Abstract: In this paper we present a study of the impact of connection schemes on the performance of iterative decoding of Generalized Parallel Concatenated block (GPCB) constructed from one step majority logic decodable (OSMLD) codes and we propose a new connection scheme for decoding them. All iterative decoding connection schemes use a soft-input soft-output threshold decoding algorithm as a component decoder. Numerical result for GPCB codes transmitted over Additive White Gaussian Noise (AWGN) channel are provided. It will show that the proposed scheme is better than Hagenauer-s scheme and Lucas-s scheme [1] and slightly better than the Pyndiah-s scheme.
Abstract: This paper presents a solution for the behavioural
animation of autonomous virtual agent navigation in virtual environments.
We focus on using Dempster-Shafer-s Theory of Evidence
in developing visual sensor for virtual agent. The role of the visual
sensor is to capture the information about the virtual environment
or identifie which part of an obstacle can be seen from the position
of the virtual agent. This information is require for vitual agent to
coordinate navigation in virtual environment. The virual agent uses
fuzzy controller as a navigation system and Fuzzy α - level for
the action selection method. The result clearly demonstrates the path
produced is reasonably smooth even though there is some sharp turn
and also still not diverted too far from the potential shortest path.
This had indicated the benefit of our method, where more reliable
and accurate paths produced during navigation task.
Abstract: Clustering categorical data is more complicated than
the numerical clustering because of its special properties. Scalability
and memory constraint is the challenging problem in clustering large
data set. This paper presents an incremental algorithm to cluster the
categorical data. Frequencies of attribute values contribute much in
clustering similar categorical objects. In this paper we propose new
similarity measures based on the frequencies of attribute values and
its cardinalities. The proposed measures and the algorithm are
experimented with the data sets from UCI data repository. Results
prove that the proposed method generates better clusters than the
existing one.
Abstract: The effect of different combinations of response
feedback on the performance of active control system on nonlinear
frames has been studied in this paper. To this end different feedback
combinations including displacement, velocity, acceleration and full
response feedback have been utilized in controlling the response of
an eight story bilinear hysteretic frame which has been subjected to a
white noise excitation and controlled by eight actuators which could
fully control the frame. For active control of nonlinear frame
Newmark nonlinear instantaneous optimal control algorithm has been
used which a diagonal matrix has been selected for weighting
matrices in performance index. For optimal design of active control
system while the objective has been to reduce the maximum drift to
below the yielding level, Distributed Genetic Algorithm (DGA) has
been used to determine the proper set of weighting matrices. The
criteria to assess the effect of each combination of response feedback
have been the minimum required control force to reduce the
maximum drift to below the yielding drift. The results of numerical
simulation show that the performance of active control system is
dependent on the type of response feedback where the velocity
feedback is more effective in designing optimal control system in
comparison with displacement and acceleration feedback. Also using
full feedback of response in controller design leads to minimum
control force amongst other combinations. Also the distributed
genetic algorithm shows acceptable convergence speed in solving the
optimization problem of designing active control systems.
Abstract: In order to consider the effects of the higher modes in
the pushover analysis, during the recent years several multi-modal
pushover procedures have been presented. In these methods the
response of the considered modes are combined by the square-rootof-
sum-of-squares (SRSS) rule while application of the elastic modal
combination rules in the inelastic phases is no longer valid. In this
research the feasibility of defining an efficient alternative
combination method is investigated. Two steel moment-frame
buildings denoted SAC-9 and SAC-20 under ten earthquake records
are considered. The nonlinear responses of the structures are
estimated by the directed algebraic combination of the weighted
responses of the separate modes. The weight of the each mode is
defined so that the resulted response of the combination has a
minimum error to the nonlinear time history analysis. The genetic
algorithm (GA) is used to minimize the error and optimize the weight
factors. The obtained optimal factors for each mode in different cases
are compared together to find unique appropriate weight factors for
each mode in all cases.
Abstract: Through inward perceptions, we intuitively expect
distributed software development to increase the risks associated with
achieving cost, schedule, and quality goals. To compound this
problem, agile software development (ASD) insists one of the main
ingredients of its success is cohesive communication attributed to
collocation of the development team. The following study identified
the degree of communication richness needed to achieve comparable
software quality (reduce pre-release defects) between distributed and
collocated teams. This paper explores the relevancy of
communication richness in various development phases and its
impact on quality. Through examination of a large distributed agile
development project, this investigation seeks to understand the levels
of communication required within each ASD phase to produce
comparable quality results achieved by collocated teams. Obviously,
a multitude of factors affects the outcome of software projects.
