International Journal of Innovative

Technology and Exploring Engineering

ISSN : 2278 - 3075Website: www.ijitee.org

Volume-8 Issue-3, JANUARY 2019

Published by: Blue Eyes Intelligence Engineering and Sciences Publication

Published by: Blue Eyes Intelligence Engineering and Sciences Publication

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Exploring Innovation

www.ijitee.org

IjItEeIjItEe

EXPLORING INNOVA

TION

Editor-In-Chief Chair Dr. Shiv Kumar

Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT), Senior Member of IEEE

Professor, Department of Computer Science & Engineering, Lakshmi Narain College of Technology Excellence (LNCTE), Bhopal

(M.P.), India

Associated Editor-In-Chief Chair Dr. Vinod Kumar Singh

Associate Professor and Head, Department of Electrical Engineering, S.R.Group of Institutions, Jhansi (U.P.), India

Associated Editor-In-Chief Members Dr. Hai Shanker Hota

Ph.D. (CSE), MCA, MSc (Mathematics)

Professor & Head, Department of CS, Bilaspur University, Bilaspur (C.G.), India

Dr. Gamal Abd El-Nasser Ahmed Mohamed Said

Ph.D(CSE), MS(CSE), BSc(EE)

Department of Computer and Information Technology , Port Training Institute, Arab Academy for Science ,Technology and Maritime

Transport, Egypt

Dr. Mayank Singh

PDF (Purs), Ph.D(CSE), ME(Software Engineering), BE(CSE), SMACM, MIEEE, LMCSI, SMIACSIT

Department of Electrical, Electronic and Computer Engineering, School of Engineering, Howard College, University of KwaZulu-

Natal, Durban, South Africa.

Scientific Editors Prof. (Dr.) Hamid Saremi

Vice Chancellor of Islamic Azad University of Iran, Quchan Branch, Quchan-Iran

Dr. Moinuddin Sarker

Vice President of Research & Development, Head of Science Team, Natural State Research, Inc., 37 Brown House Road (2nd Floor)

Stamford, USA.

Dr. Shanmugha Priya. Pon

Principal, Department of Commerce and Management, St. Joseph College of Management and Finance, Makambako, Tanzania, East

Africa, Tanzania

Dr. Veronica Mc Gowan

Associate Professor, Department of Computer and Business Information Systems,Delaware Valley College, Doylestown, PA, Allman,

China.

Dr. Fadiya Samson Oluwaseun

Assistant Professor, Girne American University, as a Lecturer & International Admission Officer (African Region) Girne, Northern

Cyprus, Turkey.

Dr. Robert Brian Smith

International Development Assistance Consultant, Department of AEC Consultants Pty Ltd, AEC Consultants Pty Ltd, Macquarie

Centre, North Ryde, New South Wales, Australia

Dr. Durgesh Mishra

Professor & Dean (R&D), Acropolis Institute of Technology, Indore (M.P.), India

Executive Editor Chair Dr. Deepak Garg

Professor & Head, Department Of Computer Science And Engineering, Bennett University, Times Group, Greater Noida (UP), India

Executive Editor Members Dr. Vahid Nourani

Professor, Faculty of Civil Engineering, University of Tabriz, Iran.

Dr. Saber Mohamed Abd-Allah

Associate Professor, Department of Biochemistry, Shanghai Institute of Biochemistry and Cell Biology, Shanghai, China.

Dr. Xiaoguang Yue

Associate Professor, Department of Computer and Information, Southwest Forestry University, Kunming (Yunnan), China.

Dr. Labib Francis Gergis Rofaiel

Associate Professor, Department of Digital Communications and Electronics, Misr Academy for Engineering and Technology,

Mansoura, Egypt.

Dr. Hugo A.F.A. Santos

ICES, Institute for Computational Engineering and Sciences, The University of Texas, Austin, USA.

Dr. Sunandan Bhunia

Associate Professor & Head, Department of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia

(Bengal), India.

Dr. Awatif Mohammed Ali Elsiddieg

Assistant Professor, Department of Mathematics, Faculty of Science and Humatarian Studies, Elnielain University, Khartoum Sudan,

Saudi Arabia.

Technical Program Committee Chair Dr. Mohd. Nazri Ismail

Associate Professor, Department of System and Networking, University of Kuala (UniKL), Kuala Lumpur, Malaysia.

Technical Program Committee Members Dr. Srilalitha Girija Kumari Sagi

Associate Professor, Department of Management, Gandhi Institute of Technology and Management, Visakhapatnam (A.P.) India.

Dr. Vishnu Narayan Mishra

Associate Professor, Department of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev Dumas

Road, Surat (Gujarat), India.

Dr. Sripada Rama Sree

Vice Principal, Associate Professor, Department of Computer Science and Engineering, Aditya Engineering College, Surampalem

(Andhra Pradesh), India.

Dr. Ramzi Raphael Ibraheem Al Barwari

Assistant Professor, Department of Mechanical Engineering, College of Engineering, Salahaddin University – Hawler (SUH) Erbil –

Kurdistan, Erbil Iraq.

Dr. Kapil Chandra Agarwal

H.O.D. & Professor, Department of Applied Sciences & Humanities, Radha Govind Engineering College, U. P. Technical University,

Jai Bheem Nagar, Meerut, (U.P). India.

Convener Chair Mr. Jitendra Kumar Sen

Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd., Bhopal (M.P.), India

Editorial Chair Dr. Saeed Balochian

Associate Professor, Gonaabad Branch, Islamic Azad University, Gonabad, Iran.

Editorial Members Dr. Gyanesh Shrivastava

Associate Professor, Department of Information Technology, MATS University, Raipur (Chhattisgarh), India.

Dr. Swapnil B. Mohod

Assistant Professor, Department of Electrical Engineering, Prof. Ram Meghe College of Engineering & Management, Badnera,

Amravati (Maharashtra), India.

Dr. Subramani Roychoudri

Professor, Department of Computer Science and Engineering, Usha Rama College of Engineering and Technology, Telaprolu (Andhra

Pradesh), India.

Dr. KPNV Satyasree

Professor, Department of Computer Science and Engineering, Usha Rama College of Engineering and Technology, Telaprolu (Andhra

Pradesh), India.

Dr. Parul Mishra

Assistant Professor, Department of English, GD Goenka University Gurugram, Gurgaon (Haryana), India.

S.

No

Volume-8 Issue-3, January 2019, ISSN: 2278-3075 (Online)

Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Page

No.

1.

Authors: Pankaj, Ramesh Kumar Garg, Amit Goyal

Paper Title: Experimental Investigations of Mechanical and Microstructural Properties of FSWed Cu-Zn30 Joints

Abstract: The present work focuses on studying the effect of pin profile and tilt angle of the tool on the mechanical

and microstructural characteristics of FSWed Cu-Zn30 brass joints. Three different pin profiles viz. Conical, threaded

cylindrical and cylindrical were used to fabricate the joints at five different tilt angles viz. 0°, 1°, 2°, 3° and 4°. Fifteen

joints were produced by different combinations of input parameters. The fabricated joints were tested for tensile,

hardness and microstructural properties in order to explore the weld quality. The results of a current study clearly show

that tool tilt angle and pin profile significantly affects the weld quality. Threaded pin profiled tool is observed to

produce better quality joint at 3° tool tilt angle as compared to conical and cylindrical pin profiled tools. The results of

mechanical testing were also correlated with the microstructural changes occur during the welding process.

