Abstract: In this manuscript, a wavelet-based blind
watermarking scheme has been proposed as a means to provide
security to authenticity of a fingerprint. The information used for
identification or verification of a fingerprint mainly lies in its
minutiae. By robust watermarking of the minutiae in the fingerprint
image itself, the useful information can be extracted accurately even
if the fingerprint is severely degraded. The minutiae are converted in
a binary watermark and embedding these watermarks in the detail
regions increases the robustness of watermarking, at little to no
additional impact on image quality. It has been experimentally shown
that when the minutiae is embedded into wavelet detail coefficients
of a fingerprint image in spread spectrum fashion using a
pseudorandom sequence, the robustness is observed to have a
proportional response while perceptual invisibility has an inversely
proportional response to amplification factor “K". The DWT-based
technique has been found to be very robust against noises,
geometrical distortions filtering and JPEG compression attacks and is
also found to give remarkably better performance than DCT-based
technique in terms of correlation coefficient and number of erroneous
minutiae.
Abstract: The most severe damage of the turbine rotor is its
distortion. The rotor straightening process must lead, at the first
stage, to removal of the stresses from the material by annealing and
next, to straightening of the plastic distortion without leaving any
stress by hot spotting. The straightening method does not produce
stress accumulations and the heating technique, developed
specifically for solid forged rotors and disks, enables to avoid local
overheating and structural changes in the material. This process also
does not leave stresses in the shaft material. An experimental study
of hot spotting is carried out on a large turbine rotor and some of the
most important effective parameters that must be considered on
annealing and hot spotting processes are investigated in this paper.
Abstract: This paper presents a boarding on biometric
authentication through the Keystrokes Dynamics that it intends to
identify a person from its habitual rhythm to type in conventional
keyboard. Seven done experiments: verifying amount of prototypes,
threshold, features and the variation of the choice of the times of the
features vector. The results show that the use of the Keystroke
Dynamics is simple and efficient for personal authentication, getting
optimum resulted using 90% of the features with 4.44% FRR and 0%
FAR.
Abstract: Skin color based tracking techniques often assume a
static skin color model obtained either from an offline set of library
images or the first few frames of a video stream. These models
can show a weak performance in presence of changing lighting or
imaging conditions. We propose an adaptive skin color model based
on the Gaussian mixture model to handle the changing conditions.
Initial estimation of the number and weights of skin color clusters
are obtained using a modified form of the general Expectation
maximization algorithm, The model adapts to changes in imaging
conditions and refines the model parameters dynamically using spatial
and temporal constraints. Experimental results show that the method
can be used in effectively tracking of hand and face regions.
Abstract: Content-based music retrieval generally involves analyzing, searching and retrieving music based on low or high level features of a song which normally used to represent artists, songs or music genre. Identifying them would normally involve feature extraction and classification tasks. Theoretically the greater features analyzed, the better the classification accuracy can be achieved but with longer execution time. Technique to select significant features is important as it will reduce dimensions of feature used in classification and contributes to the accuracy. Artificial Immune System (AIS) approach will be investigated and applied in the classification task. Bio-inspired audio content-based retrieval framework (B-ACRF) is proposed at the end of this paper where it embraces issues that need further consideration in music retrieval performances.
Abstract: This research aims to create a model for analysis of student motivation behavior on e-Learning based on association rule mining techniques in case of the Information Technology for Communication and Learning Course at Suan Sunandha Rajabhat University. The model was created under association rules, one of the data mining techniques with minimum confidence. The results showed that the student motivation behavior model by using association rule technique can indicate the important variables that influence the student motivation behavior on e-Learning.
Abstract: In this paper, a novel contrast enhancement technique
for contrast enhancement of a low-contrast satellite image has been
proposed based on the singular value decomposition (SVD) and
discrete cosine transform (DCT). The singular value matrix
represents the intensity information of the given image and any
change on the singular values change the intensity of the input image.
The proposed technique converts the image into the SVD-DCT
domain and after normalizing the singular value matrix; the enhanced
image is reconstructed by using inverse DCT. The visual and
quantitative results suggest that the proposed SVD-DCT method
clearly shows the increased efficiency and flexibility of the proposed
method over the exiting methods such as Linear Contrast Stretching
technique, GHE technique, DWT-SVD technique, DWT technique,
Decorrelation Stretching technique, Gamma Correction method
based techniques.
