Abstract: This paper describes a segmentation algorithm based
on the cooperation of an optical flow estimation method with edge
detection and region growing procedures.
The proposed method has been developed as a pre-processing
stage to be used in methodologies and tools for video/image indexing
and retrieval by content. The addressed problem consists in
extracting whole objects from background for producing images of
single complete objects from videos or photos. The extracted images
are used for calculating the object visual features necessary for both
indexing and retrieval processes.
The first task of the algorithm exploits the cues from motion
analysis for moving area detection. Objects and background are then
refined using respectively edge detection and region growing
procedures. These tasks are iteratively performed until objects and
background are completely resolved.
The developed method has been applied to a variety of indoor and
outdoor scenes where objects of different type and shape are
represented on variously textured background.
Abstract: One of the most challengeable issues in ESL
(Electronic System Level) design is the lack of a general modeling
scheme for on chip communication architecture. In this paper some
of the mostly used methodologies for modeling and representation of
on chip communication are investigated. Our goal is studying the
existing methods to extract the requirements of a general
representation scheme for communication architecture synthesis. The
next step, will be introducing a modeling and representation method
for being used in automatically synthesis process of on chip
communication architecture.
Abstract: In this study we focus on improvement performance
of a cue based Motor Imagery Brain Computer Interface (BCI). For
this purpose, data fusion approach is used on results of different
classifiers to make the best decision. At first step Distinction
Sensitive Learning Vector Quantization method is used as a feature
selection method to determine most informative frequencies in
recorded signals and its performance is evaluated by frequency
search method. Then informative features are extracted by packet
wavelet transform. In next step 5 different types of classification
methods are applied. The methodologies are tested on BCI
Competition II dataset III, the best obtained accuracy is 85% and the
best kappa value is 0.8. At final step ordered weighted averaging
(OWA) method is used to provide a proper aggregation classifiers
outputs. Using OWA enhanced system accuracy to 95% and kappa
value to 0.9. Applying OWA just uses 50 milliseconds for
performing calculation.
Abstract: Most standard software development methodologies
are often not applied to software projects in many developing
countries of the world. The approach generally practice is close to
what eXtreme Programming (XP) is likely promoting, just keep
coding and testing as the requirement evolves. XP is an agile
software process development methodology that has inherent
capability for improving efficiency of Business Software
Development (BSD). XP can facilitate Business-to-Development
(B2D) relationship due to its customer-oriented advocate. From
practitioner point of view, we applied XP to BSD and result shows
that customer involvement has positive impact on productivity, but
can as well frustrate the success of the project. In an effort to
promote software engineering practice in developing countries of
Africa, we present the experiment performed, lessons learned,
problems encountered and solution adopted in applying XP
methodology to BSD.
Abstract: Pattern recognition is the research area of Artificial
Intelligence that studies the operation and design of systems that
recognize patterns in the data. Important application areas are image
analysis, character recognition, fingerprint classification, speech
analysis, DNA sequence identification, man and machine
diagnostics, person identification and industrial inspection. The
interest in improving the classification systems of data analysis is
independent from the context of applications. In fact, in many
studies it is often the case to have to recognize and to distinguish
groups of various objects, which requires the need for valid
instruments capable to perform this task. The objective of this article
is to show several methodologies of Artificial Intelligence for data
classification applied to biomedical patterns. In particular, this work
deals with the realization of a Computer-Aided Detection system
(CADe) that is able to assist the radiologist in identifying types of
mammary tumor lesions. As an additional biomedical application of
the classification systems, we present a study conducted on blood
samples which shows how these methods may help to distinguish
between carriers of Thalassemia (or Mediterranean Anaemia) and
healthy subjects.
Abstract: Recently, the health of retired National Football
League players, particularly lineman has been investigated. A
number of studies have reported increased cardiometabolic risk,
premature cardiovascular disease and incidence of type 2 diabetes.
Rugby union players have somatotypes very similar to National
Football League players which suggests that rugby players may have
similar health risks. The International Golden Oldies World Rugby
Festival (GORF) provided a unique opportunity to investigate the
demographics of veteran rugby players. METHODOLOGIES: A
cross-sectional, observational study was completed using an online
web-based questionnaire that consisted of medical history and
physiological measures. Data analysis was completed using a one
sample t-test (50yrs) and Chi-square test. RESULTS:
A total of 216 veteran rugby competitors (response rate = 6.8%)
representing 10 countries, aged 35-72 yrs (mean 51.2, S.D. ±8.0),
participated in the online survey. As a group, the incidence of current
smokers was low at 8.8% (avg 72.4 cigs/wk) whilst the percentage
consuming alcohol was high (93.1% (avg 11.2 drinks/wk).