Keywords: FSW, Aluminium Alloy, Mechanical Properties, Microstructure.

References: 1. Mironov, S., Inagaki, K., Sato, Y.S. and Kokawa, H., 2014. Development of grain structure during friction-stir welding of Cu–30Zn

brass. Philosophical Magazine, 94(27), pp.3137-3148.

2. Heidarzadeh, A. and Saeid, T., 2016. A comparative study of microstructure and mechanical properties between friction stir welded single and double phase brass alloys. Materials Science and Engineering: A, 649, pp.349-358.

3. Sun, Y.F., Xu, N. and Fujii, H., 2014. The microstructure and mechanical properties of friction stir welded Cu–30Zn brass alloys. Materials

Science and Engineering: A, 589, pp.228-234. 4. Heidarzadeh, A., 2019. Tensile behavior, microstructure, and substructure of the friction stir welded 70/30 brass joints: RSM, EBSD, and TEM

study. Archives of Civil and Mechanical Engineering, 19(1), pp.137-146.

5. Xu, N., Ueji, R. and Fujii, H., 2015. Enhanced mechanical properties of 70/30 brass joint by multi-pass friction stir welding with rapid cooling. Science and Technology of Welding and Joining, 20(2), pp.91-99.

6. Heidarzadeh, A., Saeid, T. and Klemm, V., 2016. Microstructure, texture, and mechanical properties of friction stir welded commercial brass

alloy. Materials Characterization, 119, pp.84-91. 7. Thomas, W.M., Friction Stir Butt Welding International Patent Application No PCT/GB92 Patent Application No. 9125978. 8.

8. Goyal, A. and Garg, R.K., 2018. Selection of FSW Tool Parameters for Joining Al-Mg4. 2 Alloy: An Experimental Approach. Metallography,

Microstructure, and Analysis, 7(5), pp.524-532. 9. Park, H.S., Kimura, T., Murakami, T., Nagano, Y., Nakata, K. and Ushio, M., 2004. Microstructures and mechanical properties of friction stir

welds of 60% Cu–40% Zn copper alloy. Materials Science and Engineering: A, 371(1-2), pp.160-169.

10. Meran, C. and Kovan, V., 2008. Microstructures and mechanical properties of friction stir welded dissimilar copper/brass

joints. Materialwissenschaft und Werkstofftechnik: Entwicklung, Fertigung, Prüfung, Eigenschaften und Anwendungen technischer

Werkstoffe, 39(8), pp.521-530. 11. Emamikhah, A., Abbasi, A., Atefat, A. and Givi, M.B., 2014. Effect of tool pin profile on friction stir butt welding of high-zinc brass

(CuZn40). The International Journal of Advanced Manufacturing Technology, 71(1-4), pp.81-90.

12. Emami, S. and Saeid, T., 2015. Effects of welding and rotational speeds on the microstructure and hardness of friction stir welded single-phase brass. Acta Metallurgica Sinica (English Letters), 28(6), pp.766-771.

1-5

2.

Authors: Rohit Gupta, Rahul Gupta, Dinesh Verma

Paper Title: Eigen Energy Values and Eigen Functions of a Particle in an Infinite Square Well Potential by

Laplace Transforms

Abstract: Quantum mechanics is one of the branches of physics and is a fundamental theory which explains the

nature of atoms and subatomic particles at the smallest scale of energy. In this mechanics, physical problems are solved

by algebraic and analytic methods. By applying Laplace Transforms we can find the general solutions of one

dimensional Schrodinger’s time-independent wave equation for a particle in an infinite square well potential. In this

paper, we will discuss the Eigen energy values and Eigen functions of a particle in an infinite square well potential. We

will find that general solutions (Eigen functions) of one dimensional Schrodinger’s time- independent wave equation

for a particle in an infinite square well potential have very interesting characteristics that each Eigen function is

associated with a particular value of energy (Eigen energy value) of the particle confined inside an infinite square well

potential

Keywords: Eigen functions, Eigen values, Infinite square well, Laplace Transforms.

References: 1. Advanced Engineering Mathematics by Erwin Kreysig 10th edition, 2014

2. Higher Engineering Mathematics byDr.B.S.Grewal 43rd edition 2015.

3. Advanced engineering mathematics by H.K. Dass. Edition: Reprint, 2014. 4. Quantum mechanics by Satya Parkash. Edition: Reprint, 2016.

5. A text book of Engineering Physics by M.N. Avadhanulu. Revised edition 2014.

6. Introduction to Quantum mechanics by David j. Griffiths. Edition 2nd, 2017. 7. Principles of quantum mechanics by P.A.M. Dirac. Edition: Reprint, 2016.

8. Quantum Mechanics and Path Integralsby Richard P. Feynman, Albert R. Hibbs, Daniel F. Styer

9. Quantum mechanics by B.N. Srivastava, R.M. Richaria. 16th edition, 2017. 10. An introduction to Laplace transforms and Fourier series by Dyke and Phil.

6-9

3.

Authors: Kapil Kumar Singh, Sandhya Tarar

Paper Title: Watermarking Techniques based on DCT and SVD using PSO

Abstract: Digital watermarking is the most important technique in today’s world for hiding the secret information in

a carrier image or cover and to avoid illegal copying of data. This technique can be applied to audio, video, text or 10-15

images, the features and concepts pertaining to the various watermarking techniques using as DCT and SVD.Herein

research paper we are providing a comparative analysis of all the above mention algorithms with Particle Swarm

Optimization (PSO).The proposed scheme meets the objective as MSE, PSNR, SSIMand NCC. Finally, it has been

concluded that all the proposed schemes are well efficient in watermarking in the still image data. All schemes are

works really fast and hence suitable and real-time applications.

Keywords: Discrete Cosine Transform (DCT), Singular Values Decomposition (SVD), Particle Swarm Optimization

(PSO).

References: 1. I.J. Cox, M.L. Miller, Jeffrey A. Bloom, J. Fridrich and T. Kalker, Digital Watermarking and Steganography, Second Edition, Elsevier,2008,

ISBN:978-0-12-372585. 2. Feng-Hsing Wang, Jeng-Shyang Pan, Lakshmi C Jain, Innovations in Digital Watermarking Techniques, Springer, 2009, ISBN: 978-3-642-

03186-1 (Print)978-3-642-03187-8 (Online).

3. I. Cox, J. Kilian, F.T. Leighton and T. Shamoon, Secure spread spectrum watermarking for multimedia, IEEE Transactions on Image Processing, 1997,Vol.6, pp. 1673-1687.

