Abstract: The objective is to split a simply connected polygon
into a set of convex quadrilaterals without inserting new
boundary nodes. The presented approach consists in repeatedly
removing quadrilaterals from the polygon. Theoretical results
pertaining to quadrangulation of simply connected polygons are
derived from the usual 2-ear theorem. It produces a quadrangulation
technique with O(n) number of quadrilaterals. The
theoretical methodology is supplemented by practical results
and CAD surface segmentation.
Abstract: XML files contain data which is in well formatted manner. By studying the format or semantics of the grammar it will be helpful for fast retrieval of the data. There are many algorithms which describes about searching the data from XML files. There are no. of approaches which uses data structure or are related to the contents of the document. In these cases user must know about the structure of the document and information retrieval techniques using NLPs is related to content of the document. Hence the result may be irrelevant or not so successful and may take more time to search.. This paper presents fast XML retrieval techniques by using new indexing technique and the concept of RXML. When indexing an XML document, the system takes into account both the document content and the document structure and assigns the value to each tag from file. To query the system, a user is not constrained about fixed format of query.
Abstract: The security of computer networks plays a strategic
role in modern computer systems. Intrusion Detection Systems (IDS)
act as the 'second line of defense' placed inside a protected
network, looking for known or potential threats in network traffic
and/or audit data recorded by hosts. We developed an Intrusion
Detection System using LAMSTAR neural network to learn patterns
of normal and intrusive activities, to classify observed system
activities and compared the performance of LAMSTAR IDS with
other classification techniques using 5 classes of KDDCup99 data.
LAMSAR IDS gives better performance at the cost of high
Computational complexity, Training time and Testing time, when
compared to other classification techniques (Binary Tree classifier,
RBF classifier, Gaussian Mixture classifier). we further reduced the
Computational Complexity of LAMSTAR IDS by reducing the
dimension of the data using principal component analysis which in
turn reduces the training and testing time with almost the same
performance.
Abstract: The recognition of handwritten numeral is an
important area of research for its applications in post office, banks
and other organizations. This paper presents automatic recognition of
handwritten Kannada numerals based on structural features. Five
different types of features, namely, profile based 10-segment string,
water reservoir; vertical and horizontal strokes, end points and
average boundary length from the minimal bounding box are used in
the recognition of numeral. The effect of each feature and their
combination in the numeral classification is analyzed using nearest
neighbor classifiers. It is common to combine multiple categories of
features into a single feature vector for the classification. Instead,
separate classifiers can be used to classify based on each visual
feature individually and the final classification can be obtained based
on the combination of separate base classification results. One
popular approach is to combine the classifier results into a feature
vector and leaving the decision to next level classifier. This method
is extended to extract a better information, possibility distribution,
from the base classifiers in resolving the conflicts among the
classification results. Here, we use fuzzy k Nearest Neighbor (fuzzy
k-NN) as base classifier for individual feature sets, the results of
which together forms the feature vector for the final k Nearest
Neighbor (k-NN) classifier. Testing is done, using different features,
individually and in combination, on a database containing 1600
samples of different numerals and the results are compared with the
results of different existing methods.
Abstract: This paper presents a recognition system for isolated
words like robot commands. It’s carried out by Time Delay Neural
Networks; TDNN. To teleoperate a robot for specific tasks as turn,
close, etc… In industrial environment and taking into account the
noise coming from the machine. The choice of TDNN is based on its
generalization in terms of accuracy, in more it acts as a filter that
allows the passage of certain desirable frequency characteristics of
speech; the goal is to determine the parameters of this filter for
making an adaptable system to the variability of speech signal and to
noise especially, for this the back propagation technique was used in
learning phase. The approach was applied on commands pronounced
in two languages separately: The French and Arabic. The results for
two test bases of 300 spoken words for each one are 87%, 97.6% in
neutral environment and 77.67%, 92.67% when the white Gaussian
noisy was added with a SNR of 35 dB.
Abstract: This article concerns with the accessibility of Business
process modelling tools (BPMo tools) and business process
modelling languages (BPMo languages). Therefore the reader will be
introduced to business process management and the authors'
motivation behind this inquiry. Afterwards, the paper will reflect
problems when applying inaccessible BPMo tools. To illustrate these
problems the authors distinguish between two different categories of
issues and provide practical examples. Finally the article will present
three approaches to improve the accessibility of BPMo tools and
BPMo languages.
