Abstract: In this paper, we focus on the fusion of images from
different sources using multiresolution wavelet transforms. Based on
reviews of popular image fusion techniques used in data analysis,
different pixel and energy based methods are experimented. A novel
architecture with a hybrid algorithm is proposed which applies pixel
based maximum selection rule to low frequency approximations and
filter mask based fusion to high frequency details of wavelet
decomposition. The key feature of hybrid architecture is the
combination of advantages of pixel and region based fusion in a
single image which can help the development of sophisticated
algorithms enhancing the edges and structural details. A Graphical
User Interface is developed for image fusion to make the research
outcomes available to the end user. To utilize GUI capabilities for
medical, industrial and commercial activities without MATLAB
installation, a standalone executable application is also developed
using Matlab Compiler Runtime.
Abstract: On-line (near infrared) spectroscopy is widely used to support the operation of complex process systems. Information extracted from spectral database can be used to estimate unmeasured product properties and monitor the operation of the process. These techniques are based on looking for similar spectra by nearest neighborhood algorithms and distance based searching methods. Search for nearest neighbors in the spectral space is an NP-hard problem, the computational complexity increases by the number of points in the discrete spectrum and the number of samples in the database. To reduce the calculation time some kind of indexing could be used. The main idea presented in this paper is to combine indexing and visualization techniques to reduce the computational requirement of estimation algorithms by providing a two dimensional indexing that can also be used to visualize the structure of the spectral database. This 2D visualization of spectral database does not only support application of distance and similarity based techniques but enables the utilization of advanced clustering and prediction algorithms based on the Delaunay tessellation of the mapped spectral space. This means the prediction has not to use the high dimension space but can be based on the mapped space too. The results illustrate that the proposed method is able to segment (cluster) spectral databases and detect outliers that are not suitable for instance based learning algorithms.
Abstract: In this paper, we introduce a novel platform
encryption method, which modify its keys and random number
generators step by step during encryption algorithms. According to
complexity of the proposed algorithm, it was safer than any other
method.
Abstract: Genetic algorithms (GAs) have been widely used for
global optimization problems. The GA performance depends highly
on the choice of the search space for each parameter to be optimized.
Often, this choice is a problem-based experience. The search space
being a set of potential solutions may contain the global optimum
and/or other local optimums. A bad choice of this search space
results in poor solutions. In this paper, our approach consists in
extending the search space boundaries during the GA optimization,
only when it is required. This leads to more diversification of GA
population by new solutions that were not available with fixed search
space boundaries. So, these dynamic search spaces can improve the
GA optimization performances. The proposed approach is applied to
power system stabilizer optimization for multimachine power system
(16-generator and 68-bus). The obtained results are evaluated and
compared with those obtained by ordinary GAs. Eigenvalue analysis
and nonlinear system simulation results show the effectiveness of the
proposed approach to damp out the electromechanical oscillation and
enhance the global system stability.
Abstract: This paper proposes a VPN Accelerator Board
(VPN-AB), a virtual private network (VPN) protocol designed for
trust channel security system (TCSS). TCSS supports safety
communication channel between security nodes in internet. It
furnishes authentication, confidentiality, integrity, and access control
to security node to transmit data packets with IPsec protocol. TCSS
consists of internet key exchange block, security association block,
and IPsec engine block. The internet key exchange block negotiates
crypto algorithm and key used in IPsec engine block. Security
Association blocks setting-up and manages security association
information. IPsec engine block treats IPsec packets and consists of
networking functions for communication. The IPsec engine block
should be embodied by H/W and in-line mode transaction for high
speed IPsec processing. Our VPN-AB is implemented with high speed
security processor that supports many cryptographic algorithms and
in-line mode. We evaluate a small TCSS communication environment,
and measure a performance of VPN-AB in the environment. The
experiment results show that VPN-AB gets a performance throughput
of maximum 15.645Gbps when we set the IPsec protocol with
3DES-HMAC-MD5 tunnel mode.
Abstract: Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, a density based clustering algorithm (DCBRD) is presented, relying on a knowledge acquired from the data by dividing the data space into overlapped regions. The proposed algorithm discovers arbitrary shaped clusters, requires no input parameters and uses the same definitions of DBSCAN algorithm. We performed an experimental evaluation of the effectiveness and efficiency of it, and compared this results with that of DBSCAN. The results of our experiments demonstrate that the proposed algorithm is significantly efficient in discovering clusters of arbitrary shape and size.
Abstract: We present a genetic algorithm application to the problem of object registration (i.e., object detection, localization and recognition) in a class of medical images containing various types of blood cells. The genetic algorithm approach taken here is seen to be most appropriate for this type of image, due to the characteristics of the objects. Successful cell registration results on real life microscope images of blood cells show the potential of the proposed approach.
