Volume-7 Issue-3

S. No

Volume-7 Issue-3, July 2017, ISSN:  2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Vipul Dalal, Latesh Malik

Paper Title:

Data Clustering Approach for Automatic Text Summarization of Hindi Documents using Particle Swarm Optimization and Semantic Graph

Abstract: Automatic text summarization is a process of describing important information from given document using intelligent algorithms. A lot of methods have been proposed by researchers for summarization of English text. Automatic summarization of Indian text has received a very little attention so far. In this paper, we have proposed a data clustering approach for summarizing Hindi text using semantic graph of the document and Particle Swarm Optimization (PSO) algorithm. PSO is one of the most powerful bio-inspired algorithms used to obtain optimal solution. The subject-object-verb (SOV) triples are extracted from the document. These triples are used to construct semantic graph of the document and finally clustered into summary and non-summary groups. A classifier   is trained using PSO algorithm which is then used to obtain document summary.

Keywords:  bio-inspired algorithms, text mining, text summarization, semantic graph, PSO, data clustering

References:
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5.       Massih R. Amini, Nicolas Usunier, and Patrick Gallinari, "Automatic Text Summarization Based on Word-Clusters and Ranking Algorithms", ECIR 2005, LNCS 3408, pp. 142–156, (2005).
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8.       Aysun Güran, Eren Bekar, Selim Akyokuş “A Comparison of Feature and Semantic-Based Summarization Algorithms or Turkish”. INISTA 2010, International Symposium on Innovations in Intelligent Systems and Applicaitons, 21-24June 2010, Kayseri & Cappadocia,TURKEY.
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11.    Giuseppe Carenini and Jackie Chi Kit Cheung, “Extractive vs. NLG-based Abstractive Summarization of Evaluative Text: The Effect of Corpus Controversiality”.
12.    Pierre-Etienne Genest, Guy Lapalme, “Framework for Abstractive Summarization using Text-to-Text Generation”, Workshop on Monolingual Text-To-Text Generation, pages 64–73,Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 64–73,Portland, Oregon, 24 June 2011. c 2011 Association for
Computational Linguistics.
13.    Vipul Dalal, Dr. Latesh Malik.: “A Survey of Extractive & Abstractive Text Summarization”, 6th International Conference on Emerging Trends in Engineering & Tecnology (ICETET), 2013
14.    M. S. Binwahlan, Salim, N., & Suanmali, L.: “Swarm based features selection for text summarization”, International Journal of Computer Science and Network Security IJCSNS, vol. 9, pp. 175-179, 2009b.
15.    M. S. Binwahlan, Salim, N., & Suanmali, L.: “Swarm Based Text Summarization”,  Computer Science and Information Technology – Spring Conference, 2009. IACSITSC '09. International Association of, 2009, pp. 145-150.
16.    Albaraa Abuobieda M. Ali, Naomie Salim, Rihab Eltayeb Ahmed, Mohammed Salem Binwahlan, Ladda Sunamali, Ahmed Hamza.: “Pseudo Genetic And Probabilistic-Based Feature Selection Method For Extractive Single Document Summarization”, Journal of Theoretical and Applied Information Technology, 15th October 2011. Vol. 32 No.1,
ISSN: 1992-8645, E-ISSN: 1817-3195.
17.    Alkesh Patel,  Tanveer Siddiqui,  U. S. Tiwary.: “A language independent approach to multilingual text summarization”, Conference RIAO2007, Pittsburgh PA, U.S.A. May 30-June 1, 2007 - Copyright C.I.D. Paris, France
18.    Naresh Kumar Nagwani, Shrish Verma.: “A Frequent Term and Semantic Similarity based Single Document Text Summarization Algorithm”, International Journal of Computer Applications (0975 – 8887) Volume 17– No.2, March 2011.
19.    Kamal Sarkar.: “Bengali Text Summarization By Sentence Extraction”
20.    Upendra Mishra, Chandra Prakash.: MAULIK: “An Effective Stemmer for Hindi Language”, International Journal on Computer Science and Engineering (IJCSE), ISSN : 0975-3397, Vol. 4 No. 05 May 2012
21.    Vishal Gupta, Gurpreet Singh Lehal.: “Preprocessing Phase of Punjabi language Text Summarization”
22.    Jurij Leskovec, Natasa Milic-Frayling, Marko Grobelnik.: “Extracting Summary Sentences Based on the Document Semantic Graph, Microsoft Research, Microsoft Corporation
23.    Regina Barzilay, Michael Elhadad.: “Using Lexical Chains for Text Summarization”, In Proceedings of the Intelligent Scalable Text Summarization Workshop (ISTS'97). Madrid: ACL, 1997. 10-17.
24.    Kavita Ganesan, ChengXiang Zhai, Jiawei Han.: “Opinosis: A Graph-Based Approach to Abstractive Summarization of Highly Redundant Opinions”.
25.    Eduard Hovy and Chin-Yew Lin.: “Automated Text Summarization in SUMMARIST”, In I. Mani and M. Maybury (eds), Advances in Automated Text Summarization. MIT Press.26.    Udo Hahn, Inderjeet Mani. : “The Challenges of Automatic Text Summarization”, IEEE Computer Society Press Los Alamitos, CA, USA, Volume 33 Issue 11, November 2000, Page 29-36  ISSN:0018-9162.
27.    Chetana Thaokar, Latesh Malik, “Test Model for Summarizing Hindi Text using Extraction Method”, Proceedings of 2013 IEEE Conference on Information and Communication Technologies (ICT 2013).
28.    Reddy Siva. Natural Language Processing Tools. December. 2012 URL: http://sivareddy.in/downloads

1-3

2.

Authors:

Haeeder Munther Noman

Paper Title:

PCF and DCF Performances Evaluation for a Non Transition 802.11 Wireless Network using OPNET Modular

Abstract:  Wireless Local Area Networks (WLANs) take increased a percentage of acceptance as they can offer an access to independent site network among computing systems. IEEE 802.11 WLAN is the best organized wireless knowledge with probably show a key role in the wireless tele-communication networks for the next generation. Many access techniques have been utilized in Wireless Networks, mainly DCF in addition to PCF can be the essential access methods. Main features to the 802.11 WLAN technologies deal with simplicities, flexibilities, and effectiveness of cost. 802.11 standards specify Many _mechanisms of essential access: Distributed Coordination Function (DCF) and Point Coordination Function (PCF) present in the MAC layer of the OSI Protocol  stack. This paper mainly deals with a performance presented at these mechanisms from where the end to end delay, throughput and average delays.

