Abstract: This paper is mainly concerned with the application of
a novel technique of data interpretation for classifying measurements
of plasma columns in Tokamak reactors for nuclear fusion
applications. The proposed method exploits several concepts derived
from soft computing theory. In particular, Artificial Neural Networks
and Multi-Class Support Vector Machines have been exploited to
classify magnetic variables useful to determine shape and position of
the plasma with a reduced computational complexity. The proposed
technique is used to analyze simulated databases of plasma equilibria
based on ITER geometry configuration. As well as demonstrating the
successful recovery of scalar equilibrium parameters, we show that
the technique can yield practical advantages compared with earlier
methods.
Abstract: This paper describes the study of cryptographic hash functions, one of the most important classes of primitives used in recent techniques in cryptography. The main aim is the development of recent crypt analysis hash function. We present different approaches to defining security properties more formally and present basic attack on hash function. We recall Merkle-Damgard security properties of iterated hash function. The Main aim of this paper is the development of recent techniques applicable to crypt Analysis hash function, mainly from SHA family. Recent proposed attacks an MD5 & SHA motivate a new hash function design. It is designed not only to have higher security but also to be faster than SHA-256. The performance of the new hash function is at least 30% better than that of SHA-256 in software. And it is secure against any known cryptographic attacks on hash functions.
Abstract: This paper presents a new approach to tackle the problem of recognizing machine-printed Arabic texts. Because of the difficulty of recognizing cursive Arabic words, the text has to be normalized and segmented to be ready for the recognition stage. The new scheme for recognizing Arabic characters depends on multiple parallel neural networks classifier. The classifier has two phases. The first phase categories the input character into one of eight groups. The second phase classifies the character into one of the Arabic character classes in the group. The system achieved high recognition rate.
Abstract: Given that entrepreneurship is a very significant factor of regional development, it is necessary to approach systematically the development with measures of regional politics. According to international classification The Nomenclature of Territorial Units for Statistics (NUTS II), there are three regions in Croatia. The indicators of entrepreneurial activities on the national level of Croatia are analyzed in the paper, taking into consideration the results of referent research. The level of regional development is shown based on the analysis of entrepreneurs- operations. The results of the analysis show a very unfavorable situation in entrepreneurial activities on the national level of Croatia. The origin of this situation is to be found in the surroundings with an expressed inequality of regional development, which is caused by the non-existence of a strategically directed regional policy. In this paper recommendations which could contribute to the reduction of regional inequality in Croatia, have been made.
Abstract: This paper presents various classifiers results from a system that can automatically recognize four different static human body postures in video sequences. The considered postures are standing, sitting, squatting, and lying. The three classifiers considered are a naïve one and two based on the belief theory. The belief theory-based classifiers use either a classic or restricted plausibility criterion to make a decision after data fusion. The data come from the people 2D segmentation and from their face localization. Measurements consist in distances relative to a reference posture. The efficiency and the limits of the different classifiers on the recognition system are highlighted thanks to the analysis of a great number of results. This system allows real-time processing.
Abstract: This paper focuses on the problem of a common linear copositive Lyapunov function(CLCLF) existence for discrete-time switched positive linear systems(SPLSs) with bounded time-varying delays. In particular, applying system matrices, a special class of matrices are constructed in an appropriate manner. Our results reveal that the existence of a common copositive Lyapunov function can be related to the Schur stability of such matrices. A simple example is provided to illustrate the implication of our results.
Abstract: Recently, bianisotropic media again received
increasing importance in electromagnetic theory because of advances
in material science which enable the manufacturing of complex
bianisotropic materials. By using Maxwell's equations and
corresponding boundary conditions, the electromagnetic field
distribution in bianisotropic solenoid coils is determined and the
influence of the bianisotropic behaviour of coil to the impedance and
Q-factor is considered. Bianisotropic media are the largest class of
linear media which is able to describe the macroscopic material
properties of artificial dielectrics, artificial magnetics, artificial chiral
materials, left-handed materials, metamaterials, and other composite
materials. Several special cases of coils, filled with complex
substance, have been analyzed. Results obtained by using the
analytical approach are compared with values calculated by
numerical methods, especially by our new hybrid EEM/BEM method
and FEM.
