Ataollah Ebrahimzadeh

Babol Noshirvani University of Technology, Barfrush, Māzandarān, Iran

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Publications (54)33.05 Total impact

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    ABSTRACT: In this paper, an energy-efficient scheme is proposed for cooperative spectrum sensing in cognitive sensor networks. In our scheme, we introduce a technique to select the sensing nodes and to set energy detection threshold so that energy saving can be accomplished in the nodes. Our objective is to minimize the energy consumed in distributed sensing subject to constraints on global probability of detection and probability of false alarm by determining the detection threshold and selection of the sensing nodes. The energy detector is applied to detect the primary-user activity for the sake of simplicity. At first, it is assumed that the instantaneous signal-to-noise ratio (SNR) for each node is known. Then, the optimal conditions are obtained, and a closed-form equation is expressed to determine the priority of nodes for spectrum sensing, as well as the optimum detection threshold. This problem is also solved when the average SNRs of sensors are available according to real situations. To achieve more energy savings, the problem of joint sensing node selection, detection threshold, and decision node selection is analyzed, and an efficient solution is extracted based on the convex optimization framework. Simulation results show that the proposed algorithms lead to significant energy savings in cognitive sensor networks.
    IEEE Transactions on Vehicular Technology 04/2015; 64(4):1565-1577. DOI:10.1109/TVT.2014.2331681 · 2.64 Impact Factor
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    Milad Azarbad, Hamed Azami, Saeid Sanei, Ataollah Ebrahimzadeh
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    ABSTRACT: Global positioning system (GPS) is the most widely used military and commercial positioning tool for real-time navigation and location. Geometric dilution of precision (GDOP) stands as a relevant measure of positioning accuracy and consequently, the performance quality of the GPS positioning algorithm. Since the calculation of GPS GDOP has a time and power burden that involves complicated transformation and inversion of measurement matrices, in this paper we propose hybrid intelligent methods, namely adaptive neuro-fuzzy inference system (ANFIS), improved ANFIS, and radial basis function (RBF), for GPS GDOP classification. Through investigation it is verified that the ANFIS is a high performance and valuable classifier. In the ANFIS training, the radius vector has very important role for its recognition accuracy. Therefore, in the optimization module, bee algorithm (BA) is proposed for finding the optimum vector of radius. In order to improve the performance of the proposed method, a new improvement for the BA is used. In addition, to enhance the accuracy of the method, principal component analysis (PCA) is utilized as a pre-processing step. Experimental results clearly indicate that the proposed intelligent methods have high classification accuracy rates comparing with conventional ones.
    Applied Soft Computing 10/2014; 25:285–292. DOI:10.1016/j.asoc.2014.09.022 · 2.68 Impact Factor
  • A. Ebrahimzadeh, B. Shakiba, A. Khazaee
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    ABSTRACT: Automatic detection of electrocardiogram (ECG) signals is very important for clinical diagnosis of heart disease. This paper investigates the design of a three-step system for recognition of the five types of ECG beat. In the first step, stationary wavelet transform (SWT) is used for noise reduction of the electrocardiogram (ECG) signals. Feature extraction module extracts higher order statistics of ECG signals in combination with three timing interval features. Then hybrid Bees algorithm-radial basis function (RBF_BA) technique is used to classify the five types of electrocardiogram (ECG) beat. The suggested method can accurately classify and discriminate normal (Normal) and abnormal heartbeats. Abnormal heartbeats include left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contractions (APC) and premature ventricular contractions (PVC). Finally, the classification capability of five different classes of ECG signals is attained over eight files from the MIT/BIH arrhythmia database. Simulation results show that classification accuracy of 95.79% for the first dataset (4000 beats) and an overall accuracy of detection of 95.18% are achieved over eight files from the MIT/BIH arrhythmia database.
    Applied Soft Computing 09/2014; 22:108–117. DOI:10.1016/j.asoc.2014.05.003 · 2.68 Impact Factor
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    Maryam Najimi, Ataollah Ebrahimzadeh, S.M.H. Andargoli, Afshin Fallahi