Keywords: Wireless LAN, IEEE 802.11, DCF, PCF, Opnet Simulator.

References:
1.       Bhaskar, B. Mallick, “Performance Evaluation of MAC Protocol for IEEE 802.11, 802.11Ext. WLAN and IEEE 802.15.4 WPAN using NS-2”, International Journal of Computer Applications, Volume 119 – No.16, June 2015.
2.       Boskovic, B. and Markovic, M. (2000). On Spread Spectrum Modulation Techniques Applied in IEEE 802.11 Wireless LAN Standard. 4, 238-241.
3.       Kaur, M. Bala, H. Bajaj, “Performance Evaluation of Wlan by Varying Pcf, Dcf and Enhanced Dcf Slots to Improve Quality of Service”, IOSR Journal of Computer Engineering (IOSRJCE), Vol. 2, Issue 5 (July-Aug. 2012), PP 29-33.
4.       Sarah Shaaban, Dr. Hesham M. El Badawy, Prof. Dr. Attallah Hashad, "Performance Evaluation of the IEEE 802.11 Wireless LAN Standards," Proceedings of World Congress on Engineering, vol. I, 2-4, 2008.
5.       J. Alonso-Zárate, C. Crespo, Ch.Skianis, L. Alonso, Ch. Verikoukis, “Distributed Point Coordination Function for IEEE 802.11 Wireless Ad hoc Networks”,  Elsevier Ad Hoc Networks Journal, October 2011, doi:10.1016/j.adhoc.2011.09.004.
6.       Moustafa A. Youssef, Arunchandar Vasan, Raymond E. Miller, "Specification and analysis of the DCF and PCF protocols in the 802.11 standard using systems of communicating machines", 2002, ISSN:1092- 1648,pp:132 – 141.Symposium, 4, 11-14.Telecommunications Review-4, 5, 287-291.7.       Mohammad Hussain Ali, Manal Kadhim Odah, “Simulation Study 0f 802.11b   DCF Using OPNET Simulator”, Eng. & Tech. Journal, Vol. 27, No6, 2009.
8.       N. Singha, K. Aroraa, S. Goyal, “Performance of Wireless LAN in DCF and EDCF using OPNET”, IJESM Vol.2, No.3 (2012).
9.       I.Kaur, M. Bala, H. Bajaj, “Performance Evaluation of Wlan by Varying Pcf, Dcf and Enhanced Dcf Slots To Improve Quality of Service”, IOSR Journal of Computer Engineering (IOSRJCE), Volume 2, Issue 5 (July-Aug. 2012), PP 29-33.
10.    OPNET LABS, “Creating Wireless Network,” 200. 

4-8

3.

Authors:

Neety Bansal, Parvinder Kaur

Paper Title:

A Survey on Soft Computing Based Approaches for Fuzzy Model Identification

Abstract: The identification of an optimized fuzzy model is one of the key issues in the field of fuzzy system modeling. This can be formulated as a search and optimisation problem and many hard computing as well as soft computing approaches are available in the literature to solve this problem. In this paper we have made an attempt to present a survey on fuzzy model identification using some soft computing techniques like ACO, BBO, BB-BC, ABC, etc.

Keywords:  Fuzzy system, Fuzzy model identification, Soft computing, Nature inspired approaches.