4. Acken, J.M.: ‘How Watermarking adds value to digital content’, Commun. ACM, 1998, 41, (7), pp. 74–77.

5. Piva, A., Barni, M., Bartolini, F., et al.: ‘DCT-based watermark recovering without resorting to the uncorrupted original image’. Image Processing, 1997.Proc., Int. Conf. on. IEEE, 1997, vol. 1, pp. 520–523.

6. Joshi, A.M., Mishra, V., Patrikar, R.M.: ‘Design of real-time video watermarking based on Integer DCT for H.: 264 encoder’, Int. J. Electron.,

2015, 102, (1), pp. 141–155. 7. Jagadeesh, B., Kumar, P.R., Reddy, P.C.: ‘Robust digital image watermarking based on fuzzy inference system and back propagation neural

networks using DCT’, Soft Comput., 2016, 20, (9), pp. 3679–3686.

8. Fazli, S., Moeini, M.: ‘A robust image watermarking method based on DWT,DCT, and SVD using a new technique for correction of main geometric attacks’, Optik-Int. J. Light Electron Opt., 2016, 127, (2), pp. 964–972.

9. Dong, L., Yan, Q., Lv, Y., et al.: ‘Full band watermarking in DCT domain with Weibull model’, Multimedia Tools Appl., 2017, 76, (2), pp.

1983–2000. 10. Panah, A.S., Schyndel, R.V., Sellis, T., et al.: ‘On the properties of non-media digital watermarking: a review of state of the art techniques’,

IEEE Access, 2016, 4, pp. 2670–2704.

11. Parah, S.A., Sheikh, J.A., Loan, N.A., et al.: ‘Robust and blind watermarking technique in DCT domain using inter-block coefficient differencing’,Digit.Signal Process., 2016, 53, pp. 11–24.

12. Ali, M., Ahn, C.W., Pant, M.: ‘A robust image watermarking technique using SVD and differential evolution in DCT domain’, Optik-Int. J.

Light Electron Opt., 2014, 125, (1), pp. 428–434. 13. Joshi, A.M., Mishra, V., Patrikar, R.M.: ‘Design of real-time video watermarking based on Integer DCT for H. 264 encoder’, Int. J.

Electron.,2015, 102, (1), pp. 141–155.

14. Ye, X., Chen, X., Deng, M., et al.: ‘A SIFT-based DWT-SVD blind watermark method against geometrical attacks’.2014 7th Int. Congress on Image and Signal Processing (CISP) IEEE, 2014, pp. 323–329.

15. Wang, S., Zheng, D., Zhao, J., et al.: ‘Adaptive watermarking and tree structure based image quality estimation’, IEEE Trans. Multimed., 2014,

16,(2), pp. 311–325. 16. Guo, J.M., Lai, G.H., Wong, K., et al.: ‘Progressive halftone watermarking using multilayer table lookup strategy’, IEEE Trans. Image

Process., 2015, 24, (7), pp. 2009–2024.

17. Sadreazami, H., Ahmad, M.O., Swamy, M.N.S.: ‘Multiplicative watermark decoder in contourlet domain using the normal inverse Gaussian distribution’, IEEE Trans. Multimed., 2016, 18, (2), pp. 196–207.

18. Feng-Hsing Wang, Jeng-Shyang Pan, Lakhmi C Jain, Innovations in Digital Watermarking Techniques, Springer, 2009, ISBN: 978-3-642-

03186-1 (Print) 978-3-642-03187-8 (Online). 19. R. C. Gonzalez, R. E. Woods, Digital Image Processing, second Edition, Prentice Hall, 2002, ISBN-13: 978-0201180756.

4.

Authors: Om Prakash Yadav, Shashwati Ray

Paper Title: An Efficient ECG Approximation using Chebyshev Polynomial Interpolation

Abstract: An ECG (Electrocardiography) is a simple, noninvasive method plotting potentials generated due to

cardiac activity. The recorded signal is also called as ECG (Electrocardiogram) consist of waves viz., P, QRS, T and U

are variable shape and timing characteristics. These signals are often contaminated with noises of variable frequency

and amplitude during acquisition and transmission. These noises must be reduced for better clinical evaluation.

Volume of data generated through ECG recorders is also very large. Lagrange-Chebyshev interpolation technique

along with total variation approach has been presented for approximation of ECG signals of MIT-BIH database. The

standard ECG assessment tools has been utilized to measure the performance the proposed method. The results

obtained are found to be better than exiting techniques.

Keywords: ECG signal; Total Variation Denoising; First Difference; Second Differences; Majorization-Minorization

optimization; Bottom-Up Algorithms; Chebyshev Nodes; Lagrange Interpolation.

References: 1. G. Walraven, G. Walraven, “Basic arrhythmias”, Brady Communications Company, 1986.

2. R. Acharya, S.M. Krishnan, J.A. Spaan, J.S. Suri, “Advances in cardiac signal processing” Springer: Berlin, Heidelberg, 2007.

3. G.M. Friesen, T.C. Jannett, M.A. Jadallah, S.L. Yates, S.R. Quint, H.T. Nagle, “A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Transactions on biomedical engineering ,Vol. 37,pp 85–98,1990.

4. A. Bharadwaj, U. Kamath, “Techniques for accurate ECG signal processing”. EE Times Vol. 14, pp. 1-7, 2011.

5. W. Ma, Y. Zhang, F. Yang, “A fast recursive-least-squares adaptive notch filter and its applications to biomedical signals”, Medical & biological engineering & computing,Vol. 37, pp. 99–103,1999

6. S. Rani, A. Kaur, J. Ubhi, “Comparative study of FIR and IIR filters for the removal of Baseline noises from ECG signal” International

Journal of Computer Science and Information Technologies, Vol. 2, No. 3,pp. 1105-1108, 2011. 7. S. Poungponsri, X,H, Y, “Electrocardiogram ECG Signal Modeling and Noise Reduction Using Wavelet Neural Networks”. IEEE Conference

on Automation and Logistics. IEEE, August, pp. 394–398, 2009.

8. N.V. Thakor, Y.S. Zhu, “Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection” . IEEE transactions on biomedical engineering, Vol. 38,pp. 785–794, 1991.

9. S. Javed, N.A. Ahmad, “ An adaptive noise cancelation model for removal of noise from modeled ECG signals” , Region 10 Symposium,

2014 IEEE., pp. 471–475, 2014. 10. J.S. Wang, Y. Zhang, P. Zhang, S.F, Sun, “ Research on denoising algorithm for ECG signals” , IEEE 29th Chinese Control Conference

16-22

(CCC), pp. 2936–2940, 2010.

11. P.B. Patil, M.S. Chavan, “A wavelet basedmethod for denoising of biomedical signal.”, International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME), IEEE, pp. 278–283, 2012.

12. P. Karthikeyan, M. Murugappan, S. Yaacob, “ ECG signal denoising using wavelet thresholding techniques in human stress assessment” .

