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Salehi N.,Islamic Azad University at Tehran | Dana A.,Iran Telecommunication Research Center
International Conference on Advanced Communication Technology, ICACT | Year: 2010

Network-on-chip (NoC) is being proposed as a scalable and reusable communication platform for future embedded systems platforms. In this paper a novel routing algorithm for avoiding congested areas using a fuzzy-based routing decision is proposed.The routing path is established by minimizing a cost which is calculated by fuzzy controller and considers the power consumption and free slots input buffer of the neighbor cores. The proposed routing scheme can effectively regulate power distribution to meet power balance requirement and avoid hotspot with low hardware overhead.


Razavi M.,Islamic Azad University at Tehran | Aliee F.S.,Shahid Beheshti University | Badie K.,Iran Telecommunication Research Center
Knowledge and Information Systems | Year: 2011

Enterprise Architecture (EA) as a discipline that manages large amount of models and information about different aspects of the enterprise, can support decision making on enterprise-wide issues. In order to provide such support, EA information should be amenable to analysis of various utilities and quality attributes. In this regard, we have proposed the idea of characterizing and using enterprise architecture quality attributes. And this paper provides a quantitative AHP-based method toward expert-based EA analysis. Our method proposes a step-by-step process of assessing quality attribute achievement of different scenarios using AHP. By this method, most suitable EA scenarios are selected according to prioritized enterprise utilities and this selection has an important affect on decision making in enterprises. The proposed method also introduces a data structure that contains required information about quality attribute achievement of different EA scenarios in enterprises. The stored asset can be used for further decision making and progress assessment in future. Sensitivity analysis is also part of the process to identify sensitive points in the decision process. The applicability of the proposed method is demonstrated using a practical case study. © 2010 Springer-Verlag London Limited.


Mardani M.,University of Minnesota | Seifali Harsini J.,Guilan University | Lahouti F.,University of Tehran | Eliasi B.,Iran Telecommunication Research Center
IEEE Transactions on Vehicular Technology | Year: 2011

In a cooperative relay network, a relay (R) node may facilitate data transmission to the destination (D) node when the latter node cannot correctly decode the source (S) node data. This paper considers such a system model and presents a cross-layer approach to jointly design adaptive modulation and coding (AMC) at the physical layer and the truncated cooperative automatic repeat request (C-ARQ) protocol at the data-link layer for quality-of-service (QoS)-constrained applications. The average spectral efficiency and packet loss rate of the joint C-ARQ and AMC scheme are first derived in closed form. Aiming to maximize the system spectral efficiency, AMC schemes for the S-D and R-D links are optimized, whereas a prescribed packet-loss-rate constraint is satisfied. As an interesting application, joint link adaptation and blockage mitigation in land mobile satellite communications (LMSC) with temporally correlated channels is then investigated. In LMSC, the S node data can be delivered to the D node when the S-D is in the outage, therefore provisioning the QoS requirements. For applications without instantaneous feedback, an optimized rate selection scheme based on the channel statistics is also devised. Detailed and insightful numerical results are presented, which indicate the superior performance of the proposed joint AMC and C-ARQ schemes over their optimized joint AMC and traditional ARQ counterparts. © 2010 IEEE.


Sheibani K.,Iran Telecommunication Research Center
IEEM2010 - IEEE International Conference on Industrial Engineering and Engineering Management | Year: 2010

This paper describes a hybrid meta-heuristic for combinatorial optimization problems with specific reference to the travelling salesman problem (TSP). The method is a combination of genetic algorithms (GA) and greedy randomized adaptive search procedures (GRASP). A new adaptive fuzzy greedy search operator is developed for this hybrid method. Computational experiments using a wide range of standard benchmark problems indicate that the proposed hybrid meta-heuristic is very efficient. ©2010 IEEE.


Khaleie S.,Iran Telecommunication Research Center | Fasanghari M.,Iran Telecommunication Research Center
Soft Computing | Year: 2012

Group decision making is a process in which experts rank and choose the most desirable alternatives based on some accepted criteria. The aim of this paper was to introduce a method to solve group decision making problems with Atanassov's intuitionistic fuzzy sets. First, the weight of each criterion is calculated using intuitionistic fuzzy entropy. Then, the total criteria weight vector is calculated by aggregating the calculated weights. Using the obtained weight vector, the alternatives are ranked based on the association coefficient of the performance of alternatives related to each criterion and the positive ideal intuitionistic fuzzy set value and the negative ideal intuitionistic fuzzy set value. Finally, to show the application of the proposed method, it is implemented in software vendor selection. © 2012 Springer-Verlag.


