Journal of Computer and Robotics
Volume:8 Issue: 2, Summer and Autumn 2015

In this paper an optimal controller is proposed for a self-balancing electrical vehicle called Segway PT. This vehicle has one platform and two wheels on the sides and the rider stands on the platform. A handlebar, as a navigator, is attached to the body of Segway, with which the rider controls the vehicle. Since Segway uses electrical energy produced by batteries, resource consumption management is of utmost importance. On the other hand, complex nonlinear dynamics cause difficulties in controlling the vehicle. Our proposed controller reduces energy consumption and enhance response speed of system instead of classic PID controller which proposed before.  Simulation results show the desired performance of the proposed controller.

Generally human vision system searches for salient regions and movements in video scenes to lessen the search space and effort. Using visual saliency map for modelling gives important information for understanding in many applications. In this paper we present a simple method with low computation load using visual saliency map for background subtraction in video stream. The proposed technique is based on finding image segments whose intensity values can be distinguished accurate. The practical implementation uses a sliding window approach, where the distributions of the objects and surroundings are estimated using semi-local intensity histograms. This introduced method requires no training so it can be used in embedded systems like cameras due to low load in calculation. So with our background subtraction algorithm we can detect pre-defined targets. Also the automatically video regions detected by proposed model are consistent with the ground truth saliency maps of eye movement data. Comparisons with state-of-the-art background subtraction techniques indicate that the introduced approach results in high performance and accuracy.

Today, with rapid growth of the World Wide Web and creation of Internet sites and online text resources, text summarization issue is highly attended by various researchers. Extractive-based text summarization is an important summarization method which is included of selecting the top representative sentences from the input document. When, we are facing into large data volume documents, the extractive-based text summarization seems to be an unsolvable problem. Therefore, to deal with such problems, meta-heuristic techniques are applied as a solution. In this paper, we used Cuckoo Search Optimization Algorithm (CSOA) to improve performance of extractive-based summarization method. The proposed approach is examined on Doc. 2002 standard documents and analyzed by Rouge evaluation software. The obtained results indicate better performance of proposed method compared with other similar techniques.

In recent years mobile networks have expanded dramatically, compared with other wireless networks. Routing protocols in these networks are designed with the assumption that there is no attacker node, so routing protocols are vulnerable to various attacks in these networks. In this paper, we review the network layer attacks and then we simulate the impact of black hole attack on ad hoc on demand distance vector routing protocol with NS-2 simulation. Then we review all kinds of intrusion detection systems (IDS) in large and small mobile ad hoc networks. We simulate these networks when they are under single black hole attack and with the existence of IDS byNS-2 simulator software. Finally, we compared the results according to throughput, packet loss ratio and packet delivery rate with each other.

Knowing the fact that the main weakness of the most standard methods including k-means and hierarchical data clustering is their sensitivity to initialization and trapping to local minima, this paper proposes a modification of convex data clustering  in which there is no need to  be peculiar about how to select initial values. Due to properly converting the task of optimization to an equivalent convex optimization problem, the proposed data clustering algorithm can be indeed considered as a global minimizer. In this paper, a splitting method for solving the convex clustering problem is used called as Alterneting Direction Method of Multipliers (ADMM), a simple but powerful algorithm that is well suited to convex optimization. We demonstrate the performance of the proposed algorithm on real data examples. The simulation result easily approve that the Modified Convex Data Clustering (MCDC) algorithm provides separation more than the Convex Data Clustering (CDC) algorithm. Furthermore, complexity of solving the second part of MCDC problem is reduced from O(n2) to O(n).

In recent years, collaborative filtering (CF) methods are important and widely accepted techniques are available for recommender systems. One of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. However, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. With the development of social network, trust measure introduced as a new approach to overcome the CF problems. On the other hand, trust-aware recommender systems are techniques to make use of trust statements and user personal data in social networks to improve the accuracy of rating prediction for cold start users. In addition, clustering-based recommender systems are other kind of systems that to be efficient and scalable to large-scale data sets but these systems suffer from relatively low accuracy and especially coverage too. Therefore to address these problems, in this paper we proposed a multi-view clustering based on Euclidean distance by combination both similarity view and trust relationships that is including explicit and implicit trusts. In order to analyze the effectiveness of the proposed method we used the real-world FilmTrust dataset. The experimental results on this data sets show that our approach can effectively improve both the accuracy and especially coverage of recommendations as well as in the cold start problem.