Abstract: The performance results of the athletes competed in
the 1988-2008 Olympic Games were analyzed (n = 166). The data
were obtained from the IAAF official protocols. In the principal
component analysis, the first three principal components explained
70% of the total variance. In the 1st principal component (with
43.1% of total variance explained) the largest factor loadings were
for 100m (0.89), 400m (0.81), 110m hurdle run (0.76), and long jump
(–0.72). This factor can be interpreted as the 'sprinting performance'.
The loadings on the 2nd factor (15.3% of the total variance)
presented a counter-intuitive throwing-jumping combination: the
highest loadings were for throwing events (javelin throwing 0.76;
shot put 0.74; and discus throwing 0.73) and also for jumping events
(high jump 0.62; pole vaulting 0.58). On the 3rd factor (11.6% of
total variance), the largest loading was for 1500 m running (0.88); all
other loadings were below 0.4.
Abstract: This paper discusses effects of adhesive thickness,
overlap length and material combinations on the single-lap
joints strength from the point of singular stress fields. A useful
method calculating the ratio of intensity of singular stress is
proposed using FEM for different adhesive thickness and
overlap length. It is found that the intensity of singular stress
increases with increasing adhesive thickness, and decreases
with increasing overlap length. The increment and decrement
are different depending on material combinations between
adhesive and adherent.
Abstract: In this paper, a necessary and sufficient coefficient are given for functions in a class of complex valued meromorphic harmonic univalent functions of the form f = h + g using Salagean operator. Furthermore, distortion theorems, extreme points, convolution condition and convex combinations for this family of meromorphic harmonic functions are obtained.
Abstract: Reducing river sediments through path correction and
preservation of river walls leads to considerable reduction of
sedimentation at the pumping stations. Path correction and
preservation of walls is not limited to one particular method but,
depending on various conditions, a combination of several methods
can be employed. In this article, we try to review and evaluate
methods for preservation of river banks in order to reduce sediments.
Abstract: The protection of parallel transmission lines has been a challenging task due to mutual coupling between the adjacent circuits of the line. This paper presents a novel scheme for detection and classification of faults on parallel transmission lines. The proposed approach uses combination of wavelet transform and neural network, to solve the problem. While wavelet transform is a powerful mathematical tool which can be employed as a fast and very effective means of analyzing power system transient signals, artificial neural network has a ability to classify non-linear relationship between measured signals by identifying different patterns of the associated signals. The proposed algorithm consists of time-frequency analysis of fault generated transients using wavelet transform, followed by pattern recognition using artificial neural network to identify the type of the fault. MATLAB/Simulink is used to generate fault signals and verify the correctness of the algorithm. The adaptive discrimination scheme is tested by simulating different types of fault and varying fault resistance, fault location and fault inception time, on a given power system model. The simulation results show that the proposed scheme for fault diagnosis is able to classify all the faults on the parallel transmission line rapidly and correctly.
Abstract: This paper presents an approach for early breast
cancer diagnostic by employing combination of artificial neural
networks (ANN) and multiwaveletpacket based subband image
decomposition. The microcalcifications correspond to high-frequency
components of the image spectrum, detection of microcalcifications
is achieved by decomposing the mammograms into different
frequency subbands,, reconstructing the mammograms from the
subbands containing only high frequencies. For this approach we
employed different types of multiwaveletpacket. We used the result
as an input of neural network for classification. The proposed
methodology is tested using the Nijmegen and the Mammographic
Image Analysis Society (MIAS) mammographic databases and
images collected from local hospitals. Results are presented as the
receiver operating characteristic (ROC) performance and are
quantified by the area under the ROC curve.
Abstract: This paper proposes a new approach for image encryption
using a combination of different permutation techniques.
