Journal of Advances in Computer Engineering and Technology
Volume:1 Issue: 2, Spring 2015

With the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. One of the major problems in text classification relates to the high dimensional feature spaces. Therefore, the main goal of text classification is to reduce the dimensionality of features space. There are many feature selection methods. However, only a few methods are utilized for huge text classification problems. In this paper, we propose a new wrapper method based on Particle Swarm Optimization (PSO) algorithm and Support Vector Machine (SVM). We combine it with Learning Automata in order to make it more efficient. This helps to select better features using the reward and penalty system of automata. To evaluate the efficiency of the proposed method, we compare it with a method which selects features based on Genetic Algorithm over the Reuters-21578 dataset. The simulation results show that our proposed algorithm works more efficiently.

Peer-to-Peer ( P2P ) systems have been the center of attention in recent years due to their advantage . Since each node in such networks can act both as a service provider and as a client , they are subject to different attacks . Therefore it is vital to manage confidence for these vulnerable environments in order to eliminate unsafe peers . This paper investigates the use of genetic programing for achieving trust of a peer without central monitoring . A model of confidence management is proposed here in which every peer ranks other peers according to calculated local confidence based on recommendations and previous interactions . The results show that this model identifies malicious nodes without the use of a central supervisor or overall confidence value and thus the system functions.Index Terms — peer - to - peer systems , confidence , genetic programing , malicious nodes .

Multiple people detection and tracking is a challenging task in real-world crowded scenes. In this paper, we have presented an online multiple people tracking-by-detection approach with a single camera. We have detected objects with deformable part models and a visual background extractor. In the tracking phase we have used a combination of support vector machine (SVM) person-specific classifiers, similarity scores, the Hungarian algorithm and inter-object occlusion handling. Detections have been used for training person-specific classifiers and to help guide the trackers by computing a similarity score based on them and spatial information and assigning them to the trackers with the Hungarian algorithm. To handle inter-object occlusion we have used explicit occlusion reasoning. The proposed method does not require prior training and does not impose any constraints on environmental conditions. Our evaluation showed that the proposed method outperformed the state of the art approaches by 10% and 15% or achieved comparable performance.

This paper presents the design and simulation of a typical Ka band satellite beacon receiver for propagation experimentation. Using satellite beacon signal as a reference signal in satellite wave propagation study, is one of the most important methods. Satellite beacons are frequently available for pointing large antennas, but such signals can be used for measuring the effect of natural phenomena such as atmospheric gases, water vapor, oxygen molecules, clouds, rain, dust and fog existing in different layers of the atmosphere, including troposphere and ionosphere.In recent years, different designs of satellite beacon receiver (analog and digital) are proposed and implemented. Beacon signals are used for various applications including precise orientation of the earth station to the satellite, automatic frequency control and satellite propagation research. These cases demonstrate the importance of using the reference beacon signal. To receive satellite beacon, an appropriate receiver is needed. Locking on satellite beacon signal is hard. Though conventional satellite receivers are also generally able to track the beacon signal, the nature of the signal is led to create a special receiver. In this paper, the design and simulation of a satellite signal beacon receiver have been done in Ka band.

This study proposes a modified version of cultural algorithms (CAs) which benefits from rule-based system for influence function. This rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. This is important to use appropriate influence function to apply to a specific individual, regarding to its role in the search process. This rule based system is optimized using Genetic Algorithm (GA). The proposed modified CA algorithm is compared with several other optimization algorithms including GA, particle swarm optimization (PSO), especially standard version of cultural algorithm. The obtained results demonstrate that the proposed modification enhances the performance of the CA in terms of global optimality.Optimization is an important issue in different scientific applications. Many researches dedicated to algorithms that can be used to find an optimal solution for different applications. Intelligence optimizations which are generally classified as, evolutionary computations techniques like Genetic Algorithm, evolutionary strategy, and evolutionary programming, and swarm intelligence algorithms like particle swarm intelligence algorithm and ant colony optimization, etc are powerful tools for solving optimization problems

Network sensors consist of sensor nodes in which every node covers a limited area. The most common use ofthese networks is in unreachable fields.Sink is a node that collects data from other nodes.One of the main challenges in these networks is the limitation of nodes battery (power supply). Therefore, the use oftopology control is required to decrease power consumption and increase network accessibility.In this paper, a network is modeled by a graph. Each vertex in the graphindicatesa sensor node and the edges display the communication links between them.Changes in the graph show changes in network topology and a different path to the sink.In this research, “fuzzy logic” is used for topology control. As the fuzzy logic utilizes optimized sensing radius comparing with minimum-maximum sensing radius, we expect less dead nodes, more mean residual energy and relatively more load balance in the network. At first, 2-input fuzzy algorithm was chosen. However 3-input fuzzy algorithm was also observed due to reasons explained in the main text. In both algorithms, we haveload balance in network and prolong network lifetime. Unreachable paths are less encountered with higher rates of packet delivery. The final standard deviation (STD) reaches to its minimum level, while the residual energy in sensors remains close to each other.