Abstract: Data mining is an extraordinarily demanding field referring to extraction of implicit knowledge and relationships, which are not explicitly stored in databases. A wide variety of methods of data mining have been introduced (classification, characterization, generalization...). Each one of these methods includes more than algorithm. A system of data mining implies different user categories,, which mean that the user-s behavior must be a component of the system. The problem at this level is to know which algorithm of which method to employ for an exploratory end, which one for a decisional end, and how can they collaborate and communicate. Agent paradigm presents a new way of conception and realizing of data mining system. The purpose is to combine different algorithms of data mining to prepare elements for decision-makers, benefiting from the possibilities offered by the multi-agent systems. In this paper the agent framework for data mining is introduced, and its overall architecture and functionality are presented. The validation is made on spatial data. Principal results will be presented.
Abstract: The group mutual exclusion (GME) problem is an
interesting generalization of the mutual exclusion problem. Several
solutions of the GME problem have been proposed for message
passing distributed systems. However, none of these solutions is
suitable for real time distributed systems. In this paper, we propose a
token-based distributed algorithms for the GME problem in soft real
time distributed systems. The algorithm uses the concepts of priority
queue, dynamic request set and the process state. The algorithm uses
first come first serve approach in selecting the next session type
between the same priority levels and satisfies the concurrent
occupancy property. The algorithm allows all n processors to be
inside their CS provided they request for the same session. The
performance analysis and correctness proof of the algorithm has also
been included in the paper.
Abstract: The advantage of solving the complex nonlinear
problems by utilizing fuzzy logic methodologies is that the
experience or expert-s knowledge described as a fuzzy rule base can
be directly embedded into the systems for dealing with the problems.
The current limitation of appropriate and automated designing of
fuzzy controllers are focused in this paper. The structure discovery
and parameter adjustment of the Branched T-S fuzzy model is
addressed by a hybrid technique of type constrained sparse tree
algorithms. The simulation result for different system model is
evaluated and the identification error is observed to be minimum.
Abstract: Block replacement algorithms to increase hit ratio
have been extensively used in cache memory management. Among
basic replacement schemes, LRU and FIFO have been shown to be
effective replacement algorithms in terms of hit rates. In this paper,
we introduce a flexible stack-based circuit which can be employed in
hardware implementation of both LRU and FIFO policies. We
propose a simple and efficient architecture such that stack-based
replacement algorithms can be implemented without the drawbacks
of the traditional architectures. The stack is modular and hence, a set
of stack rows can be cascaded depending on the number of blocks in
each cache set. Our circuit can be implemented in conjunction with
the cache controller and static/dynamic memories to form a cache
system. Experimental results exhibit that our proposed circuit
provides an average value of 26% improvement in storage bits and its
maximum operating frequency is increased by a factor of two
Abstract: The problem of manipulator control is a highly
complex problem of controlling a system which is multi-input, multioutput,
non-linear and time variant. In this paper some adaptive
fuzzy, and a new hybrid fuzzy control algorithm have been
comparatively evaluated through simulations, for manipulator
control. The adaptive fuzzy controllers consist of self-organizing,
self-tuning, and coarse/fine adaptive fuzzy schemes. These
controllers are tested for different trajectories and for varying
manipulator parameters through simulations. Various performance
indices like the RMS error, steady state error and maximum error are
used for comparison. It is observed that the self-organizing fuzzy
controller gives the best performance. The proposed hybrid fuzzy
plus integral error controller also performs remarkably well, given its
simple structure.
Abstract: Wireless sensor networks include small nodes which
have sensing ability; calculation and connection extend themselves
everywhere soon. Such networks have source limitation on
connection, calculation and energy consumption. So, since the nodes
have limited energy in sensor networks, the optimized energy
consumption in these networks is of more importance and has created
many challenges. The previous works have shown that by organizing
the network nodes in a number of clusters, the energy consumption
could be reduced considerably. So the lifetime of the network would
be increased. In this paper, we used the Queen-bee algorithm to
create energy efficient clusters in wireless sensor networks. The
Queen-bee (QB) is similar to nature in that the queen-bee plays a
major role in reproduction process. The QB is simulated with J-sim
simulator. The results of the simulation showed that the clustering by
the QB algorithm decreases the energy consumption with regard to
the other existing algorithms and increases the lifetime of the
network.
Abstract: Advances in computing applications in recent years
have prompted the demand for more flexible scheduling models for
QoS demand. Moreover, in practical applications, partly violated
temporal constraints can be tolerated if the violation meets certain
distribution. So we need extend the traditional Liu and Lanland model
to adapt to these circumstances. There are two extensions, which are
the (m, k)-firm model and Window-Constrained model. This paper
researches on weakly hard real-time constraints and their combination
to support QoS. The fact that a practical application can tolerate some
violations of temporal constraint under certain distribution is
employed to support adaptive QoS on the open real-time system. The
experiment results show these approaches are effective compared to
traditional scheduling algorithms.
Abstract: Multicast Network Technology has pervaded our
lives-a few examples of the Networking Techniques and also for the
improvement of various routing devices we use. As we know the
Multicast Data is a technology offers many applications to the user
such as high speed voice, high speed data services, which is presently
dominated by the Normal networking and the cable system and
digital subscriber line (DSL) technologies. Advantages of Multi cast
Broadcast such as over other routing techniques. Usually QoS
(Quality of Service) Guarantees are required in most of Multicast
applications. The bandwidth-delay constrained optimization and we
use a multi objective model and routing approach based on genetic
algorithm that optimizes multiple QoS parameters simultaneously.
