مجله رایانش نرم و فناوری اطلاعات
سال یکم شماره 1 (بهار 1391)

In the last decades huristic algorithms optimization are used in various fiels of science and engineering problems. Most of these methods are inspired by natural phenomena, such as biological behaviors or physical principles. In this paper, a new optimization method based on the dynamic of sliding motion along a frictionless inclined plane is introduced. In the proposed algorithm, a collection of agents cooperate with each other and move toward better positions in the search space by employing Newton’s second law and equations of motion. Our method is compared with other popular optimization algorithms and the results on 23 standard benchmark functions show its effectiveness in most cases.

In the last decades huristic algorithms optimization are used in various fiels of science and engineering problems. Most of these methods are inspired by natural phenomena, such as biological behaviors or physical principles. In this paper, a new optimization method based on the dynamic of sliding motion along a frictionless inclined plane is introduced. In the proposed algorithm, a collection of agents cooperate with each other and move toward better positions in the search space by employing Newton’s second law and equations of motion. Our method is compared with other popular optimization algorithms and the results on 23 standard benchmark functions show its effectiveness in most cases.

Inasmuch as sensor nodes are typically used in inaccessible environments, they are vulnerable and insecure against environmental destructive factors and against deliberate devastating attempts of enemies. Hence, fault occurrence in wireless sensor networks (WSNs) is deemed to be an unavoidable phenomenon. The main drawback of comparative fault detection methods are that in case more than half of the neighboring nodes are faulty or the nodes become faulty due to a common mode failure (CMF), they will fail to detect faulty nodes properly. Thus, in order to address this issue, the authors introduced a cluster-based hierarchical fault detection method which increases the influence of non adjacent sensor nodes’ data in determining of sensor’s status. Therefore the proposed method not only compares the data of neighboring nodes but also compares the data of non-neighboring nodes at an upper layer in order to adopt the proper decision upon the status of the nodes. Since applying fault detection methods in determined intervals and static manner are considered as inefficient, in this paper, we put forward an intelligent and dynamic method to determine the appropriate time for the implementation of the fault detection algorithm; hence, the right time and the required number of the implementation of the algorithm are intelligently and dynamically specified and as a result, the network lifetime increases. The related simulations were carried out by means of Matlab software was conducted under different densities of the nodes and with differing probability of being faulty nodes. The simulations results indicated that the fault detection accuracy of the proposed algorithm is significantly high and its false alarm rate is noticeably low. The results obviously demonstrate that the proposed method is scalable.

The Global Positioning System (GPS) is a space-based satellite navigation system that provides location and time information in all weather, anywhere on or near the Earth, where there is an unobstructed line of sight to four or more GPS satellites. The GPS program provides critical capabilities to military, civil and commercial users around the world. Traditionally GPS navigation equations can be solved using iterated methods, difference linearization, and extended Kalman filter. All these techniques attempt to linearize the equations and then solve them by traditional means such as Least Squares (LS) solvers. The linearization introduces a small error in the equations as the higher order terms are neglected from the equations to be solved. Neural Networks (NNs) are used to approximate non-linear functions using a training set composed of past data to learn NN weights from data. In this paper, an approach to solving the GPS pseudo-range carrier phase measurements equations using multi-layer perceptron NNs is proposed. The experimental results obtained from a Coarse Acquisition (C/A)-code single-frequency GPS receiver are provided to confirm the efficacy of the proposed method to provide a high level of positioning accuracy. The results of comparison by LS method show NN approach has less RMS error, less calculation volume and more precision. Also, simulations demonstrate that NN has stable behavior even under bad geometry conditions.

When an image is taken from a paper-based documents, such as scanned paper, the picture on the back page of the front page of the paper leaves effect behind. Pictures of the back and the front page of the paper, make a nonlinear combination and thus a practical problem of blind source separation (BSS) provides. Here a version of the highly nonlinear mixtures using onion skin paper, is used. Separation was performed by applying an enhanced InfoMax algorithm based on a novel nonlinear preprocessing method, and competition between high frequency components of the wavelet decomposition of the images. Separation results with proposed algorithm were evaluated by objective and subjective criteria. that indicate the precision, speed and quality of the estimated image with proposed algorithm, outperform the other nonlinear BSS methods. In order to accurately evaluate the separating algorithms and comparison the quality of estimated images with original sources, in addition to the usual criteria, the similarity Line Profile and measure of Euclidean distance between standard deviation of images introduced. Nonlinear separation results with the proposed algorithm was tested by the quantitive and qualitative measures, and a significant improvement was observed.

Given a set P of red points and a set Q of blue points in the plane of total size n, we investigate the problem of finding two discrete axis-align boxes that (a) contain all points of Q, (b) avoid any point of P. Intersection of these rectangles is empty and the sides of them are parallel to x-y coordinate. Such rectangles are called separating rectangles. Our algorithm reports them in O(n log n) time.

In the recent years, cameras with disjoint views are often used in surveillance system to cover a wide area to reduce the cost posed by utilizing many cameras. Tracking in surveillance systems is performed in two distinct steps. During the first step, identification and tracking of persons is done in the field of view (FOV) of one camera. In the second step, re-identification of persons is carried out in the other available cameras to measure performance of the tracking system. Various conventional features such as clothes and appearance of person have been used to identify peoples in the cameras. However, similarity between appearance and clothes can cause tracking system to give unreliable results. Thus, much sophisticated features are required to increase the ability of tracking system. The aim of this paper is to propose a suitable method to identify peoples in a network of cameras with disjoints views. In our proposed methodology, according to relative distance of various parts, human body has been partitioned into three portions of head, torso and bottom part. Histograms of these portions are used to match and distinguish between persons in FOV of cameras. Since the color space is one of the effective parameters to use histograms, experiments have been conducted in RGB, YCbCr and HSV color spaces. It was found that RGB color space has a superior efficiency compared to those color spaces used in this study.