Abstract: Microarrays technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining this data one can identify the dynamics of the gene expression time series. By recourse of principal component analysis, we uncover the circadian rhythmic patterns underlying the gene expression profiles from Cyanobacterium Synechocystis. We applied PCA to reduce the dimensionality of the data set. Examination of the components also provides insight into the underlying factors measured in the experiments. Our results suggest that all rhythmic content of data can be reduced to three main components.
Abstract: In this research, we have developed a new efficient
heuristic algorithm for the dynamic facility layout problem with
budget constraint (DFLPB). This heuristic algorithm combines two
mathematical programming methods such as discrete event
simulation and linear integer programming (IP) to obtain a near
optimum solution. In the proposed algorithm, the non-linear model
of the DFLP has been changed to a pure integer programming (PIP)
model. Then, the optimal solution of the PIP model has been used in
a simulation model that has been designed in a similar manner as the
DFLP for determining the probability of assigning a facility to a
location. After a sufficient number of runs, the simulation model
obtains near optimum solutions. Finally, to verify the performance of
the algorithm, several test problems have been solved. The results
show that the proposed algorithm is more efficient in terms of speed
and accuracy than other heuristic algorithms presented in previous
works found in the literature.
Abstract: This paper presents an overview of the multiobjective shortest path problem (MSPP) and a review of essential and recent issues regarding the methods to its solution. The paper further explores a multiobjective evolutionary algorithm as applied to the MSPP and describes its behavior in terms of diversity of solutions, computational complexity, and optimality of solutions. Results show that the evolutionary algorithm can find diverse solutions to the MSPP in polynomial time (based on several network instances) and can be an alternative when other methods are trapped by the tractability problem.
Abstract: The particular interests of this paper is to explore if the simple Genetic Algorithms (GA) starts with population of only two individuals and applying different crossover technique over these parents to produced 104 children, each one has different attributes inherited from their parents; is better than starting with population of 100 individuals; and using only one type crossover (order crossover OX). For this reason we implement GA with 52 different crossover techniques; each one produce two children; which means 104 different children will be produced and this may discover more search space, also we implement classic GA with order crossover and many experiments were done over 3 Travel Salesman Problem (TSP) to find out which method is better, and according to the results we can say that GA with Multi-crossovers is much better.
Abstract: The electrical potentials generated during eye movements and blinks are one of the main sources of artifacts in Electroencephalogram (EEG) recording and can propagate much across the scalp, masking and distorting brain signals. In recent times, signal separation algorithms are used widely for removing artifacts from the observed EEG data. In this paper, a recently introduced signal separation algorithm Mutual Information based Least dependent Component Analysis (MILCA) is employed to separate ocular artifacts from EEG. The aim of MILCA is to minimize the Mutual Information (MI) between the independent components (estimated sources) under a pure rotation. Performance of this algorithm is compared with eleven popular algorithms (Infomax, Extended Infomax, Fast ICA, SOBI, TDSEP, JADE, OGWE, MS-ICA, SHIBBS, Kernel-ICA, and RADICAL) for the actual independence and uniqueness of the estimated source components obtained for different sets of EEG data with ocular artifacts by using a reliable MI Estimator. Results show that MILCA is best in separating the ocular artifacts and EEG and is recommended for further analysis.
Abstract: We present a hybrid architecture of recurrent neural
networks (RNNs) inspired by hidden Markov models (HMMs). We
train the hybrid architecture using genetic algorithms to learn and
represent dynamical systems. We train the hybrid architecture on a
set of deterministic finite-state automata strings and observe the
generalization performance of the hybrid architecture when presented
with a new set of strings which were not present in the training data
set. In this way, we show that the hybrid system of HMM and RNN
can learn and represent deterministic finite-state automata. We ran
experiments with different sets of population sizes in the genetic
algorithm; we also ran experiments to find out which weight
initializations were best for training the hybrid architecture. The
results show that the hybrid architecture of recurrent neural networks
inspired by hidden Markov models can train and represent dynamical
systems. The best training and generalization performance is
achieved when the hybrid architecture is initialized with random real
weight values of range -15 to 15.
Abstract: A lot of matching algorithms with different characteristics have been introduced in recent years. For real time systems these algorithms are usually based on minutiae features. In this paper we introduce a novel approach for feature extraction in which the extracted features are independent of shift and rotation of the fingerprint and at the meantime the matching operation is performed much more easily and with higher speed and accuracy. In this new approach first for any fingerprint a reference point and a reference orientation is determined and then based on this information features are converted into polar coordinates. Due to high speed and accuracy of this approach and small volume of extracted features and easily execution of matching operation this approach is the most appropriate for real time applications.
