Abstract: In this paper, a new approach for target recognition based on the Empirical mode decomposition (EMD) algorithm of Huang etal. [11] and the energy tracking operator of Teager [13]-[14] is introduced. The conjunction of these two methods is called Teager-Huang analysis. This approach is well suited for nonstationary signals analysis. The impulse response (IR) of target is first band pass filtered into subsignals (components) called Intrinsic mode functions (IMFs) with well defined Instantaneous frequency (IF) and Instantaneous amplitude (IA). Each IMF is a zero-mean AM-FM component. In second step, the energy of each IMF is tracked using the Teager energy operator (TEO). IF and IA, useful to describe the time-varying characteristics of the signal, are estimated using the Energy separation algorithm (ESA) algorithm of Maragos et al .[16]-[17]. In third step, a set of features such as skewness and kurtosis are extracted from the IF, IA and IMF energy functions. The Teager-Huang analysis is tested on set of synthetic IRs of Sonar targets with different physical characteristics (density, velocity, shape,? ). PCA is first applied to features to discriminate between manufactured and natural targets. The manufactured patterns are classified into spheres and cylinders. One hundred percent of correct recognition is achieved with twenty three echoes where sixteen IRs, used for training, are free noise and seven IRs, used for testing phase, are corrupted with white Gaussian noise.
Abstract: In this work, we are interested in developing a speech denoising tool by using a discrete wavelet packet transform (DWPT). This speech denoising tool will be employed for applications of recognition, coding and synthesis. For noise reduction, instead of applying the classical thresholding technique, some wavelet packet nodes are set to zero and the others are thresholded. To estimate the non stationary noise level, we employ the spectral entropy. A comparison of our proposed technique to classical denoising methods based on thresholding and spectral subtraction is made in order to evaluate our approach. The experimental implementation uses speech signals corrupted by two sorts of noise, white and Volvo noises. The obtained results from listening tests show that our proposed technique is better than spectral subtraction. The obtained results from SNR computation show the superiority of our technique when compared to the classical thresholding method using the modified hard thresholding function based on u-law algorithm.
Abstract: This Paper proposes a new facial feature extraction approach, Wash-Hadamard Transform (WHT). This approach is based on correlation between local pixels of the face image. Its primary advantage is the simplicity of its computation. The paper compares the proposed approach, WHT, which was traditionally used in data compression with two other known approaches: the Principal Component Analysis (PCA) and the Discrete Cosine Transform (DCT) using the face database of Olivetti Research Laboratory (ORL). In spite of its simple computation, the proposed algorithm (WHT) gave very close results to those obtained by the PCA and DCT. This paper initiates the research into WHT and the family of frequency transforms and examines their suitability for feature extraction in face recognition applications.
Abstract: Modular multiplication is the basic operation
in most public key cryptosystems, such as RSA, DSA, ECC,
and DH key exchange. Unfortunately, very large operands
(in order of 1024 or 2048 bits) must be used to provide
sufficient security strength. The use of such big numbers
dramatically slows down the whole cipher system, especially
when running on embedded processors.
So far, customized hardware accelerators - developed on
FPGAs or ASICs - were the best choice for accelerating
modular multiplication in embedded environments. On the
other hand, many algorithms have been developed to speed
up such operations. Examples are the Montgomery modular
multiplication and the interleaved modular multiplication
algorithms. Combining both customized hardware with
an efficient algorithm is expected to provide a much faster
cipher system.
This paper introduces an enhanced architecture for computing
the modular multiplication of two large numbers X
and Y modulo a given modulus M. The proposed design is
compared with three previous architectures depending on
carry save adders and look up tables. Look up tables should
be loaded with a set of pre-computed values. Our proposed
architecture uses the same carry save addition, but replaces
both look up tables and pre-computations with an enhanced
version of sign detection techniques. The proposed architecture
supports higher frequencies than other architectures.
It also has a better overall absolute time for a single operation.
Abstract: The purpose of this paper is to demonstrate the ability
of a genetic programming (GP) algorithm to evolve a team of data
classification models. The GP algorithm used in this work is
“multigene" in nature, i.e. there are multiple tree structures (genes)
that are used to represent team members. Each team member assigns
a data sample to one of a fixed set of output classes. A majority vote,
determined using the mode (highest occurrence) of classes predicted
by the individual genes, is used to determine the final class
prediction. The algorithm is tested on a binary classification problem.
For the case study investigated, compact classification models are
obtained with comparable accuracy to alternative approaches.