International Journal on Electrical Engineering and Informatics , Vol.4,No. 2, 306-319, 2012. 13. M. Alfaouri, K. Daqrouq, “ ECG denoising by sparse wavelet shrinkage” . American Journal of Applied Sciences , Vol. 5,pp. 276–281,

2008.

14. B.N. Singh, A.K. Tiwari, “ Optimal selection of wavelet basis function applied to ECG signal denoising” . Digital signal processing , Vol. 16,pp. 275–287, 2006

15. H.D. Kim, C.H. Min, T.S. Kim, “Adaptable noise reduction of ECG signals for feature extraction” , International Symposium on Neural

Networks. Springer, pp. 586–591, 2006 16. X. Ning, I.W. Selesnick, “ ECG enhancement and QRS detection based on sparse derivatives” , Biomedical Signal Processing and Control,

Vol. 8,pp. 713–723, 2013. 17. I. Selesnick, “ Total variation denoising (an MM algorithm)” . NYU Polytechnic School of Engineering Lecture Notes, 2012.

18. O.P. Yadav,S. Ray, “Total Variational Denoising of ECG Signals using Majorization-Minorization Technique” , Indian Control Conference.

IIT Madras, Chennai, pp. 165–169, 2015 19. L.I. Rudin, S. Osher, E. Fatemi, “Nonlinear total variation based noise removal algorithms” . Physica D: Nonlinear Phenomena Vol. 60,pp.

259–268, 1992

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22. J . De Leeuw, K. Lange, “ Sharp quadratic majorization in one dimension” , Computational statistics & data analysis Vol. 53,pp. 2471–2484, 2009

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32. O.P. Yadav, S. Ray, “Smoothening and Segmentation of ECG Signals Using Total Variation Denoising–Minimization-Majorization and Bottom-Up Approach” . Procedia Computer Science ,Vol. 85,pp. 483–489, 2016.

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URL: http://www. dt. fee. unicamp. br/~ivanil/adaptive svd encoderanalise metodo wei. Pdf

5.

Authors: Seyed Amin Ahmadi Olounabadi, Avula. Damodaram, V. Kamakshi Prasad

Paper Title:

People Movement on The Throughput of Wi-Fi Networks and the Significant Changes that Affect

WLAN Performance by using Random Waypoint Mobility Model (RWMM) & Random Direction

Mobility Model (RDMM) to Model the JNT University Hyderabad India

Abstract: The use of network technologies and facilities has always been explored by users, researchers and

experts. One of the issues that the network has to deal with, how to send and receive, and network communications. In

this research, we are looking at how people are moving on the throughput of Wi-Fi networks and the significant

changes that affect WLAN performance. We examined the background of the research and related protocols, we were

trying to model the Jawaharlal Nehru Technological University Hyderabad in India.

In conducting research, we concluded that the behavior and scenario of moving network nodes can be simulated into

two models and scenarios:

1. Random Waypoint Mobility Model (RWMM),

2. Random Direction Mobility Model (RDMM),

In our study, we examined the operation of these two models with two methods without any grouping and with

grouping and clustering and we proved that the use of clustering or grouping algorithms in the model of Jawaharlal

Nehru Technological University Hyderabad, India, Optimizes network utilization and can provide better quality and

better bandwidth. One of the reasons for this quality is the lower number of packets dropped. In this research work we

used MobiSim v3 – A Framework to Manage Mobility Models, software.

Keywords: People Movement, Wi-Fi Networks, Random Waypoint Mobility Model (RWMM), Random Direction

Mobility Model (RDMM), JNTUH

References: 1. Watson, D. (2003). "IT vital part of NZ$200m Auckland hospital." Retrieved 24/03/2012, 2012, from

http://www.computerworld.com.au/article/64827/it_vital_part_nz_200m_auckland_hospital/.

2. Evans, R. and N. Sarkar (2004). "Mobile commerce implementation in the hospital environment: Issues, challenges and future trends." Journal

of Applied Computing and Information Technology 2(1).

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devices in further and higher education institutions." JISC Technology and Standards Watch Report (TSW0305): 1-21.

4. Bing, B. (1999). Measured performance of the IEEE 802.11 wireless LAN, IEEE.

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7. Xiao, Yang, and Jon Rosdahl. "Throughput and delay limits of IEEE 802.11." IEEE Communications letters 6.8 (2002): 355-357.

8. Golmie, N., R. E. Van Dyck, et al. (2003). "Interference evaluation of Bluetooth and IEEE 802.11 b systems." Wireless Networks 9(3): 201-211.

9. Jain, K., J. Padhye, et al. (2005). "Impact of interference on multi-hop wireless network performance." Wireless Networks 11(4): 471-487.

10. Doefexi, A., S. Armour, et al. (2003). An evaluation of the performance of IEEE 802.11a and 802.11 g wireless local area networks in a corporate office environment, IEEE.

11. Johnsson, A., M. Björkman, et al. (2004). A study of dispersion-based measurement methods in IEEE 802.11 ad-hoc networks.

12. Perahia, E. (2008). "IEEE 802.11 n development: History, process, and technology." Communications Magazine, IEEE 46(7): 48-55. 13. Xiao, Y. (2005). "IEEE 802.11 n: enhancements for higher throughput in wireless LANs." Wireless Communications, IEEE 12(6): 82-91.

14. Aharony, N., T. Zehavi, et al. (2005). Learning Wireless Network Association Control with Gaussian Process Temporal Difference Methods, Citeseer.

15. Mishra, A., M. Shin, et al. (2003). "An empirical analysis of the IEEE 802.11 MAC layer handoff process." ACM SIGCOMM Computer

Communication Review 33(2): 93-102. 16. Youssef, M. A. and R. E. Miller (2002). "Analyzing the point coordination function of the ieee 802.11 wlan protocol using a systems of

communicating machines specification."

17. Wu, H., S. Cheng, et al. (2002). IEEE 802.11 distributed coordination function (DCF): analysis and enhancement, IEEE. 18. Heusse, M., F. Rousseau, et al. (2003). Performance anomaly of 802.11 b, IEEE.

19. Lo, E. C. C. (2007). An investigation of the impact of signal strength on Wi-Fi link throughput through propagation measurement, AUT

University. 20. Sarkar, N. I. and K. W. Sowerby (2006). Wi-Fi performance measurements in the crowded office environment: a case study, IEEE.

21. Stein, J. C. (1998). "Indoor radio WLAN performance part II: Range performance in a dense office environment." Intersil Corporation.

22. Zhao, M., Li, Y., Wang, W. (2012). "Modeling and analytical study of link properties in Multihop wireless networks". IEEE Transactions on

Communications, ISSN: 0090-6778, Volume: 60, Issue: 2: 445-455.

