Abstract: Nothing that an effective cure for infertility happens
when we can find a unique solution, a great deal of study has been
done in this field and this is a hot research subject for to days study.
So we could analyze the men-s seaman and find out about fertility
and infertility and from this find a true cure for this, since this will be
a non invasive and low risk procedure, it will be greatly welcomed.
In this research, the procedure has been based on few Algorithms
enhancement and segmentation of images which has been done on
the images taken from microscope in different fertility institution and
have obtained a suitable result from the computer images which in
turn help us to distinguish these sperms from fluids and its
surroundings.
Abstract: Although so far, many methods for ranking fuzzy numbers
have been discussed broadly, most of them contained some shortcomings,
such as requirement of complicated calculations, inconsistency
with human intuition and indiscrimination. The motivation of
this study is to develop a model for ranking fuzzy numbers based
on the lexicographical ordering which provides decision-makers with
a simple and efficient algorithm to generate an ordering founded on
a precedence. The main emphasis here is put on the ease of use
and reliability. The effectiveness of the proposed method is finally
demonstrated by including a comprehensive comparing different
ranking methods with the present one.
Abstract: This research proposes an algorithm for the simulation
of time-periodic unsteady problems via the solution unsteady Euler
and Navier-Stokes equations. This algorithm which is called Time
Spectral method uses a Fourier representation in time and hence
solve for the periodic state directly without resolving transients
(which consume most of the resources in a time-accurate scheme).
Mathematical tools used here are discrete Fourier transformations. It
has shown tremendous potential for reducing the computational cost
compared to conventional time-accurate methods, by enforcing
periodicity and using Fourier representation in time, leading to
spectral accuracy. The accuracy and efficiency of this technique is
verified by Euler and Navier-Stokes calculations for pitching airfoils.
Because of flow turbulence nature, Baldwin-Lomax turbulence
model has been used at viscous flow analysis. The results presented
by the Time Spectral method are compared with experimental data. It
has shown tremendous potential for reducing the computational cost
compared to the conventional time-accurate methods, by enforcing
periodicity and using Fourier representation in time, leading to
spectral accuracy, because results verify the small number of time
intervals per pitching cycle required to capture the flow physics.
Abstract: A neuron can emit spikes in an irregular time basis and by averaging over a certain time window one would ignore a lot of information. It is known that in the context of fast information processing there is no sufficient time to sample an average firing rate of the spiking neurons. The present work shows that the spiking neurons are capable of computing the radial basis functions by storing the relevant information in the neurons' delays. One of the fundamental findings of the this research also is that when using overlapping receptive fields to encode the data patterns it increases the network-s clustering capacity. The clustering algorithm that is discussed here is interesting from computer science and neuroscience point of view as well as from a perspective.
Abstract: In this article we explore the application of a formal
proof system to verification problems in cryptography. Cryptographic
properties concerning correctness or security of some cryptographic
algorithms are of great interest. Beside some basic lemmata, we
explore an implementation of a complex function that is used in
cryptography. More precisely, we describe formal properties of this
implementation that we computer prove. We describe formalized
probability distributions (σ-algebras, probability spaces and conditional
probabilities). These are given in the formal language of the
formal proof system Isabelle/HOL. Moreover, we computer prove
Bayes- Formula. Besides, we describe an application of the presented
formalized probability distributions to cryptography. Furthermore,
this article shows that computer proofs of complex cryptographic
functions are possible by presenting an implementation of the Miller-
Rabin primality test that admits formal verification. Our achievements
are a step towards computer verification of cryptographic primitives.
They describe a basis for computer verification in cryptography.
Computer verification can be applied to further problems in cryptographic
research, if the corresponding basic mathematical knowledge
is available in a database.
Abstract: Recently, information security has become a key issue
in information technology as the number of computer security
breaches are exposed to an increasing number of security threats. A
variety of intrusion detection systems (IDS) have been employed for
protecting computers and networks from malicious network-based or
host-based attacks by using traditional statistical methods to new data
mining approaches in last decades. However, today's commercially
available intrusion detection systems are signature-based that are not
capable of detecting unknown attacks. In this paper, we present a
new learning algorithm for anomaly based network intrusion
detection system using decision tree algorithm that distinguishes
attacks from normal behaviors and identifies different types of
intrusions. Experimental results on the KDD99 benchmark network
intrusion detection dataset demonstrate that the proposed learning
algorithm achieved 98% detection rate (DR) in comparison with
other existing methods.
