Journal of Computer and Robotics
Volume:9 Issue: 1, Winter and Spring 2016

Mobile ad-hoc networks have attracted a great deal of attentions over the past few years. Considering their applications, the security issue has a great significance in them. Security scheme utilization that includes prevention and detection has the worth of consideration. In this paper, a method is presented that includes a multi-level security scheme to identify intrusion by sensors and authenticates using biosensors. Optimizing authentication and intrusion detection combination, we formulate the problem as a partially observable distributed stochastic system. In order to reduce the computation time, the parallel forward algorithm of Hidden Markov Model has been used. Due to the possibility of misdetection of the sensor and in order to increase the accuracy of observations, more than one sensor is selected in every step, the observations obtained from the sensors are combined for more accurate identification, and the system decides about the security status based on combined observations of the sensors. Bayesian theory has been used in sensors evidence fusion brought by increased accuracy and network security, which will be observed in the simulations. The use of this theory causes the increase of accuracy and security on networks.

This paper demonstrates a method to how reserve capacity and cost allocation could be determined in a pool-based and disaggregated market model. The method considers both the spinning reserve and interruptible loads as the operating reserve services. In the proposed market, generators and consumers (including participation of interruptible loads) submit offers and bids to the independent system operator. Firstly, the energy market is cleared according to GENCOs' offers and customers' energy requirements. To make; the more competitive market, interruptible customers participate in reserve market and supply operating reserve. It is assumed that the operating reserve market structure cleared in two-stages. Based on the reliability evaluation of the generators, market operator (MO) clears the reserve market. According to the contribution of generation units to the system expected energy not supplied, reserve cost of this level is allocated among them. In the second section of the reserve market clearing, customers can choose their desired reliability requirements. The independent system operator is cleared reserve market such that the required reliability levels of the customers are met. Reserve cost associated with this part is allocated among customers that are willing to have a higher reliability level than the standard level. To determine the share of each consumer from a shortage in the real time operation, Deficiency Factor is introduced. Finally, numerical results are presented to illustrate the impact of the reserve cost allocation and effectiveness of participations’ demand side on the operating reserve market.

The sales proceeds are the most important factors for keeping alive profitable companies. So sales and budget sales are considered as important parameters influencing all other decision variables in an organization. Therefore, poor forecasting can lead to great loses in organization caused by inaccurate and non-comprehensive production and human resource planning. In this research a coherent solution has been proposed for forecasting sales besides refining and revising it continuously by ANFIS model with consideration of time series relations. The relevant data has been collected from the public and accessible annual financial reports being related to a famous Iranian company. Moreover, for more accuracy in forecasting, solution has been examined by Back Propagation neural Network (BPN) and Particle swarm Optimization (PSO). The comparison between prediction taken and real data shows that PSO can optimize some parts of prediction in contrast to the rest which is more coincident to the output of BPN analysis with more precise results relatively.

The computer industry has defined the IEEE 802.16 family of standards that will enable mobile devices to access a broadband network as an alternative to digital subscriber line technology. As the mobile devices join and leave a network, security measures must be taken to ensure the safety of the network against unauthorized usage by encryption and group key management. IEEE 802.16e uses Multicast and Broadcast Service (MBS) as an efficient mechanism to distribute the same data concurrently to Multiple mobile Stations (MSs) through one Base Station (BS). To generate, update and distribute the group keys, the MBS applies Multicast and Broadcast Rekeying Algorithm (MBRA). The main performance parameters of group key management schemes are typically communications, computation and storage cost as well as scalability. The purpose of this paper is to review and investigate the challenges and security issues of performance parameters in different group key managements in IEEE 802.16e.

Disguised face recognition is a major challenge in the field of face recognition which has been taken less attention. Therefore, in this paper a disguised face recognition algorithm based on Local Phase Quantization (LPQ) method and Singular Value Decomposition (SVD) is presented which deals with two main challenges. The first challenge is when an individual intentionally alters the appearance by using disguise accessories, and the second one is when gallery images are limited for recognition. LPQ has been used for extraction of the statistical feature of the phase in windows with different sizes for each pixel of the image. SVD is used to cope with the challenge of the gallery images limitation and also with the help of original images extracted from that, every single image turns to three renovated images. In this study, disguise is intended as a blur in the image and Local phase quantization method is robust against the disguised mode, due to the use of the statistical feature of the Fourier transform phase. Also the use of different-sized window instead of fixed window in feature extraction stage, the performance of the proposed method has increased. The distance of images from each other is computed by using Manhattan and Euclidean distance for recognition in the proposed method. The Performance of the proposed algorithm has been evaluated by using three series of experiments on two real and synthesized databases. The first test has been performed by evaluating all the possible combinations of the different-sized windows created in the feature extraction stage, and the second experiment has been done by reducing the number of gallery images and then the third one has been carried out in different disguise. In all cases, the proposed method is competitive with to several existing well-known algorithms and when there is only an image of the person it even outperforms them.

Community detection is a task of fundamental importance in social network analysis. Community structures enable us to discover the hidden interactions among the network entities and summarize the network information that can be applied in many applied domains such as bioinformatics, finance, e-commerce and forensic science. There exist a variety of methods for community detection based on different metrics and domain of applications. Most of these methods are based on the existing of the non-overlapping or sparse overlapping communities. Moreover, the experimental analysis showed that, overlapping areas of communities become denser than non-overlapping area of communities. In this paper, significant methods of overlapping community detection are compared according to well-known evaluation criteria. The experimental analyses on artificial network generation have shown that earlier methods of community detection will not discover overlapping communities properly and we offered suggestions for resolving them.