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    ABSTRACT: This paper proposes two methods for maximizing the lifetime of the cognitive sensor networks by focusing on node selection criteria for spectrum sensing in such networks. We maximize the network lifetime with constraints on the detection performance by determining the sensors, which sense the spectrum. This is a NP-complete problem and cannot be solved by standard methods. Therefore, we relax it to a more tractable form. Based on convex optimization framework, the optimal conditions are obtained and the sensors, which sense the spectrum, are determined. Simulation results show that our proposed algorithm is very efficient in terms of network lifetime maximization.
    IEEE Sensors Journal 07/2014; 14(7):2376-2383. DOI:10.1109/JSEN.2014.2311154 · 1.85 Impact Factor
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    Milad Azarbad, Hamed Azami, Saeid Sanei, Ataollah Ebrahimzadeh
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    Dataset: paper2
    Ataollah Ebrahimzadeh, Mahdi Hossienzadeh
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    Milad Azarbad, Ataollah Ebrahimzadeh, Jalil Addeh
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    ABSTRACT: In this paper a novel automatic method to identify digital communication signals is presented for different signal to noise ranges (SNRs). The method is based on the idea of optimization of the adaptive neuro-fuzzy inference system (ANFIS) including three major modules: the feature extraction the classification and optimization module. In the feature extraction module, an effective combination of the higher order moments (up to eighth), higher order cumulants (up to eighth) and spectral characteristics are proposed as the efficient features. For classification of the extracted features, ANFIS is investigated as a powerful classifier. In the training of ANFIS, the vector of radius has very important roles for its recognition accuracy. Therefore, in the optimization module, cuckoo optimization algorithm (COA) is proposed for optimization of the classifier to find the optimum value of radius. Experimental results clearly indicate that the proposed hybrid intelligent method has a high classification accuracy to discriminate different types of digital signals even at very low SNRs.
    Journal of VLSI signal processing systems for signal, image, and video technology 06/2013; 73(02):16. DOI:10.1007/s11265-013-0829-0
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    Maryam Najimi, Ataollah Ebrahimzadeh, S.M.H. Andargoli, Afshin Fallahi
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    ABSTRACT: In this paper, we address the problem of sensor selection for energy efficient spectrum sensing in cognitive sensor networks. We consider minimizing energy consumption and improving spectrum sensing performance simultaneously. For this purpose, we employ the energy detector for spectrum sensing and formulate the problem of sensor selection in order to achieve energy efficiency in spectrum sensing while reducing complexity. Due to the NP-complete nature of the problem, we simplify the problem to a more tractable form through mapping assignment indices from integer to the real domain. Based on the standard optimization techniques, the optimal conditions are obtained and a closed-form equation is expressed to determine the priority of nodes for spectrum sensing. In the next step, to save more energy, the decision node (DN) selection procedure is proposed to address the problem of direct transmissions to fusion center. Then, the problem of joint sensing node selection and DN selection is analyzed and an efficient solution is extracted based on the convex optimization framework. The novelty of the proposed work is to address the selection of the best sensing nodes while minimizing energy consumption. Simulation results show that significant energy is saved due to the proposed schemes in different scenarios.
    IEEE Sensors Journal 05/2013; 13(5):1610-1621. DOI:10.1109/JSEN.2013.2240900 · 1.85 Impact Factor
  • Ali Khazaee, Ataollah Ebrahimzadeh
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    ABSTRACT: This paper deals with the discrimination of premature ventricular contraction (PVC) arrhythmia using support vector machine (SVM) and genetic algorithm (GA). Feature extraction module extracts ten electrocardiogram (ECG) morphological features and two timing interval features. Then a number of SVM classifiers with different values of C and the GRBF kernel parameter, sigma, are designed and compared their ability for classification of three different classes of ECG signals. However, the parameters were not optimum choices. So, GAs are used to find the optimum values of C and sigma. An overall classification accuracy of detection of 99.8112% were achieved using proposed method over nine files from the MIT/BIH arrhythmia database.
    Intelligent Automation and Soft Computing 02/2013; 19(1). DOI:10.1080/10798587.2013.771456 · 0.19 Impact Factor
