2010
1
1
1
0
1

Moving Objects Tracking Using Statistical Models
http://jacr.iausari.ac.ir/article_631388.html
1
Object detection plays an important role in successfulness of a wide range of applications that involve images as input data. In this paper we have presented a new approach for background modeling by nonconsecutive frames differencing. Direction and velocity of moving objects have been extracted in order to get an appropriate sequence of frames to perform frame subtraction. Stationary parts of background are extracted from differenced frames and joined as patches to complete the background model. There is also a special stage to handle changing regions of dynamic scenes. During the detection phase, the modeled background is updated for every new frame. Since it's not necessary to estimate each pixel gray value like the most common statistical methods, modeling process is not timeconsuming. Different experiments show successful results even for challenging phenomena like environmental changes.
0

1
9


Sara
Sharifzadeh
Department of Microelectronics and Electronic Systems Universitat AutÃ²noma de Barcelona (UAB) 08193, Bellaterra, Spain
Iran
sarasharifzade@yahoo.com
Object tracking
Background modeling
Frame subtraction
1

An Adaptive Segmentation Method Using Fractal Dimension and Wavelet Transform
http://jacr.iausari.ac.ir/article_631389.html
1
In analyzing a signal, especially a nonstationary signal, it is often necessary the desired signal to be segmented into small epochs. Segmentation can be performed by splitting the signal at time instances where signal amplitude or frequency change. In this paper, the signal is initially decomposed into signals with different frequency bands using wavelet transform. Then, fractal dimension of the decomposed signal is computed and used as a feature for adaptively segmenting the signal. Any changes on the signal amplitude or frequency are reflected on the fractal dimension of the signal. The proposed method was applied on a synthetic signal and real EEG to evaluate its performance on segmenting nonstationary signals. The results indicate that the proposed approach outperforms the existing method in signal segmentation.
0

11
18


S.M.
Anisheh
Department of Computer and Electrical Engineering
Babol Noushirvani University of Technology,
P.O.Box 47144, Babol, Iran
Iran
s_m_anisheh@yahoo.com
Segmentation
Nonstationary
Wavelet transform
fractal dimension
1

Translation Invariant Approach for Measuring Similarity of Signals
http://jacr.iausari.ac.ir/article_631390.html
1
In many signal processing applications, an appropriate measure to compare two signals plays a fundamental role in both implementing the algorithm and evaluating its performance. Several techniques have been introduced in literature as similarity measures. However, the existing measures are often either impractical for some applications or they have unsatisfactory results in some other applications. This problem becomes more evident when signals involve translations in amplitude and time. This paper presents a new one dimensional similarity measure to compare two signals. The proposed measure accepts transformations like timeshift, amplitudescale, amplitudeshift, and phase delay in measuring the similarity. The results in this paper indicate that the proposed approach overcomes exiting techniques in measuring similarity among different signals.
0

19
27


A.
Darvishi
Department of Computer and Electrical Engineering
Babol Noushirvani University of Technology,
P.O.Box 47144, Babol, Iran
Iran
alidarvishi.nit@gmail.com
Similarity measure
Time shift
Amplitude shift
Amplitude scale
Phase delay
1

Transforming Fuzzy State Diagram to Fuzzy Petri net
http://jacr.iausari.ac.ir/article_631391.html
1
UML is known as one of the most common methods in software engineering. Since this language is semiformal, many researches and efforts have been performed to transform this language into formal methods including Petri nets. Thus, the operation of verification and validation of the qualitative and nonfunctional parameters could be achieved with more ability. Since the majority of the real world information is uncertain, therefore fuzzy UML diagram has been extensively used by system analyzers. This paper is an attempt to transform state diagrams created in fuzzy UML into fuzzy Petri net, so that the verification and performance evaluation operation could be performed formally, rather than exact visual analysis.
0

29
44


H.
Motameni
Department of Computer Engineering
Islamic Azad University, Sari Branch,
Sari, Iran
Iran
eng.hamed.sa@gmail.com


I.
Daneshfar
Department of Computer Engineering
University of Science and Technology of Mazandaran Babol, Iran
Iran
dopofa@yahoo.com


J.
Bakhshi
Department of Computer Engineering
University of Science and Technology of Mazandaran Babol, Iran
Iran
javadbakhshi@yahoo.com


