International Journal of Information & Communication Technology Research 2018-08-13T15:14:41+0430 IJICTR Technical Support- Open Journal Systems <p class="show">International Journal of Information &amp; Communication Technology Research (IJICTR) publishes high quality scientific papers covering all aspects of information and communication technology also is indexed in MSRT(Iran Ministry of Science, Research and Technology) and ISC (Islamic World Science Citation Center) and many other Scientific Citation Centers.</p> Subspace-Based Approaches for Hybrid Millimeter-Wave Channel Estimation 2018-08-13T15:14:38+0430 Majid Shakhsi Dastgahian Hossein Khoshbin Ghomash <p>Millimeter wave communication (mmWC) is a promising volunteer for 5G communication systems with high data rates. To subdue the channel propagation characteristics in this frequency band, high dimensional antenna arrays need to be deployed at transceiver. Employing such a deployment, prevents to use of ADC or RF chain in each branch of MIMO system because of power constraints. Thus, such systems impose to have a hybrid analog/digital precoding/combining architecture. Hence, channel estimation revision seems to be essential. This paper propose new algorithms to estimate the mmW channel by exploiting the sparse nature of the channel and finding the subspace of received signal vectors based on MUSIC. By combining the multiple measurement vector (MMV) concept, MISIC , subspace augmentation (SA) and two-stage orthogonal subspace matching pursuit (TOSMP) approaches, we try to recover the indices of non-zero elements of an unknown channel matrix accurately even under the defective- rank condition. These indices are called support in the context. Simulation results indicate MUSIC-based approaches offer lower estimation error and higher sum rates compared with conventional MMV solutions.</p> 2018-08-11T12:37:55+0430 ##submission.copyrightStatement## A Fairness-Guaranteed Game-Theoretic Perspective in Multi-User Interference Channel 2018-08-13T15:14:38+0430 Atena Ebrahimkhani Bahareh Akhbari Babak Seyfe <p>In this paper, a novel game theoretic perspective with pricing scheme over a multi-user Gaussian interference channel is presented. The Kalai-Smorodinsky bargaining solution (KSBS) as a measure for guaranteeing fairness in resource allocation among users on the weak Gaussian interference channel is investigated. By using the treating interference as noise (TIN) scenario and applying proper prices for the transmit power of each user the result of the proposed game settles on a unique fair point. Also, an iterative algorithm is proposed that converges to the KSBS when users update their transmit powers and prices. Numerical results confirm analytical development.</p> 2018-08-11T13:31:34+0430 ##submission.copyrightStatement## Hub location Allocation Problem in Computer Networks Using Intelligent Optimization Algorithms 2018-08-13T15:14:39+0430 Armond Hartoonian Ahmad Khadem Zadeh <p>One of the new issues that have been raised in recent years is the hub network design problem. The hubs are collection and distribution centers that are used for the purpose of less connections and more of indirect than direct communications. They are interface facilities which are used as switch centers to collect and distribute flows in the network. They determine routes and organize traffic between source-destination in order to provide high performance and be more inexpensive. In the hub location problem, the aim is to find a suitable location for the hub and routes for sending information from a source to a destination, in order to reduce costs and gain desired purpose by multiple transfers between the hubs. In this paper, teaching and learning based optimization, particle swarm optimization and imperialist competitive algorithm were studied for locating optimally hubs and allocating nodes to the nearest located hub nodes. Experimental results show that optimal location for hubs by using cluster-based optimization algorithm (TLBO) successfully has been performed with extreme accuracy and precision.</p> 2018-08-11T14:03:45+0430 ##submission.copyrightStatement## Cloud-Based Large-Scale Sensor Networks: Motivation, Taxonomies, and Open Challenges 2018-08-13T15:14:40+0430 Fatemeh Banaie Heravan Mohammad Hossein Yaghmaee Seyed Amin Hosseini Seno <p>Recently, the integration of ubiquitous wireless sensor network (WSN) and powerful cloud computing (CC) has attracted growing attention and efforts in both academic and industry. In this new paradigm, cloud computing can be exploited to perform analysis of online as well as offline data streams provided by sensor networks. This can help to deal with the inherent limitations of WSN in combining and analyzing of the heterogeneous large number of sensory data. The study we present in this paper provides a comprehensive analysis and discussion of the representative works on large-scale WSNs, the need for integrating sensor with the cloud, the main challenges deriving from such integration, and future research directions in this promising field.