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    ABSTRACT: In this paper we address sensor selection problem for spectrum sensing in cognitive radio network. The limited sensor's energy is an important issue which has attracted more attention in recent years. We propose an energy efficient cooperative spectrum sensing when Multi-Input Multi-Output (MIMO) sensors are used. Two decision techniques for combination of antennas signals were used in each sensor: hard decision and soft decision. The OR rule is used for hard decision and Selection Combining (SC) and Equal Gain Combining (EGC). In each combination scheme, the problem was the sensor selection so that the energy consumption was minimized while detection performance was satisfied. The problem was solved based on the standard convex optimization method. Simulation results show that significant energy saving is achieved in comparison with the networks using Single-Input Single-Output (SISO) sensors specifically in low Signal to Noise Ratio (SNR) regime. Among of all methods, EGC combining has the least energy consumption while satisfy desired detection performance.
    Computer and Knowledge Engineering (ICCKE), 2013 3th International eConference on; 01/2013
  • A. Ebrahimzadeh, S. Mavaddati
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    ABSTRACT: Blind source separation (BSS) technique plays an important role in many areas of signal processing. A BSS technique separates the mixed signals blindly without information about the mixing system. This paper proposes a novel BSS technique using the bees colony algorithm (BCA) in order to achieve the de-mixing system. Cost function is one the important modules for operation of the BCA. So, we have investigated different types of the cost function. These cost functions are based on the balanced combination of two important paradigms, i.e., higher order statistics and information theory. Experimental results show the proposed technique has high separation accuracy, robustness against the local minima, high degree of flexibility and high speed of convergence in noisy and noiseless environments.
    01/2013; DOI:10.1016/j.swevo.2013.08.002
  • MReza Khorramian, Bijan Zakeri, Ataollah Ebrahimzadeh
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    ABSTRACT: Fractal Antennas are one of the most important and useful types which exhibit wide band and multiband behavior. In this paper a Sierpinski monopole fractal antenna is designed and analyzed with FDTD method. It has both multiband and wideband features and have stable radiation pattern in wide frequency ranges such as 3.3GHz to 4.1 GHz and 8.2 GHz to 9.2 GHz. The antenna is designed on a substrate with permittivity of 2.1, 1.5 mm thickness and fed with 50 ohm microstrip line. The antenna has a compact size of 89.9mm × 102.65 mm.
    Telecommunications Forum (TELFOR), 2013 21st; 01/2013
  • Leila Montazeri, Reza Ghaderi, Ataollah Ebrahimzadeh
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    ABSTRACT: Fuzzy classification systems play an important role in dealing with uncertainty and vagueness inherent in multi-dimensional pattern classification problems. Finding an optimal fuzzy rule set is a milestone in order for fuzzy classification systems to be built. In this paper, a fuzzy Genetic Algorithm (GA) is developed to generate fuzzy classification rules with appropriate number of rules in order to maximize the number of correctly classified patterns. The proposed algorithm is applied to a collection of data contained in 2D images captured by a video camera to identify the class of moving objects in a traffic scene. The simulation results are compared with other methods in order to illustrate the efficiency of the proposed method.
    Majlesi Conference on Electrical Engineering; 10/2012
  • Mobin Sefidgaran, Mohammad Mirzaie, Ataollah Ebrahimzadeh
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    ABSTRACT: This paper extracts a comprehensive reliability model for a transformer with oil natural air forced (ONAF) cooling system, which is the most common type of power transformers. The transformer is first classified into three subsystems and a reliability model is thereafter developed corresponding to each subsystem. Markov process and frequency/duration approaches are applied to analyze the extracted models and achieve their equalized representations. Merging subsystem models leads to the transformer entire reliability model consisting 17 states. The model is then reduced to an 11-state one which is more computationally manageable. Numerical analyses and sensitivity studies are conducted to demonstrate the performance of the proposed model and the results are thoroughly discussed.
    International Journal of Electrical Power & Energy Systems 02/2012; 35(1). DOI:10.1016/j.ijepes.2011.10.002 · 3.43 Impact Factor
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    ABSTRACT: In this paper, we propose an energy-efficient technique for cooperative spectrum sensing in cognitive sensor networks. In cooperative spectrum sensing, information is collected from different sensors to make a final decision. We use an "on/off" method for cognitive wireless sensor networks and also formulate the determination of the number of sensing nodes, such that energy consumption in spectrum sensing reduces and satisfies the constraint on the detection performance. The constraint on the detection performance is given by a minimum global probability of detection and a maximum global probability of false alarm. We use the energy detector as the spectrum sensing technique and solve the problem using the convex optimization methods while consider the computational complexity. Simulation results show that our proposed technique saves significant energy in different conditions.