Competitors reported the following top six chronic
diseases/disorders; hypertension (18.6%), arthritis (OA/RA, 11.5%),
asthma (9.3%), hyperlipidemia (8.2%), diabetes (all types, 7.5%) and
gout (6%), there were significant differences between groups with
regard to cancer (all types) and migraines. When compared to the
Australian general population (Australian Bureau of Statistics data,
n=18,000), GORF competitors had a significantly lower incidence of
anxiety (p
Abstract: Full adders are important components in applications
such as digital signal processors (DSP) architectures and
microprocessors. In addition to its main task, which is adding two
numbers, it participates in many other useful operations such as
subtraction, multiplication, division,, address calculation,..etc. In
most of these systems the adder lies in the critical path that
determines the overall speed of the system. So enhancing the
performance of the 1-bit full adder cell (the building block of the
adder) is a significant goal.Demands for the low power VLSI have
been pushing the development of aggressive design methodologies to
reduce the power consumption drastically. To meet the growing
demand, we propose a new low power adder cell by sacrificing the
MOS Transistor count that reduces the serious threshold loss
problem, considerably increases the speed and decreases the power
when compared to the static energy recovery full (SERF) adder. So a
new improved 14T CMOS l-bit full adder cell is presented in this
paper. Results show 50% improvement in threshold loss problem,
45% improvement in speed and considerable power consumption
over the SERF adder and other different types of adders with
comparable performance.
Abstract: In this work, a radial basis function (RBF) neural network is developed for the identification of hyperbolic distributed parameter systems (DPSs). This empirical model is based only on process input-output data and used for the estimation of the controlled variables at specific locations, without the need of online solution of partial differential equations (PDEs). The nonlinear model that is obtained is suitably transformed to a nonlinear state space formulation that also takes into account the model mismatch. A stable robust control law is implemented for the attenuation of external disturbances. The proposed identification and control methodology is applied on a long duct, a common component of thermal systems, for a flow based control of temperature distribution. The closed loop performance is significantly improved in comparison to existing control methodologies.
Abstract: This paper gives an overview of the mapping
mechanism of SEAM-a methodology for the automatic generation of
knowledge models and its mapping onto Java codes. It discusses the
rules that will be used to map the different components in the
knowledge model automatically onto Java classes, properties and
methods. The aim of developing this mechanism is to help in the
creation of a prototype which will be used to validate the knowledge
model which has been generated automatically. It will also help to
link the modeling phase with the implementation phase as existing
knowledge engineering methodologies do not provide for proper
guidelines for the transition from the knowledge modeling phase to
development phase. This will decrease the development overheads
associated to the development of Knowledge Based Systems.
Abstract: Simulation of the flow and sedimentation process in
the reservoir dams can be made by two methods of physical and mathematical modeling. The study area was within a region which
ranged from the Jelogir hydrometric station to the Karkheh reservoir
dam aimed at investigating the effects of stream tubes on the
GSTARS-3 model behavior. The methodologies was to run the model based on 5 stream tubes in order to observe the influence of
each scenario on longitudinal profiles, cross-section, flow velocity and bed load sediment size. Results further suggest that the use of
two stream tubes or more which result in the semi-two-dimensional
model will yield relatively closer results to the observational data
than a singular stream tube modeling. Moreover, the results of
modeling with three stream tubes shown to yield a relatively close
results with the observational data. The overall conclusion of the paper is with applying various stream tubes; it would be possible to yield a significant influence on the modeling behavior Vis-a Vis the bed load sediment size.
Abstract: Six Sigma is a well known discipline that reduces
variation using complex statistical tools and the DMAIC model. By
integrating Goldratts-s Theory of Constraints, the Five Focusing
Points and System Thinking tools, Six Sigma projects can be selected
where it can cause more impact in the company. This research
defines an integrated model of six sigma and constraint management
that shows a step-by-step guide using the original methodologies
from each discipline and is evaluated in a case study from the
production line of a Automobile engine monoblock V8, resulting in
an increase in the line capacity from 18.7 pieces per hour to 22.4
pieces per hour, a reduction of 60% of Work-In-Process and a
variation decrease of 0.73%.
Abstract: New methodologies for XOR-XNOR circuits are
proposed to improve the speed and power as these circuits are basic
building blocks of many arithmetic circuits. This paper evaluates and
compares the performance of various XOR-XNOR circuits. The
performance of the XOR-XNOR circuits based on TSMC 0.18μm
process models at all range of the supply voltage starting from 0.6V
to 3.3V is evaluated by the comparison of the simulation results
obtained from HSPICE. Simulation results reveal that the proposed
circuit exhibit lower PDP and EDP, more power efficient and faster
when compared with best available XOR-XNOR circuits in the
literature.
Abstract: The main purpose of this paper is to research on the
methodologies of BYD to implement the opportune innovation. BYD
is a Chinese company which has the IT component manufacture, the
rechargeable battery and the automobile businesses. The paper deals
with the innovation methodology as the same as the IPR management
BYD implements in order to obtain the rapid growth of technology
development with the reasonable cost of money and time.
Abstract: Distributed denial-of-service (DDoS) attacks pose a
serious threat to network security. There have been a lot of
methodologies and tools devised to detect DDoS attacks and reduce
the damage they cause. Still, most of the methods cannot
simultaneously achieve (1) efficient detection with a small number of
false alarms and (2) real-time transfer of packets. Here, we introduce
a method for proactive detection of DDoS attacks, by classifying the
network status, to be utilized in the detection stage of the proposed
anti-DDoS framework. Initially, we analyse the DDoS architecture
and obtain details of its phases. Then, we investigate the procedures
of DDoS attacks and select variables based on these features. Finally,
we apply the k-nearest neighbour (k-NN) method to classify the
network status into each phase of DDoS attack. The simulation result
showed that each phase of the attack scenario is classified well and
we could detect DDoS attack in the early stage.