References:
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2.          John Yen and Reza Langari, “Fuzzy Logic Intelligence, Control and Information,” Prentice Hall, New Jersey, 1999.
3.          Plamen A. et al., “Identification of Evolving Fuzzy Rule-Based Models,” IEEE Transactions on Fuzzy Systems, Vol. 10, No.5, pp.667-677, 2002.
4.          P. Bhalla et al., “Soft Computing Approaches to Fuzzy System Identification: A Survey,” 3rd International Conference on Intelligent Systems and Networks (IISN-2009), February 14-16,2009.
5.          L.A. Zadeh, “Fuzzy logic, neural networks, and soft computing,” Commun. ACM, vol. 37, pp. 77-84, 1994.
6.          W.A. Farag, V.H. Quintana, and G. Lambert-Torres, “A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems,” IEEE Trans. Neural Networks, vol. 9, pp. 756-767, 1998.
7.          C.L. Karr and E.J. Gentry, “Fuzzy Control of pH using genetic algorithms,” IEEE Transactions on Fuzzy Systems, Vol. 1, No. 1, pp.46-53, 1993.
8.          J. Liska and S. Melsheimer, “Complete Design of Fuzzy Logic Systems using Genetic Algorithms,” Proceedings of International Conference on Fuzzy Systems, Orlando, FL, pp.1377-1382, 1994.
9.          T. Pal and Nikhil R. Pal, “SOGARG: A self organized genetic algorithm based rule generation scheme for fuzzy controllers,” IEEE Transactions on Evolutionary Computation, vol. 7, no. 4, Aug. 2003.
10.       Andreas Bastian, “Identifying fuzzy models utilizing genetic programming”, Fuzzy sets and systems– Elsevier, 2000.
11.       M. Setnes and H. Roubos, “GA-fuzzy modeling and classification: complexity and performance”, IEEE transactions on Fuzzy Systems, vol. 8, 2000.
12.       Haralambos Sarimveis, George Bafas, “Fuzzy model predictive control of non-linear processes using genetic algorithms”, Fuzzy sets and systems– Elsevier, Oct. 2002.
13.       Eghbal G. Mansoori, M.J. Zolghadri and S.D. Katebi, “SGERD: A steady-state genetic algorithm for extracting fuzzy classification rules from data,” IEEE
Transactions on Fuzzy Systems, Vol.16, No.4, pp. 1061-1071, Aug. 2008.
14.       Z. Ning, Y S. Ong, K.W. Wong and K.T. Seow, “Parameter identification using Memetic algorithms for fuzzy systems,” Proc. of the fourth Int’l conf. on intelligent technologies (Intech’03), pp 833-839, 2003.
15.       M. Ishikawa, “Structural learning with forgetting,” Neural Networks, vol. 9, pp. 509–521, 1996.
16.       W. Duch, R. Adamczak, and K. Grabczewski, “Extraction of logical rules from neural networks,” Neural Process. Lett., vol. 7, pp. 211–219, 1998.
17.       L. Fu, “Rule generation from neural networks,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 24, No. 8, pp. 1114-1124, Aug. 1994.18.       R. Setiono and W. K. Leow, “FERNN: An algorithm for fast extraction of rules from neural networks,” Appl. Intell., 2000, to be published.
19.       R. Setiono and H. Liu, “Neuro Linear: From neural networks to oblique decision rules,” Neurocomputation, vol. 17, pp. 1–24, 1997.
20.       Wen Yu and Xiaoou Li, “Fuzzy Identification Using Fuzzy Neural Networks With Stable Learning Algorithms”, IEEE Transactions On Fuzzy Systems, Vol. 12, No. 3, June 2004.
21.       G Leng, TM McGinnity, G Prasad, “An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network”, Fuzzy sets and systems- Elsevier, 2005.
22.       Rahib Hidayat Abiyev, Okyay Kaynak, “Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants—A Novel Structure and a Comparative Study”, IEEE Transactions on Industrial Electronics, Vol. 55, Issue: 8, Aug. 2008.
23.       Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. New York: Oxford University Press.
24.       He Zhenya, Wei Chengjian, Yang Luxi, Gao Xiqi, Yao Susu, R.C. Eberhart, Yuhui Shi, “Extracting rules from fuzzy neural network by particle swarm optimisation”, Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, 1998.
25.       R. Marinke ; E. Araujo ; Ld.S. Coelho ; I. Matiko, “Particle swarm optimization (PSO) applied to fuzzy modeling in a thermal-vacuum system”, Hybrid Intelligent Systems, 2005. HIS '05.
26.       A.A.A. Esmin, A.R. Aoki, G. Lambert-Torres, “Particle swarm optimization for fuzzy membership functions optimization,” IEEE Int’l Conf. on Syst., Man and Cybern., vol. 3, Oct. 2002.
27.       Arun Khosla, Shakti Kumar, K.K.Aggarwal, Jagatpreet Singh, “Particle Swarm Optimizer for building fuzzy models,” Proceeding of one week workshop on applied soft computing SOCO-2005, Haryana Engg.College, Jagadhri, India, July 25-30, pp 43-71, 2005.
28.       Ernesto Araujo, Leandro dos S. Coelho, “Particle swarm approaches using Lozi map chaotic sequences to fuzzy modeling of an experimental thermal-vacuum system”, Applied Soft Computing- Elsevier, October 2007.
29.       Marco Dorigo and Thomas Stutzle, Ant Colony Optimization, Eastern Economy Edition, PHI, 2005.
30.       J. Casillas, O. Cordon and F. Herrera, “Learning fuzzy rules using ant colony optimization algorithms,” Proc. 2nd Int. Workshop Ant Algorithms, 2000, pp. 13-21.
31.       Shakti Kumar, “Rulebase generation using ant colony optimization,” Proc. of the one-week workshop on applied soft computing (SOCO-2006), Haryana Engg. College, Jgadhri, July 2006.
32.       Shakti K., P. Bhalla and S.Sharma, “Automatic Fuzzy Rule-base Generation for Intersystem Handover using Ant Colony Optimization Algorithm,” International Conference on Intelligent Systems and Networks (IISN-2007), Feb 23-25, 2007, MAIMT, Jagadhri, Haryana, India, pp. 764-773.
33.       Shakti K., P. Bhalla, “Fuzzy Rulebase Generation from Numerical Data using Ant Colony Optimization,” MAIMT- Journal of IT & Management. Vol.1, No.1 May - Oct. 2007, pp. 33-47.
34.       Chia-Feng Juang ; Po-Han Chang, “Designing Fuzzy-Rule-Based Systems Using Continuous Ant-Colony Optimization”, IEEE Transactions on Fuzzy Systems (Vol.: 18, Issue: 1, Feb. 2010).
35.       SM Vieira, JMC Sousa, TA Runkler, “Two cooperative ant colonies for feature selection using fuzzy models”, Expert Systems with Applications, 2010 – Elsevier.
36.       D Simon. ‘Biogeography-Based Optimization’. IEEE Transaction on Evolutionary Computation, vol 12, no 6, December 2008, pp 702-713.
37.       S. Kumar, P. Bhalla, A. Singh, “Fuzzy Rule base Generation from Numerical Data using Biogeography-based Optimization”, Journal of Institution of Engineers IE (I), vol. 90, pp. 8-13, July 2009.
38.       Yu-Jun Zheng ; Hai-Feng Ling ; Sheng-Yong Chen ; Jin-Yun Xue, “A Hybrid Neuro-Fuzzy Network Based on Differential Biogeography-Based Optimization for Online Population Classification in Earthquakes”, IEEE Transactions on Fuzzy Systems ( Volume: 23, Issue: 4, Aug. 2015 ).
39.       Erol, O. K. and Eksin, I. 2006. A new optimization method: Big Bang-Big Crunch, Advances in Engineering Software. 37(2): 106-111.
40.       S. Kumar, P. Bhalla, A. Singh, “Fuzzy Rule base Generation from Numerical Data using Big Bang-Big Crunch Optimization”, Journal of Institution of Engineers IE (I), vol. 91, pp. 18-25, January 2011.
41.       Tufan Kumbasar ; Engin Yesil ; Ibrahim Eksin ; Mujde Guzelkaya, “Inverse fuzzy Model Control with online adaptation via Big Bang-Big Crunch optimization”, Communications, Control and Signal Processing, 2008. ISCCSP 2008.
42.       S. Kumar, S.S Walia, A. Singh, “Parallel Big Bang-Big Crunch Algorithm”, International Journal of Advanced Computing, ISSN:2051-0845,Vol.46, Issue.3, Sept. 2013.
43.       Engin Yesil, “Interval type-2 fuzzy PID load frequency controller using Big Bang–Big Crunch optimization”, Applied Soft Computing, Volume 15, February 2014, Pages 100–112.
44.       Karaboga, D. 2005. An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department.
45.       S. Kumar, P. Bhalla, A. Singh, “Fuzzy Rule base Generation: An Artificial Bee Colony Approach”, 5th International Multi Conference on Intelligent Systems, Sustainable, New and Renewable Energy Technology and Nanotechnology (IISN-2011), February 18 -20, 2011.
46.       Dervis Karaboga and Celal Ozturk, “Fuzzy clustering with artificial bee colony algorithm”, Scientific Research and Essays Vol. 5(14), pp. 1899-1902, 18 July, 2010.
47.       Fayssal Beloufa, M.A. Chikh, “Design of fuzzy classifier for diabetes disease using Modified Artificial Bee Colony algorithm”, Computer methods and programs in biomedicine, 2013 – Elsevier.
48.       Hacene Habbi, Yassine Boudouaoui, Dervis Karaboga, Celal Ozturk, “Self-generated fuzzy systems design using artificial bee colony optimization”, Information Sciences, 2015 – Elsevier.
49.       S. Kumar, S.S. Walia, P. Kaur, “Fuzzy System Identification: A Firefly Optimisation Approach”, International Journal of Electronics and Communication Engineering (IJECE) ISSN(P): 2278-9901; ISSN(E): 2278-991X Vol. 3, Issue 6, Nov 2014.
50.       N. Susila, S. Chandramathi, Rohit Kishore, “A Fuzzy-based Firefly Algorithm for Dynamic Load Balancing in Cloud Computing Environment”, Journal Of Emerging Technologies In Web Intelligence, Vol. 6, No. 4, November 2014.
51.       Nguyen Cong Long and Phayung Meesad, “An optimal design for type–2 fuzzy logic system using hybrid of chaos firefly algorithm and genetic algorithm and its application to sea level prediction”, Journal of Intelligent & Fuzzy Systems 27 (2014) 1335–1346.52.       K. Mohana Sundaram, R. Senthil Kumar, C. Krishnakumar and K. R. Sugavanam, “Fuzzy Logic and Firefly Algorithm based Hybrid System for Energy Efficient Operation of Three Phase Induction Motor Drives”, Indian Journal of Science and Technology, Vol 9(1), January 2016.
53.       P. Kaur, S. Kumar, A.P. Singh, “Nature Inspired Approaches for Identification of Optimized Fuzzy Model: A Comparative Study”, J. of Mult.-Valued Logic & Soft Computing, Vol. 25, pp. 555-587, March 2014.
54.       Sana Bouzaida, Anis Sakly and Faouzi M’Sahli, “Extracting TSK-type Neuro-Fuzzy model using the Hunting search algorithm”, International Journal of General Systems, Vol. 43, 2014.

