مجله فناوری اطلاعات در طراحی مهندسی
سال هشتم شماره 2 (پاییز و زمستان 1394)

Social networks are social structures between individuals and their relationship which play a significant role in information spread, nowadays. An appropriate solution for being informed about the process of information and news spread in these networks is the identification of the critical edges between members of the society. In a lot of articles identifying the edge will be static that such important challenges in this field as well as the interactions between users is not considered.
In this paper, a new method is introduced for the identification of effective communication channels that has the ability to comply with the various social networks and real-world structures. This method identifies the effective communication channels based on the data volume transferred between members of the society dynamically, the centrality of interstitial of edges and the features of the two nodes. The model presented in this paper can maintain the specificity of data. The results of the evaluation indicate that this method has desired results compared to similar methods.

recently, the website social networks provide a rich resource of Heterogeneous data which the analysis of these data can lead to discover unknown information and relations in these networks. The discovery of community including “similar” nodes is a challenging issue in analysis of social networks data, and it has widely studied in the social networking community in the context of the structure of the underlying graphs. The online social networks, additionally having graph structures, include effective information of users within networks, which using this information can lead to improve the quality of communitie's discovery.
In this paper, for detecting community, a method is provided that in addition to the communication among nodes, to improve the quality of the discovered communities, content information is used, as well. This method is a new approach based on a frequent pattern and the actions of users in the network and in particular, is performed on social networking sites where users select their favorite acts. First, based on Interests and activities of users in networks, we discover some small communities of similar users, and then by using social relations, extend (those) communities.
The F-measure evaluated results on two real-world datasets (Blogcatalog and Flicker) demonstrate that the proposed method leads to improvethe quality of community detection.

Nowadays, due to advances in technology and information technology fast processing of data is a growing need. Residue Number System is an unconventional and non-Weighted System that parallel computing on remainders of dividing number on several modules is performed. The computations are performed cause to increase the speed of computing and reduce the power consumption.
One of the problems in this system is published overflow that due to the modular operations and nature of non-weighting numbers. In order to solve this problem in many cases using to binary number system converter and convert the number to the weighted numbers to comparison and overflow detection is necessary.
In this article, the novel methods to improve the detection of overflow in moduli set {2n-1, 2n, 2n} without doing a full comparison and need the reverse convert process. Also The proposed method is compared with previous approaches, in addition to fewer hardware components, the delay is very low.

The tremendous growth of Internet, both in terms of the number of users and the usage time, and thus bandwidth taken by each user, is a major factor that has made the need of optical telecommunication networks and modulations with high spectrum efficiency undeniable. In this paper, the performance of each modulation CDMA, OFDM can be individually investigated and simulated. The combination between OFDM and CDMA systems to achieve better spectrum efficiency in opical fiber is proposed and its performance in the presence of AWGN and dispersion is simulated. The OOC codes are used to asign to the user and the reciever is a PIN detector. he simulation results are compared in different situation and combinations of the wo modulation is preffered other methods.

Data access optimization is one of the most important challenges for organizations and competitive businesses therefore selecting useful index is one of the significant techniques to optimize their databases. With the creation of very large databases and consequently need for more advanced query optimizer; database administrator role was not singly enough for finding suitable indexes in database management systems and finding indexes automatically by these systems was considered by researchers in this field. So far several techniques such as data mining techniques are proposed for finding indexes automatically. However there is a challenge in using data mining techniques, whether all indexes have been found are useful and necessary or not? Obviously creating unnecessary indexes is costly in terms of time and memory. For solving the problem, in this research has been proposed an effective way using Maximal frequent Pattern to reduce time for finding indexes automatically. Also in the proposed method using the allocated proper weight to the patterns which have been found, unnecessary indexes are not created. The proposed method like previous works is evaluated on standard data and queries by several experiments, considering the TPC-H benchmark assessment. Test results show that the automatic creation of required indexes using the proposed method compared to the previous method, which makes use of frequent patterns is less time required.