Abstract: System is using multiple processors for computing and information processing, is increasing rapidly speed operation of these systems compared with single processor systems, very significant impact on system performance is increased .important differences to yield a single multi-processor cpu, the scheduling policies, to reduce the implementation time of all processes. Notwithstanding the famous algorithms such as SPT, LPT, LSPT and RLPT for scheduling and there, but none led to the answer are not optimal.In this paper scheduling using genetic algorithms and innovative way to finish the whole process faster that we do and the result compared with three algorithms we mentioned.
Abstract: This paper compares the heuristic Global Search
Techniques; Genetic Algorithm, Particle Swarm Optimization,
Simulated Annealing, Generalized Pattern Search, genetic algorithm
hybridized with Nelder–Mead and Generalized pattern search
technique for tuning of fuzzy PID controller for Puma 560. Since the
actual control is in joint space ,inverse kinematics is used to generate
various joint angles correspoding to desired cartesian space
trajectory. Efficient dynamics and kinematics are modeled on Matlab
which takes very less simulation time. Performances of all the tuning
methods with and without disturbance are compared in terms of ITSE
in joint space and ISE in cartesian space for spiral trajectory tracking.
Genetic Algorithm hybridized with Generalized Pattern Search is
showing best performance.
Abstract: Optical network uses a tool for routing which is called
Latin router. These routers use particular algorithms for routing. In this paper, we present algorithm for configuration of optical network that is optimized regarding previous algorithm. We show that by decreasing the number of hops for source-destination in lightpath number of satisfied request is less. Also we had shown that more than
single-hop lightpath relating single-hop lightpath is better.
Abstract: An optimal solution for a large number of constraint
satisfaction problems can be found using the technique of
substitution and elimination of variables analogous to the technique
that is used to solve systems of equations. A decision function
f(A)=max(A2) is used to determine which variables to eliminate. The
algorithm can be expressed in six lines and is remarkable in both its
simplicity and its ability to find an optimal solution. However it is
inefficient in that it needs to square the updated A matrix after each
variable elimination. To overcome this inefficiency the algorithm is
analyzed and it is shown that the A matrix only needs to be squared
once at the first step of the algorithm and then incrementally updated
for subsequent steps, resulting in significant improvement and an
algorithm complexity of O(n3).
Abstract: In this research work, a novel parallel manipulator
with high positioning and orienting rate is introduced. This
mechanism has two rotational and one translational degree of
freedom. Kinematics and Jacobian analysis are investigated.
Moreover, workspace analysis and optimization has been performed
by using genetic algorithm toolbox in Matlab software. Because of
decreasing moving elements, it is expected much more better
dynamic performance with respect to other counterpart mechanisms
with the same degrees of freedom. In addition, using couple of
cylindrical and revolute joints increased mechanism ability to have
more extended workspace.
Abstract: In this paper, we propose a new algorithm for joint time-delay and direction-of-arrival (DOA) estimation, here called two-dimensional code acquisition, in an asynchronous directsequence code-division multiple-access (DS-CDMA) array system. This algorithm depends on eigenvector-eigenvalue decomposition of sample correlation matrix, and requires to know desired user-s training sequence. The performance of the algorithm is analyzed both analytically and numerically in uncorrelated and coherent multipath environment. Numerical examples show that the algorithm is robust with unknown number of coherent signals.
Abstract: This paper presents a text clustering system developed based on a k-means type subspace clustering algorithm to cluster large, high dimensional and sparse text data. In this algorithm, a new step is added in the k-means clustering process to automatically calculate the weights of keywords in each cluster so that the important words of a cluster can be identified by the weight values. For understanding and interpretation of clustering results, a few keywords that can best represent the semantic topic are extracted from each cluster. Two methods are used to extract the representative words. The candidate words are first selected according to their weights calculated by our new algorithm. Then, the candidates are fed to the WordNet to identify the set of noun words and consolidate the synonymy and hyponymy words. Experimental results have shown that the clustering algorithm is superior to the other subspace clustering algorithms, such as PROCLUS and HARP and kmeans type algorithm, e.g., Bisecting-KMeans. Furthermore, the word extraction method is effective in selection of the words to represent the topics of the clusters.
Abstract: Segmentation and quantification of stenosis is an
important task in assessing coronary artery disease. One of the main
challenges is measuring the real diameter of curved vessels.
Moreover, uncertainty in segmentation of different tissues in the
narrow vessel is an important issue that affects accuracy. This paper
proposes an algorithm to extract coronary arteries and measure the
degree of stenosis. Markovian fuzzy clustering method is applied to
model uncertainty arises from partial volume effect problem. The
algorithm employs: segmentation, centreline extraction, estimation of
orthogonal plane to centreline, measurement of the degree of
stenosis. To evaluate the accuracy and reproducibility, the approach
has been applied to a vascular phantom and the results are compared
with real diameter. The results of 10 patient datasets have been
visually judged by a qualified radiologist. The results reveal the
superiority of the proposed method compared to the Conventional
thresholding Method (CTM) on both datasets.