However, within distributed agile software development teams, the
mode of communication is one of the critical components required to
achieve team cohesiveness and effectiveness. As such, this study
constructs a distributed agile communication model (DAC-M) for
potential application to similar distributed agile development efforts
using the measurement of the suitable level of communication. The
results of the study show that less rich communication methods, in
the appropriate phase, might be satisfactory to achieve equivalent
quality in distributed ASD efforts.
Abstract: Music segmentation is a key issue in music information
retrieval (MIR) as it provides an insight into the
internal structure of a composition. Structural information about
a composition can improve several tasks related to MIR such
as searching and browsing large music collections, visualizing
musical structure, lyric alignment, and music summarization.
The authors of this paper present the MTSSM framework, a twolayer
framework for the multi-track segmentation of symbolic
music. The strength of this framework lies in the combination of
existing methods for local track segmentation and the application
of global structure information spanning via multiple tracks.
The first layer of the MTSSM uses various string matching
techniques to detect the best candidate segmentations for each
track of a multi-track composition independently. The second
layer combines all single track results and determines the best
segmentation for each track in respect to the global structure of
the composition.
Abstract: Data mining techniques have been used in medical
research for many years and have been known to be effective. In order
to solve such problems as long-waiting time, congestion, and delayed
patient care, faced by emergency departments, this study concentrates
on building a hybrid methodology, combining data mining techniques
such as association rules and classification trees. The methodology is
applied to real-world emergency data collected from a hospital and is
evaluated by comparing with other techniques. The methodology is
expected to help physicians to make a faster and more accurate
classification of chest pain diseases.
Abstract: This paper presents an automatic feature recognition
method based on center-surround difference detecting and fuzzy logic
that can be applied in ground-penetrating radar (GPR) image
processing. Adopted center-surround difference method, the salient
local image regions are extracted from the GPR images as features of
detected objects. And fuzzy logic strategy is used to match the
detected features and features in template database. This way, the
problem of objects detecting, which is the key problem in GPR image
processing, can be converted into two steps, feature extracting and
matching. The contributions of these skills make the system have the
ability to deal with changes in scale, antenna and noises. The results of
experiments also prove that the system has higher ratio of features
sensing in using GPR to image the subsurface structures.
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: Case-Based Reasoning (CBR) is one of machine
learning algorithms for problem solving and learning that caught a lot
of attention over the last few years. In general, CBR is composed of
four main phases: retrieve the most similar case or cases, reuse the
case to solve the problem, revise or adapt the proposed solution, and
retain the learned cases before returning them to the case base for
learning purpose. Unfortunately, in many cases, this retain process
causes the uncontrolled case base growth. The problem affects
competence and performance of CBR systems. This paper proposes
competence-based maintenance method based on deletion policy
strategy for CBR. There are three main steps in this method. Step 1,
formulate problems. Step 2, determine coverage and reachability set
based on coverage value. Step 3, reduce case base size. The results
obtained show that this proposed method performs better than the
existing methods currently discussed in literature.
Abstract: Multivariate quality control charts show some advantages to monitor several variables in comparison with the simultaneous use of univariate charts, nevertheless, there are some disadvantages. The main problem is how to interpret the out-ofcontrol signal of a multivariate chart. For example, in the case of control charts designed to monitor the mean vector, the chart signals showing that it must be accepted that there is a shift in the vector, but no indication is given about the variables that have produced this shift. The MEWMA quality control chart is a very powerful scheme to detect small shifts in the mean vector. There are no previous specific works about the interpretation of the out-of-control signal of this chart. In this paper neural networks are designed to interpret the out-of-control signal of the MEWMA chart, and the percentage of correct classifications is studied for different cases.
Abstract: In this paper, a new proposed system for Persian
printed numeral characters recognition with emphasis on
representation and recognition stages is introduced. For the first time,
in Persian optical character recognition, geometrical central moments
as character image descriptor and fuzzy min-max neural network for
Persian numeral character recognition has been used. Set of different
experiments on binary images of regular, translated, rotated and
scaled Persian numeral characters has been done and variety of
results has been presented. The best result was 99.16% correct
recognition demonstrating geometrical central moments and fuzzy
min-max neural network are adequate for Persian printed numeral
character recognition.
Abstract: This paper describes the results of an extensive study
and comparison of popular hash functions SHA-1, SHA-256,
RIPEMD-160 and RIPEMD-320 with JERIM-320, a 320-bit hash
function. The compression functions of hash functions like SHA-1
and SHA-256 are designed using serial successive iteration whereas
those like RIPEMD-160 and RIPEMD-320 are designed using two
parallel lines of message processing. JERIM-320 uses four parallel
lines of message processing resulting in higher level of security than
other hash functions at comparable speed and memory requirement.