Abstract: Transmission network expansion planning (TNEP) is
a basic part of power system planning that determines where, when
and how many new transmission lines should be added to the
network. Up till now, various methods have been presented to solve
the static transmission network expansion planning (STNEP)
problem. But in all of these methods, transmission expansion
planning considering network adequacy restriction has not been
investigated. Thus, in this paper, STNEP problem is being studied
considering network adequacy restriction using discrete particle
swarm optimization (DPSO) algorithm. The goal of this paper is
obtaining a configuration for network expansion with lowest
expansion cost and a specific adequacy. The proposed idea has been
tested on the Garvers network and compared with the decimal
codification genetic algorithm (DCGA). The results show that the
network will possess maximum efficiency economically. Also, it is
shown that precision and convergence speed of the proposed DPSO
based method for the solution of the STNEP problem is more than
DCGA approach.
Abstract: This paper describes the design of a real-time audiorange
digital oscilloscope and its implementation in 90nm CMOS
FPGA platform. The design consists of sample and hold circuits,
A/D conversion, audio and video processing, on-chip RAM, clock
generation and control logic. The design of internal blocks and
modules in 90nm devices in an FPGA is elaborated. Also the key
features and their implementation algorithms are presented.
Finally, the timing waveforms and simulation results are put
forward.
Abstract: This paper presents a novel iris recognition system
using 1D log polar Gabor wavelet and Euler numbers. 1D log polar
Gabor wavelet is used to extract the textural features, and Euler
numbers are used to extract topological features of the iris. The
proposed decision strategy uses these features to authenticate an
individual-s identity while maintaining a low false rejection rate. The
algorithm was tested on CASIA iris image database and found to
perform better than existing approaches with an overall accuracy of
99.93%.
Abstract: The Combination of path planning and path following is the main purpose of this paper. This paper describes the developed practical approach to motion control of the MRL small size robots. An intelligent controller is applied to control omni-directional robots motion in simulation and real environment respectively. The Brain Emotional Learning Based Intelligent Controller (BELBIC), based on LQR control is adopted for the omni-directional robots. The contribution of BELBIC in improving the control system performance is shown as application of the emotional learning in a real world problem. Optimizing of the control effort can be achieved in this method too. Next the implicit communication method is used to determine the high level strategies and coordination of the robots. Some simple rules besides using the environment as a memory to improve the coordination between agents make the robots' decision making system. With this simple algorithm our team manifests a desirable cooperation.
Abstract: In this work, we consider an application of neural networks in LD converter. Application of this approach assumes a reliable prediction of steel temperature and reduces a reblow ratio in steel work. It has been applied a conventional model to charge calculation, the obtained results by this technique are not always good, this is due to the process complexity. Difficulties are mainly generated by the noisy measurement and the process non linearities. Artificial Neural Networks (ANNs) have become a powerful tool for these complex applications. It is used a backpropagation algorithm to learn the neural nets. (ANNs) is used to predict the steel bath temperature in oxygen converter process for the end condition. This model has 11 inputs process variables and one output. The model was tested in steel work, the obtained results by neural approach are better than the conventional model.
Abstract: Nowadays, with the emerging of the new applications
like robot control in image processing, artificial vision for visual
servoing is a rapidly growing discipline and Human-machine
interaction plays a significant role for controlling the robot. This
paper presents a new algorithm based on spatio-temporal volumes for
visual servoing aims to control robots. In this algorithm, after
applying necessary pre-processing on video frames, a spatio-temporal
volume is constructed for each gesture and feature vector is extracted.
These volumes are then analyzed for matching in two consecutive
stages. For hand gesture recognition and classification we tested
different classifiers including k-Nearest neighbor, learning vector
quantization and back propagation neural networks. We tested the
proposed algorithm with the collected data set and results showed the
correct gesture recognition rate of 99.58 percent. We also tested the
algorithm with noisy images and algorithm showed the correct
recognition rate of 97.92 percent in noisy images.
Abstract: In this work, we present for the first time in our perception an efficient digital watermarking scheme for mpeg audio layer 3 files that operates directly in the compressed data domain, while manipulating the time and subband/channel domain. In addition, it does not need the original signal to detect the watermark. Our scheme was implemented taking special care for the efficient usage of the two limited resources of computer systems: time and space. It offers to the industrial user the capability of watermark embedding and detection in time immediately comparable to the real music time of the original audio file that depends on the mpeg compression, while the end user/audience does not face any artifacts or delays hearing the watermarked audio file. Furthermore, it overcomes the disadvantage of algorithms operating in the PCMData domain to be vulnerable to compression/recompression attacks, as it places the watermark in the scale factors domain and not in the digitized sound audio data. The strength of our scheme, that allows it to be used with success in both authentication and copyright protection, relies on the fact that it gives to the users the enhanced capability their ownership of the audio file not to be accomplished simply by detecting the bit pattern that comprises the watermark itself, but by showing that the legal owner knows a hard to compute property of the watermark.
