Abstract: This paper describes a novel method for automatic
estimation of the contours of weld defect in radiography images.
Generally, the contour detection is the first operation which we apply
in the visual recognition system. Our approach can be described as a
region based maximum likelihood formulation of parametric
deformable contours. This formulation provides robustness against
the poor image quality, and allows simultaneous estimation of the
contour parameters together with other parameters of the model.
Implementation is performed by a deterministic iterative algorithm
with minimal user intervention. Results testify for the very good
performance of the approach especially in synthetic weld defect
images.
Abstract: A Decision Support System/Expert System for stock
portfolio selection presented where at first step, both technical and
fundamental data used to estimate technical and fundamental return
and risk (1st phase); Then, the estimated values are aggregated with
the investor preferences (2nd phase) to produce convenient stock
portfolio.
In the 1st phase, there are two expert systems, each of which is
responsible for technical or fundamental estimation. In the technical
expert system, for each stock, twenty seven candidates are identified
and with using rough sets-based clustering method (RC) the effective
variables have been selected. Next, for each stock two fuzzy rulebases
are developed with fuzzy C-Mean method and Takai-Sugeno-
Kang (TSK) approach; one for return estimation and the other for
risk. Thereafter, the parameters of the rule-bases are tuned with backpropagation
method. In parallel, for fundamental expert systems,
fuzzy rule-bases have been identified in the form of “IF-THEN" rules
through brainstorming with the stock market experts and the input
data have been derived from financial statements; as a result two
fuzzy rule-bases have been generated for all the stocks, one for return
and the other for risk.
In the 2nd phase, user preferences represented by four criteria and
are obtained by questionnaire. Using an expert system, four estimated
values of return and risk have been aggregated with the respective
values of user preference. At last, a fuzzy rule base having four rules,
treats these values and produce a ranking score for each stock which
will lead to a satisfactory portfolio for the user.
The stocks of six manufacturing companies and the period of
2003-2006 selected for data gathering.
Abstract: Packing problems arise in a wide variety of application
areas. The basic problem is that of determining an efficient arrangement
of different objects in a region without any overlap and
with minimal wasted gap between shapes. This paper presents a
novel population based approach for optimizing arrangement of irregular
shapes. In this approach, each shape is coded as an agent and
the agents' reproductions and grouping policies results in arrangements
of the objects in positions with least wasted area between
them. The approach is implemented in an application for cutting
sheets and test results on several problems from literature are presented.
Abstract: The development and use of mobile devices as well as its integration within education systems to deliver electronic contents and to support real-time communications was the focus of this research. In order to investigate the software engineering issues in using mobile devices a research on electronic content was initiated. The Developed MP3 mobile software solution was developed as a prototype for testing and developing a strategy for designing a usable m-learning environment. The mobile software solution was evaluated using mobile device using the link: http://projects.seeu.edu.mk/mlearn. The investigation also tested the correlation between the two mobile learning indicators: electronic content and attention, based on the Task Based learning instructional method. The mobile software solution ''M-Learn“ was developed as a prototype for testing the approach and developing a strategy for designing usable m-learning environment. The proposed methodology is about what learning modeling approach is more appropriate to use when developing mobile learning software.
Abstract: This research paper designs a unique motion planner
of multiple platoons of nonholonomic car-like robots as a feasible
solution to the lane changing/merging maneuvers. The decentralized
planner with a leaderless approach and a path-guidance principle
derived from the Lyapunov-based control scheme generates collision
free avoidance and safe merging maneuvers from multiple lanes to a
single lane by deploying a split/merge strategy. The fixed obstacles
are the markings and boundaries of the road lanes, while the moving
obstacles are the robots themselves. Real and virtual road lane
markings and the boundaries of road lanes are incorporated into a
workspace to achieve the desired formation and configuration of the
robots. Convergence of the robots to goal configurations and the
repulsion of the robots from specified obstacles are achieved by
suitable attractive and repulsive potential field functions,
respectively. The results can be viewed as a significant contribution
to the avoidance algorithm of the intelligent vehicle systems (IVS).