H.
Nematzadeh
Department of Computer Science & Information System, University Technology Malaysia
Iran
hn_61@yahoo.com
Software engineering
Fuzzy UML
Fuzzy Petri net
Fuzzy state diagrams
1

A High Performance Feedback Active Noise Control System
http://jacr.iausari.ac.ir/article_631392.html
1
In many active noise control (ANC) applications, an online secondary path modelling method that uses a white noise as a training signal is required. This paper proposes a new feedback ANC system. Here we modified both the FxLMS and the VSSLMS algorithms to raised noise attenuation and modelling accuracy for the overall system. The proposed algorithm stops injection of the white noise at the optimum point and reactivate the injection during the operation, if needed, to maintain performance of the system. Preventing continuous injection of the white noise increases the performance of the proposed method significantly and makes it more desirable for practical ANC systems. Computer simulation results shown in this paper indicate effectiveness of the proposed method.
0

45
52


Pooya
Davari
Department of Computer and Information Technology,
Shahrood University of Technology,
Shahrood, Iran
Iran
pooya.davari@gmail.com
Active noise control
Secondary path
Feedback ANC
White noise
1

A Novel Noise Reduction Method Based on Subspace Division
http://jacr.iausari.ac.ir/article_631393.html
1
This article presents a new subspacebased technique for reducing the noise of signals in timeseries. In the proposed approach, the signal is initially represented as a data matrix. Then using Singular Value Decomposition (SVD), noisy data matrix is divided into signal subspace and noise subspace. In this subspace division, each derivative of the singular values with respect to rank order is used to reduce the effect of space intersections on altering the structure of important information in the signal. On the other hand, since singular vectors are the span bases of the matrix, reducing the effect of noise from the singular vectors and using them in reproducing the matrix, enhances the information embedded in the matrix. The proposed technique utilizes the SavitzkyGolay lowpass filter for noise attenuation from the singular vectors. The enhanced matrix is finally transformed to a timeseries signal. The obtained results in this research indicate that the proposed method excels the other existing timedomain approaches in noise reduction.
0

53
59


Amin
Zehtabian
Department of Computer and Electrical Engineering
Babol Noshirvani University of Technology,
Babol, Iran
Iran
amin_zehtabian@yahoo.com


Behzad
Zehtabian
Department of Computer and Electrical Engineering
Babol Noshirvani University of Technology,
Babol, Iran
Iran
behzadz@ymail.com
Time Series
Noise reduction
Singular valve
Singular vector
1

Mobile Robot Navigation Error Handling Using an Extended Kalman Filter
http://jacr.iausari.ac.ir/article_631394.html
1
Obviously navigation is one of the most complicated issues in mobile robots. Intelligent algorithms are often used for error handling in robot navigation. This Paper deals with the problem of Inertial Measurement Unit (IMU) error handling by using Extended Kalman Filter (EKF) as an Expert Algorithms. Our focus is put on the field of mobile robot navigation in the 2D environments. The main challenge in this issue is to keep track of the position and orientation within a global frame of reference using a variety of sensors providing DeadReckoned Odometry, Inertial and Absolute data.
0

61
75


Aydin
Saderzadeh
Mechatronics Research Lab
Faculty of Electronic, Computer Engineering& IT
Islamic Azad university, Qazvin Branch
Qazvin, Iran
Iran
aydin_saderzadeh@yahoo.com
Inertial navigation system
Extended Kalman Filter
Error handling
1

Research of Blind Signals Separation with Genetic Algorithm and Particle Swarm Optimization Based on Mutual Information
http://jacr.iausari.ac.ir/article_631395.html
1
Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evaluate and compare the performance of these methods, we have focused on separation of noisy and noiseless sources. Simulations results demonstrate that proposed method for employing fitness function have rapid convergence, simplicity and a more favorable signal to noise ratio for separation tasks based on particle swarm optimization and continuous genetic algorithm than binary genetic algorithm. Also, particle swarm optimization enjoys shorter computation time than the other two algorithms for solving these optimization problems for multiple sources.
0

77
88
Blind source separation
mutual information
high order statistics
Continuous and Binary genetic algorithm
Particle Swarm Optimization