The performance evaluation of these methods has been done by using
practical implementation and also by using step computation
methods. JERIM-320 proves to be secure and ensures the integrity of
messages at a higher degree. The focus of this work is to establish
JERIM-320 as an alternative of the present day hash functions for the
fast growing internet applications.
Abstract: In this paper, a new secure watermarking scheme for
color image is proposed. It splits the watermark into two shares using
(2, 2)- threshold Visual Cryptography Scheme (V CS) with Adaptive
Order Dithering technique and embeds one share into high textured
subband of Luminance channel of the color image. The other share
is used as the key and is available only with the super-user or the
author of the image. In this scheme only the super-user can reveal
the original watermark. The proposed scheme is dynamic in the sense
that to maintain the perceptual similarity between the original and the
watermarked image the selected subband coefficients are modified
by varying the watermark scaling factor. The experimental results
demonstrate the effectiveness of the proposed scheme. Further, the
proposed scheme is able to resist all common attacks even with strong
amplitude.
Abstract: Star graphs are Cayley graphs of symmetric groups of permutations, with transpositions as the generating sets. A star graph is a preferred interconnection network topology to a hypercube for its ability to connect a greater number of nodes with lower degree. However, an attractive property of the hypercube is that it has a Hamiltonian decomposition, i.e. its edges can be partitioned into disjoint Hamiltonian cycles, and therefore a simple routing can be found in the case of an edge failure. The existence of Hamiltonian cycles in Cayley graphs has been known for some time. So far, there are no published results on the much stronger condition of the existence of Hamiltonian decompositions. In this paper, we give a construction of a Hamiltonian decomposition of the star graph 5-star of degree 4, by defining an automorphism for 5-star and a Hamiltonian cycle which is edge-disjoint with its image under the automorphism.
Abstract: Image data holds a large amount of different context
information. However, as of today, these resources remain largely
untouched. It is thus the aim of this paper to present a basic technical
framework which allows for a quick and easy exploitation of context
information from image data especially by non-expert users.
Furthermore, the proposed framework is discussed in detail
concerning important social and ethical issues which demand special
requirements in system design. Finally, a first sensor prototype is
presented which meets the identified requirements. Additionally,
necessary implications for the software and hardware design of the
system are discussed, rendering a sensor system which could be
regarded as a good, acceptable and justifiable technical and thereby
enabling the extraction of context information from image data.
Abstract: Trust is essential for further and wider acceptance of
contemporary e-services. It was first addressed almost thirty years
ago in Trusted Computer System Evaluation Criteria standard by
the US DoD. But this and other proposed approaches of that
period were actually solving security. Roughly some ten years ago,
methodologies followed that addressed trust phenomenon at its core,
and they were based on Bayesian statistics and its derivatives, while
some approaches were based on game theory. However, trust is a
manifestation of judgment and reasoning processes. It has to be dealt
with in accordance with this fact and adequately supported in cyber
environment. On the basis of the results in the field of psychology
and our own findings, a methodology called qualitative algebra has
been developed, which deals with so far overlooked elements of trust
phenomenon. It complements existing methodologies and provides a
basis for a practical technical solution that supports management of
trust in contemporary computing environments. Such solution is also
presented at the end of this paper.
Abstract: In recent times, the problem of Unsolicited Bulk
Email (UBE) or commonly known as Spam Email, has increased at a
tremendous growth rate. We present an analysis of survey based on
classifications of UBE in various research works. There are many
research instances for classification between spam and non-spam
emails but very few research instances are available for classification
of spam emails, per se. This paper does not intend to assert some
UBE classification to be better than the others nor does it propose
any new classification but it bemoans the lack of harmony on number
and definition of categories proposed by different researchers. The
paper also elaborates on factors like intent of spammer, content of
UBE and ambiguity in different categories as proposed in related
research works of classifications of UBE.
Abstract: Fast forecasting of stock market prices is very important for
strategic planning. In this paper, a new approach for fast forecasting of
stock market prices is presented. Such algorithm uses new high speed
time delay neural networks (HSTDNNs). The operation of these
networks relies on performing cross correlation in the frequency
domain between the input data and the input weights of neural
networks. It is proved mathematically and practically that the number
of computation steps required for the presented HSTDNNs is less
than that needed by traditional time delay neural networks
(TTDNNs). Simulation results using MATLAB confirm the
theoretical computations.