Computer simulations highlight the effectiveness of the split/merge
strategy and the acceleration-based controllers.
Abstract: Content-Based Image Retrieval has been a major area
of research in recent years. Efficient image retrieval with high
precision would require an approach which combines usage of both
the color and texture features of the image. In this paper we propose
a method for enhancing the capabilities of texture based feature
extraction and further demonstrate the use of these enhanced texture
features in Texture-Based Color Image Retrieval.
Abstract: Conceptualization strengthens intelligent systems in generalization skill, effective knowledge representation, real-time inference, and managing uncertain and indefinite situations in addition to facilitating knowledge communication for learning agents situated in real world. Concept learning introduces a way of abstraction by which the continuous state is formed as entities called concepts which are connected to the action space and thus, they illustrate somehow the complex action space. Of computational concept learning approaches, action-based conceptualization is favored because of its simplicity and mirror neuron foundations in neuroscience. In this paper, a new biologically inspired concept learning approach based on the probabilistic framework is proposed. This approach exploits and extends the mirror neuron-s role in conceptualization for a reinforcement learning agent in nondeterministic environments. In the proposed method, instead of building a huge numerical knowledge, the concepts are learnt gradually from rewards through interaction with the environment. Moreover the probabilistic formation of the concepts is employed to deal with uncertain and dynamic nature of real problems in addition to the ability of generalization. These characteristics as a whole distinguish the proposed learning algorithm from both a pure classification algorithm and typical reinforcement learning. Simulation results show advantages of the proposed framework in terms of convergence speed as well as generalization and asymptotic behavior because of utilizing both success and failures attempts through received rewards. Experimental results, on the other hand, show the applicability and effectiveness of the proposed method in continuous and noisy environments for a real robotic task such as maze as well as the benefits of implementing an incremental learning scenario in artificial agents.
Abstract: This paper describes a novel approach for deriving
modules from protein-protein interaction networks, which combines
functional information with topological properties of the network.
This approach is based on weighted clustering coefficient, which
uses weights representing the functional similarities between the
proteins. These weights are calculated according to the semantic
similarity between the proteins, which is based on their Gene
Ontology terms. We recently proposed an algorithm for identification
of functional modules, called SWEMODE (Semantic WEights for
MODule Elucidation), that identifies dense sub-graphs containing
functionally similar proteins. The rational underlying this approach is
that each module can be reduced to a set of triangles (protein triplets
connected to each other). Here, we propose considering semantic
similarity weights of all triangle-forming edges between proteins. We
also apply varying semantic similarity thresholds between
neighbours of each node that are not neighbours to each other (and
hereby do not form a triangle), to derive new potential triangles to
include in module-defining procedure. The results show an
improvement of pure topological approach, in terms of number of
predicted modules that match known complexes.
Abstract: The significance of emissions from the road transport
sector (such as air pollution, noise, etc) has grown considerably in
recent years. In Australia, 14.3% of national greenhouse gas
emissions in 2000 were the transport sector-s share which 12.9% of
net national emissions were related to a road transport alone.
Considering the growing attention to the green house gas(GHG)
emissions, this paper attempts to provide air pollution modeling
aspects of environmental consequences of the road transport by using
one of the best computer based tools including the Geographic
Information System (GIS). In other word, in this study, GIS and its
applications is explained, models which are used to model air
pollution and GHG emissions from vehicles are described and GIS is
applied in real case study that attempts to forecast GHG emission
from people who travel to work by car in 2031 in Melbourne for
analysing results as thematic maps.
Abstract: The winding hot-spot temperature is one of the most
critical parameters that affect the useful life of the power
transformers. The winding hot-spot temperature can be calculated as
function of the top-oil temperature that can estimated by using the
ambient temperature and transformer loading measured data. This
paper proposes the estimation of the top-oil temperature by using a
method based on Least Squares Support Vector Machines approach.
