Mojtaba Hosseini

Solving heterogeneous coverage problem in Wireless Multimedia Sensor Networks in a dynamic environment using Evolutionary Strategies

consist of a large number of low-power camera nodes to monitor a general environment with some targets in it. One of the most important problems in this context is energy conservation and covering the entire targets. This paper uses Evolutionary Strategy as a method based on population gradual adjustment by environmental conditions to solve the heterogeneous coverage problem in Wireless Multimedia Sensor Networks (WMSNs) in a dynamic environment by mobile targets with minimum energy consumption.

Sensor selection and configuration in visual sensor networks

In this article, the problem of selection and configuration of visual sensors in Visual Sensor Networks (VSNs) is studied for coverage of the existing targets in the three-dimensional network area. It is assumed that visual sensors are capable of Pan-Tilt-Zoom (PTZ) ability and able to choose the covered area and desirable target through configuration of the angle of view in two horizontal and vertical dimensions and alteration of the enlargement scale. In order to solve the problem of selection and configuration, a method is presented according to Binary Integer Programming (BIP). It ensures to find the best solution. In addition, it has less calculation complexity compared with other optimization methods. Applying the proposed solution, two problems of k-coverage and full angle coverage have been assessed using two models of point target model and circular target model. The perceived results properly confirm the efficiency of the proposed method in terms of execution speed and decreasing the computation complexity.

Mobile target tracking in non-overlapping wireless visual sensor Networks using Neural Networks

Target tracking using Wireless Visual Sensors Networks (WVSN), is an interesting research area, especially if visual sensors have non-overlapping Field-Of-Views (FOV). In this paper, we propose a new prediction based method to efficient sensor selection. These method uses a Neural Network (NN) to predict the next target movement. We implemented and tested this tracking approach in a flat environment, simulation shows this approach is efficient non overlapping tracking with acceptable accuracy, configuration of WVSN cost and energy conservation.

Particle filter supported with the neural network for aircraft tracking based on kernel and active contour

In this paper we present a new method for aircraft tracking in color video sequences based on contour and kernel. The aim of this work is to overcome the problem of losing target in changing light conditions, large displacement, speed changing, and occlusion. In fact, we want to achieve an exact contour of the target in each frame of the video. The proposed method is made in three steps, estimating the location of the target by the particle filter, segmentation of the region of the target using neural networks and finding the exact contours by greedy snake algorithm. In the proposed method we have used both regions and contour information to create target candidate model and this model is dynamically updated during tracking. To avoid the accumulation error during the update step and achieving higher segmentation accuracy, the target region is given to a perceptron neural network to separate the target from the background, after estimation of the target location. The output is used for exact calculation of the size and the center of the target. Moreover, it is used as the initial contour for the greedy snake algorithm to find the exact edge of the target. The proposed algorithm has been tested on two databases which contain challenges like highspeed and agility of aircrafts, background clutter, occlusions and camera movements. The experimental results show that our method increases the accuracy of tracking and segmentation.

Region-based multi-spectral image segmentation using evolutionary strategies

One of the most important stages of image processing is image segmentation to extract different features. In this paper, we present a region-based algorithm for multi spectral image segmentation using evolutionary strategies (ESs). We use ES to find the center of regions of the image and then classify it using Euclidean or angular distances. We also use different indices to evaluate the fitness of the resulted centers of ES (chromosomes) and compare their results to choose the best.