23. Kotz, D., Essien, K. (2002). "Characterizing usage of a campus-wide wireless network". Dartmouth Computer Science Technical Report

TR2002-423.March 12, 2002. 24. Rahim, A., et al. (2006). "Impact of people movement on received signal in fixed indoor radio communications". 2006 IEEE 17th International

Symposium on Personal, Indoor and Mobile Radio Communications. ISSN: 2166-9570. DOI: 10.1109/PIMRC.2006.254304.

25. Choi, S. W., Shim, Y. S., Park, S. K. (2012). "A study on throughput difference of station according to separation distance among WLAN Aps". 2012 International Conference on ICT Convergence (ICTC). IEEE Xplore: 24 December 2012. DOI: 10.1109/ICTC.2012.6386866.

ISSN: 2162-1233.

26. Sarkar, N. I., Mussa, O. (2013). "The effect of people movement on Wi-Fi link throughput in indoor propagation environments". IEEE 2013 Tencon - spring. DOI: 10.1109/TENCONSpring.2013.6584508. ISBN: 978-1-4673-6347-1.

27. Balachandran, A., et al. (2002). "Characterizing user behavior and network performance in a public wireless LAN". ACM Sigmetrics

Performance Evaluation Review - Measurement and modeling of computer systems. Vol. 30. No. 1. ACM, 2002: 195-205. 28. Chinchilla, F., Lindsey, M., Papadopouli, M. (2004). "Analysis of wireless information locality and association patterns in a campus".

IEEE INFOCOM 2004. Twenty-third Annual Joint Conference of the IEEE Computer and Communications Societies.

DOI: 10.1109/INFCOM.2004.1356978. ISSN: 0743-166X. Vol. 2. 29. Choi, N., Nam, Y., Seok, Y., Choi, Y., Kim, S. (2005). "Analysis of Campus Users’ Pattern in NESPOT Wireless LANs". The 7th International

Conference on Advanced Communication Technology (ICACT) IEEE. Vol. 2.

30. Henderson, T., Kotz, D., Abyzov, I. (2004). "The changing usage of a mature campus-wide wireless network". MobiCom 04 Proceedings of the 10th annual international conference on Mobile computing and networking (ACM): 187-201.

31. Kotz, D., Essien, K. (2005). "Analysis of a campus-wide wireless network". Wireless Networks Vol. 11. Issue. 1-2 (January 2005): 115-133.

32. Schwab, D., Bunt, R. (2004). "Characterizing the use of a campus wireless network". INFOCOM 2004. Twenty-third Annual Joint Conference of the IEEE Computer and Communications Societies. DOI: 10.1109/INFCOM.2004.1356974. ISSN: 0743-166X. Vol. 2.

33. Shaffer, J., Siewiorek, D. P., Smailagic, A. (2005). "Analysis of movement and mobility of wireless network users". Ninth IEEE International

Symposium on Wearable Computers (ISWC'05). DOI: 10.1109/ISWC.2005.10. ISSN: 1550-4816. 34. Tang, D., Baker, M. (2000). "Analysis of a local-area wireless network". Proceedings of the 6th annual international conference on Mobile

computing and networking (ACM): 1-10.

35. Zola, E., Barcelo-Arroyo, F. (2011). "A comparative analysis of the user behavior in academic Wi-Fi networks". Proceedings of the 6th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks (ACM): 59-66.

36. Yamkhin, D., Won, Y. (2009). "Modeling and Analysis of Wireless LAN Traffic". Journal of Information Science & Engineering 25:1783-

1801. 37. Jiang, L., and et al. (2018). "A Hot Area Mobility Model for Ad- Hoc Networks Based on Mining Real Traces of Human". Conference on

Complex, Intelligent, and Software Intensive Systems. CISIS 2017. Springer, Cham, 2017. Vol 611. Pp: 689-698.

38. Heinzelman, W. R., Chandrakasan, A., and Balakrishnan, H., (2000). "Energy-efficient communication protocol for wireless microsensor networks". Proceedings of the 33rd annual Hawaii international conference on System sciences IEEE. ISBN: 0-7695-0493-0.

DOI: 10.1109/HICSS.2000.926982.

39. Liu, J. L., Ravishankar, C. V. (2011). "LEACH-GA: Genetic algorithm-based energy-efficient adaptive clustering protocol for wireless sensor networks". International Journal of Machine Learning and Computing 1.1. Pp: 79.

40. Parmar, B., et al. (2014). "A Survey of routing protocol LEACH for WSN". International Journal of Scientific and Research Publications. Vol

2. Issue 2. ISSN: 2250-3153. 41. Bettstetter, C., Resta, G., Santi, P. (2003). "The node distribution of the random waypoint mobility model for wireless ad-hoc networks". IEEE

Transactions on mobile computing 3. Pp: 257-269. 42. Royer, E., Melliar-Smith, P., Moser, L. (2001). "An analysis of the optimum node density for ad-hoc mobile networks". In Proc. IEEE

International Conference on Communications (ICC). Pp: 857–861.

43. Bai, Fan, Sadagopan, N., Helmy, A. (2003). "The IMPORTANT framework for analyzing the Impact of Mobility on Performance of Routing protocols for Ad-hoc Networks". Ad-hoc networks 1.4. Pp: 383-403.

44. Hong, Xiaoyan, et al. (1999). "A group mobility model for ad-hoc wireless networks". Proceedings of the 2nd ACM international workshop on

Modeling, analysis and simulation of wireless and mobile systems. 45. Bheemalingaiah, M., et al. (2017). "Survey of Routing Protocols, Simulation Tools and Mobility Models in Mobile Ad-Hoc Networks".

International Journal of Innovations & Advancement in Computer Science (IJIACS) ISSN (2017): 2347-8616.

6.

Authors: Saher Manaseer, Metib Alzghoul, Mazen Mohmad

Paper Title: An Advanced Algorithm for Load Balancing in Cloud Computing using MEMA Technique

Abstract: Recently Cloud computing has become one of the most significant technologies for its impact in every

aspect in networking and new technologies such as security, capacity, quality of service, cost, and accessibility etc.

One of the major challenges in networks is Load Balancing. Many algorithms were proposed to solve the problem.

Some considered static variables while others considered dynamic ones. In this paper static variables techniques are

used with the new proposed algorithm “MEMA Technique”. In the proposed algorithm few steps are added to the

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weighted round robin (WRR). Moreover, a comparison of performance between the (WRR) and MEMA is presented.

Keywords: Cloud Computing, Load Balancing, Static Algorithms, Weighted Round Robin (WRR) MEMA

Technique.

References: 1. P. M. B. M.Padmavathi, "Dynamic And Elasticity ACO Load Balancing Algorithm for Cloud Computing," International Conference on

Intelligent Computing and Control Systems ICICCS 2017, 2017. 2. P. U. T. Prof. Satvik Khara, "A Novel Approach for Enhancing Selection of Load Balancing Algorithms Dynamically in Cloud Computing,"

International Conference on Computer, Communications and Electronics (Comptelix) Manipal University Jaipur, Malaviya National Institute

of Technology Jaipur & IRISWORLD, 2017. 3. M. R. D. D. R. S. Khusboo K. Patel, "Dynamic Priority Based Load Balancing Technique For VM Placement In Cloud Computing,"

Proceedings of the IEEE 2017 International Conference on Computing Methodologies and Communication (ICCMC), 2017. 4. H.-C. H. W.-C. T. M.-C. K. Mao-Lun Chiang, "An Improved Task Scheduling and Load Balancing Algorithm under the Heterogeneous Cloud

Computing Network," IEEE 8th International Conference on Awareness Science and Technology (iCAST 2017), 2017.