9-13

4.

Authors:

Amrapali Bansal, A. K. Upadhyay

Paper Title:

Microsoft Power BI

Abstract: With the changeable business circumstances, the significance of Business Intelligence has gained lots of deliberation. Business Intelligence tools can provide the standarization with a fast and persuasive decision making process based on the multiple data sources, which might be able to affect the survival of the organization on the market. And because of the changes in the extrinsic business environment and the profession needs, a new access of BI solution, Self-service BI solution, is introduced and during the last few years, the number of the market players using the approach has increased expeditiously. The objective of this paper was to build a BI solution according to one of Self-service BI solutions: Power BI presents by Microsoft, one of the leading professionals in the area of MSBI. This research contains two parts. The first part is the theory package which covers the BI and Self-service BI approaches in order to provide the readers with an overall under-standing of these concepts. It also sets up the understructure for the empirical part of this research paper project. The research paper started with analyzing BI and Self-service BI and the relationship between them. After this, the Microsoft BI solution was introduced before moving to the back-ground facts about Power BI. The second part of this research represents how to use Power BI to build a best BI solution based on the business scenario. During this testing process, the compulsory steps for building a BI solution were popularized also covering the main range of capabilities in the tool package. The consequence of this research paper was a BI solution built using Power BI and it met the requirements set for it. The observational case presented in this study can be used as a supportive user guide for all those who are concerned about Microsoft Power BI.

Keywords: Auto-Scheduling; analytics; exploring; Integration; impactful; Intelligence; Visualization

References:
1.       https://support.office.com/en-us/article/Power-BI-Getting-Started-Guides-bd30711a-7ccf-49e8-aafa-2e8f481e675d?ui=en-US&rs=en-US&ad=US
2.       https://powerbi.microsoft.com/en-us/documentation/powerbi-desktop-getting-started/
3.       Microsoft Community : http://community.powerbi.com/

14-20

5.

Authors:

Fernandes R. J, Javali F. M, Patil S. B

Paper Title:

Analysis and Design of Reinforced Concrete Beams and Columns using open STAAD

Abstract:  Structural designers especially in India use STAAD software to execute the structural analysis, but for the design purpose still manual calculations and excel spread sheets are being used. It leads to cumbersome and time consuming process to obtain analysis results from STAAD Pro to design calculations, hence to automate this process an MS Excel spread sheet has been developed. A vba program has been developed to access the analysis results from STAAD Pro to MS Excel such that the design process is fully automated which reduces manual interference.

Keywords:  MS Excel, Open STAAD, VBA, IS 456:2000, Analysis, Design, Beam, Column.

References:
1.       Jonathan Meyer, “SCR Pile Cap Foundation Design Using STAAD v8i & Excel,” Structures Congress 2011, pp. 2485-2495, April 2012
2.       Ishwaragouda S. Patil and Dr. Satish A. Annigeri, “Introduction to PSA as a Free Structural Analysis Software,” Bonfring International Journal of Man Machine Interface, Vol. 4, Special Issue, July 2016
3.       P.Mujumdar and J. U. Maheswari, “Integrated Framework for Automating the Structural Design Iteration,” Proceedings of the International Symposium on Automation and Robotics in Construction, 2015
4.       Purva Mujumdar and Vasant Matsagar, “Design Optimization of Steel Members Using Openstaad and Genetic Algorithm,” Advances in Structural Engineering, V. Matsagar (ed.),  Springer India 2015, pp.233-244
5.       Bentley, “OpenSTAAD V8i (SELECT series 4) Reference manual,” 2012. Available: http://www.bentley.com
6.       Bentley, STAAD Pro V8i. Available: http://www.bentley.com
7.       Microsoft Excel. Visual Basic Applications for Excel. http://www.office.microsoft.com
8. Tim Burnett (2009, November), “VBA for office 2010,” Kingfisher Computer Consulting [online]. https://msdn.microsoft.com/en-us/library/office/ee814735(v=office.14).aspx#VBA Programming 101
9.       IS 456:2000, “Indian standard code of practice for plain and reinforced concrete – code of practice,” Bureau of Indian Standards, New Delhi, 2000.10.    SP 16-1980, “Design aids for reinforced concrete to IS 456-1978,” Bureau of Indian Standards, New Delhi, 1980.
11.    SP 24-1983, “Explanatory Handbook on Indian standard code of practice for plain and reinforced concrete (IS 456-1978),” Bureau of Indian Standards, New Delhi, 1983.
12.    IS 875(Part 1)-1987, “Design Loads (Other than Earthquake) For Buildings and Structures,” Bureau of Indian Standards, New Delhi, 1987.
13.    IS 875(Part 2)-1987, “Design Loads (Other than Earthquake) For Buildings and Structures,” Bureau of Indian Standards, New Delhi, 1987.
14.    Guy Hart-Davis, “Mastering VBA,” 2nd edition, WILEY dreamtech, 2006.
15.    Website:http://www.civilnstructural.com/soft-tools/(OpenSTAAD learning videos).
16.    Website: https://www.excelcampus.com/ (MS Excel VBA coding_ language).