Abstract: Digital watermarking is a way to provide the facility of secure multimedia data communication besides its copyright protection approach. The Spread Spectrum modulation principle is widely used in digital watermarking to satisfy the robustness of multimedia signals against various signal-processing operations. Several SS watermarking algorithms have been proposed for multimedia signals but very few works have discussed on the issues responsible for secure data communication and its robustness improvement. The current paper has critically analyzed few such factors namely properties of spreading codes, proper signal decomposition suitable for data embedding, security provided by the key, successive bit cancellation method applied at decoder which have greater impact on the detection reliability, secure communication of significant signal under camouflage of insignificant signals etc. Based on the analysis, robust SS watermarking scheme for secure data communication is proposed in wavelet domain and improvement in secure communication and robustness performance is reported through experimental results. The reported result also shows improvement in visual and statistical invisibility of the hidden data.
Abstract: In this study, the use of silicon NAM (Non-Audible
Murmur) microphone in automatic speech recognition is presented.
NAM microphones are special acoustic sensors, which are attached
behind the talker-s ear and can capture not only normal (audible)
speech, but also very quietly uttered speech (non-audible murmur).
As a result, NAM microphones can be applied in automatic speech
recognition systems when privacy is desired in human-machine communication.
Moreover, NAM microphones show robustness against
noise and they might be used in special systems (speech recognition,
speech conversion etc.) for sound-impaired people. Using a small
amount of training data and adaptation approaches, 93.9% word
accuracy was achieved for a 20k Japanese vocabulary dictation
task. Non-audible murmur recognition in noisy environments is also
investigated. In this study, further analysis of the NAM speech has
been made using distance measures between hidden Markov model
(HMM) pairs. It has been shown the reduced spectral space of NAM
speech using a metric distance, however the location of the different
phonemes of NAM are similar to the location of the phonemes
of normal speech, and the NAM sounds are well discriminated.
Promising results in using nonlinear features are also introduced,
especially under noisy conditions.
Abstract: In this paper, a method for decision making in fuzzy environment is presented.A new subjective and objective integrated approach is introduced that used to assign weight attributes in fuzzy multiple attribute decision making (FMADM) problems and alternatives and fmally ranked by proposed method.
Abstract: Knowledge is indispensable but voluminous knowledge becomes a bottleneck for efficient processing. A great challenge for data mining activity is the generation of large number of potential rules as a result of mining process. In fact sometimes result size is comparable to the original data. Traditional data mining pruning activities such as support do not sufficiently reduce the huge rule space. Moreover, many practical applications are characterized by continual change of data and knowledge, thereby making knowledge voluminous with each change. The most predominant representation of the discovered knowledge is the standard Production Rules (PRs) in the form If P Then D. Michalski & Winston proposed Censored Production Rules (CPRs), as an extension of production rules, that exhibit variable precision and supports an efficient mechanism for handling exceptions. A CPR is an augmented production rule of the form: If P Then D Unless C, where C (Censor) is an exception to the rule. Such rules are employed in situations in which the conditional statement 'If P Then D' holds frequently and the assertion C holds rarely. By using a rule of this type we are free to ignore the exception conditions, when the resources needed to establish its presence, are tight or there is simply no information available as to whether it holds or not. Thus the 'If P Then D' part of the CPR expresses important information while the Unless C part acts only as a switch changes the polarity of D to ~D. In this paper a scheme based on Dempster-Shafer Theory (DST) interpretation of a CPR is suggested for discovering CPRs from the discovered flat PRs. The discovery of CPRs from flat rules would result in considerable reduction of the already discovered rules. The proposed scheme incrementally incorporates new knowledge and also reduces the size of knowledge base considerably with each episode. Examples are given to demonstrate the behaviour of the proposed scheme. The suggested cumulative learning scheme would be useful in mining data streams.