5. M. S. G. a. J. P. Nithin Das K.C, "Incorporating Weighted Round Robin in Honeybee Algorithm for Enhanced Load Balancing in Cloud Environment," International Conference on Communication and Signal Processing, April 6-8, 2017, India, 2017.

6. K. R. K. D. Kripa Sekaran, "SIQ Algorithm for Efficient Load Balancing In Cloud," International Conference on Algorithms, Methodology,

Models and Applications in Emerging Technologies (ICAMMAET), 2017. 7. P. B. Bharat Khatavkar, "Efficient WMaxMin Static Algorithm For Load Balancing In Cloud Computation," International Conference on

Innovations in Power and Advanced Computing Technologies [i-PACT2017], 2017.

8. B. R. Venkateshwarlu Velde, "An Advanced Algorithm for Load Balancing in Cloud Computing using Fuzzy Technique," International Conference on Intelligent Computing and Control Systems ICICCS 2017, 2017.

9. G. P. R.Kanniga Devi, "A Graph-based Mathematical Model for an Efficient Load Balancing and Fault tolerance in Cloud Computing,"

Second International Conference on Recent Trends and Challenges in Computational Models, 2017. 10. M. S. H. Tahira Islam, "A Performance Comparison of Load Balancing Algorithms for Cloud Computing," 978-1-5386-3148-5/17/$31.00 ©

2017 IEEE, 2017.

11. D. V. R. U. Sridevi S, "A Survey of Soft Computing Techniques Applied in Cloud Load Balancing," IEEE Eighth International Conference on Advanced Computing (ICoAC), 2016.

12. S. M. S. N. I. P. Jananta Permata Putra1, "Live Migration Based on Cloud Computing to Increase Load Balancing," International Seminar on

Intelligent Technology and Its Application, 2017. 13. R. G. Ashish Gupta, "Load Balancing Based Task Scheduling with ACO in Cloud Computing," international conference on computer and

applications (ICCA), 2017.

14. O. H. O. H. Sofiane Mounine Hemam, "Load Balancing Between Nodes in a Volunteer Cloud Computing by Taking Into Consideration the Number of Cloud Services Replicas," IEEE, 2017.

15. D. S. P. D. N. J. D. K. K. D. D. Jaimeel M Shah, "Load Balancing in cloud computing: Methodological Survey on different types of

algorithm," International Conference on Trends in Electronics and Informatics ICEI 2017, 2017. 16. N. B. MONIKA LAGWAL, "Load balancing in Cloud Computing using Genetic Algorithm," International Conference on Intelligent

Computing and Control Systems ICICCS 2017, 2017.

17. S. P. N. A. A. Mrs. Shruti Tripathi, "MODIFIED OPTIMAL ALGORITHM FOR LOAD BALANCING IN CLOUD COMPUTING," International Conference on Computing, Communication and Automation (ICCCA2017), 2017.

18. E. A. R. Muhammad Sohaib Shakir1, "Performance Comparison of Load Balancing Algorithms using Cloud Analyst in Cloud Computing.,"

IEEE, 2017. 19. P. C. Umadevi.K.S, "Predictive Load Balancing Algorithm for Cloud Computing," IEEE, 2017.

20. M. A. K. B. S. D. P. a. M. S. O. F. o. I. a. K. H. Sambit Kumar Mishra, "Time Efficient Dynamic Threshold-based load balancing technique for

cloud computing," IEEE, 2017. 21. Manaseer, S. and , Alawneh, A. “Fairness based on backoff algorithms for enhancing MANETs performance: a comprehensive review”,

Journal of Mathematical and Computational Science, Vol 8, No.3, Pp: 394-420.

7.

Authors: M. Rajeswari, K.S. Meena, C. Sivamani, Sheethal M. S, Sreetha E.S.

Paper Title: Performability of VBRCP in VANET using UGFT

Abstract: A reliable Vehicular Based Reliable Clustering Protocol (VBRCP) for transmitting road safety information

between vehicular nodes is presented in this paper. Transmission range of vehicular nodes are said to be identified

during phase-I of VBRCP. Nodes are grouped to form a cluster based on this transmission range of vehicular nodes.

The parameters like Degree, Degree In Difference, Sum of Degree, Battery power, Node movement and Combined

weight are used to elect the cluster head. The reliability of transportation system is considered as the probability of

successful delivery of information from a source vehicle to the destination vehicle. This is attained by using UGF

(Universal Generating Function). Node UGF of a member node / cluster head and link UGF are used to define the

reliability of VBRCP. Algorithm is proposed in UGF in order to calculate the link reliability which represents the

successful delivery of traffic information between the ongoing vehicles on the roadside. Proposed protocol is compared

with some other existing protocols. Simulation results show that VBRCP outperforms other protocols in terms of

delay, packet delivery and throughput.

Keywords: UGF, VANET, VBRCP, Transmission Probability.

References: 1. R.S. Bali, N. Kumar, J.P.C. Rodrigues, Clustering in vehicular ad hoc networks: Taxonomy, challenges and solutions, 2014, pp. 134-152. 2. Salhi, M. Cherif, S. Senouci, Data collection in vehicular networks, in: Autonomous and Spontaneous Networks Symposium, 2008, pp.20–

21.

3. Z. Wang, L. Liu, M. Zhou, N. Ansari, A position based clustering technique for ad hoc inter vehicle communication, IEEE Trans. Syst. Man Cybern., Part C, Appl. Rev. 38(2) (2008) 201–208.

4. F. Ahammed, J. Taheri, A. Zomaya, LICA: robust localization using cluster analysis to improve GPS coordinates, in: First ACM International

Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, New York, USA, 2011, pp.39–46. 5. W. Fan, Y. Shi, S. Chen, L. Zou, A mobility metric based dynamic clustering algorithm (DCA) for VANETs, in: International Conference on

Communication Technology and Application, Beijing, 2011, pp.752–756.

6. P. Fan, Improving broadcasting performance by clustering with hicle communication, in: 65th IEEE Conference on Vehicular Technology, Dublin, 2007, pp.2491–2495.

7. Z. Zhang, A. Boukerche, R.W. Pazzi, A novel multi-hop clustering scheme for vehicular ad-hoc networks, in: MobiWac’11, 9th ACM

International Symposium on Mobility Management and Wireless Access, 2011, pp.19–26.