21-26

6.

Authors:

Chander Shekhar Devra

Paper Title:

Issues and Challenges with Product Life Cycle Management (PLM) System Implementation Guidelines

Abstract: Deployment of PLM System is today’s need for all commercial organizations. For successful implementation of PLM system solution, Commercial organisation adopt various available implementation guidelines. Sometime available implementation guidelines may results into un-successful implementation/ re-implementation. Each Unsuccessful / re-implementation leads to waste of time, money & efforts. There is a need for analysis of current available implementation guidelines with bit detailing in real PLM implementation project in Indian context specifically process manufacturing industry. Paper will provide reliable guideline for successful PLM implementation specific to Indian Process manufacturing Industries. It will reduce the failure rate of PLM implementation. It will provide faster PLM implementation. It will save cost & efforts for implementation.

Keywords: PLM System, successful, specifically process manufacturing industry.

References:
1.       Stark, J. (2004) Product Lifecycle Management: 21st century Paradigm for Product Realisation, Springer-Verlag, New York.
2.       Mattias B. (2012) „Evaluating PLM Implementations Using a Guidelines-based Approach‟ thesis for the degree of licentiate of engineering, Department of Product and Production Development, Chalmers University of Technology, Gothenburg, Sweden.
3.       Pikosz, P., Malmström, J. and Malmqvist, J. (1997) „Strategies for introducing PDM systems in engineering companies‟, Advances in Concurrent Engineering – CE97, 20–22 August, Rochester Hills, MI, USA, pp.425–434
4.       Rangan, R., Rohde, S., Peak, R., Chadha, B. and Bliznakov, P. (2005) „Streamlining product lifecycle processes: a survey of product lifecycle management implementations, directions, and challenges‟, Journal of Computing and Information Science in Engineering, Vol. 5, No. 3, pp.227–237
5.       Jennings, M. and Rangan, R. (2004) „Managing complex vehicle system simulation models for manufacturing system development‟, Journal of Computing and Information Science in Engineering, Vol. 4, No. 4, pp.372–378.
6.       Illback, J. and Sholberg, J. (2000) „Application integration in the Boeing enterprise‟, Paper presented at the Fourth International Enterprise Distributed Object Computing Conference (EDOC 2000), 25–28 September, Makuhari, Japan
7.       Chadha, B. and Welsh, J. (2000) „Architecture concepts for simulation-based acquisition  of complex systems‟, Paper presented at the 2000 Summer Computer Simulation Conference, 16–20 July, Vancouver, Canada.
8.       Grieves, M. (2006) Product Lifecycle Management: Driving the Next Generation of Lean Thinking, McGraw-Hill, New York.
9.       Brown, C. and Vessey, I. (2003) „Managing the next wave of enterprise systems: leveraging lessons from ERP‟, MIS Quarterly Executive, Vol. 2, No. 1, pp.65–77.
10.    Wognum, P. and Kerssens-van Drongelen, I. (2005) „Process and impact of product data management implementation‟, International Journal of Product Development, Vol. 2, Nos. 1–2, pp.5–23.
11.    Hartman, N. and Miller, C. (2006) „Examining industry perspectives related to legacy data and technology toolset implementation‟, Engineering Design Graphics Journal, Vol. 70, No. 3, pp.12–21.
12.    Berle, A. (2006) „PLM development and implementation at Volvo 3P, using Catia V5 and Enovia V5‟, Paper Presented at the 1st Nordic Conference on Product Lifecycle Management, 25–26 January, Gothenburg, Sweden.
13.    Zimmerman, T. (2008) „Implementing PLM across organization – for multi-disciplinary and cross-functional product development‟, PhD thesis, Chalmers University of Technology, Gothenburg, Sweden.
14.    Aras implementation methodology, best practices for implementing the Aras plm solution suite, Aras Corporation, USA , Rapid results business – driven PLM Implementation Methodology; website http://kalypso.com/rapidresults/

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7.

Authors:

Mahmoud Al-Zyood

Paper Title:

Forecast Car Accident in Saudi Arabia with ARIMA Models

Abstract:  Traffic accidents are the main cause of deaths and injury in Saudi Arabia, this work is a challenge to examine the best ARIMA model for forecast a car accident. Results show that an appropriate model is simply an ARIMA (1, 0, 0, 0) due to the fact that, the ACF has an exponential decay and the PACF has a spike at lag2 which is an indication of the said model. The forecasted car accident cases from 1998 to 2016. The selected model with least AIC value will be selected. We entertained nine tentative ARMA models and Chose that model which has minimum AIC (Akaike Information Criterion).The chosen model is the first one AIC (-0.274306) The selected ARIMA (1, 0) (0, 0), model to forecast for the future values of our time series (car accident). Forecasted for the next 7 years with (95%) prediction intervals The prediction values of traffic accidents show that there will be increasing in deaths and injury coming years

Keywords:  Forecasting, ARIMA models, car accident, Akaike Information Criterion (AIC), Bayessian Information Criterion (BIC).