Abstract: Evolutionary robotics is concerned with the design of
intelligent systems with life-like properties by means of simulated
evolution. Approaches in evolutionary robotics can be categorized
according to the control structures that represent the behavior and the
parameters of the controller that undergo adaptation. The basic idea
is to automatically synthesize behaviors that enable the robot to
perform useful tasks in complex environments. The evolutionary
algorithm searches through the space of parameterized controllers
that map sensory perceptions to control actions, thus realizing a
specific robotic behavior. Further, the evolutionary algorithm
maintains and improves a population of candidate behaviors by
means of selection, recombination and mutation. A fitness function
evaluates the performance of the resulting behavior according to the
robot-s task or mission. In this paper, the focus is in the use of
genetic algorithms to solve a multi-objective optimization problem
representing robot behaviors; in particular, the A-Compander Law is
employed in selecting the weight of each objective during the
optimization process. Results using an adaptive fitness function show
that this approach can efficiently react to complex tasks under
variable environments.
Abstract: State Dependent Riccati Equation (SDRE) approach is
a modification of the well studied LQR method. It has the capability of being applied to control nonlinear systems. In this paper the technique
has been applied to control the single inverted pendulum (SIP) which represents a rich class of nonlinear underactuated systems. SIP
modeling is based on Euler-Lagrange equations. A procedure is developed
for judicious selection of weighting parameters and constraint handling. The controller designed by SDRE technique here gives better results than existing controllers designed by energy based techniques.
Abstract: The link between urban planning and design principles and the built environment of an urban renewal area is of interest to the field of urban studies. During the past decade, there has also been increasing interest in urban planning and design; this interest is motivated by the possibility that design policies associated with the built environment can be used to control, manage, and shape individual activity and behavior. However, direct assessments and design techniques of the links between how urban planning design policies influence individuals are still rare in the field. Recent research efforts in urban design have focused on the idea that land use and design policies can be used to increase the quality of design projects for an urban renewal area-s built environment. The development of appropriate design techniques for the built environment is an essential element of this research. Quality function deployment (QFD) is a powerful tool for improving alternative urban design and quality for urban renewal areas, and for procuring a citizen-driven quality system. In this research, we propose an integrated framework based on QFD and an Analytic Network Process (ANP) approach to determine the Alternative Technical Requirements (ATRs) to be considered in designing an urban renewal planning and design alternative. We also identify the research designs and methodologies that can be used to evaluate the performance of urban built environment projects. An application in an urban renewal built environment planning and design project evaluation is presented to illustrate the proposed framework.
Abstract: Mixed Model Production is the practice of assembling
several distinct and different models of a product on the same
assembly line without changeovers and then sequencing those models
in a way that smoothes the demand for upstream components. In this
paper, we consider an objective function which minimizes total
stoppage time and total idle time and it is presented sequence
dependent set up time. Many studies have been done on the mixed
model assembly lines. But in this paper we specifically focused on
reducing the idle times. This is possible through various help policies.
For improving the solutions, some cases developed and about 40 tests
problem was considered. We use scatter search for optimization and
for showing the efficiency of our algorithm, experimental results
shows behavior of method. Scatter search and help policies can
produce high quality answers, so it has been used in this paper.
Abstract: Realistic 3D face model is desired in various
applications such as face recognition, games, avatars, animations, and
etc. Construction of 3D face model is composed of 1) building a face
shape model and 2) rendering the face shape model. Thus, building a
realistic 3D face shape model is an essential step for realistic 3D face
model. Recently, 3D morphable model is successfully introduced to
deal with the various human face shapes. 3D dense correspondence
problem should be precedently resolved for constructing a realistic 3D
dense morphable face shape model. Several approaches to 3D dense
correspondence problem in 3D face modeling have been proposed
previously, and among them optical flow based algorithms and TPS
(Thin Plate Spline) based algorithms are representative. Optical flow
based algorithms require texture information of faces, which is
sensitive to variation of illumination. In TPS based algorithms
proposed so far, TPS process is performed on the 2D projection
representation in cylindrical coordinates of the 3D face data, not
directly on the 3D face data and thus errors due to distortion in data
during 2D TPS process may be inevitable.
In this paper, we propose a new 3D dense correspondence algorithm
for 3D dense morphable face shape modeling. The proposed algorithm
does not need texture information and applies TPS directly on 3D face
data. Through construction procedures, it is observed that the proposed
algorithm constructs realistic 3D face morphable model reliably and
fast.
