Abstract: Load balancing in distributed computer systems is the
process of redistributing the work load among processors in the
system to improve system performance. Most of previous research in
using fuzzy logic for the purpose of load balancing has only
concentrated in utilizing fuzzy logic concepts in describing
processors load and tasks execution length. The responsibility of the
fuzzy-based load balancing process itself, however, has not been
discussed and in most reported work is assumed to be performed in a
distributed fashion by all nodes in the network. This paper proposes a
new fuzzy dynamic load balancing algorithm for homogenous
distributed systems. The proposed algorithm utilizes fuzzy logic in
dealing with inaccurate load information, making load distribution
decisions, and maintaining overall system stability. In terms of
control, we propose a new approach that specifies how, when, and by
which node the load balancing is implemented. Our approach is
called Centralized-But-Distributed (CBD).
Abstract: An early and accurate detection of Alzheimer's disease (AD) is an important stage in the treatment of individuals suffering from AD. We present an approach based on the use of structural magnetic resonance imaging (sMRI) phase images to distinguish between normal controls (NC), mild cognitive impairment (MCI) and AD patients with clinical dementia rating (CDR) of 1. Independent component analysis (ICA) technique is used for extracting useful features which form the inputs to the support vector machines (SVM), K nearest neighbour (kNN) and multilayer artificial neural network (ANN) classifiers to discriminate between the three classes. The obtained results are encouraging in terms of classification accuracy and effectively ascertain the usefulness of phase images for the classification of different stages of Alzheimer-s disease.
Abstract: One of the major parts of a jet engine is air intake,
which provides proper and required amount of air for the engine to
operate. There are several aerodynamic parameters which should be
considered in design, such as distortion, pressure recovery, etc. In
this research, the effects of lip ice accretion on pitot intake
performance are investigated. For ice accretion phenomenon, two
supervised multilayer neural networks (ANN) are designed, one for
ice shape prediction and another one for ice roughness estimation
based on experimental data. The Fourier coefficients of transformed
ice shape and parameters include velocity, liquid water content
(LWC), median volumetric diameter (MVD), spray time and
temperature are used in neural network training. Then, the subsonic
intake flow field is simulated numerically using 2D Navier-Stokes
equations and Finite Volume approach with Hybrid mesh includes
structured and unstructured meshes. The results are obtained in
different angles of attack and the variations of intake aerodynamic
parameters due to icing phenomenon are discussed. The results show
noticeable effects of ice accretion phenomenon on intake behavior.
Abstract: In this paper, we use Generalized Hamiltonian systems approach to synchronize a modified sixth-order Chua's circuit, which generates hyperchaotic dynamics. Synchronization is obtained between the master and slave dynamics with the slave being given by an observer. We apply this approach to transmit private information (analog and binary), while the encoding remains potentially secure.
Abstract: In geometrical camera calibration, the objective is to
determine a set of camera parameters that describe the mapping
between 3D references coordinates and 2D image coordinates. In this
paper, a technique of calibration and tracking based on both a least
squares method is presented and a correlation technique developed as
part of an augmented reality system. This approach is fast and it can
be used for a real time system
Abstract: This study sought to uncover the complex role of
stress in the workplace by investigating both positive (eustress) and
negative (distress) stress responses. In particular, the study tested a
mediation model in which organisational stressors (person-job fit and
role overload) influence employee affective wellbeing, both directly
and indirectly through stress responses. Participants were recruited
from retail and finance organisations in Australia and New Zealand,
and asked to complete an anonymous online questionnaire. A total of
140 individuals returned completed questionnaires. The results show
that person-job fit influenced eustress, which in turn had a positive
effect on employee affective wellbeing; and role overload impacted
distress, which in turn held a negative influence on affective
wellbeing. These findings indicate that different organisational
stressors have unique relationships with eustress and distress
responses. Limitations and implications of the study are discussed.
Abstract: This paper presents an innovative computer system
that contributes to sustainable development of the enterprise. The
research refers to a rethinking of traditional systems of collaboration
and risk assessment, present in any organization, leading to a
sustainable enterprise. This concept integrates emerging tools that
allow the implementation and exploitation of the collective
intelligence of the enterprise, allowing the exchange of contextual,
agile and simplified information, and collaboration with networks of
customers and partners in an environment where risks are controlled.
Risk assessment is done in a systemic way: the enterprise as the
system compared to the contained departments and the enterprise as a
subsystem compared to: families of international standards and
sustainability-s responsibilities. The enterprise, in this systemic
vision, responds to the requirements that any existing system to
operate continuously in an indefinite future without reaching key
resource depletion. The research is done by integrating collaborative
science, engineering, management, psychology, obtaining thus a
cornerstone of sustainable development of the enterprise.