References:
1.       Armstrong, J. S. (2001). Principles of forecasting: A handbook for researchers and practitioners. Boston: Kluwer Academic.  
2.       Box, G.E. and Jenkins, G.M. (1994) Time Series Analysis: Forecasting and Control. Prentice Hall, Englewood Cliffs.
3.       Berube, M. S. (Ed.). (1985). American heritage dictionary (2nd ed.). Boston, MA: Houghton Mifflin.
4.       Box, G. E., & Jenkins, G. M. (1994). Time series analysis: Forecasting and control (3rd ed.). Englewood Cliffs, NJ: Prentice Hall.
5.       Box, G. E., Jenkins, G. M., & Bacon, D. W. (1967). Models for forecasting seasonal and nonseasonal time series. In B. Harris (Ed.), Spectral analysis of time series. New York, NY: John Wiley & Sons.
6.       Boylan, J. (2005). Intermittent and lumpy demand: A forecasting challenge. The International Journal of Applied Forecasting, 1, 36-42. 
7.       Caldwell, J. G. (n.d.) The Box-Jenkins forecasting technique. Retrieved March 3,2012, from http://www.foundationwebsite.org/BoxJenkins.htm
8.       Cryer, J.D. and Chan, K.S. (2008) Time Series Analysis with Application in R. Springer, New York. http://dx.doi.org/10.1007/978-0-387-75959-3
9.       Hamilton, J. D. (1994). Time series analysis (Vol. 2). Princeton: Princeton university press. commandeur JJ, Bijleveld FD, Bergel-Hayat R, Antoniou C, Yannis G,
10.    Papadimitriou E. On statistical inference in time series analysis of the evolution of road safety. Accid Anal Prev. 2013; 60:424–3doi:10.1016/j.aap.2012.11.006. [PubMed: 23260716].
11.    G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis Forecasting and Control, Third ed. Englewood Cliffs, NJ: PrenticeHall, 1994.
12.    Hannan, E., (1980), The Estimation of the Order of ARMA Process, Annals of Statistics, Vol. 8,pp. 1071-1081.

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8.

Authors:

Manju, Rajesh Kumar

Paper Title:

Complexity of  and Its Connection with Logic

Abstract: The authors investigate the state complexity of some operations on regular languages. We prove that the upper bounds on the state complexity of these operations, which were known to be tight for larger alphabets, are tight also for binary alphabet. Upper and lower bounds for the finite-state complexity of arbitrary strings, and for strings of particular types, are given and incompressible strings are studied.

Keywords:  Finite Automata, Formal Languages, Logic, Regular languages, State Complexity

References:
1.        J.C. Birget, Intersection and union of regular languages, and state-complexity, Inform. Process. Lett. 43 (1992) 185- 190.
2.        J.C. Birget, Partial orders on words, minimal elements of regular languages, and state-complexity, Theoret. Comput. Sci. 119 (1993) 267-291.
3.        J. Btzozowski and E. Leiss. On equations for regular languages, finite automata, and sequential networks, Theoret. Comput. Sci. 10 (1980) 19-35.
4.        A. Chandra, D. Kozen and L. Stockmeyer, Alternation, J. ACM 28 (1981) 114-133.
5.        J. Cohen, D. Penin and J.-E. Pin, On the expressive power of temporal logic, J. Comput. System Sci. 46 (1993) 271-294.
6.        J. Hopcroft and J. Ullman, Introduction to Automata, L   anguages and Computation (Addison-Wesley, Reading, MA,   19791.
7.        D. Kozen, On parallelism in Turing machines, in: Proc. Ann. Symp. on Founaiuions of Computer Science (1976) 89-97.
8.        E. Leiss, Succinct representation of regular languages by boolean automata, Theoret. Comput. Sci. 13 ( 198 1) 323-330.
9.        E. Leiss, Succinct representation of regular languages by boolean automata, Part II, Theoret. Comput. Sci. 38 (1985)    133-136.
10.     A.R. Meyer and M.J. Fischer, Economy of description by automata, grammars, and formal systems, in: Proc. 12Th IEEE Ann. Symp. on Switching atul Automata Theory (1971) 188-191.

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9.

Authors:

Dragos Ionut ONESCU

Paper Title:

EU and Cyber Security

Abstract:  Securing network and information systems in the European Union is essential to ensure prosperity and to keep the online economy running. The quick and constant development of information and communication technologies, globalization, the drastic increase in data volumes and the growing number of different types of equipment connected to data networks have an impact on daily life, the economy and the functioning of the state. On the one hand, this level of ICT development will contribute to the improved availability and usability of services, enhance transparency and citizen participation in governance, and cut public as well as private sector costs. Instead, the increasing importance of technology is accompanied by an increase in the state's growing dependence on already entrenched e-solutions, and cements the expectation of technology operating aimlessly. Social processes are also becoming increasingly dependent on a growing number of information technology resources, and in the future attention must be drawn to the fact that society at large, and each individual in particular, will be able to maintain control over the corresponding processes. The number of actors and state in cyberspace that are involved in cyber espionage targeted at computers connected to the Internet as well as closed networks continues to grow, with their aim being to collect information on both national security as well as economic interests. The amount and activeness of states capable of cyber-attacks are increasing. Meaningful and effective cooperation between the public and private sector in the development of cyber security organization as well as in preventing and resolving cyber incidents is becoming increasingly unavoidable. National defense and internal security are dependent on the private sector's infrastructure and resources, while at the same time the state can assist vital service providers and guarantors of national critical information infrastructure as a coordinator and balancer of various interests, please download TEMPLATE HELP FILE from the website.

Keywords: European Union; security; cyber security

References:
1.       Cyber Security Strategy was published as the two British security strategies under the direction of the Cabinet Office, the document is available at http://www.cabinetoffice.gov.uk/reports/cyber_security.aspex.
2.       Estonia hit by „Moscow cyber war”, „The Economist”, the document is available at: http://news.bbc.co.uk/2/hi/europe/6665145.stm
3.       Douglas W. Hubbard, Richard Seiersen, Patrick Cronin, How to Measure Anything in Cybersecurity Risk, Audible Studios, 2016
4.       Robert K. Knake, Pete Larkin, Richard A. Clarke, Cyber War: The Next Threat to National Security and What to Do About It, Tantor Audio, 2014
5.       George Cristian Maior,2009 strategic thinking and Uncertainty in international relations in the twenty-first century, RAO, Bucharest
6.       The National Security Strategy of the United Kingdom-2008, (5.6)7.       http://nato.mae.ro/node/435
8.       P. W. Singer, Allan Friedman, Cybersecurity and Cyberwar: What Everyone Needs to Know®, New York Times, 2003
9.       Rid Thomas, Peter McBurney, Cyber-Weapons, The RUSI Journal, 157:1
10.    R. J. Vidmar. (1992, August). On the use of atmospheric plasmas as electromagnetic reflectors. IEEE Trans. Plasma Sci. [Online]. 21(3). pp. 876—880.   Available: http://www.halcyon.com/pub/journals/21ps03-vidmar

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10.