The main idea behind the present work is that an image can be
viewed as an arrangement of bits, pixels and blocks. The intelligible
information present in an image is due to the correlations among the
bits, pixels and blocks in a given arrangement. This perceivable information
can be reduced by decreasing the correlation among the bits,
pixels and blocks using certain permutation techniques. This paper
presents an approach for a random combination of the aforementioned
permutations for image encryption. From the results, it is observed
that the permutation of bits is effective in significantly reducing the
correlation thereby decreasing the perceptual information, whereas
the permutation of pixels and blocks are good at producing higher
level security compared to bit permutation. A random combination
method employing all the three techniques thus is observed to be
useful for tactical security applications, where protection is needed
only against a casual observer.
Abstract: This paper introduces two decoders for binary linear
codes based on Metaheuristics. The first one uses a genetic algorithm
and the second is based on a combination genetic algorithm with
a feed forward neural network. The decoder based on the genetic
algorithms (DAG) applied to BCH and convolutional codes give good
performances compared to Chase-2 and Viterbi algorithm respectively
and reach the performances of the OSD-3 for some Residue
Quadratic (RQ) codes. This algorithm is less complex for linear
block codes of large block length; furthermore their performances
can be improved by tuning the decoder-s parameters, in particular the
number of individuals by population and the number of generations.
In the second algorithm, the search space, in contrast to DAG which
was limited to the code word space, now covers the whole binary
vector space. It tries to elude a great number of coding operations
by using a neural network. This reduces greatly the complexity of
the decoder while maintaining comparable performances.
Abstract: The intrusion detection problem has been frequently studied, but intrusion detection methods are often based on a single point of view, which always limits the results. In this paper, we introduce a new intrusion detection model based on the combination of different current methods. First we use a notion of distance to unify the different methods. Second we combine these methods using the Pearson correlation coefficients, which measure the relationship between two methods, and we obtain a combined distance. If the combined distance is greater than a predetermined threshold, an intrusion is detected. We have implemented and tested the combination model with two different public data sets: the data set of masquerade detection collected by Schonlau & al., and the data set of program behaviors from the University of New Mexico. The results of the experiments prove that the combination model has better performances.
Abstract: Artificial Immune System is applied as a Heuristic
Algorithm for decades. Nevertheless, many of these applications
took advantage of the benefit of this algorithm but seldom proposed
approaches for enhancing the efficiency. In this paper, a
Self-evolving Artificial Immune System is proposed via developing
the T and B cell in Immune System and built a self-evolving
mechanism for the complexities of different problems. In this
research, it focuses on enhancing the efficiency of Clonal selection
which is responsible for producing Affinities to resist the invading of
Antigens. T and B cell are the main mechanisms for Clonal
Selection to produce different combinations of Antibodies.
Therefore, the development of T and B cell will influence the
efficiency of Clonal Selection for searching better solution.
Furthermore, for better cooperation of the two cells, a co-evolutional
strategy is applied to coordinate for more effective productions of
Antibodies. This work finally adopts Flow-shop scheduling
instances in OR-library to validate the proposed algorithm.
Abstract: This paper deals with efficient computation of
probability coefficients which offers computational simplicity as
compared to spectral coefficients. It eliminates the need of inner
product evaluations in determination of signature of a combinational
circuit realizing given Boolean function. The method for computation
of probability coefficients using transform matrix, fast transform
method and using BDD is given. Theoretical relations for achievable
computational advantage in terms of required additions in computing
all 2n probability coefficients of n variable function have been
developed. It is shown that for n ≥ 5, only 50% additions are needed
to compute all probability coefficients as compared to spectral
coefficients. The fault detection techniques based on spectral
signature can be used with probability signature also to offer
computational advantage.
Abstract: One major difficulty that faces developers of
concurrent and distributed software is analysis for concurrency based
faults like deadlocks. Petri nets are used extensively in the
verification of correctness of concurrent programs. ECATNets [2] are
a category of algebraic Petri nets based on a sound combination of
algebraic abstract types and high-level Petri nets. ECATNets have
'sound' and 'complete' semantics because of their integration in
rewriting logic [12] and its programming language Maude [13].
Rewriting logic is considered as one of very powerful logics in terms
of description, verification and programming of concurrent systems.