Abstract: Partitioning is a critical area of VLSI CAD. In order to build complex digital logic circuits its often essential to sub-divide multi -million transistor design into manageable Pieces. This paper looks at the various partitioning techniques aspects of VLSI CAD, targeted at various applications. We proposed an evolutionary time-series model and a statistical glitch prediction system using a neural network with selection of global feature by making use of clustering method model, for partitioning a circuit. For evolutionary time-series model, we made use of genetic, memetic & neuro-memetic techniques. Our work focused in use of clustering methods - K-means & EM methodology. A comparative study is provided for all techniques to solve the problem of circuit partitioning pertaining to VLSI design. The performance of all approaches is compared using benchmark data provided by MCNC standard cell placement benchmark net lists. Analysis of the investigational results proved that the Neuro-memetic model achieves greater performance then other model in recognizing sub-circuits with minimum amount of interconnections between them.
Abstract: Today's business environment requires that companies have access to highly relevant information in a matter of seconds.
Modern Business Intelligence tools rely on data structured mostly in traditional dimensional database schemas, typically represented by
star schemas. Dimensional modeling is already recognized as a
leading industry standard in the field of data warehousing although
several drawbacks and pitfalls were reported. This paper focuses on
the analysis of another data warehouse modeling technique - the
anchor modeling, and its characteristics in context with the standardized dimensional modeling technique from a query performance perspective. The results of the analysis show
information about performance of queries executed on database
schemas structured according to principles of each database modeling
technique.
Abstract: Wireless Sensor Networks (WSNs) are used to monitor/observe vast inaccessible regions through deployment of large number of sensor nodes in the sensing area. For majority of WSN applications, the collected data needs to be combined with geographic information of its origin to make it useful for the user; information received from remote Sensor Nodes (SNs) that are several hops away from base station/sink is meaningless without knowledge of its source. In addition to this, location information of SNs can also be used to propose/develop new network protocols for WSNs to improve their energy efficiency and lifetime. In this paper, range free localization protocols for WSNs have been proposed. The proposed protocols are based on weighted centroid localization technique, where the edge weights of SNs are decided by utilizing fuzzy logic inference for received signal strength and link quality between the nodes. The fuzzification is carried out using (i) Mamdani, (ii) Sugeno, and (iii) Combined Mamdani Sugeno fuzzy logic inference. Simulation results demonstrate that proposed protocols provide better accuracy in node localization compared to conventional centroid based localization protocols despite presence of unintentional radio frequency interference from radio frequency (RF) sources operating in same frequency band.
Abstract: This paper presents an approach for an unequal error
protection of facial features of personal ID images coding. We
consider unequal error protection (UEP) strategies for the efficient
progressive transmission of embedded image codes over noisy
channels. This new method is based on the progressive image
compression embedded zerotree wavelet (EZW) algorithm and UEP
technique with defined region of interest (ROI). In this case is ROI
equal facial features within personal ID image. ROI technique is
important in applications with different parts of importance. In ROI
coding, a chosen ROI is encoded with higher quality than the
background (BG). Unequal error protection of image is provided by
different coding techniques and encoding LL band separately. In our
proposed method, image is divided into two parts (ROI, BG) that
consist of more important bytes (MIB) and less important bytes
(LIB). The proposed unequal error protection of image transmission
has shown to be more appropriate to low bit rate applications,
producing better quality output for ROI of the compresses image.
The experimental results verify effectiveness of the design. The
results of our method demonstrate the comparison of the UEP of
image transmission with defined ROI with facial features and the
equal error protection (EEP) over additive white gaussian noise
(AWGN) channel.
Abstract: In this paper, we have presented the effect of varying
time-delays on performance and stability in the single-channel multirate
sampled-data system in hard real-time (RT-Linux) environment.
The sampling task require response time that might exceed the
capacity of RT-Linux. So a straight implementation with RT-Linux is
not feasible, because of the latency of the systems and hence,
sampling period should be less to handle this task. The best sampling
rate is chosen for the sampled-data system, which is the slowest rate
meets all performance requirements. RT-Linux is consistent with its
specifications and the resolution of the real-time is considered 0.01
seconds to achieve an efficient result. The test results of our
laboratory experiment shows that the multi-rate control technique in
hard real-time operating system (RTOS) can improve the stability
problem caused by the random access delays and asynchronization.
Abstract: Data Mining aims at discovering knowledge out of
data and presenting it in a form that is easily comprehensible to
humans. One of the useful applications in Egypt is the Cancer
management, especially the management of Acute Lymphoblastic
Leukemia or ALL, which is the most common type of cancer in
children.
This paper discusses the process of designing a prototype that can
help in the management of childhood ALL, which has a great
significance in the health care field. Besides, it has a social impact
on decreasing the rate of infection in children in Egypt. It also
provides valubale information about the distribution and
segmentation of ALL in Egypt, which may be linked to the possible
risk factors.
Undirected Knowledge Discovery is used since, in the case of this
research project, there is no target field as the data provided is
mainly subjective. This is done in order to quantify the subjective
variables. Therefore, the computer will be asked to identify
significant patterns in the provided medical data about ALL. This
may be achieved through collecting the data necessary for the
system, determimng the data mining technique to be used for the
system, and choosing the most suitable implementation tool for the
domain.