Abstract: Tablet computers and Multifunctional Hardcopy Devices (MHDs) are common devices in daily life. Though, many scientific studies have not been published. The tablet computers are straightforward to use whereas the MHDs are comparatively difficult to use. Thus, to assist different levels of users, we propose combining these two devices to achieve straightforward intelligent user interface (UI) and versatile What You See Is What You Get (WYSIWYG) document management and production. Our approach to this issue is to design an intelligent user dependent UI for a MHD applying a tablet computer. Furthermore, we propose hardware interconnection and versatile intelligent software between these two devices. In this study, we first provide a state-of-the-art survey on MHDs and tablet computers, and their interconnections. Secondly we provide a comparative UI survey on two state-of-the-art MHDs with a proposal of a novel UI for the MHDs using Jakob Nielsen-s Ten Usability Heuristics Evaluation.
Abstract: In this research, the laminar heat transfer of natural convection on vertical surfaces has been investigated. Most of the studies on natural convection have been considered constantly whereas velocity and temperature domain, do not change with time, transient one are used a lot. Governing equations are solved using a finite volume approach. The convective terms are discretized using the power-law scheme, whereas for diffusive terms the central difference is employed. Coupling between the velocity and pressure is made with SIMPLE algorithm. The resultant system of discretized linear algebraic equations is solved with an alternating direction implicit scheme. Then a configuration of rectangular fins is put in different ways on the surface and heat transfer of natural convection on these surfaces without sliding is studied and finally optimization is done.
Abstract: Component-Based software engineering provides an
opportunity for better quality and increased productivity in software
development by using reusable software components [10]. One of the
most critical aspects of the quality of a software system is its
performance. The systematic application of software performance
engineering techniques throughout the development process can help
to identify design alternatives that preserve desirable qualities such
as extensibility and reusability while meeting performance objectives
[1]. In the present scenario, software engineering methodologies
strongly focus on the functionality of the system, while applying a
“fix- it-later" approach to software performance aspects [3]. As a
result, lengthy fine-tunings, expensive extra hard ware, or even
redesigns are necessary for the system to meet the performance
requirements. In this paper, we propose design based,
implementation independent, performance prediction approach to
reduce the overhead associated in the later phases while developing a
performance guaranteed software product with the help of Unified
Modeling Language (UML).
Abstract: This paper describes dynamic analysis using proposed
fast finite element method for a shock absorbing structure including a
sponge. The structure is supported by nonlinear concentrated springs.
The restoring force of the spring has cubic nonlinearity and linear
hysteresis damping. To calculate damping properties for the structures
including elastic body and porous body, displacement vectors as
common unknown variable are solved under coupled condition. Under
small amplitude, we apply asymptotic method to complex eigenvalue
problem of this system to obtain modal parameters. And then
expressions of modal loss factor are derived approximately. This
approach was proposed by one of the authors previously. We call this
method as Modal Strain and Kinetic Energy Method (MSKE method).
Further, using the modal loss factors, the discretized equations in
physical coordinate are transformed into the nonlinear ordinary
coupled equations using normal coordinate corresponding to linear
natural modes. This transformation yields computation efficiency. As
a numerical example of a shock absorbing structures, we adopt double
skins with a sponge. The double skins are supported by nonlinear
concentrated springs. We clarify influences of amplitude of the input
force on nonlinear and chaotic responses.
Abstract: In this paper, we present a new learning algorithm for
anomaly based network intrusion detection using improved self
adaptive naïve Bayesian tree (NBTree), which induces a hybrid of
decision tree and naïve Bayesian classifier. The proposed approach
scales up the balance detections for different attack types and keeps
the false positives at acceptable level in intrusion detection. In
complex and dynamic large intrusion detection dataset, the detection
accuracy of naïve Bayesian classifier does not scale up as well as
decision tree. It has been successfully tested in other problem
domains that naïve Bayesian tree improves the classification rates in
large dataset. In naïve Bayesian tree nodes contain and split as
regular decision-trees, but the leaves contain naïve Bayesian
classifiers. The experimental results on KDD99 benchmark network
intrusion detection dataset demonstrate that this new approach scales
up the detection rates for different attack types and reduces false
positives in network intrusion detection.