Abstract: Under the limitation of investment budget, a utility
company is required to maximize the utilization of their existing
assets during their life cycle satisfying both engineering and financial
requirements. However, utility does not have knowledge about the
status of each asset in the portfolio neither in terms of technical nor
financial values. This paper presents a knowledge based model for
the utility companies in order to make an optimal decision on power
transformer with their utilization. CommonKADS methodology, a
structured development for knowledge and expertise representation,
is utilized for designing and developing knowledge based model. A
case study of One MVA power transformer of Nepal Electricity
Authority is presented. The results show that the reusable knowledge
can be categorized, modeled and utilized within the utility company
using the proposed methodologies. Moreover, the results depict that
utility company can achieve both engineering and financial benefits
from its utilization.
Abstract: Web applications have become very complex and crucial, especially when combined with areas such as CRM (Customer Relationship Management) and BPR (Business Process Reengineering), the scientific community has focused attention to Web applications design, development, analysis, and testing, by studying and proposing methodologies and tools. This paper proposes an approach to automatic multi-dimensional concern mining for Web Applications, based on concepts analysis, impact analysis, and token-based concern identification. This approach lets the user to analyse and traverse Web software relevant to a particular concern (concept, goal, purpose, etc.) via multi-dimensional separation of concerns, to document, understand and test Web applications. This technique was developed in the context of WAAT (Web Applications Analysis and Testing) project. A semi-automatic tool to support this technique is currently under development.
Abstract: A number of competing methodologies have been developed
to identify genes and classify DNA sequences into coding
and non-coding sequences. This classification process is fundamental
in gene finding and gene annotation tools and is one of the most
challenging tasks in bioinformatics and computational biology. An
information theory measure based on mutual information has shown
good accuracy in classifying DNA sequences into coding and noncoding.
In this paper we describe a species independent iterative
approach that distinguishes coding from non-coding sequences using
the mutual information measure (MIM). A set of sixty prokaryotes is
used to extract universal training data. To facilitate comparisons with
the published results of other researchers, a test set of 51 bacterial
and archaeal genomes was used to evaluate MIM. These results
demonstrate that MIM produces superior results while remaining
species independent.
Abstract: Benefits to the organisation are just as important as technical ability when it comes to software success. The challenge is to provide industry with professionals who understand this. In other words: How to teach computer engineering students to look beyond technology, and at the benefits of software to organizations? This paper reports on the conceptual design of a section of the computer networks module aimed to sensitize the students to the organisational context.
Checkland focuses on different worldviews represented by various role players in the organisation. He developed the Soft Systems Methodology that guides purposeful action in organisations, while incorporating different worldviews in the modeling process. If we can sensitize students to these methods, they are likely to appreciate the wider context of application of system software. This paper will provide literature on these concepts as well as detail on how the students will be guided to adopt these concepts.
Abstract: Enterprise applications are complex systems that are hard to develop and deploy in organizations. Although software application development tools, frameworks, methodologies and patterns are rapidly developing; many projects fail by causing big costs. There are challenging issues that programmers and designers face with while working on enterprise applications. In this paper, we present the three of the significant issues: Architectural, technological and performance. The important subjects in each issue are pointed out and recommendations are given. In architectural issues the lifecycle, meta-architecture, guidelines are pointed out. .NET and Java EE platforms are presented in technological issues. The importance of performance, measuring performance and profilers are explained in performance issues.
Abstract: This paper focuses on the development of bond graph
dynamic model of the mechanical dynamics of an excavating mechanism
previously designed to be used with small tractors, which are
fabricated in the Engineering Workshops of Jomo Kenyatta University
of Agriculture and Technology. To develop a mechanical dynamics
model of the manipulator, forward recursive equations similar to
those applied in iterative Newton-Euler method were used to obtain
kinematic relationships between the time rates of joint variables
and the generalized cartesian velocities for the centroids of the
links. Representing the obtained kinematic relationships in bondgraphic
form, while considering the link weights and momenta as
the elements led to a detailed bond graph model of the manipulator.
The bond graph method was found to reduce significantly the number
of recursive computations performed on a 3 DOF manipulator for a
mechanical dynamic model to result, hence indicating that bond graph
method is more computationally efficient than the Newton-Euler
method in developing dynamic models of 3 DOF planar manipulators.
The model was verified by comparing the joint torque expressions
of a two link planar manipulator to those obtained using Newton-
Euler and Lagrangian methods as analyzed in robotic textbooks. The
expressions were found to agree indicating that the model captures
the aspects of rigid body dynamics of the manipulator. Based on
the model developed, actuator sizing and valve sizing methodologies
were developed and used to obtain the optimal sizes of the pistons
and spool valve ports respectively. It was found that using the pump
with the sized flow rate capacity, the engine of the tractor is able to
power the excavating mechanism in digging a sandy-loom soil.