The proposed approach is non-dominated routes and the performance
with high efficiency of GA. Its betterment and high optimization has
been verified. We have also introduced and correlate the result of
multicast GA with the Broadband wireless to minimize the delay in
the path.
Abstract: The design of a gravity dam is performed through an
interactive process involving a preliminary layout of the structure
followed by a stability and stress analysis. This study presents a
method to define the optimal top width of gravity dam with genetic
algorithm. To solve the optimization task (minimize the cost of the
dam), an optimization routine based on genetic algorithms (GAs) was
implemented into an Excel spreadsheet. It was found to perform well
and GA parameters were optimized in a parametric study. Using the
parameters found in the parametric study, the top width of gravity
dam optimization was performed and compared to a gradient-based
optimization method (classic method). The accuracy of the results
was within close proximity. In optimum dam cross section, the ratio
of is dam base to dam height is almost equal to 0.85, and ratio of dam
top width to dam height is almost equal to 0.13. The computerized
methodology may provide the help for computation of the optimal
top width for a wide range of height of a gravity dam.
Abstract: Because of importance of energy, optimization of
power generation systems is necessary. Gas turbine cycles are
suitable manner for fast power generation, but their efficiency is
partly low. In order to achieving higher efficiencies, some
propositions are preferred such as recovery of heat from exhaust
gases in a regenerator, utilization of intercooler in a multistage
compressor, steam injection to combustion chamber and etc.
However thermodynamic optimization of gas turbine cycle, even
with above components, is necessary. In this article multi-objective
genetic algorithms are employed for Pareto approach optimization of
Regenerative-Intercooling-Gas Turbine (RIGT) cycle. In the multiobjective
optimization a number of conflicting objective functions
are to be optimized simultaneously. The important objective
functions that have been considered for optimization are entropy
generation of RIGT cycle (Ns) derives using Exergy Analysis and
Gouy-Stodola theorem, thermal efficiency and the net output power
of RIGT Cycle. These objectives are usually conflicting with each
other. The design variables consist of thermodynamic parameters
such as compressor pressure ratio (Rp), excess air in combustion
(EA), turbine inlet temperature (TIT) and inlet air temperature (T0).
At the first stage single objective optimization has been investigated
and the method of Non-dominated Sorting Genetic Algorithm
(NSGA-II) has been used for multi-objective optimization.
Optimization procedures are performed for two and three objective
functions and the results are compared for RIGT Cycle. In order to
investigate the optimal thermodynamic behavior of two objectives,
different set, each including two objectives of output parameters, are
considered individually. For each set Pareto front are depicted. The
sets of selected decision variables based on this Pareto front, will
cause the best possible combination of corresponding objective
functions. There is no superiority for the points on the Pareto front
figure, but they are superior to any other point. In the case of three
objective optimization the results are given in tables.
Abstract: We present in this paper a new approach for specific JPEG steganalysis and propose studying statistics of the compressed DCT coefficients. Traditionally, steganographic algorithms try to preserve statistics of the DCT and of the spatial domain, but they cannot preserve both and also control the alteration of the compressed data. We have noticed a deviation of the entropy of the compressed data after a first embedding. This deviation is greater when the image is a cover medium than when the image is a stego image. To observe this deviation, we pointed out new statistic features and combined them with the Multiple Embedding Method. This approach is motivated by the Avalanche Criterion of the JPEG lossless compression step. This criterion makes possible the design of detectors whose detection rates are independent of the payload. Finally, we designed a Fisher discriminant based classifier for well known steganographic algorithms, Outguess, F5 and Hide and Seek. The experiemental results we obtained show the efficiency of our classifier for these algorithms. Moreover, it is also designed to work with low embedding rates (< 10-5) and according to the avalanche criterion of RLE and Huffman compression step, its efficiency is independent of the quantity of hidden information.
Abstract: In this paper the direct kinematic model of a multiple
applications three degrees of freedom industrial manipulator, was
developed using the homogeneous transformation matrices and the
Denavit - Hartenberg parameters, likewise the inverse kinematic
model was developed using the same method, verifying that in the
workload border the inverse kinematic presents considerable errors,
therefore a genetic algorithm was implemented to optimize the model
improving greatly the efficiency of the model.
Abstract: Cell formation is the first step in the design of cellular
manufacturing systems. In this study, a general purpose
computational scheme employing a hybrid tabu search algorithm as
the core is proposed to solve the cell formation problem and its
variants. In the proposed scheme, great flexibilities are left to the
users. The core solution searching algorithm embedded in the scheme
can be easily changed to any other meta-heuristic algorithms, such as
the simulated annealing, genetic algorithm, etc., based on the
characteristics of the problems to be solved or the preferences the
users might have. In addition, several counters are designed to control
the timing of conducting intensified solution searching and diversified
solution searching strategies interactively.