We proposed in [4] a method for translating Ada-95 tasking
programs to ECATNets formalism (Ada-ECATNet). In this paper,
we show that ECATNets formalism provides a more compact
translation for Ada programs compared to the other approaches based
on simple Petri nets or Colored Petri nets (CPNs). Such translation
doesn-t reduce only the size of program, but reduces also the number
of program states. We show also, how this compact Ada-ECATNet
may be reduced again by applying reduction rules on it. This double
reduction of Ada-ECATNet permits a considerable minimization of
the memory space and run time of corresponding Maude program.
Abstract: One of the major disadvantages of the minimally
invasive surgery (MIS) is the lack of tactile feedback to the surgeon.
In order to identify and avoid any damage to the grasped complex
tissue by endoscopic graspers, it is important to measure the local
softness of tissue during MIS. One way to display the measured
softness to the surgeon is a graphical method. In this paper, a new
tactile sensor has been reported. The tactile sensor consists of an
array of four softness sensors, which are integrated into the jaws of a
modified commercial endoscopic grasper. Each individual softness
sensor consists of two piezoelectric polymer Polyvinylidene Fluoride
(PVDF) films, which are positioned below a rigid and a compliant
cylinder. The compliant cylinder is fabricated using a micro molding
technique. The combination of output voltages from PVDF films is
used to determine the softness of the grasped object. The theoretical
analysis of the sensor is also presented.
A method has been developed with the aim of reproducing the
tactile softness to the surgeon by using a graphical method. In this
approach, the proposed system, including the interfacing and the data
acquisition card, receives signals from the array of softness sensors.
After the signals are processed, the tactile information is displayed
by means of a color coding method. It is shown that the degrees of
softness of the grasped objects/tissues can be visually differentiated
and displayed on a monitor.
Abstract: Through the course of this paper we outline how
mobile Business Intelligence (m-BI) can help businesses to work
smarter and to improve their agility. When we analyze the industry
from the usage perspective or how interaction with the enterprise BI
system happens via mobile devices, we may easily understand that
there are two major types of mobile BI: passive and active. Active
mobile BI gives provisions for users to interact with the BI systems
on-the-fly. Active mobile business intelligence often works as a
combination of both “push and pull" techniques. Some mistakes were
done in the up-to-day progress of mobile technologies and mobile BI,
as well as some problems that still have to be resolved. We discussed
in the paper rather broadly.
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: The noteworthy point in the advancement of Brain Machine Interface (BMI) research is the ability to accurately extract features of the brain signals and to classify them into targeted control action with the easiest procedures since the expected beneficiaries are of disabled. In this paper, a new feature extraction method using the combination of adaptive band pass filters and adaptive autoregressive (AAR) modelling is proposed and applied to the classification of right and left motor imagery signals extracted from the brain. The introduction of the adaptive bandpass filter improves the characterization process of the autocorrelation functions of the AAR models, as it enhances and strengthens the EEG signal, which is noisy and stochastic in nature. The experimental results on the Graz BCI data set have shown that by implementing the proposed feature extraction method, a LDA and SVM classifier outperforms other AAR approaches of the BCI 2003 competition in terms of the mutual information, the competition criterion, or misclassification rate.
Abstract: As the material used for fuselage structure must
possess low density, high strength to weight ratio, the selection of
appropriate materials for fuselage structure is one of the most
important tasks. Aluminum metal itself is soft and low in strength. It
can be made stronger by giving proper combination of suitable alloy
addition, mechanical treatment and thermal treatment. The usual
thermal treatment given to aluminum alloys is called age-hardening
or precipitation hardening. In this paper, the studies are carried out on
7075 aluminum alloy which is how to improve strength level for
fuselage structure. The marked effect of the strength on the ternary
alloy is clearly demonstrated at several ageing times and
temperatures. It is concluded that aluminum-zinc-magnesium alloy
can get the highest strength level in natural ageing.