Abstract: The paper presents the multi-element synthetic
transmit aperture (MSTA) method with a small number of elements
transmitting and all elements apertures in medical ultrasound
imaging. As compared to the other methods MSTA allows to
increase the system frame rate and provides the best compromise
between penetration depth and lateral resolution.
In the experiments a 128-element linear transducer array with
0.3 mm pitch excited by a burst pulse of 125 ns duration were used.
The comparison of 2D ultrasound images of tissue mimicking
phantom obtained using the STA and the MSTA methods is
presented to demonstrate the benefits of the second approach. The
results were obtained using SA algorithm with transmit and receive
signals correction based on a single element directivity function.
Abstract: A new approach has been used for optimized design of multipliers based upon the concepts of Vedic mathematics. The design has been targeted to state-of-the art field-programmable gate arrays (FPGAs). The multiplier generates partial products using Vedic mathematics method by employing basic 4x4 multipliers designed by exploiting 6-input LUTs and multiplexers in the same slices resulting in drastic reduction in area. The multiplier is realized on Xilinx FPGAs using devices Virtex-5 and Virtex-6.Carry Chain Adder was employed to obtain final products. The performance of the proposed multiplier was examined and compared to well-known multipliers such as Booth, Carry Save, Carry ripple, and array multipliers. It is demonstrated that the proposed multiplier is superior in terms of speed as well as power consumption.
Abstract: This paper addresses a novel technique for placement of distributed generation (DG) in electric power systems. A GA based approach for sizing and placement of DG keeping in view of system power loss minimization in different loading conditions is explained. Minimal system power loss is obtained under voltage and line loading constraints. Proposed strategy is applied to power distribution systems and its effectiveness is verified through simulation results on 16, 37-bus and 75-bus test systems.
Abstract: The future of business intelligence (BI) is to integrate
intelligence into operational systems that works in real-time
analyzing small chunks of data based on requirements on continuous
basis. This is moving away from traditional approach of doing
analysis on ad-hoc basis or sporadically in passive and off-line mode
analyzing huge amount data. Various AI techniques such as expert
systems, case-based reasoning, neural-networks play important role
in building business intelligent systems. Since BI involves various
tasks and models various types of problems, hybrid intelligent
techniques can be better choice. Intelligent systems accessible
through web services make it easier to integrate them into existing
operational systems to add intelligence in every business processes.
These can be built to be invoked in modular and distributed way to
work in real time. Functionality of such systems can be extended to
get external inputs compatible with formats like RSS. In this paper,
we describe a framework that use effective combinations of these
techniques, accessible through web services and work in real-time.
We have successfully developed various prototype systems and done
few commercial deployments in the area of personalization and
recommendation on mobile and websites.
Abstract: Optimal supplementary damping controller design for Thyristor Controlled Series Compensator (TCSC) is presented in this paper. For the proposed controller design, a multi-objective fitness function consisting of both damping factors and real part of system electromachanical eigenvalue is used and Real- Coded Genetic Algorithm (RCGA) is employed for the optimal supplementary controller parameters. The performance of the designed supplementary TCSC-based damping controller is tested on a weakly connected power system with different disturbances and loading conditions with parameter variations. Simulation results are presented and compared with a conventional power system stabilizer and also with the TCSC-based supplementary controller when the controller parameters are not optimized to show the effectiveness and robustness of the proposed approach over a wide range of loading conditions and disturbances.
Abstract: As the number of networked computers grows,
intrusion detection is an essential component in keeping networks
secure. Various approaches for intrusion detection are currently
being in use with each one has its own merits and demerits. This
paper presents our work to test and improve the performance of a
new class of decision tree c-fuzzy decision tree to detect intrusion.
The work also includes identifying best candidate feature sub set to
build the efficient c-fuzzy decision tree based Intrusion Detection
System (IDS). We investigated the usefulness of c-fuzzy decision
tree for developing IDS with a data partition based on horizontal
fragmentation. Empirical results indicate the usefulness of our
approach in developing the efficient IDS.
Abstract: We study different types of aggregation operators such
as the ordered weighted averaging (OWA) operator and the
generalized OWA (GOWA) operator. We analyze the use of OWA
operators in the Minkowski distance. We will call these new distance
aggregation operator the Minkowski ordered weighted averaging
distance (MOWAD) operator. We give a general overview of this
type of generalization and study some of their main properties. We
also analyze a wide range of particular cases found in this
generalization such as the ordered weighted averaging distance
(OWAD) operator, the Euclidean ordered weighted averaging
distance (EOWAD) operator, the normalized Minkowski distance,
etc. Finally, we give an illustrative example of the new approach
where we can see the different results obtained by using different
aggregation operators.
