Ataollah Ebrahimzadeh

Bio: Ataollah Ebrahimzadeh is an academic researcher from Babol Noshirvani University of Technology. The author has contributed to research in topics: Wireless sensor network & Artificial neural network. The author has an hindex of 15, co-authored 56 publications receiving 896 citations.

Improved Fruit-Fly Optimization Algorithm and Its Applications in Antenna Arrays Synthesis

Abstract: Synthesizing antenna arrays is one of the most influential optimization problems in the electromagnetics community. In this paper, an improved fruit-fly optimization algorithm (FOA) [entitled averager engine linear generation mechanism of candidate solution of FOA (AE-LGMS-FOA)] is proposed to be used in antenna array synthesis. This improvement includes adding a new search mechanism to enhance the efficiency of algorithm during high-dimensional problems. After investigating its performance through a variety of benchmark functions, the proposed method is applied to several linear and planar array problems in terms of sidelobe reduction, null control, and thinning. During the problems, some properties of the algorithm are analyzed and the associated results are compared with other state-of-the-art methods and popular algorithms. Furthermore, various boundary conditions are reformulated for the algorithm and examined during the planar array synthesis. Finally, the AE-LGMS-FOA is utilized in synthesizing a U-slot microstrip array antenna with ultrawideband characteristics to verify its versatility and robustness in real-world array antenna problems. The optimized structure has an impedance bandwidth of 3.38 GHz, which indicates 181.6% improvement over the original structure’s bandwidth.

A Novel Sensing Nodes and Decision Node Selection Method for Energy Efficiency of Cooperative Spectrum Sensing in Cognitive Sensor Networks

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.

Control chart pattern recognition using K-MICA clustering and neural networks.

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.

A survey of network lifetime maximization techniques in wireless sensor networks

Abstract: Emerging technologies, such as the Internet of Things, smart applications, smart grids, and machine-to-machine networks stimulate the deployment of autonomous, self-configuring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints, and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteria.

Wavelet-based representations for the 1/f family of fractal processes : Fractals in electrical engineering

Abstract: The 1/f family of fractal random processes model a truly extraordinary range of natural and man-made phenomena, many of which arise in a variety of signal processing scenarios. Yet despite their apparent importance, the lack of convenient representations for 1/f processes has, at least until recently, strongly limited their popularity. In this paper, we demonstrate that 1/f processes are, in a broad sense, optimally represented in terms of orthonormal wavelet bases. Specifically, via a useful frequency domain characterization for 1/f processes, we develop the wavelet expansion's role as a Karhunen-Loeve-type expansion for 1/f processes. As an illustration of potential, we show that wavelet based representations naturally lead to highly efficient solutions to some fundamental detection and estimation problems involving 1/f processes