Abstract: A new approach for the improvement of coding gain
in channel coding using Advanced Encryption Standard (AES) and
Maximum A Posteriori (MAP) algorithm is proposed. This new
approach uses the avalanche effect of block cipher algorithm AES
and soft output values of MAP decoding algorithm. The performance
of proposed approach is evaluated in the presence of Additive White
Gaussian Noise (AWGN). For the verification of proposed approach,
computer simulation results are included.
Abstract: Selective harmonic elimination-pulse width modulation techniques offer a tight control of the harmonic spectrum of a given voltage waveform generated by a power electronic converter along with a low number of switching transitions. Traditional optimization methods suffer from various drawbacks, such as prolonged and tedious computational steps and convergence to local optima; thus, the more the number of harmonics to be eliminated, the larger the computational complexity and time. This paper presents a novel method for output voltage harmonic elimination and voltage control of PWM AC/AC voltage converters using the principle of hybrid Real-Coded Genetic Algorithm-Pattern Search (RGA-PS) method. RGA is the primary optimizer exploiting its global search capabilities, PS is then employed to fine tune the best solution provided by RGA in each evolution. The proposed method enables linear control of the fundamental component of the output voltage and complete elimination of its harmonic contents up to a specified order. Theoretical studies have been carried out to show the effectiveness and robustness of the proposed method of selective harmonic elimination. Theoretical results are validated through simulation studies using PSIM software package.
Abstract: This paper presents the application of an enhanced
Particle Swarm Optimization (EPSO) combined with Gaussian
Mutation (GM) for solving the Dynamic Economic Dispatch (DED)
problem considering the operating constraints of generators. The
EPSO consists of the standard PSO and a modified heuristic search
approaches. Namely, the ability of the traditional PSO is enhanced
by applying the modified heuristic search approach to prevent the
solutions from violating the constraints. In addition, Gaussian
Mutation is aimed at increasing the diversity of global search, whilst
it also prevents being trapped in suboptimal points during search. To
illustrate its efficiency and effectiveness, the developed EPSO-GM
approach is tested on the 3-unit and 10-unit 24-hour systems
considering valve-point effect. From the experimental results, it can
be concluded that the proposed EPSO-GM provides, the accurate
solution, the efficiency, and the feature of robust computation
compared with other algorithms under consideration.
Abstract: Performance control law is studied for an
interconnected fractional nonlinear system. Applying a backstepping
algorithm, a backstepping sliding mode controller (BSMC) is
developed for fractional nonlinear system. To improve control law
performance, BSMC is coupled to an adaptive sliding mode observer
have a filtered error as a sliding surface. The both architecture
performance is studied throughout the inverted pendulum mounted on
a cart. Simulation result show that the BSMC coupled to an adaptive
sliding mode observer have stable control law and eligible control
amplitude than the BSMC.
Abstract: The article presents a new method for detection of
artificial objects and materials from images of the environmental
(non-urban) terrain. Our approach uses the hue and saturation (or Cb
and Cr) components of the image as the input to the segmentation
module that uses the mean shift method. The clusters obtained as the
output of this stage have been processed by the decision-making
module in order to find the regions of the image with the significant
possibility of representing human. Although this method will detect
various non-natural objects, it is primarily intended and optimized for
detection of humans; i.e. for search and rescue purposes in non-urban
terrain where, in normal circumstances, non-natural objects shouldn-t
be present. Real world images are used for the evaluation of the
method.
Abstract: This study introduces a new method for detecting,
sorting, and localizing spikes from multiunit EEG recordings. The
method combines the wavelet transform, which localizes distinctive
spike features, with Super-Paramagnetic Clustering (SPC) algorithm,
which allows automatic classification of the data without assumptions
such as low variance or Gaussian distributions. Moreover, the method
is capable of setting amplitude thresholds for spike detection. The
method makes use of several real EEG data sets, and accordingly the
spikes are detected, clustered and their times were detected.
Abstract: The goal of data mining algorithms is to discover
useful information embedded in large databases. One of the most
important data mining problems is discovery of frequently occurring
patterns in sequential data. In a multidimensional sequence each
event depends on more than one dimension. The search space is quite
large and the serial algorithms are not scalable for very large
datasets. To address this, it is necessary to study scalable parallel
implementations of sequence mining algorithms.
In this paper, we present a model for multidimensional sequence
and describe a parallel algorithm based on data parallelism.
Simulation experiments show good load balancing and scalable and
acceptable speedup over different processors and problem sizes and
demonstrate that our approach can works efficiently in a real parallel
computing environment.