Abstract: With the exponentially increasing demand for
wireless communications the capacity of current cellular systems will
soon become incapable of handling the growing traffic. Since radio
frequencies are diminishing natural resources, there seems to be a
fundamental barrier to further capacity increase. The solution can be
found in smart antenna systems.
Smart or adaptive antenna arrays consist of an array of antenna
elements with signal processing capability, that optimize the
radiation and reception of a desired signal, dynamically. Smart
antennas can place nulls in the direction of interferers via adaptive
updating of weights linked to each antenna element. They thus cancel
out most of the co-channel interference resulting in better quality of
reception and lower dropped calls. Smart antennas can also track the
user within a cell via direction of arrival algorithms. This implies that
they are more advantageous than other antenna systems. This paper
focuses on few issues about the smart antennas in mobile radio
networks.
Abstract: Traffic incident has bad effect on all parts of society
so controlling road networks with enough traffic devices could help
to decrease number of accidents, so using the best method for
optimum site selection of these devices could help to implement good
monitoring system. This paper has considered here important criteria
for optimum site selection of traffic camera based on aggregation
methods such as Bagging and Dempster-Shafer concepts. In the first
step, important criteria such as annual traffic flow, distance from
critical places such as parks that need more traffic controlling were
identified for selection of important road links for traffic camera
installation, Then classification methods such as Artificial neural
network and Decision tree algorithms were employed for
classification of road links based on their importance for camera
installation. Then for improving the result of classifiers aggregation
methods such as Bagging and Dempster-Shafer theories were used.
Abstract: This paper analyses the performance of a genetic algorithm using a new concept, namely a fractional-order dynamic fitness function, for the synthesis of combinational logic circuits. The experiments reveal superior results in terms of speed and convergence to achieve a solution.
Abstract: The objective of this contribution is to study the
performances in terms of bit error rate, of space-time code algorithms
applied to MIMO communication in tunnels. Indeed, the channel
characteristics in a tunnel are quite different than those of urban or
indoor environment, due to the guiding effect of the tunnel.
Therefore, MIMO channel matrices have been measured in a straight
tunnel, in a frequency band around 3GHz. Correlation between array
elements and properties of the MIMO matrices are first studied as a
function of the distance between the transmitter and the receiver.
Then, owing to a software tool simulating the link, predicted values
of bit error rate are given for VLAST, OSTBC and QSTBC
algorithms applied to a MIMO configuration with 2 or 4 array
elements. Results are interpreted from the analysis of the channel
properties.
Abstract: Asynchronous Transfer Mode (ATM) is widely used
in telecommunications systems to send data, video and voice at a
very high speed. In ATM network optimizing the bandwidth through
dynamic routing is an important consideration. Previous research
work shows that traditional optimization heuristics result in suboptimal
solution. In this paper we have explored non-traditional
optimization technique. We propose comparison of two such
algorithms - Genetic Algorithm (GA) and Tabu search (TS), based on
non-traditional Optimization approach, for solving the dynamic
routing problem in ATM networks which in return will optimize the
bandwidth. The optimized bandwidth could mean that some
attractive business applications would become feasible such as high
speed LAN interconnection, teleconferencing etc. We have also
performed a comparative study of the selection mechanisms in GA
and listed the best selection mechanism and a new initialization
technique which improves the efficiency of the GA.
Abstract: Modeling the behavior of the dialogue management in
the design of a spoken dialogue system using statistical methodologies
is currently a growing research area. This paper presents a work
on developing an adaptive learning approach to optimize dialogue
strategy. At the core of our system is a method formalizing dialogue
management as a sequential decision making under uncertainty whose
underlying probabilistic structure has a Markov Chain. Researchers
have mostly focused on model-free algorithms for automating the
design of dialogue management using machine learning techniques
such as reinforcement learning. But in model-free algorithms there
exist a dilemma in engaging the type of exploration versus exploitation.
Hence we present a model-based online policy learning
algorithm using interconnected learning automata for optimizing
dialogue strategy. The proposed algorithm is capable of deriving
an optimal policy that prescribes what action should be taken in
various states of conversation so as to maximize the expected total
reward to attain the goal and incorporates good exploration and
exploitation in its updates to improve the naturalness of humancomputer
interaction. We test the proposed approach using the most
sophisticated evaluation framework PARADISE for accessing to the
railway information system.