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8. Meena KS and Vasanthi T “Reliability Design for a MANET with Cluster Head Gateway Routing Protocol”, Communication in Statistics-

Theory and Methods, 2015; 45(13): 3904-3918. 9. [9] Meena KS and Vasanthi T “Reliability Analysis of Mobile Adhoc Networks using Universal Generating Function”, Quality and

Reliability Engineering International, 2014; 32(1): 111-122.

10. Meena, KS&Vasanthi, T 2015, 'The Performance Assessment of a MANET using WUGF', Research Journal of Applied Sciences, Engineering and Technology, vol. 10, no.8 , pp. 942-950.

11. Meena KS et al., “Reliability Analysis of MANET with RCFP: Reliable Cluster Forming Protocol”, International Journal of Applied

Engineering Research, 2016; 11(1): 440-447. 12. Meena, KS et al., “Reliable Cluster Forming Protocol in VANET”, World Journal of Engineering research and Technology, vol. 3, no. 3,

pp.390-321, DOI: 10.1080/03610926.2014.911908.

13. P.UmaMaheswari and M. Rajeswari, “Reliability Calculation of VANET with RSU and SP”, Research Journal of Applied Sciences, Engineering and Technology 8(13): 1607-1612, 2014.

8.

Authors: Vasujadevi Midasala, P. Siddaiah

Paper Title: Design & Simulation of Array DGS using HFSS

Abstract: Array antennas are useful in military applications due to high gain and more directivity. But the individual

elements of array radiation may interfere each other and resulting in Mutual coupling. In this research paper, Defected

Ground Structure (DGS) is used to reduce the mutual coupling and further array of DGS is used for the optimization of

output parameters. The proposed antenna of 1x2 array and 4x4 array of DGS is designed and simulated using HFSS

software. It operates at 15GHz (Ku band) with minimum mutual coupling of -25dB. The gain is 8dB and VSWR and

Return loss are in optimal Range. The output parameters are plotted and compared using MATLAB software.

Keywords: DGS, Ku-band, Mutual Coupling, HFSS, Return loss, VSWR.

References: 1. Ankit Arora, N. Anish Kumar “To reduce mutual coupling in microstrip patch antenna arrays elements using electromagnetic band gap

structures for X-band”, ICNETS 2017.

2. Mohamed Ezzat and Choon Sae Lee “A simple optimization technique for reducing mutual coupling between two coupled antennas”, IEEE

international Sympoisum on Antennas, 2015. 3. Prakash K Kuravatti, T. S. Rukmini “Reduction of mutual coupling in antenna arrays using periodic structures”, IEEE-RTEICT 2016.

4. Qian Li , Chong Ding “Mutual Coupling Reduction between Patch Antennas Using Meander Line”, International Journal of Antennas and

Propagation 2018:1-7. 5. S. Chakrabarti and A. Chakraborty, "Dual-band dual sense microstrip antenna with improved characteristics," in Electronics Letters, vol. 54, no.

20, pp. 1149-1150, 4 10 2018.

6. B.S Nagakishor, R.S Kalyan "Design of a Novel Coaxial-Feed Triple Frequency PatchAntenna with Slots and Shorting Pin", Procedia Computer Science, 1877-0509, Vol. 85, 2016, 345–351.

7. Vasujadevi, M., Madhav, B.T.P., Shiva Skandan “Maple leaf shaped array antenna for multiband applications”, International Journal of

Engineering and Technology (UAE), 2018. 8. B. S Nagakishore, "Design & Simulation of TrippleFrequency Triangular PatchAntenna by Using HFSS 14.0", International Journal of Applied

Engineering Research, vol. 10, No. 20, 2015, 1087-1090, 18585-18588.

9. M. Sekhar, B.S.N.Kishore "Triplefrequency Circular PatchAntenna" IEEE ICCIC, 2014, pp.1231-1233.

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9.

Authors: Mohamed.M.Elgazzar, Mahmoud Maher El-Sayed Mohammed

Paper Title: A Comparison of Piplined and Parallel CORDIC Architecures

Abstract: Coordinate Rotation Digital Computer (CORDIC) is efficient and simple algorithm that is used to

calculate the trigonometric and hyperbolic functions. The CORDIC is used in multiple applications like hardware

multiplier in the field programmable gate array (FPGA) or in simple microcontroller. The advantage of the CORDIC is

that it uses only shift and add operations. The CORDIC can be implemented in different architectures like the parallel

(or combinational) architecture, pipelined architecture, and iterative architecture. We want to know what is the pros

and cons for every architecture, so our research will be very important to the integrated circuit (IC) designer. The

designer should know the advantages of each architecture to choose the suitable architecture for his design. In this

paper we will make comparison between pipelined and parallel CORDIC architectures. The comparison steps are

implementing the parallel and the pipelined CORDIC architectures in Verilog register transfer level (RTL) codes, then

simulating these two Verilog codes using Modelsim. The both architectures will be simulated using the same test

sequence. The sequence flow is inserting angles from zero degree to ninety degree and compare the outputs which are

cosine and sine for the inserted angle to the expected values. The test sequence will be the same for both architectures.

This will make the same simulation environment for the two architectures. From simulation results, we can expect the

parallel CORDIC is more efficient in the initial delay, area, power consumption, and the pipelined CORDIC is more

efficient in the clock frequency, critical path.

Keywords: CORDIC, E. Volder, FPGA, Parallel CORDIC, Pipelined CORDIC.

References: 1. VOLDER, J. E. (2000). The Birth of CORDIC. Journal of VLSI Signal Processing , 101-105. 2. Takagi, Naofumi, Tohru Asada, and Shuzo Yajima., “Redundant CORDIC methods with a constant scale factor for sine and cosine

computation”, IEEE Transactions on Computers, vol.40, no.9, pp.989-995, 1991.

3. J. Chen and K. J. R. Liu, “A fully pipelined parallel CORDIC architecture for half-pel motion estimation,” in Proc. IEEE Int. Conf. Image Processing, Santa Barbara, CA, Oct. 1997, vol. 2,pp. 574–577.

4. T. B. Juang and H. F. Lin, “CORDIC algorithm for vectoring mode

5. without constant scaling factors,” Electron. Lett., vol. 35, no. 12, pp. 971–972, Jun. 10, 1999 6. T. B. Juang, "Area/delay efficient recoding methods for parallel CORDIC rotations," in Proc. IEEE Int. Symp. Circuits And Systems, June 2006,

pp. 1539-1542.

7. G.Gopikiran, R.Thilagavathy, “FPGA Implementation of Floatingpoint Rotation Mode CORDIC Algorithm,” " in Signal Processing, Communication, Computing and Networking Technologies (ICSCCN), 2011.

8. A. Syed, M. Lourde R., “Hardware Security Threats to DSP Applications in an IoT network,” IEEE International Symposium on Nanoelectronic

and Information Systems, 2016.

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9. Aggarwal, Supriya, Pramod K. Meher, and KavitaKhare., “Concept, Design, and Implementation of Reconfigurable CORDIC”, IEEE

Transactions on Very Large Scale Integration (VLSI) Systems, vol.24, no.4, pp.1588-1592, 2016.