Abstract: A direct connection between ElectroEncephaloGram
(EEG) and the genetic information of individuals has been
investigated by neurophysiologists and psychiatrists since 1960-s;
and it opens a new research area in the science. This paper focuses on
the person identification based on feature extracted from the EEG
which can show a direct connection between EEG and the genetic
information of subjects. In this work the full EO EEG signal of
healthy individuals are estimated by an autoregressive (AR) model
and the AR parameters are extracted as features. Here for feature
vector constitution, two methods have been proposed; in the first
method the extracted parameters of each channel are used as a
feature vector in the classification step which employs a competitive
neural network and in the second method a combination of different
channel parameters are used as a feature vector. Correct classification
scores at the range of 80% to 100% reveal the potential of our
approach for person classification/identification and are in agreement
to the previous researches showing evidence that the EEG signal
carries genetic information. The novelty of this work is in the
combination of AR parameters and the network type (competitive
network) that we have used. A comparison between the first and the
second approach imply preference of the second one.
Abstract: Global approximation using metamodel for complex
mathematical function or computer model over a large variable
domain is often needed in sensibility analysis, computer simulation,
optimal control, and global design optimization of complex, multiphysics
systems. To overcome the limitations of the existing
response surface (RS), surrogate or metamodel modeling methods for
complex models over large variable domain, a new adaptive and
regressive RS modeling method using quadratic functions and local
area model improvement schemes is introduced. The method applies
an iterative and Latin hypercube sampling based RS update process,
divides the entire domain of design variables into multiple cells,
identifies rougher cells with large modeling error, and further divides
these cells along the roughest dimension direction. A small number
of additional sampling points from the original, expensive model are
added over the small and isolated rough cells to improve the RS
model locally until the model accuracy criteria are satisfied. The
method then combines local RS cells to regenerate the global RS
model with satisfactory accuracy. An effective RS cells sorting
algorithm is also introduced to improve the efficiency of model
evaluation. Benchmark tests are presented and use of the new
metamodeling method to replace complex hybrid electrical vehicle
powertrain performance model in vehicle design optimization and
optimal control are discussed.
Abstract: Thyroid dysfunction is one of the most frequently
reported complications of chronic blood transfusion therapy in patients with beta-thalassemia major (BTM). However, the occurrence of thyroid dysfunction and its possible association with
iron overload in BTM patients is still under debate. Therefore, this
study aimed to investigate the status of thyroid functions and iron overload in adolescent and young adult patients with BTM in Jordan population. Thirty six BTM patients aged 12-28 years and matched controls were included in this study. All patients have been receiving frequent blood transfusion to maintain pretransfusion hemoglobin
concentration above 10 g dl-1 and deferoxamine at a dose of 45 mg kg-1 day-1 (8 h, 5-7 days/week) by subcutaneous infusion. Blood
samples were drawn from patients and controls. The status of thyroid functions and iron overload was evaluated by measurements of serum
free thyroxine (FT4), triiodothyronine (FT3), thyrotropin (TSH) and
serum ferritin level. A number of some hematological and
biochemical parameters were also measured. It was found that hematocrit, serum ferritin, hemoglobin, FT3 and zinc, copper mean values were significantly higher in the patients than in the controls (P< 0.05). On other hand, leukocyte, FT4 and TSH mean values were
similar to that of the controls. In addition, our data also indicated that
all of the above examined parameters were not significantly affected
by the patient-s age and gender. Deferoxamine approach for removing excess iron from our BTM patient did not normalize the
values of serum ferritin, copper and zinc, suggesting poor compliance
with deferoxamine chelation therapy. Thus, we recommend the use
of a combination of deferoxamine and deferiprone to reduce the risk
of excess of iron in our patients. Furthermore, thyroid dysfunction
appears to be a rare complication, because our patients showed
normal mean levels for serum TSH and FT4. However, high mean
levels of serum ferritin, zinc, copper might be seen as potential risk
factors for initiation and development of thyroid dysfunctions and
other diseases. Therefore, further studies must be carried out at
yearly intervals with large sample number, to detect subclinical
thyroid dysfunction cases.