The estimated top-oil temperature is compared with measured data of
a power transformer in operation. The results are also compared with
methods based on the IEEE Standard C57.91-1995/2000 and
Artificial Neural Networks. It is shown that the Least Squares
Support Vector Machines approach presents better performance than
the methods based in the IEEE Standard C57.91-1995/2000 and
artificial neural networks.
Abstract: Organizational communication is an administrative
function crucial especially for executives in the implementation of
organizational and administrative functions. Executives spend a
significant part of their time on communicative activities. Doing his or her daily routine, arranging meeting schedules, speaking on the telephone, reading or replying to business correspondence, or
fulfilling the control functions within the organization, an executive typically engages in communication processes.
Efficient communication is the principal device for the adequate implementation of administrative and organizational activities. For
this purpose, management needs to specify the kind of
communication system to be set up and the kind of communication
devices to be used. Communication is vital for any organization.
In conventional offices, communication takes place within the hierarchical pyramid called the organizational structure, and is known as formal or informal communication. Formal communication
is the type that works in specified structures within the organizational rules and towards the organizational goals. Informal communication, on the other hand, is the unofficial type taking place among staff as
face-to-face or telephone interaction.
Communication in virtual as well as conventional offices is
essential for obtaining the right information in administrative
activities and decision-making. Virtual communication technologies
increase the efficiency of communication especially in virtual teams.
Group communication is strengthened through an inter-group central
channel. Further, ease of information transmission makes it possible
to reach the information at the source, allowing efficient and correct decisions. Virtual offices can present as a whole the elements of information which conventional offices produce in different
environments.
At present, virtual work has become a reality with its pros and
cons, and will probably spread very rapidly in coming years, in line
with the growth in information technologies.
Abstract: Phytases (myo-inositol hexakisphosphate
phosphohydrolases; EC 3.1.3.8) catalyze the hydrolysis of phytic acid
(myoinositol hexakisphosphate) to the mono-, di-, tri-, tetra-, and
pentaphosphates of myo-inositol and inorganic phosphate.
Therrmophilic bacteria isolated from water sampled from hot spring.
About 120 isolates of bacteria were successfully isolated form hot
spring water sample and tested for extracellular phytase producing.
After 5 passages of the screening on the PSM media, 4 isolates were
found stable in producing phytase enzyme. The 16s RDNA
sequencing for identification of bacteria using molecular technique
revealed that all isolates those positive in phytase producing are
belong to Geobacillus spp. And Anoxybacillus spp. Anoxybacillus
rupiensis UniSZA-7 were identified for their carbon source utilization
using Phenotype Microarray Plate of Biolog and found they utilize
several kind of carbon source provided.
Abstract: Performance of any continuous speech recognition system is highly dependent on performance of the acoustic models. Generally, development of the robust spoken language technology relies on the availability of large amounts of data. Common way to cope with little data for training each state of Markov models is treebased state tying. This tying method applies contextual questions to tie states. Manual procedure for question generation suffers from human errors and is time consuming. Various automatically generated questions are used to construct decision tree. There are three approaches to generate questions to construct HMMs based on decision tree. One approach is based on misrecognized phonemes, another approach basically uses feature table and the other is based on state distributions corresponding to context-independent subword units. In this paper, all these methods of automatic question generation are applied to the decision tree on FARSDAT corpus in Persian language and their results are compared with those of manually generated questions. The results show that automatically generated questions yield much better results and can replace manually generated questions in Persian language.
Abstract: As a popular rank-reduced vector space approach,
Latent Semantic Indexing (LSI) has been used in information
retrieval and other applications. In this paper, an LSI-based content
vector model for text classification is presented, which constructs
multiple augmented category LSI spaces and classifies text by their
content. The model integrates the class discriminative information
from the training data and is equipped with several pertinent feature
selection and text classification algorithms. The proposed classifier
has been applied to email classification and its experiments on a
benchmark spam testing corpus (PU1) have shown that the approach
represents a competitive alternative to other email classifiers based
on the well-known SVM and naïve Bayes algorithms.
