en
jalali
1397
12
1
gregorian
2019
3
1
11
1
online
1
fulltext
en
Transmit Power Optimization in Optical Coherent Transmission Systems: Analytical, Simulation, and Experimental Results
In this paper, we propose to use discretized version of the so-called Enhanced Gaussian Noise (EGN) model to estimate the non-linearity effects of fiber on the performance of optical coherent and uncompensated transmission (CUT) systems. By computing the power of non-linear interference noises and considering optical amplifier noises, we obtain the signal-to-noise (SNR) ratio and achievable rate of CUT. To allocate power of each CUT channel, we consider two optimization problems with the objectives of maximizing minimum SNR margin and achievable rate. We show that by using the discretized EGN model, the complexity of the introduced optimization problems is reduced compared with the existing optimization problems developed based on the so-called discretized Gaussian Noise (GN) model. In addition, the optimization based on the disctretized EGN model leads to a better SNR and achievable rate. We validate our analytical results with simulations and experimental results. We simulate a five-channel coherent system on OptiSystem software, where a close agreement is observed between optimizations and simulations. Furthermore, we measured SNR of commercial 100Gbps coherent transmitter over 300 km single-mode fiber (SMF) and non-zero dispersion shifted fiber (NZDSF), by considering single-channel and three-channel coherent systems. We observe there are performance gaps between experimental and analytical results, which is mainly due to other sources of noises such as transmitter imperfection noise, thermal noise, and shot noise, in experiments. By including these sources of noises in the analytical model, the gaps between analytical and experimental results are reduced.
Optical Coherent Transmission Systems, Fiber Non-linear Interference Noise, Power Allocation, Minimum Signal-to-Noise-Ration (SNR) Margin, Maximum Achievable Capacity.
1
11
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-164-1&slc_lang=en&sid=1
2019/03/5
1397/12/14
2022/03/2
1400/12/11
Ramin
Hashemi
Amirkabir University of Technology
raminhashemi@aut.ac.ir
0031947532846001418
0031947532846001418
No
Mehdi
Habibi
Amirkabir University of Technology
mehdi_habibi@aut.ac.ir
0031947532846001419
0031947532846001419
No
Hamzeh
Beyranvand
Amirkabir University of Technology
beyranvand@aut.ac.ir
0031947532846001420
0031947532846001420
Yes
Ali
Emami
Iran Telecommunication Research Center
emami@itrc.ac.ir
0031947532846001421
0031947532846001421
No
Mahdi
Hashemi
Iran Telecommunication Research Center
mhashemi@itrc.ac.ir
0031947532846001422
0031947532846001422
No
Davood
Ranjbar Rafie
Iran Telecommunication Research Center
ranjbar@itrc.ac.ir
0031947532846001423
0031947532846001423
No
en
Numerical Full wave Analysis and Modeling of Plasmonic HEMT Performance Using Hydrodynamic Model
The two dimensional (2D) plasmon propagation in the channel of a high electron mobility transistor (HEMT) is numerically modeled using the full wave technique. To analyze the proposed structure, the Maxwell’s equations are solved inconjuction to the hydrodynamic transport equations, using the finite difference time domain (FDTD) method. The properties of the 2D plasmons propagation along the channel are investigated by applying different bias voltages to the drain and the gate terminals. The obtained results show that the wavelengths and the propagation constants of the 2D plasmons are considerably affected by varying the bias voltages. Moreover, the proposed full wave model is employed to investigate the tunability of a grating gate HEMT detector over terahertz (THz) frequencies. Our studies demonstrate that it is possible to control the characteristics of the 2D plasmon propagation along the channel of HEMT devices to produce various types of reconfigurable structures used for THz applications.
2DEG channel, 2D plasmon, full wave model, hydrodynamic equations, HEMT structure, resonant detector
12
18
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-4250-1&slc_lang=en&sid=1
2019/03/52019/04/30
1398/2/10
2022/03/22019/10/16
1398/7/24
Farzaneh
Daneshmandian
Microwave/mm-Wave & Wireless Communication Research Lab
f.daneshmandian@aut.ac.ir
0031947532846001424
0031947532846001424
No
Abdolali
Abdipour
Microwave/mm-Wave & Wireless Communication Research Lab
abdipour@aut.ac.ir
0031947532846001425
0031947532846001425
Yes
Amir Nader
Askarpour
Microwave/mm-Wave & Wireless Communication Research Lab
askarpour@aut.ac.ir
0031947532846001426
0031947532846001426
No
other
Continuous Double Auction Scheduling in Federated Cloud Services
In recent years, researchers have introduced many different mechanisms to improve resource allocation in the cloud. One of these resource allocation methods is market-based resource allocation which exploits different models used in exchanging goods and services. In this research, a two-way auction model is used for allocating cloud resources based on the market model. In the case of federated clouds, as the providers may face a shortage of resources during their operation; therefore, the continuous double auction model is suggested to create a cloud federation environment to support a suitable resource allocation among different providers. In our experiment 1, fixed pricing with Reputation-Aware Continuous Double Auction, Continuous Double Auction, and Market-Driven Continuous Double Auction models will be executed for resource allocation. It shows that both the resource efficiency and the income of the providers are improved in the federated clouds using these models. In experiment 2, with changing the type and number of the requested resources by customers and providers, the proposed federated model is also tested. The results of the experiments show that our proposed model for implementing federated clouds based on the continuous double auction model, in terms of successful allocation rates, resource efficiency and provider revenue, is better than other marketoriented models.