Abstract: In the context of spectrum surveillance, a new method
to recover the code of spread spectrum signal is presented, while the
receiver has no knowledge of the transmitter-s spreading sequence. In
our previous paper, we used Genetic algorithm (GA), to recover
spreading code. Although genetic algorithms (GAs) are well known
for their robustness in solving complex optimization problems, but
nonetheless, by increasing the length of the code, we will often lead
to an unacceptable slow convergence speed. To solve this problem we
introduce Particle Swarm Optimization (PSO) into code estimation in
spread spectrum communication system. In searching process for
code estimation, the PSO algorithm has the merits of rapid
convergence to the global optimum, without being trapped in local
suboptimum, and good robustness to noise. In this paper we describe
how to implement PSO as a component of a searching algorithm in
code estimation. Swarm intelligence boasts a number of advantages
due to the use of mobile agents. Some of them are: Scalability, Fault
tolerance, Adaptation, Speed, Modularity, Autonomy, and
Parallelism. These properties make swarm intelligence very attractive
for spread spectrum code estimation. They also make swarm
intelligence suitable for a variety of other kinds of channels. Our
results compare between swarm-based algorithms and Genetic
algorithms, and also show PSO algorithm performance in code
estimation process.
Abstract: This paper presents an architecture to assist in the
development of tools to perform experimental analysis. Existing
implementations of tools based on this architecture are also described
in this paper. These tools are applied to the real world problem of
fault attack emulation and detection in cryptographic algorithms.
Abstract: Most of the biclustering/projected clustering algorithms are based either on the Euclidean distance or correlation coefficient which capture only linear relationships. However, in many applications, like gene expression data and word-document data, non linear relationships may exist between the objects. Mutual Information between two variables provides a more general criterion to investigate dependencies amongst variables. In this paper, we improve upon our previous algorithm that uses mutual information for biclustering in terms of computation time and also the type of clusters identified. The algorithm is able to find biclusters with mixed relationships and is faster than the previous one. To the best of our knowledge, none of the other existing algorithms for biclustering have used mutual information as a similarity measure. We present the experimental results on synthetic data as well as on the yeast expression data. Biclusters on the yeast data were found to be biologically and statistically significant using GO Tool Box and FuncAssociate.
Abstract: this paper focuses on designing of PSS and SVC
controller based on chaos and PSO algorithms to improve the
stability of power system. Single machine infinite bus (SMIB) system
with SVC located at the terminal of generator has been considered to
evaluate the proposed controllers where both SVC and PSS have the
same controller. The coefficients of PSS and SVC controller have
been optimized by chaos and PSO algorithms. Finally the system
with proposed controllers has been simulated for the special
disturbance in input power of generator, and then the dynamic
responses of generator have been presented. The simulation results
showed that the system composed with recommended controller has
outstanding operation in fast damping of oscillations of power system.
Abstract: In this contribution, a way to enhance the performance of the classic Genetic Algorithm is proposed. The idea of restarting a Genetic Algorithm is applied in order to obtain better knowledge of the solution space of the problem. A new operator of 'insertion' is introduced so as to exploit (utilize) the information that has already been collected before the restarting procedure. Finally, numerical experiments comparing the performance of the classic Genetic Algorithm and the Genetic Algorithm with restartings, for some well known test functions, are given.
Abstract: Traditional principal components analysis (PCA)
techniques for face recognition are based on batch-mode training
using a pre-available image set. Real world applications require that
the training set be dynamic of evolving nature where within the
framework of continuous learning, new training images are
continuously added to the original set; this would trigger a costly
continuous re-computation of the eigen space representation via
repeating an entire batch-based training that includes the old and new
images. Incremental PCA methods allow adding new images and
updating the PCA representation. In this paper, two incremental
PCA approaches, CCIPCA and IPCA, are examined and compared.
Besides, different learning and testing strategies are proposed and
applied to the two algorithms. The results suggest that batch PCA is
inferior to both incremental approaches, and that all CCIPCAs are
practically equivalent.
Abstract: Discrimination between different classes of environmental
sounds is the goal of our work. The use of a sound recognition
system can offer concrete potentialities for surveillance and
security applications. The first paper contribution to this research
field is represented by a thorough investigation of the applicability
of state-of-the-art audio features in the domain of environmental
sound recognition. Additionally, a set of novel features obtained by
combining the basic parameters is introduced. The quality of the
features investigated is evaluated by a HMM-based classifier to which
a great interest was done. In fact, we propose to use a Multi-Style
training system based on HMMs: one recognizer is trained on a
database including different levels of background noises and is used
as a universal recognizer for every environment. In order to enhance
the system robustness by reducing the environmental variability, we
explore different adaptation algorithms including Maximum Likelihood
Linear Regression (MLLR), Maximum A Posteriori (MAP)
and the MAP/MLLR algorithm that combines MAP and MLLR.
Experimental evaluation shows that a rather good recognition rate
can be reached, even under important noise degradation conditions
when the system is fed by the convenient set of features.