
for building our three systems, obtaining 77.78%, 55.47%, and
39.06% from the person, culture, and topic recognition systems
respectively. In addition, we combined the above GMM systems with
Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and
40.63% accuracy for person, culture, and topic recognition
respectively.
Although direct comparison among these three recognition
systems is difficult, it seems that our person recognition system
performs best for both GMM and GMM-SVM, suggesting that intersubject
differences (i.e. subject’s personality traits) are a major
source of variation. When removing these traits from culture and
topic recognition systems using the Nuisance Attribute Projection
(NAP) and the Intersession Variability Compensation (ISVC)
techniques, we obtained 73.44% and 46.09% accuracy from culture
and topic recognition systems respectively.
Abstract: Analyzing DNA microarray data sets is a great
challenge, which faces the bioinformaticians due to the complication
of using statistical and machine learning techniques. The challenge
will be doubled if the microarray data sets contain missing data,
which happens regularly because these techniques cannot deal with
missing data. One of the most important data analysis process on
the microarray data set is feature selection. This process finds the
most important genes that affect certain disease. In this paper, we
introduce a technique for imputing the missing data in microarray
data sets while performing feature selection.
Abstract: These days, the field of tissue engineering is getting
serious attention due to its usefulness. Bone tissue engineering helps
to address and sort-out the critical sized and non-healing orthopedic
problems by the creation of manmade bone tissue. We will design
and validate an efficient numerical model, which will simulate the
effective diffusivity in bone tissue engineering. Our numerical model
will be based on the finite element analysis of the diffusion-reaction
equations. It will have the ability to optimize the diffusivity, even
at multi-scale, with the variation of time. It will also have a special
feature “parametric sweep”, with which we will be able to predict
the oxygen, glucose and cell density dynamics, more accurately. We
will fix these problems by modifying the governing equations, by
selecting appropriate spatio-temporal finite element schemes and by
transient analysis.
Abstract: Since columns are the most important elements of the
structures, failure of one column in a critical location can cause a
progressive collapse. In this respect, the repair and strengthening of
columns is a very important subject to reduce the building failure and
to keep the columns capacity. Twenty columns with different
parameters is tested and analysis. Eleven typical confined reinforced
concrete (RC) columns with different types of techniques are
assessment. And also, four confined concrete columns with plastic
tube (PVC) are tested with and with four paralleling tested of
unconfined plain concrete. The techniques of confined RC columns
are mortar strengthening, Steel rings strengthening, FRP
strengthening. Moreover, the technique of confined plain concrete
(PC) column is used PVC tubes. The columns are tested under
uniaxial compressive loads studied the effect of confinement on the
structural behavior of circular RC columns. Test results for each
column are presented in the form of crack patterns, stress-strain
curves. Test results show that confining of the RC columns using
different techniques of strengthening results significant improvement
of the general behavior of the columns and can used in construction.
And also, tested confined PC columns with PVC tubes results shown
that the confined PC with PVC tubes can be used in economical
building. The theoretical model for predicted column capacity is
founded with experimental factor depends on the confined techniques
used and the strain reduction.
Abstract: A knowledge-based expert system with the acronym
RASPE is developed as an application tool to help decision makers in
construction companies make informed decisions about managing
risks in pipeline construction projects. Choosing to use expert
systems from all available artificial intelligence techniques is due to
the fact that an expert system is more suited to representing a
domain’s knowledge and the reasoning behind domain-specific
decisions. The knowledge-based expert system can capture the
knowledge in the form of conditional rules which represent various
project scenarios and potential risk mitigation/response actions. The
built knowledge in RASPE is utilized through the underlying
inference engine that allows the firing of rules relevant to a project
scenario into consideration. Paper provides an overview of the
knowledge acquisition process and goes about describing the
knowledge structure which is divided up into four major modules.
The paper shows one module in full detail for illustration purposes
and concludes with insightful remarks.