Authors:

Rajesh Kumar, Manju

Paper Title:

Complexity of Binary and Uniary Operations on Regular Grammar 

Abstract: It appears that the state complexity of each operation has its own special features. Thus, it is important and practical to calculate good estimates for some commonly used general cases. In this paper, the author consider the state complexity of combined Boolean operations on regular language and give an exact bound for all of them in the case when the alphabet is not fixed. Moreover, the author shows that for any fixed alphabet, this bound can be reached in infinite cases.

Keywords:  Alternating finite automaton, Automata, Combined operations, Estimation, Formal languages, Multiple operations, State complexity

References:
1.       Chandra, D. Kozen and L. Stockmeyer, Alternation, J. ACM 28 (1981) 114-133
2.       E. Leiss, Succint representation of regular languages by boolean automata, Theoret. Comput. Sci. 13 (1981) 323- 330.
3.       G. Liu, C. Martin-Vide, A. Salomaa, S. Yu, State complexity of basic language operations combined with reversal, Information and Computation 206 (2008) 1178–1186.
4.       G. Rozenberg, A. Salomaa, Handbook of Formal Languages, Springer-Verlag, Berlin, Heidelbergm, New York, 1997
5.       J. Berstel, D. Perrin, Theory of Codes, Academic Press Inc., 1985.
6.       J. Hopcroft, J. Ullman, Introduction to Automata Theory Languages and Computation, 2nd ed., Addison-Wesley, Reading, MA, 1979.
7.       K. Salomaa, S. Yu, On the state complexity of combined operations and their estimation, International Journal of Foundations of Computer Science 18 (4) (2007) 683–698.
8.       M. Domaratzki, State complexity of proportional removals, Journal of Automata Languages and Combinatorics 7 (4) (2002) 455–468.
9.       M. Domaratzki, K. Salomaa, State complexity of shuffle on trajectories, Journal of Automata Languages and Combinatorics 9 (2–3) (2004) 217–232.
10.    S. Yu, Q. Zhuang, K. Salomaa, The state complexities of some basic operations on regular languages, Theoretical Computer Science 125 (2) (1994)315–328
11.    S. Yu, Regular Languages, In [23] Ch.1 (1997) 41–110.
12.    S. Yu, State complexity: Recent results and open problems, invited talk at International Colloquium on Automata, Languages and Programming 2004 Formal Language Workshop, also appears in  Fundamenta Informaticae 64 1–4 (2005) 471–480.
13.    S. Yu, On the state complexity of combined operations, in: invited talk at 11th International Conference on Implementation and Application of Automata, in: Lecture Notes in Computer Science, vol. 4094, Springer, 2006, pp. 11–22.   

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11.

Authors:

Ngo Tung Son, Tran Binh Duong, Bui Ngoc Anh, Luong Duy Hieu

Paper Title:

An Empirical Research in Autonomous Vehicles Control

Abstract:  Recent years have witnessed a growing attention to automatic-driving vehicles as this is one of the key technologies for the future industry. Even though being successful at many aspects, there has been a long interest in designing an efficient control system for automatic driving vehicles. This paper empirically demonstrates the efficiency of our system which only employs low cost camera for visual sensing. Our approach puts the focus on 2 main objectives in autonomous vehicle control: (1) lane detection and (2) speed and direction decisions for the sake of fast processing. This is to help the vehicle always moves in the right lane while keeping a suitable speed. For decision making fuzzy logic is used for effective reasoning. We test our system in mini automatic-vehicles to show that it is not only efficient but also reliable. At a practical test, the system has won third place at the Vietnam Digital Race challenge 2017.

Keywords: Image Processing, Lane Detection, Support Vector Machine, Automatic-Car Control, Fuzzy Logic.

References:
1.       Autonomous Cars: Self-Driving the New Auto Industry Paradigm, MORGAN STANLEY RESEARCH, November 6, 2013.
2.       Overview of Autonomous Vehicle Sensors and Systems, Jaycil Z. Varghese, Professor Randy G. Boone, Proceedings of the 2015 International Conference on Operations Excellence and Service Engineering Orlando, Florida, USA, September 10-11, 2015.
3.       Multi-sensor data fusion for autonomous vehicle navigation through adaptive particle filter, Tehrani Nik Nejad Hossein, Seiichi Mita, Han Long, Intelligent Vehicles Symposium (IV), 2010 IEEE
4.       An Empirical Evaluation of Deep Learning on Highway Driving, An Empirical Evaluation  of  Deep Learning on Highway Driving, B. Huval, T. Wang, S. Tandon, J. Kiske, W. Song, J. Pazhayampallil, M. Andriluka, P. Rajpurkar, T. Migimatsu, R. Cheng-Yue, et al., [arXiv], (2015).
5.       P. V. C. Hough, "Method and Means for Recognizing Complex Patterns", US Patent 3,069,654, Ser. No. 17, 7156 Claims, 1962.
6.       “Real-Time Lane Detection for Driving System Using Image Processing”, IRJET, Volume: 02 Issue: 05, Aug-2015.
7.       Allam Shehata Hassanein , Sherien Mohammad, Mohamed Sameer, and Mohammad Ehab Ragab, "A Survey on Hough Transform, Theory, Techniques and Applications", Informatics Department, Electronics Research Institute, El-Dokki, Giza,12622, Egypt.
8.       S.ARUNADEVI, Dr. S. DANIEL MADAN RAJA, A Survey on Image Classification Algorithm Based on Per-pixel, International Journal of Engineering Research and General Science Volume 2, Issue 6, October-November, 2014 ISSN 2091-2730.
9.       Vapnik (1995), The Nature of Statistical Learning Theory. Springer, Berlin.
10.    Jitendra Kumar, Image Classification using SVM-RBF in the field of Image Processing, International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences (IJIRMPS) Volume 1, Issue 2, December 2013.
11.    S. Agrawal, N. K. Verma, P. Tamrakar and P. Sircar, "Content Based Color Image Classification using SVM," 2011 Eighth International Conference on Information Technology: New Generations, Las Vegas, NV, 2011, pp. 1090-1094.
12.    Timothy J. Ross "Fuzzy Logic With Engineering Applications" Second Edition.
13.    J. E. Naranjo, M. A. Sotelo, C. Gonzalez, R. Garcia and T. D. Pedro, "Using Fuzzy Logic in Automated Vehicle Control," in IEEE Intelligent Systems, vol. 22, no. 1, pp. 36-45, Jan.-Feb. 2007.
14.    Design and Implementation of Autonomous Car using Raspberry Pi, Gurjashan Singh Pannu, Mohammad Dawud Ansar, Pritha Gupta, International Journal of Computer Applications (0975 – 8887) Volume 113 – No. 9, March 2015.