Resource allocation,Cloud federation,Continuous double auction,Double Auction model
19
26
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-4240-2&slc_lang=en&sid=1
2019/03/52019/04/302022/03/2
1400/12/11
2022/03/22019/10/162022/03/2
1400/12/11
Mohaddeseh
Hosseinpour
University of Science and Culture
hosseinpour.moh@gmail.com
0031947532846001405
0031947532846001405
No
Alireza
Yari
ICT research institute
a_yari@itrc.ac.ir
0031947532846001406
0031947532846001406
Yes
Hamidreza
Nasiriasayesh
ICT research institute
hr_nasiri@itrc.ac.ir
0031947532846001407
0031947532846001407
No
en
Automatic Synset Extraction from text documents using a Graph-Based Clustering Approach
Semantic relations between words like synsets are used in automatic ontology production which is a strong tool in many NLP tasks. Synset extraction is usually dependent on other languages and resources using techniques such as mapping or translation. In our proposed method, synsets are extracted merely from text and corpora. This frees us from the need for special resources including Word-Nets or dictionaries. The representation model for words of corpus is based on Vector Space model and the most similar words to each are extracted based on common features count (CFC) using a modified cosine similarity measure. Furthermore, a graph-based soft clustering approach is applied to create clusters of synonymous words.
To examine performance of the proposed method, Extracted synsets were compared to other Persian semantic resources. Results show an accuracy of 80.25%, which indicates improvement in comparison to the 69.5% accuracy of pure clustering by committee method.
Automatic Synset Extraction, Semantic Relation, Graph-based Clustering, CBC clustering, Persian
27
35
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-1586-1&slc_lang=en&sid=1
2019/03/52019/04/302022/03/22019/01/23
1397/11/3
2022/03/22019/10/162022/03/22019/10/1
1398/7/9
Mahsa
Khorasani
School of Computer Engineering Iran University of Science and Technology
khorasani_mahsa@yahoo.com
0031947532846001427
0031947532846001427
No
Behrouz
Minaei-Bidgoli
School of Computer Engineering Iran University of Science and Technology
b_minaei@iust.ac.ir
0031947532846001428
0031947532846001428
Yes
Chakaveh
Saedi
NLP Research Lab. Computer Engineering Dept. Shahid Beheshti University
ch_saedi@sbu.ac.ir
0031947532846001429
0031947532846001429
No
other
Fuzzy Sequential Pattern Mining over Quantitative Streams
Sequential pattern mining is an interesting data mining problem with many real-world applications. Though new applications introduce a new form of data called data stream, no study has been reported on mining sequential patterns from the quantitative data stream. This paper presents a novel algorithm, for mining quantitative streams. The proposed algorithm can mine exact set of fuzzy sequential patterns in sliding window and gap constraints entailing the most recent transactions in a data stream. In addition, the proposed algorithm can also mine non-quantitative or transaction-based sequential patterns over a data stream. Numerical results show the running time and the memory usage of the proposed algorithm in the case of quantitative and customer-transaction-based sequence counting are proportional to the size of the sliding window and gap constraints
Data Stream,Fuzzy Sequential Pattern Mining,Gap Constraint,Sliding Window
36
44
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-383-1&slc_lang=en&sid=1
2019/03/52019/04/302022/03/22019/01/232022/03/2
1400/12/11
2022/03/22019/10/162022/03/22019/10/12022/03/2
1400/12/11
Omid
Shakeri
Electrical & Computer Engineering Dept. Kharazmi University
omid.shakeri@khu.ac.ir
0031947532846001408
0031947532846001408
No
Manoochehr
Kelarestaghi
Electrical & Computer Engineering Dept. Kharazmi University
kelarestaghi@khu.ac.ir
0031947532846001409
0031947532846001409
Yes
Farshad
Eshghi
Electrical & Computer Engineering Dept. Kharazmi University
farshade@khu.ac.ir
0031947532846001410
0031947532846001410
No
Ahmad
Ganjtabesh
Electrical & Computer Engineering Dept. Kharazmi University
std_agt@khu.ac.ir
0031947532846001411
0031947532846001411
No
en
Converting and Completing RDF Based Ontology into Fuzzy Ontology Using Neural Tensor Network