Abstract: This paper presents an efficient method of obtaining a straight-line motion in the tool configuration space using an articulated robot between two specified points. The simulation results & the implementation results show the effectiveness of the method.
Abstract: Several works regarding facial recognition have dealt with methods which identify isolated characteristics of the face or with templates which encompass several regions of it. In this paper a new technique which approaches the problem holistically dispensing with the need to identify geometrical characteristics or regions of the face is introduced. The characterization of a face is achieved by randomly sampling selected attributes of the pixels of its image. From this information we construct a set of data, which correspond to the values of low frequencies, gradient, entropy and another several characteristics of pixel of the image. Generating a set of “p" variables. The multivariate data set with different polynomials minimizing the data fitness error in the minimax sense (L∞ - Norm) is approximated. With the use of a Genetic Algorithm (GA) it is able to circumvent the problem of dimensionality inherent to higher degree polynomial approximations. The GA yields the degree and values of a set of coefficients of the polynomials approximating of the image of a face. By finding a family of characteristic polynomials from several variables (pixel characteristics) for each face (say Fi ) in the data base through a resampling process the system in use, is trained. A face (say F ) is recognized by finding its characteristic polynomials and using an AdaBoost Classifier from F -s polynomials to each of the Fi -s polynomials. The winner is the polynomial family closer to F -s corresponding to target face in data base.
Abstract: In the recent works related with mixture discriminant
analysis (MDA), expectation and maximization (EM) algorithm is
used to estimate parameters of Gaussian mixtures. But, initial values
of EM algorithm affect the final parameters- estimates. Also, when
EM algorithm is applied two times, for the same data set, it can be
give different results for the estimate of parameters and this affect the
classification accuracy of MDA. Forthcoming this problem, we use
Self Organizing Mixture Network (SOMN) algorithm to estimate
parameters of Gaussians mixtures in MDA that SOMN is more robust
when random the initial values of the parameters are used [5]. We
show effectiveness of this method on popular simulated waveform
datasets and real glass data set.
Abstract: We address the balancing problem of transfer lines in
this paper to find the optimal line balancing that minimizes the nonproductive
time. We focus on the tool change time and face
orientation change time both of which influence the makespane. We
consider machine capacity limitations and technological constraints
associated with the manufacturing process of auto cylinder heads.
The problem is represented by a mixed integer programming model
that aims at distributing the design features to workstations and
sequencing the machining processes at a minimum non-productive
time. The proposed model is solved by an algorithm established using
linearization schemes and Benders- decomposition approach. The
experiments show the efficiency of the algorithm in reaching the
exact solution of small and medium problem instances at reasonable
time.
Abstract: Clustering algorithms help to understand the hidden
information present in datasets. A dataset may contain intrinsic and
nested clusters, the detection of which is of utmost importance. This
paper presents a Distributed Grid-based Density Clustering algorithm
capable of identifying arbitrary shaped embedded clusters as well as
multi-density clusters over large spatial datasets. For handling
massive datasets, we implemented our method using a 'sharednothing'
architecture where multiple computers are interconnected
over a network. Experimental results are reported to establish the
superiority of the technique in terms of scale-up, speedup as well as
cluster quality.
Abstract: Optimization is often a critical issue for most system
design problems. Evolutionary Algorithms are population-based,
stochastic search techniques, widely used as efficient global
optimizers. However, finding optimal solution to complex high
dimensional, multimodal problems often require highly
computationally expensive function evaluations and hence are
practically prohibitive. The Dynamic Approximate Fitness based
Hybrid EA (DAFHEA) model presented in our earlier work [14]
reduced computation time by controlled use of meta-models to
partially replace the actual function evaluation by approximate
function evaluation. However, the underlying assumption in
DAFHEA is that the training samples for the meta-model are
generated from a single uniform model. Situations like model
formation involving variable input dimensions and noisy data
certainly can not be covered by this assumption. In this paper we
present an enhanced version of DAFHEA that incorporates a
multiple-model based learning approach for the SVM approximator.
DAFHEA-II (the enhanced version of the DAFHEA framework) also
overcomes the high computational expense involved with additional
clustering requirements of the original DAFHEA framework. The
proposed framework has been tested on several benchmark functions
and the empirical results illustrate the advantages of the proposed
technique.
Abstract: Load balancing is the process of improving the
performance of a parallel and distributed system through a
redistribution of load among the processors [1] [5]. In this paper we
present the performance analysis of various load balancing
algorithms based on different parameters, considering two typical
load balancing approaches static and dynamic. The analysis indicates
that static and dynamic both types of algorithm can have
advancements as well as weaknesses over each other. Deciding type
of algorithm to be implemented will be based on type of parallel
applications to solve. The main purpose of this paper is to help in
design of new algorithms in future by studying the behavior of
various existing algorithms.