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12.

Authors:

Ashwani Kumar Aggarwal

Paper Title:

Intelligent Electronic Surveillance Systems for Personal and Team Security in Public Places

Abstract: Public security is of prime importance for establishing law and order in any society. With the advent of cheap and fast electronic systems, public security is prone to fall in the control of intruders. Whilst many electronic surveillance systems available in market claim to work effectively, their operation is questionable in crowded places where the subject under surveillance is occluded under clutter. Under such challenging task, computer vision techniques are very helpful which work on foreground segmentation of captured images to remove clutter. Further, images are preprocessed before applying many machine learning methods to log in the details of person behavior. Action recognition techniques are then used to detect unusual behavior which helps in personal security in public places.

Keywords: Artificial intelligence, computer vision, database, descriptors, feature points, image processing, machine learning, optimization, electronic surveillance.

References:
1.       M. Watney, "Intensifying State Surveillance of Electronic Communications: A Legal Solution in Addressing Extremism or Not?" Availability, Reliability and Security (ARES), 2015 10th International Conference on, Toulouse, 2015, pp. 367-373.    
2.       D. C. Andrew, "Ground stations for analysis of electronic surveillance imagery," Human Interfaces in Control Rooms, Cockpits and Command Centres, 1999. International Conference on, Bath, 1999, pp. 418-421.        
3.       C. Ovseník, J. Turán and A. K. Kolesárová, "Video surveillance systems with optical correlator," MIPRO, 2011 Proceedings of the 34th International Convention, Opatija, 2011, pp. 227-230.        
4.       M. Yaghoobi, B. Mulgrew and M. E. Davies, "An efficient implementation of the low-complexity multi-coset sub-Nyquist wideband radar electronic surveillance," Sensor Signal Processing for Defence (SSPD), 2014, Edinburgh, 2014, pp. 1-5.        
5.       J. Teng, J. Zhu, Boying Zhang, D. Xuan and Y. F. Zheng, "E-V: Efficient visual surveillance with electronic footprints," INFOCOM, 2012 Proceedings IEEE, Orlando, FL,
2012, pp. 109-117.        
6.       G. Elkana and I. Baskara Nugraha, "Low cost embedded surveillance for public transportation," ICT for Smart Society (ICISS), 2014 International Conference on, Bandung, 2014, pp. 242-245.        
7.       P. Pasupathy, S. Munukutla, D. P. Neikirk and S. L. Wood, "Versatile wireless sacrificial transducers for electronic structural surveillance sensors," Sensors, 2009 IEEE, Christchurch, 2009, pp. 979-983.        
8.       Z. B. May, "Real-time alert system for home surveillance," Control System, Computing and Engineering (ICCSCE), 2012 IEEE International Conference on, Penang, 2012, pp. 501-505.        
9.       V. M. López, A. Navarro-Crespín, C. Brañas, F. J. Azcondo, R. Schnell and R. Zane, "Frequency control and phase surveillance in resonant electronic ballast," IECON 2011 - 37th Annual Conference on IEEE Industrial Electronics Society, Melbourne, VIC, 2011, pp. 2929-2934.        
10.    Gang Kang and O. P. Gandhi, "Comparison of various safety guidelines for electronic article surveillance devices with pulsed magnetic fields," in IEEE Transactions on Biomedical Engineering, vol. 50, no. 1, pp. 107-113, Jan. 2003.        
11.    X. Pan and Y. Wu, "Modeling and simulations of ECCM of ocean surveillance satellite electronic intelligence," Biomedical Engineering and Informatics (BMEI), 2012 5th International Conference on, Chongqing, 2012, pp. 1476-1480.        
12.    M.J. Westoby, J. Brasington, N.F. Glasser, M.J. Hambrey, J.M. Reynolds, ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications, Geomorphology, Volume 179, 15 December 2012, Pages 300-314.
13.    L. Zhao, S. Huang and G. Dissanayake, "Linear SLAM: A linear solution to the feature-based and pose graph SLAM based on submap joining," Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on, Tokyo, 2013, pp. 24-30.
14.    Z. Kang and G. Medioni, "3D Urban Reconstruction from Wide Area Aerial Surveillance Video," Applications and Computer Vision Workshops (WACVW), 2015 IEEE Winter, Waikoloa, HI, 2015, pp. 28-35.
15.    J. Ventura and T. Höllerer, "Wide-area scene mapping for mobile visual tracking," Mixed and Augmented Reality (ISMAR), 2012 IEEE International Symposium on, Atlanta, GA, 2012, pp. 3-12.
16.    G. Bleser, H. Wuest and D. Stricker, "Online camera pose estimation in partially known and dynamic scenes," Mixed and Augmented Reality, 2006. ISMAR 2006. IEEE/ACM International Symposium on, Santa Barbard, CA, 2006, pp. 56-65.
17.    T. J. Cham, A. Ciptadi, W. C. Tan, M. T. Pham and L. T. Chia, "Estimating camera pose from a single urban ground-view omnidirectional image and a 2D building outline map," Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, San Francisco, CA, 2010, pp. 366-373.
18.    M. Chatzigiorgaki and A. N. Skodras, "Real-time keyframe extraction towards video content identification," Digital Signal Processing, 2009 16th International Conference on, Santorini-Hellas, 2009, pp. 1-6.

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