Abstract: Lattice Monte Carlo methods are an excellent
choice for the simulation of non-linear thermal diffusion
problems. In this paper, and for the first time, Lattice Monte
Carlo analysis is performed on thermal diffusion combined
with convective heat transfer. Laminar flow of water modeled
as an incompressible fluid inside a copper pipe with a constant
surface temperature is considered. For the simulation of
thermal conduction, the temperature dependence of the
thermal conductivity of the water is accounted for. Using the
novel Lattice Monte Carlo approach, temperature distributions
and energy fluxes are obtained.
Abstract: A simple but effective digital watermarking scheme
utilizing a context adaptive variable length coding (CAVLC) method
is presented for wireless communication system. In the proposed
approach, the watermark bits are embedded in the final non-zero
quantized coefficient of each DCT block, thereby yielding a potential
reduction in the length of the coded block. As a result, the
watermarking scheme not only provides the means to check the
authenticity and integrity of the video stream, but also improves the
compression ratio and therefore reduces both the transmission time
and the storage space requirements of the coded video sequence. The
results confirm that the proposed scheme enables the detection of
malicious tampering attacks and reduces the size of the coded H.264
file. Therefore, the current study is feasible to apply in the video
applications of wireless communication such as 3G system
Abstract: In this paper, we intend to study the synthesis of the
multibeam arrays. The synthesis implementation-s method for this
type of arrays permits to approach the appropriated radiance-s
diagram. The used approach is based on neural network that are
capable to model the multibeam arrays, consider predetermined
general criteria-s, and finally it permits to predict the appropriated
diagram from the neural model. Our main contribution in this paper is
the extension of a synthesis model of these multibeam arrays.
Abstract: Non-stationary trend in R-R interval series is
considered as a main factor that could highly influence the evaluation
of spectral analysis. It is suggested to remove trends in order to obtain
reliable results. In this study, three detrending methods, the
smoothness prior approach, the wavelet and the empirical mode
decomposition, were compared on artificial R-R interval series with
four types of simulated trends. The Lomb-Scargle periodogram was
used for spectral analysis of R-R interval series. Results indicated that
the wavelet method showed a better overall performance than the other
two methods, and more time-saving, too. Therefore it was selected for
spectral analysis of real R-R interval series of thirty-seven healthy
subjects. Significant decreases (19.94±5.87% in the low frequency
band and 18.97±5.78% in the ratio (p
Abstract: This paper explores the opportunity of using tri-axial
wireless accelerometers for supervised monitoring of sports
movements. A motion analysis system for the upper extremities of
lawn bowlers in particular is developed. Accelerometers are placed
on parts of human body such as the chest to represent the shoulder
movements, the back to capture the trunk motion, back of the hand,
the wrist and one above the elbow, to capture arm movements. These
sensors placement are carefully designed in order to avoid restricting
bowler-s movements. Data is acquired from these sensors in soft-real
time using virtual instrumentation; the acquired data is then
conditioned and converted into required parameters for motion
regeneration. A user interface was also created to facilitate in the
acquisition of data, and broadcasting of commands to the wireless
accelerometers. All motion regeneration in this paper deals with the
motion of the human body segment in the X and Y direction, looking
into the motion of the anterior/ posterior and lateral directions
respectively.
Abstract: In this paper, we present a novel approach to accurately
detect text regions including shop name in signboard images with
complex background for mobile system applications. The proposed
method is based on the combination of text detection using edge
profile and region segmentation using fuzzy c-means method. In the
first step, we perform an elaborate canny edge operator to extract all
possible object edges. Then, edge profile analysis with vertical and
horizontal direction is performed on these edge pixels to detect
potential text region existing shop name in a signboard. The edge
profile and geometrical characteristics of each object contour are
carefully examined to construct candidate text regions and classify the
main text region from background. Finally, the fuzzy c-means
algorithm is performed to segment and detected binarize text region.
Experimental results show that our proposed method is robust in text
detection with respect to different character size and color and can
provide reliable text binarization result.