The research makes use of a data mining tool, Clementine, so as to
apply Decision Trees technique. We feed it with data extracted from
real-life cases taken from specialized Cancer Institutes. Relevant
medical cases details such as patient medical history and diagnosis
are analyzed, classified, and clustered in order to improve the disease
management.
Abstract: Natural frequencies and dynamic response of a spur
gear sector are investigated using a two dimensional finite element
model that offers significant advantages for dynamic gear analyses.
The gear teeth are analyzed for different operating speeds. A primary
feature of this modeling is determination of mesh forces using a
detailed contact analysis for each time step as the gears roll through
the mesh. ANSYS software has been used on the proposed model to
find the natural frequencies by Block Lanczos technique and
displacements and dynamic stresses by transient mode super position
method. The effect of rotational speed of the gear on the dynamic
response of gear tooth has been studied and design limits have been
discussed.
Abstract: There are many real world problems in which
parameters like the arrival time of new jobs, failure of resources, and
completion time of jobs change continuously. This paper tackles the
problem of scheduling jobs with random due dates on multiple
identical machines in a stochastic environment. First to assign jobs to
different machine centers LPT scheduling methods have been used,
after that the particular sequence of jobs to be processed on the
machine have been found using simple stochastic techniques. The
performance parameter under consideration has been the maximum
lateness concerning the stochastic due dates which are independent
and exponentially distributed. At the end a relevant problem has been
solved using the techniques in the paper..
Abstract: With the advent of digital cinema and digital
broadcasting, copyright protection of video data has been one of the
most important issues.
We present a novel method of watermarking for video image data
based on the hardware and digital wavelet transform techniques and
name it as “traceable watermarking" because the watermarked data is
constructed before the transmission process and traced after it has been
received by an authorized user.
In our method, we embed the watermark to the lowest part of each
image frame in decoded video by using a hardware LSI.
Digital Cinema is an important application for traceable
watermarking since digital cinema system makes use of watermarking
technology during content encoding, encryption, transmission,
decoding and all the intermediate process to be done in digital cinema
systems. The watermark is embedded into the randomly selected
movie frames using hash functions.
Embedded watermark information can be extracted from the
decoded video data. For that, there is no need to access original movie
data. Our experimental results show that proposed traceable
watermarking method for digital cinema system is much better than the
convenient watermarking techniques in terms of robustness, image
quality, speed, simplicity and robust structure.
Abstract: In this paper, we explore the applicability of the Sinc-
Collocation method to a three-dimensional (3D) oceanography model.
The model describes a wind-driven current with depth-dependent
eddy viscosity in the complex-velocity system. In general, the
Sinc-based methods excel over other traditional numerical methods
due to their exponentially decaying errors, rapid convergence and
handling problems in the presence of singularities in end-points.
Together with these advantages, the Sinc-Collocation approach that
we utilize exploits first derivative interpolation, whose integration
is much less sensitive to numerical errors. We bring up several
model problems to prove the accuracy, stability, and computational
efficiency of the method. The approximate solutions determined by
the Sinc-Collocation technique are compared to exact solutions and
those obtained by the Sinc-Galerkin approach in earlier studies. Our
findings indicate that the Sinc-Collocation method outperforms other
Sinc-based methods in past studies.
Abstract: The aim of this paper is to determine the stress levels
at the end of a long slender shaft such as a drilling assembly used in
the oil or gas industry using a mathematical model in real-time. The
torsional deflection experienced by this type of drilling shaft (about 4
KM length and 20 cm diameter hollow shaft with a thickness of 1
cm) can only be determined using a distributed modeling technique.
The main objective of this project is to calculate angular velocity and
torque at the end of the shaft by TLM method and also analyzing of
the behavior of the system by transient response. The obtained result
is compared with lumped modeling technique the importance of these
results will be evident only after the mentioned comparison. Two
systems have different transient responses and in this project because
of the length of the shaft transient response is very important.
Abstract: Recently, a lot of attention has been devoted to
advanced techniques of system modeling. PNN(polynomial neural
network) is a GMDH-type algorithm (Group Method of Data
Handling) which is one of the useful method for modeling nonlinear
systems but PNN performance depends strongly on the number of
input variables and the order of polynomial which are determined by
trial and error. In this paper, we introduce GPNN (genetic
polynomial neural network) to improve the performance of PNN.
GPNN determines the number of input variables and the order of all
neurons with GA (genetic algorithm). We use GA to search between
all possible values for the number of input variables and the order of
polynomial. GPNN performance is obtained by two nonlinear
systems. the quadratic equation and the time series Dow Jones stock
index are two case studies for obtaining the GPNN performance.
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.