Abstract: In this research, we propose a weighted class based
queuing (WCBQ) mechanism to provide class differentiation and to
reduce the load for the IMS (IP Multimedia Subsystem) presence
server (PS). The tasks of admission controller for the PS are
demonstrated. Analysis and simulation models are developed to
quantify the performance of WCBQ scheme. An optimized dropping
time frame has been developed based on which some of the preexisting
messages are dropped from the PS-buffer. Cost functions are
developed and simulation comparison has been performed with FCFS
(First Come First Served) scheme. The results show that the PS
benefits significantly from the proposed queuing and dropping
algorithm (WCBQ) during heavy traffic.
Abstract: This work focuses on analysis of classical heat transfer equation regularized with Maxwell-Cattaneo transfer law. Computer simulations are performed in MATLAB environment. Numerical experiments are first developed on classical Fourier equation, then Maxwell-Cattaneo law is considered. Corresponding equation is regularized with a balancing diffusion term to stabilize discretizing scheme with adjusted time and space numerical steps. Several cases including a convective term in model equations are discussed, and results are given. It is shown that limiting conditions on regularizing parameters have to be satisfied in convective case for Maxwell-Cattaneo regularization to give physically acceptable solutions. In all valid cases, uniform convergence to solution of initial heat equation with Fourier law is observed, even in nonlinear case.
Abstract: In this paper, the notion of Hyperbolic Klingenberg
plane is introduced via a set of axioms like as Affine Klingenberg
planes and Projective Klingenberg planes. Models of such planes are
constructed by deleting a certain number m of equivalence classes
of lines from a Projective Klingenberg plane. In the finite case, an
upper bound for m is established and some combinatoric properties
are investigated.
Abstract: This paper proposes a novel system for monitoring the
health of underground pipelines. Some of these pipelines transport
dangerous contents and any damage incurred might have catastrophic
consequences. However, most of these damage are unintentional and
usually a result of surrounding construction activities. In order to
prevent these potential damages, monitoring systems are
indispensable. This paper focuses on acoustically recognizing road
cutters since they prelude most construction activities in modern
cities. Acoustic recognition can be easily achieved by installing a
distributed computing sensor network along the pipelines and using
smart sensors to “listen" for potential threat; if there is a real threat,
raise some form of alarm. For efficient pipeline monitoring, a novel
monitoring approach is proposed. Principal Component Analysis
(PCA) was studied and applied. Eigenvalues were regarded as the
special signature that could characterize a sound sample, and were
thus used for the feature vector for sound recognition. The denoising
ability of PCA could make it robust to noise interference. One class
SVM was used for classifier. On-site experiment results show that the
proposed PCA and SVM based acoustic recognition system will be
very effective with a low tendency for raising false alarms.
Abstract: Previously, harmonic parameters (HPs) have been
selected as features extracted from EEG signals for automatic sleep
scoring. However, in previous studies, only one HP parameter was
used, which were directly extracted from the whole epoch of EEG
signal.
In this study, two different transformations were applied to extract
HPs from EEG signals: Hilbert-Huang transform (HHT) and wavelet
transform (WT). EEG signals are decomposed by the two
transformations; and features were extracted from different
components. Twelve parameters (four sets of HPs) were extracted.
Some of the parameters are highly diverse among different stages.
Afterward, HPs from two transformations were used to building a
rough sleep stages scoring model using the classifier SVM. The
performance of this model is about 78% using the features obtained by
our proposed extractions. Our results suggest that these features may
be useful for automatic sleep stages scoring.
Abstract: The Swine flu outbreak in humans is due to a new
strain of influenza A virus subtype H1N1 that derives in part from
human influenza, avian influenza, and two separated strains of swine
influenza. It can be transmitted from human to human. A
mathematical model for the transmission of Swine flu is developed in
which the human populations are divided into two classes, the risk
and non-risk human classes. Each class is separated into susceptible,
exposed, infectious, quarantine and recovered sub-classes. In this
paper, we formulate the dynamical model of Swine flu transmission
and the repetitive contacts between the people are also considered.
We analyze the behavior for the transmission of this disease. The
Threshold condition of this disease is found and numerical results are
shown to confirm our theoretical predictions.
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: In the present paper, a numerical investigation has
been carried out to classify and clarify the effects of paramount
parameters on turbulent impinging slot jets. The effects of nozzle-s
exit turbulent intensity, distance between nozzle and impinging plate
are studied at Reynolds number 5000 and 20000. In addition, the
effect of Mach number that is varied between 0.3-0.8 at a constant
Reynolds number 133000 is investigated to elucidate the effect of
compressibility in impinging jet upon a flat plate. The wall that is
located at the same level with nozzle-s exit confines the flow. A
compressible finite volume solver is implemented for simulation the
flow behavior. One equation Spalart-Allmaras turbulent model is
used to simulate turbulent flow at this study. Assessment of the
Spalart-Allmaras turbulent model at high nozzle to plate distance,
and giving enough insights to characterize the effect of Mach number
at high Reynolds number for the complex impinging jet flow are the
remarkable results of this study.