    Telecommunications (IST), 2012 Sixth International Symposium on; 01/2012
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    Ataollah Ebrahimzadeh, Jalil Addeh, Zahra Rahmani
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    ABSTRACT: Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs. Simulation results show that a high recognition accuracy, about 99.65%, is achieved.
    ISA Transactions 01/2012; 51(1):111-9. DOI:10.1016/j.isatra.2011.08.005 · 2.26 Impact Factor
  • Leila Montazeri, Reza Ghaderi, Ataollah Ebrahimzadeh
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    ABSTRACT: This paper presents an automatic design of fuzzy rule-based classification systems which play an important role in dealing with uncertainty and vagueness inherent in multi-dimensional pattern classification problems. The performance and interpretability are two main factors which have to be considered while designing a fuzzy classification system. Finding an optimal rule set in terms of conciseness and comprehensibility is a key to build fuzzy classification systems. In this paper, a fuzzy artificial bee colony (ABC) approach is developed to generate fuzzy classification rule set with appropriate number of rules in order to maximize the number of correctly classified patterns. In order to illustrate the efficiency of the proposed method, it is applied to some well-known data sets plus a collection of data contained in 2D images captured by a video camera to identify the class of moving objects in a traffic scene. The simulation results are also compared with other methods.
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    ABSTRACT: This paper proposes the equation related to the design and dimensions of brushless permanent magnet motor. Then, an optimum design based on artificial bee colony algorithm with the purpose of increasing power density and efficiency is presented. Dimensions of brushless permanent magnet motor are calculated with optimum power density and efficiency. At the end, these results are confirmed using a 2-D numerical program based on finite element analysis.
  • S. Mavaddaty, A. Ebrahimzadeh
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    ABSTRACT: This paper proposes a novel method for blindly separating unobservable independent component signals based on the use of a bee colony optimization algorithm (BCO). It is intended for its application to the problem of blind source separation (BSS) on linear instantaneous mixtures. In this work, results obtained by BCO algorithm for solving BSS problem based on a set of cost functions are compared. These cost functions based on the fusion of two important paradigms, higher order statistics and information theory are established to measure the statistical dependence of the outputs of the demixing system. This paper demonstrates the possible benefits offered by BCO in combination with BSS, such as robustness against local minima and a high degree of flexibility in the evaluation function. Results show that the performance of the BCO is better than or similar to other evolutionary algorithms such as particle swarm optimization (PSO) with applying mutual information in combination with kurtosis on its own cost function.
    Electrical Engineering (ICEE), 2012 20th Iranian Conference on; 01/2012
  • S. Valiollahi, R. Ghaderi, A. Ebrahimzadeh
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    ABSTRACT: The proposed algorithm takes advantage of coupling fuzzy logic and Q-learning to fulfill requirements of autonomous navigations. Fuzzy if-then rules provide a reliable decision making framework to handle uncertainties, and also allow incorporation of heuristic knowledge. Dynamic structure of Q-learning makes it a promising tool to adjust fuzzy inference parameters when little or no prior knowledge is available about the world. To robot, the world is modeled into a set of state-action pairs. For each fuzzified state, there are some suggested actions. States are related to their corresponding actions via fuzzy if-then rules based on human reasoning. The robot selects the most encouraged action for each state through online experiences. Efficiency of the proposed method is validated through experiments on a simulated Khepera robot.
    Artificial Intelligence and Signal Processing (AISP), 2012 16th CSI International Symposium on; 01/2012

Publication Stats

122 Citations
33.05 Total Impact Points

Institutions

  • 2009–2014
    • Babol Noshirvani University of Technology
      • • Faculty of Electrical and Computer Engineering
      • • Department of Electronic Engineering
      Barfrush, Māzandarān, Iran
  • 2008
    • University of Mazandaran
      Meshed-i-Sar, Māzandarān, Iran
  • 2006
    • Ferdowsi University Of Mashhad
      • Department of Electrical Engineering
      Mashhad, Razavi Khorasan, Iran