Abstract: We present a method for the selection of students
in interdisciplinary studies based on the hybrid averaging
operator. We assume that the available information given in
the problem is uncertain so it is necessary to use interval
numbers. Therefore, we suggest a new type of hybrid
aggregation called uncertain induced generalized hybrid
averaging (UIGHA) operator. It is an aggregation operator
that considers the weighted average (WA) and the ordered
weighted averaging (OWA) operator in the same formulation.
Therefore, we are able to consider the degree of optimism of
the decision maker and grades of importance in the same
approach. By using interval numbers, we are able to represent
the information considering the best and worst possible results
so the decision maker gets a more complete view of the
decision problem. We develop an illustrative example of the
proposed scheme in the selection of students in
interdisciplinary studies. We see that with the use of the
UIGHA operator we get a more complete representation of the
selection problem. Then, the decision maker is able to
consider a wide range of alternatives depending on his
interests. We also show other potential applications that could
be used by using the UIGHA operator in educational problems
about selection of different types of resources such as
students, professors, etc.
Abstract: Hospital staff and managers are under pressure and
concerned for effective use and management of scarce resources. The
hospital admissions require many decisions that have complex and
uncertain consequences for hospital resource utilization and patient
flow. It is challenging to predict risk of admissions and length of stay
of a patient due to their vague nature. There is no method to capture
the vague definition of admission of a patient. Also, current methods
and tools used to predict patients at risk of admission fail to deal with
uncertainty in unplanned admission, LOS, patients- characteristics.
The main objective of this paper is to deal with uncertainty in
health system variables, and handles uncertain relationship among
variables. An introduction of machine learning techniques along with
statistical methods like Regression methods can be a proposed
solution approach to handle uncertainty in health system variables. A
model that adapts fuzzy methods to handle uncertain data and
uncertain relationships can be an efficient solution to capture the
vague definition of admission of a patient.
Abstract: In this paper, we summarize recent work of the authors on nanocomputing memory devices. We investigate two memory devices, each comprising a charged metallofullerene and carbon nanotubes. The first device involves two open nanotubes of the same radius that are joined by a centrally located nanotube of a smaller radius. A metallofullerene is then enclosed inside the structure. The second device also involves a etallofullerene that is located inside a closed carbon nanotube. Assuming the Lennard-Jones interaction energy and the continuum approximation, for both devices, the metallofullerene has two symmetrically placed equal minimum energy positions. On one side the metallofullerene represents the zero information state and by applying an external electrical field, it can overcome the energy barrier, and pass from one end of the tube to the other, where the metallofullerene then represents the one information state.
Abstract: During the past several years, face recognition in video
has received significant attention. Not only the wide range of
commercial and law enforcement applications, but also the availability
of feasible technologies after several decades of research contributes
to the trend. Although current face recognition systems have reached a
certain level of maturity, their development is still limited by the
conditions brought about by many real applications. For example,
recognition images of video sequence acquired in an open
environment with changes in illumination and/or pose and/or facial
occlusion and/or low resolution of acquired image remains a largely
unsolved problem. In other words, current algorithms are yet to be
developed. This paper provides an up-to-date survey of video-based
face recognition research. To present a comprehensive survey, we
categorize existing video based recognition approaches and present
detailed descriptions of representative methods within each category.
In addition, relevant topics such as real time detection, real time
tracking for video, issues such as illumination, pose, 3D and low
resolution are covered.
Abstract: Sign language recognition has been a topic of research since the first data glove was developed. Many researchers have attempted to recognize sign language through various techniques. However none of them have ventured into the area of Pakistan Sign Language (PSL). The Boltay Haath project aims at recognizing PSL gestures using Statistical Template Matching. The primary input device is the DataGlove5 developed by 5DT. Alternative approaches use camera-based recognition which, being sensitive to environmental changes are not always a good choice.This paper explains the use of Statistical Template Matching for gesture recognition in Boltay Haath. The system recognizes one handed alphabet signs from PSL.
Abstract: Excessive ductility demand on shorter piers is a
common problem for irregular bridges subjected to strong ground
motion. Various techniques have been developed to reduce the
likelihood of collapse of bridge due to failure of shorter piers. This
paper presents the new approach to improve the seismic behavior of
such bridges using Nitinol shape memory alloys (SMAs).
Superelastic SMAs have the ability to remain elastic under very large
deformation due to martensitic transformation. This unique property
leads to enhanced performance of controlled bridge compared with
the performance of the reference bridge. To evaluate the effectiveness
of the devices, nonlinear time history analysis is performed on a RC
single column bent highway bridge using a suite of representative
ground motions. The results show that this method is very effective in
limiting the ductility demand of shorter pier.