Abstract: Leo Breimans Random Forests (RF) is a recent
development in tree based classifiers and quickly proven to be one of
the most important algorithms in the machine learning literature. It
has shown robust and improved results of classifications on standard
data sets. Ensemble learning algorithms such as AdaBoost and
Bagging have been in active research and shown improvements in
classification results for several benchmarking data sets with mainly
decision trees as their base classifiers. In this paper we experiment to
apply these Meta learning techniques to the random forests. We
experiment the working of the ensembles of random forests on the
standard data sets available in UCI data sets. We compare the
original random forest algorithm with their ensemble counterparts
and discuss the results.
Abstract: The aged are faced with increasing risk for falls. The
aged have the easily fragile bones than others. When falls have
occurred, it is important to detect this emergency state because such
events often lead to more serious illness or even death. A
implementation of PDA system, for detection of emergency situation,
was developed using 3-axis accelerometer in this paper as follows.
The signals were acquired from the 3-axis accelerometer, and then
transmitted to the PDA through Bluetooth module. This system can
classify the human activity, and also detect the emergency state like
falls. When the fall occurs, the system generates the alarm on the
PDA. If a subject does not respond to the alarm, the system determines
whether the current situation is an emergency state or not, and then
sends some information to the emergency center in the case of urgent
situation. Three different studies were conducted on 12 experimental
subjects, with results indicating a good accuracy. The first study was
performed to detect the posture change of human daily activity. The
second study was performed to detect the correct direction of fall. The
third study was conducted to check the classification of the daily
physical activity. Each test was lasted at least 1 min. in third study.
The output of acceleration signal was compared and evaluated by
changing a various posture after attaching a 3-axis accelerometer
module on the chest. The newly developed system has some important
features such as portability, convenience and low cost. One of the
main advantages of this system is that it is available at home
healthcare environment. Another important feature lies in low cost to
manufacture device. The implemented system can detect the fall
accurately, so will be widely used in emergency situation.
Abstract: A genetic algorithm (GA) based feature subset
selection algorithm is proposed in which the correlation structure of
the features is exploited. The subset of features is validated according
to the classification performance. Features derived from the
continuous wavelet transform are potentially strongly correlated.
GA-s that do not take the correlation structure of features into
account are inefficient. The proposed algorithm forms clusters of
correlated features and searches for a good candidate set of clusters.
Secondly a search within the clusters is performed. Different
simulations of the algorithm on a real-case data set with strong
correlations between features show the increased classification
performance. Comparison is performed with a standard GA without
use of the correlation structure.
Abstract: Research into the problem of classification of sonar signals has been taken up as a challenging task for the neural networks. This paper investigates the design of an optimal classifier using a Multi layer Perceptron Neural Network (MLP NN) and Support Vector Machines (SVM). Results obtained using sonar data sets suggest that SVM classifier perform well in comparison with well-known MLP NN classifier. An average classification accuracy of 91.974% is achieved with SVM classifier and 90.3609% with MLP NN classifier, on the test instances. The area under the Receiver Operating Characteristics (ROC) curve for the proposed SVM classifier on test data set is found as 0.981183, which is very close to unity and this clearly confirms the excellent quality of the proposed classifier. The SVM classifier employed in this paper is implemented using kernel Adatron algorithm is seen to be robust and relatively insensitive to the parameter initialization in comparison to MLP NN.
Abstract: This paper gives a novel method for improving
classification performance for cancer classification with very few
microarray Gene expression data. The method employs classification
with individual gene ranking and gene subset ranking. For selection
and classification, the proposed method uses the same classifier. The
method is applied to three publicly available cancer gene expression
datasets from Lymphoma, Liver and Leukaemia datasets. Three
different classifiers namely Support vector machines-one against all
(SVM-OAA), K nearest neighbour (KNN) and Linear Discriminant
analysis (LDA) were tested and the results indicate the improvement
in performance of SVM-OAA classifier with satisfactory results on
all the three datasets when compared with the other two classifiers.