</p> 2018-08-11T14:15:33+0430 ##submission.copyrightStatement## Enhancement of Illumination scheme for Adult Image Recognition 2018-08-13T15:14:40+0430 Sasan Karamizadeh Abouzar Arabsorkhi <p>Biometric-based techniques have emerged as the most promising option for individual recognition. This task is still a challenge for computer vision systems. Several approaches to adult image recognition, which include the deep neural network and traditional classifier, have been proposed. Different image condition factors such as expressions, occlusion, poses, and illuminations affect the facial recognition system. A reasonable amount of illumination variations between the gallery and probe images need to be taken into account in adult image recognition algorithms. In the context of adult image verification, illumination variation plays a vital role and this factor will most likely result in misclassification. Different architectures and different parameters have been tested in order to improve the classification’s accuracy. This proposed method contains four steps, which begin with Fuzzy Deep Neural Network Segmentation. This step is employed in order to segment an image based on illumination intensity. Histogram Truncation and Stretching is utilized in the second step for improving histogram distribution in the segmented area. The third step is Contrast Limited Adaptive Histogram Equalization (CLAHE). This step is used to enhance the contrast of the segmented area. Finally, DCT-II is applied and low-frequency coefficients are selected in a zigzag pattern for illumination normalization. In the proposed method, AlexNet architecture is used, which consists of 5 convolutional layers, max-pooling layers, and fully connected layers. The image is passed through a stack of convolutional layers after fuzzy neural representation, where we used filter 8 × 8. The convolutional stride is fixed to 1 pixel. After every convolution, there is a subsampling layer, which consists of a 2×2 kernel to do max pooling. This can help to reduce the training time and compute complexity of the network. The proposed scheme will be analyzed and its performance in accuracy and effectiveness will be evaluated. In this research, we have used 80,400 images, which are imported from two datasets - the Compaq and Poesia datasets - and used images found on the Internet.</p> 2018-08-11T14:27:39+0430 ##submission.copyrightStatement## Dynamic Risk Assessment System for the Vulnerability Scoring 2018-08-13T15:14:41+0430 marjan keramati <p>One of the key factors that endangers network security is software vulnerabilities. So, increasing growth of vulnerability emergence is a critical challenge in security management. Also, organizations constantly encounter the limited budget problem. Therefore, to do network hardening in a cost-benefit manner, quantitative vulnerability assessment for finding the most critical vulnerabilities is a vital issue. The most prominent vulnerability scoring systems is CVSS (Common Vulnerability Scoring System) that ranks vulnerabilities based on their intrinsic characteristics. But in CVSS, Temporal features or the effect of existing patches and exploit tools in risk estimation of vulnerabilities are ignored. So, CVSS scores are not accurate. Another deficiency with CVSS that limits its application in real networks is that, in CVSS, only a small set of scores is used for discriminating between numerous numbers of vulnerabilities.&nbsp; To improve the difficulties with existing scoring systems, here some security metrics are defined that rank vulnerabilities by considering their temporal features beside their intrinsic ones. Also, by the aim of improving scores diversity in CVSS, a new method is proposed for Impact estimation of vulnerability exploitation on security parameters of the network. Performing risk assessment by considering the type of the attacker which endangers the network security most is another novelty of this paper</p> 2018-08-11T14:54:32+0430 ##submission.copyrightStatement## Hybrid of Active Learning and Dynamic Self-Training for Data Stream Classification 2018-08-13T15:14:39+0430 Sogol Haghani Mohammad Reza Keyvanpour Mahnoosh Kholghi <p>Most of the data stream classification methods need plenty of labeled samples to achieve a reasonable result. However, in a real data stream environment, it is crucial and expensive to obtain labeled samples, unlike the unlabeled ones. Although Active learning is one way to tackle this challenge, it ignores the effect of unlabeled instances utilization that can help with strength supervised learning. This paper proposes a hybrid framework named “DSeSAL”, which combines active learning and dynamic self-training to achieve both strengths. Also, this framework introduces variance based self-training that uses minimal variance as a confidence measure. Since an early mistake by the base classifier in self-training can reinforce itself by generating incorrectly labeled data, especially in multi-class condition. A dynamic approach to avoid classifier accuracy deterioration, is considered. The other capability of the proposed framework is controlling the accuracy reduction by specifying a tolerance measure. To overcome data stream challenges, i.e., infinite length and evolving nature, we use the chunking method along with a classifier ensemble. A classifier is trained on each chunk and with previous classifiers form an ensemble of M such classifiers. Experimental results on synthetic and real-world data indicate the performance of the proposed framework in comparison with other approaches.</p> 2018-08-11T00:00:00+0430 ##submission.copyrightStatement##