Online) An Open Access

ABSTRACT The main concern in Wireless Sensor Networks is how to handle with their limited energy resources. The performance of Wireless Sensor Networks strongly depends on their lifetime. As a result, Dynamic Power Management approaches with the purpose of reduction of energy consumption in sensor nodes, after deployment and designing of the network. Recently, there have been a strong interest to use intelligent tools especially Neural Networks in energy efficient approaches of Wireless Sensor Networks, due to their simple parallel distributed computation, distributed storage, data robustness, auto-classification of sensor nodes and sensor reading. This paper presents a new centralized adaptive Energy Based Clustering protocol through the application of Self organizing map neural networks (called EBC-S) which can cluster sensor nodes, based on multi parameters; energy level and coordinates of sensor nodes. We applied some maximum energy nodes as weights of SOM map units; so that the nodes with higher energy attract the nearest nodes with lower energy levels. Therefore, formed clusters may not necessarily contain adjacent nodes. The new algorithm enables us to form energy balanced clusters and equally distribute energy consumption. Simulation results and comparison with previous protocols (LEACH and LEA2C) prove that our new algorithm is able to extend the lifetime of the network. Keywords: Energy Based Clustering, Self Organizing Map Neural Networks, Wireless Sensor Networks INTRODUCTION The most important difference of Wireless Sensor Network (WSNs) with other wireless networks may be constraints of their resources, especially energy which usually arise from small size of sensor nodes and their batteries which is a prerequisite to WSNs main applications. The main and most important reason of WSNs creation was continuous monitoring of environments where are too hard or impossible for human to access or stay. So there is often low possibility to replace or recharge the dead nodes as well. The other important requirement is that we need a continuous monitoring so the lifetime and network coverage of these networks are our great concerns. As a result, as energy conservation is the main concern in WSNs, but also it should be gained with balanced distribution in whole network space. Balanced distribution of energy in whole network will lead to balanced death of nodes in all regions preventing from lacking network coverage in a rather large part of the network. Energy conservation should be gained by wisely management of energy sources. Several energy conservation schemes have been proposed in the literature while there is a comprehensive survey of energy conservation methods for WSNs and the taxonomy of all into three main approaches (duty-cycling, data reduction, and mobility based approaches

New methods of verification and identification using iris patterns

Abstract: To detect and track eye images, distinctive features of use r eye are used. Generally, an eye-tracking and detection system can be divided into four steps: Face detection, eye region detection, pupil detection and eye tracking. To find the position of pupil, first, face region must be separated from the rest of the image using mixture of Gaussian, this will cause the images background to be non-effective in our next steps. This will result in decreasing the computational complexity and ignoring some factors such as bread. Color entropy in the eye region is used to detect pupil. In the next step, we perform eye tracking. We proposed algorithm for the eye tracking by combining the pupil based Kalman filter with the mean shift algorithm. That use modal recognition technique and is based on pictures whit high equality of eye iris .Iris modals in comparison whit other properties in biometrics system are more resistance and credit .In this paper we use from fractals technique for iris recognition. Fractals are important in these aspects that can express complicated pictures with applying several simple codes. Until, That cause to iris tissue change from depart coordination to polar coordination and adjust for light rates. While performing other pre-process, fault rates will be less than EER, and lead to decreasing recognition time, account table cost and grouping precise improvement

A new method to calculate mathematical morphology using associative memory and cellular learning automata

Abstract: The methods presented in this paper include using auto-associative memory which can be defined as a supervised organizing method which is a specific type of h-sorting which is compatible with the morphologic operators over multi-variables data. Mathematical morphologies for multi-variable images require appropriate sort descriptions that allow us to define and use primitive morphologies operators without any wrong results such as wrong color. All the required calculations are defined with lattice algebra (+,^ and ∨); therefore, the proposed method will be faster with less computation overhead than the previous methods. This method does not use any assumptions of the stochastic process which means that this method is independent of the model. The presented method uses cellular learning automata which results in fewer errors than the mathematical methods due to the feedback from the network