Abstract: The dielectric properties and ionic conductivity of
novel "ceramic state" polymer electrolytes for high capacity lithium
battery are characterized by Radio frequency and Microwave
methods in two broad frequency ranges from 50 Hz to 20 KHz and 4
GHz to 40 GHz. This innovative solid polymer electrolyte which is
highly ionic conductive (10-3 S/cm at room temperature) from -40oC
to +150oC can be used in any battery application. Such polymer
exhibits properties more like a ceramic rather than polymer. The
various applied measurement methods produced accurate dielectric
results for comprehensive analysis of electrochemical properties and
ion transportation mechanism of this newly invented polymer
electrolyte. Two techniques and instruments employing air gap
measurement by Capacitance Bridge and in-waveguide measurement
by vector network analyzer are applied to measure the complex
dielectric spectra. The complex dielectric spectra are used to
determine the complex alternating current electrical conductivity and
thus the ionic conductivity.
Abstract: One image is worth more than thousand words.
Images if analyzed can reveal useful information. Low level image
processing deals with the extraction of specific feature from a single
image. Now the question arises: What technique should be used to
extract patterns of very large and detailed image database? The
answer of the question is: “Image Mining”. Image Mining deals with
the extraction of image data relationship, implicit knowledge, and
another pattern from the collection of images or image database. It is
nothing but the extension of Data Mining. In the following paper, not
only we are going to scrutinize the current techniques of image
mining but also present a new technique for mining images using
Genetic Algorithm.
Abstract: This study was carried out to evaluate the nutritional
composition of the African River Prawn (Macrobrachium
vollenhovenii) in relation to Chokor (traditional) and Altona
(improved traditional) drying techniques used in the preservation and
processing of prawns by carrying out proximate composition
analysis. The value obtained for the proximate analysis of Chokor
and Altona smoke dried prawns were; Moisture (14.90% and
15.15%), Dry matter (85.10% and 84.85%), Protein (55.80% and
58.87%), Crude fat (1.95% and 1.98%), Crude fibre (21.40% and
13.11%), Carbohydrate (0.54% and 0.54%) and Ash (19.76% and
15.86%) respectively. The proximate mineral composition of Chokor
and Altona smoke dried prawns were; Calcium (5.66% and 4.20%)
and Phosphorus (9. 22% and 6.34%) respectively. Result shows there
was no loss of nutritional value with respect to Chokor and Altona
drying techniques used in the processing of prawns.
Abstract: The use of technology in the classroom is an issue that
is constantly evolving. Digital age students learn differently than their
teachers did, so now the teacher should be constantly evolving their
methods and teaching techniques to be more in touch with the
student. In this paper a case study presents how were used some of
these technologies by accompanying a classroom course, this in order
to provide students with a different and innovative experience as their
teacher usually presented the activities to develop. As students
worked in the various activities, they increased their digital skills by
employing unknown tools that helped them in their professional
training. The twenty-first century teacher should consider the use of
Information and Communication Technologies in the classroom
thinking in skills that students of the digital age should possess. It
also takes a brief look at the history of distance education and it is
also highlighted the importance of integrating technology as part of
the student's training.
Abstract: Verification and Validation of Simulated Process
Model is the most important phase of the simulator life cycle.
Evaluation of simulated process models based on Verification and
Validation techniques checks the closeness of each component model
(in a simulated network) with the real system/process with respect to
dynamic behaviour under steady state and transient conditions. The
process of Verification and Validation helps in qualifying the process
simulator for the intended purpose whether it is for providing
comprehensive training or design verification. In general, model
verification is carried out by comparison of simulated component
characteristics with the original requirement to ensure that each step
in the model development process completely incorporates all the
design requirements. Validation testing is performed by comparing
the simulated process parameters to the actual plant process
parameters either in standalone mode or integrated mode.
A Full Scope Replica Operator Training Simulator for PFBR -
Prototype Fast Breeder Reactor has been developed at IGCAR,
Kalpakkam, INDIA named KALBR-SIM (Kalpakkam Breeder
Reactor Simulator) where in the main participants are
engineers/experts belonging to Modeling Team, Process Design and
Instrumentation & Control design team. This paper discusses about
the Verification and Validation process in general, the evaluation
procedure adopted for PFBR operator training Simulator, the
methodology followed for verifying the models, the reference
documents and standards used etc. It details out the importance of
internal validation by design experts, subsequent validation by
external agency consisting of experts from various fields, model
improvement by tuning based on expert’s comments, final
qualification of the simulator for the intended purpose and the
difficulties faced while co-coordinating various activities.