The fuzzy ontology is an ontology in which some of its aspects are fuzzified for a special application. Based on these aspects, there are different definitions of fuzzy ontology, but here, a new definition is proposed based on the aspect of the facts. In some applications, an ontology can be converted into a fuzzy one, and it may be incomplete. In this paper, a new method is introduced for the purpose of conversion and completion of fuzzy ontology based on the facts aspect. The ontology is a knowledge base in which there are many facts about the real world. The existence of uncertainties in facts of ontologies reveals the importance of fuzzy ontologies based on facts aspect. So far, many methods have been suggested to create a fuzzy ontology. Each of them focuses on some aspects to convert them into fuzzy. The focus of this paper is on facts of RDF based ontology. The first suggestion of this paper is a new algorithm in which with converting these facts into fuzzy one, fuzzy version of ontology is gained. In this algorithm neural tensor network is used as state-of-the-art in the relevant field. This network was used to complete an ontology, based on the existing facts of the ontology, but in this paper, it is used for converting and completing an ontology into a fuzzy one. For this purpose, a new definition of the fuzzy ontology for the aim of this paper will be proposed. Experimental results of the proposed method show comparing this method with similar works and then its advantages will be discussed.
Ontology, Fuzzy Ontology, Facts, Neural Tensor Network, RDF
45
56
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-391-1&slc_lang=en&sid=1
2019/03/52019/04/302022/03/22019/01/232022/03/22019/01/26
1397/11/6
2022/03/22019/10/162022/03/22019/10/12022/03/22019/09/21
1398/6/30
Farhad
Abedini
Qazvin Islamic Azad University
abedini.ac@gmail.com
0031947532846001430
0031947532846001430
No
Mohammadreza
Keyvanpour
Alzahra University
keyvanpour@alzahra.ac.ir
0031947532846001431
0031947532846001431
Yes
Mohammad Bagher
Menhaj
Amirkabir University
menhaj@aut.ac.ir
0031947532846001432
0031947532846001432
No
en
A Novel Model for MSSP Maturity Assessment
The growing threat and security risks in information and communication technology, beside the increasing use of information and communication technologies, are two main decision makers for executives of organizations, service providers and the general public. Internet Service Providers are known to be the main stakeholder of this space. If Internet Service Providers have known as untrusted party, it can put their business and community level services at serious risk. Moreover, Resource limitation and the lack of expert in cyber security have made lots of major challenge for ISPs in dealing with and managing security threats. In many developing countries, this problem has been solved using Managed Security Service Providers. Managed Security Services are network-based security services that are outsourced by a trusted third party. The diversity of Managed Security Service Providers affects the effectiveness and efficiency of decision making in this area, as well as the correct selection of them. Therefore, in order to outsource the security services, the assessment of these organizations is inevitable. This assessment can be done by various mechanisms. One of the acceptable strategies in the security is the maturity model. Maturity models are step-by-step solutions to grow organizational capabilities along a predicted, desirable, or logical path. In fact, maturity models provide standard way to assess process maturity along with business process improvement. Hitherto, no maturity model has been developed to assess the Managed Security Service Providers. Therefore, in this paper considering different issues to evaluate frameworks, we have proposed a novel model to assess the maturity of Managed Security Service Providers. The evaluation of the proposed maturity model is based on multiple case studies.
57
70
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-4261-2&slc_lang=en&sid=1
2019/03/52019/04/302022/03/22019/01/232022/03/22019/01/262019/05/29
1398/3/8
2022/03/22019/10/162022/03/22019/10/12022/03/22019/09/212022/03/2
1400/12/11
massoud
kassaee
Assistant Professor, University of Shahid Beheshti
massoudkass@yahoo.com
0031947532846001433
0031947532846001433
No
Mohammad
Gholami Merhabadi
Ph.D. student of Industrial Management, Faculty of Management and Accounting, Shahid Beheshti University
Mo_gholami@sbu.ac.ir
0031947532846001434
0031947532846001434
No
abouzar
arabsorkhi
ICT Research Institute Tehran, Iran
abouzra_arab@itrc.ac.ir
0031947532846001435
0031947532846001435
Yes