Khaleie S.,Iran Telecommunication Research Center | Fasanghari M.,Iran Telecommunication Research Center | Tavassoli E.,Payame Noor University
Applied Soft Computing Journal | Year: 2012

Supplier selection is a complicated decision-making problem involving multicriteria, alternative and decision makers (DMs). The main purpose of this paper is to demonstrate the use of a clustering-based method to solve a group decision making (GDM) problem and, also to achieve more realistic and homogeneous results. Intuitionistic fuzzy value (IFV) is used to show the decision makers' preferences and IFN clustering method is utilized to cluster around DM's preferences. Intuitionistic fuzzy weighted geometric (IFWG) is applied to aggregate the obtained clusters. Ranking process is used based on the two indices, score function and accuracy function, to rank the alternatives. Lastly, to demonstrate the efficiency of our proposed method, it is implemented to choose suppliers in a car factory. The strength of the propose approach is considering the group agreement on proposed DMs' preferences for giving different effect on their judgment. Besides, encountering the qualitative judgment of DMs using IFV concept with score function and the accuracy function for modeling the DMs' knowledge is the other contribution of this paper. © 2012 Elsevier B.V. All rights reserved.


Chimeh J.D.,Iran Telecommunication Research Center
Wireless Personal Communications | Year: 2014

Regulatory is a governmental organization that is responsible for evaluating the quality of service (QoS) of the network periodically. To verify the mobile operators' performance, Regulatory should measure network's coverage and QoS parameters like RSSI, CINR, Jitter. Thus, measuring these parameters is a main disturbance for the Regulatory. These disturbances are due to the test accuracies and both their time and cost effectiveness. Totally, drive test includes a vehicle, a laptop, a measuring software and a modem (of indoor/outdoor/USB type). Due to our review, there are many different drive test tools for measurement like Epitiro, Anite that satisfy the enough accuracy but there aren't any systematic scenarios for surveying and accomplishing the drive test. In this paper we want to survey the network quality from the Regulatory point of view. We have gathered new important equations for drive test and introduced three new field test scenarios and compared them with the existing ones. We showed our scenarios are time and cost effective in all cellular mobile networks and are applicable in GSM, UMTS, LTE and mobile WiMax networks. © 2014 Springer Science+Business Media New York.


Daeinabi A.,Sahand University of Technology | Pour Rahbar A.G.,Sahand University of Technology | Khademzadeh A.,Iran Telecommunication Research Center
Journal of Network and Computer Applications | Year: 2011

Vehicular ad hoc networks (VANETs) are appropriate networks that can be used in intelligent transportation systems. Among challenges in VANET, scalability is a critical issue for a network designer. Clustering is one solution for the scalability problem and is vital for efficient resource consumption and load balancing in large scale networks. As our first algorithm, we propose a novel clustering algorithm, vehicular clustering based on the weighted clustering algorithm (VWCA) that takes into consideration the number of neighbors based on dynamic transmission range, the direction of vehicles, the entropy, and the distrust value parameters. These parameters can increase stability and connectivity and can reduce overhead in network. On the other hand, transmission range of a vehicle is important for forwarding and receiving messages. When a fixed transmission range mechanism is used in VANET, it is likely that vehicles are not located in the range of their neighbors. This is because of the high-rate topology changes and high variability in vehicles density. Thus, we propose an adaptive allocation of transmission range (AATR) technique as our second algorithm, where hello messages and density of traffic around vehicles are used to adaptively adjust the transmission range among them. Finally, we propose a monitoring of malicious vehicle (MMV) algorithm as our third algorithm to determine a distrust value for each vehicle used in the VWCA. The effectiveness of the proposed algorithms is illustrated in a highway scenario. © 2010 Elsevier Ltd. All rights reserved.


Maleki M.,Iran Telecommunication Research Center
2nd International Conference on Software Engineering and Data Mining, SEDM 2010 | Year: 2010

One of the main preprocessing steps for having a high performance text classifier is feature weighting. Commonly used feature weighting methods such as TF and IDF-based methods only consider the distribution of a feature in the document(s) and do not consider class information for feature weighting. In this paper, we present TFCRF (Term Frequency and Category Relevancy Factor) method in which the weight of features depends on their power to discriminate the classes from each other by using class information. The results show significant improvement in the performance of SVM algorithm by using TFCRF feature weighting method in comparison to the other implemented standard feature weighting methods.


Farhoodi M.,Iran Telecommunication Research Center | Yari A.,Iran Telecommunication Research Center
Proc. - 6th Intl. Conference on Advanced Information Management and Service, IMS2010, with ICMIA2010 - 2nd International Conference on Data Mining and Intelligent Information Technology Applications | Year: 2010

Automatic document classification due to its various applications in data mining and information technology is one of the important topics in computer science. Classification plays a vital role in many information management and retrieval tasks. Document classification, also known as document categorization, is the process of assigning a document to one or more predefined category labels. Classification is often posed as a supervised learning problem in which a set of labeled data is used to train a classifier which can be applied to label future examples [1]. Document classification includes different parts such as text processing, feature extraction, feature vector construction and final classification. Thus improvement in each part should lead to better results in document classification. In this paper, we apply machine learning methods for automatic Persian news classification. In this regard, we first try to exert some language preprocess in Hamshahri dataset [2], and then we extract a feature vector for each news text by using feature weighting and feature selection algorithms. After that we train our classifier by support vector machine (SVM) and K-nearest neighbor (KNN) algorithms. In Experiments, although both algorithms show acceptable results for Persian text classification, the performance of KNN is better in comparison to SVM.

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