10.

Authors: M. Lakshmi Raviteja, N. Nagaraju, Anandbabu Gopatoti, N. Paparao

Paper Title: Processing Algorithm and Data Acquisition for Laser Range Sensor

Abstract: This research paper focuses on the use of Defense, Military, Space and Industrial applications for data

acquisition. Towards the development of this project is to develop processing Algorithm and Data Acquisition for

LASER range sensor. All processing logic will be implemented using VHDL. The Processing logic will be

implemented in serial interface 1553 from bus controller. Subsequent to range measurement it provides the range data

through 1553 interface to bus controller. As part of data Acquisition the serial communication between bus controllers

to remote terminal via 1553 interface will be implemented in C lan-guage.

Keywords: 1553 Interface, Bus controller, Remote Terminal

References: 1. Farzin, Amzajerdian, Diego Pierrottet, Larry Petway, Glennhines and Vincent Roback. “LIDAR Systems for precision navigation and safe

landing on planetary bodies”,SPIE Defense and Security Symposium, Orlando, FL,(2011).

2. F. Amzajerdian, D. Pierrottet, L. Petway, M.Vanek,” Development of LIDAR sensor and LASER systems for autonomous safe landing on planetary bodies”.

3. Amzajerdian,Fazzin,pierrottet,Petway,LarryB,Hines,alennD,Roback,VincentE, “LIDAR and LASER systems for precision Navigation and safe

landing on planetary bodies”. Langel Research centre .NASA.

4. Graham Brooker, “Introduction to Sensors for Ranging and Imaging”, Scitech Publishing,2009”.

5. LASER/ Electro –optics Technology(LEOT), “New Design and Applications for LASERS”.

6. V.E. Kisel and V.G. Shcherbitskii. (2005), "Passive Q switches for a diode-pumped erbium glass laser", Quantum Electronics 35(7) 611-614 7. M.M. Michaelisand A. Forbes. (2008), ‘Lasers in space’, Proc. of SPIE Vol. 7005

8. J.A.Kamalakar, KVSBhaskar, A.S.Laxmiprasad, R.Ranjith, K.A.Lohar, R.Venkateswaran and T.K.Alex, “Lunar Ranging Instrument for Chandrayan-1”.

9. V.L.N Sridhar Raja, V.S Bhaskar, A.S.Laxmi Prasad and J.A.Kamalakar “Solid-state Lasers for Space Applications”.

10. V.L.N Sridhar Raja, A.S.Laxmi Prasad and J.A.Kamalakar “Performance characterization of Si-APD for space Applications”. 11. Bhavana Godavarthi, Paparao Nalajala, M Lakshmi Ravi Teja,” Wireless Sensors Based Data Acquisition System Using Smart Mobile

Application”, International Journal of Advanced Trends in Computer Science and Engineering, Volume 5, Issue 1, January 2016

12. K.Kalyani, V.L.N Sridhar Raja, AdwaitaGoswami, K.Ravi Kumar, K.V.S Bhaskar ,P.Selvaraj, R.Venkateswaran,A.S.Laxmi Prasad and J.A.Kamalakar “Design and Performance Aspects of Lunar Laser Ranging Instrument”.

13. Actel “1553B RT FPGA core Data sheet”.

14. Paparao Nalajala, Bhavana Godavarth, M Lakshmi Raviteja, Deepthi Simhadri,” Morse code Generator Using Microcontroller with Alphanumeric Keypad”, International Conference on Elec-trical, Electronics, and Optimization Techniques (ICEEOT), Pages 762 –

766,Publisher-IEEE

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11.

Authors: S. Arunchaitanya, E. Arunakanthi

Paper Title: Usage of Mineral Admixtures in Self Compacting Concrete- A Review

Abstract: Self Compacting Concrete (SCC) is a special concrete, which is used to fill congested reinforcement

without any vibration. This study mainly focused to know the result of mineral admixtures on self compacting

concrete, which are prepared by partial replacement of cement and fine aggregate by mineral admixtures like copper

slag, fly ash, silica fume etc.; called as industrial by-products. The objective of this paper is to know the behavior of

the SCC, when it is in fresh and hardened states. Fresh and mechanical properties along with permeation characteristics

have been discussed.

Keywords: Fly ash, Permeation characteristics, Self Compacting Concrete, Silica fume, Copper slag.

References: 1. M.C.Nataraja, Anvit Gadkar and Giridhar Jogin, “ A simple mix proportioning method to produce SCC based on compressive strength

requirement by modification to IS 10262:2009” The India Concrete Journal Vol 92, 2018, pp. 15-23.

2. M. Sri Rama Chand, P.Rathish Kumar, P.S.N.R. Giri, G. Rajesh Kumar, “Performance studies on self compacting concrete with self curing

chemicals” The India Concrete Journal Vol 92, 2018, pp. 24-30. 3. Athiyamaan.V and G. Mohan Ganesh, “ Optimization of SCC mix design using nan-su theory embodying DOE method” The India Concrete

Journal Vol 92, 2018, pp. 31-41.

4. C. Sashidhar, B.Radhamma, J. Gurujawahar, C. Yedukondalu, “ Effect of artificial sand on fresh characteristics of self compacting geopolymer concrete” The India Concrete Journal Vol 92, 2018, pp. 42-48.

5. K Ganesh Babu and B Chandrasekhar, “High performance SCCs containing fly ash” The India Concrete Journal Vol 92, 2018, pp. 57-63.

6. M. Sri Rama Chand, P. Swamy Naga Ratna, K.L. Radhika, P. Rathish Kumar and C. Yedukondalu, “Optimization of mix proportioning for self compactinh concretes using particle packing theories” The India Concrete Journal Vol 91, 2018, pp. 20-32.

7. Shaik Khaja Sameer, B. Jagadish Chakravarti and V. Ramesh, “A quantitative approach in designing self compacting concrete using

compressible packing model” The India Concrete Journal Vol 91, 2018, pp. 33-42. 8. B. Nagendra Kumar, ”Development of self compacting concrete using quartz sand as an alternative of natural river sand” The India Concrete

Journal Vol 91, 2018, pp. 43-50.

9. S. Girish, “Importance of volume of paste on the compressive strength of SCC – A parameter to be considered in mix design” The India Concrete Journal Vol 91, 2018, pp. 51-62.

10. H.Y. Leung, J. Kim, A. Nadeem, Jayaprakash J and M. P. Anwar, “Sorptivity of self compacting concrete containing fly ash and silica fume”

Elsevier, Construction and building material, 113, 2016, pp. 369-375. 11. Daniel C, Joel Shelton J, Vincent Sam Jebadurai S, Arun Raj E, “Studies on High Strength Self Compacting Concrete with Copper Slag for

M30 grade”, International Journal of Research in Engineering and Technology”, Vol. 5, 2016, pp. 74-78.

12. Gritsada Sua-iam, Natt Mukal, “Rheological and mechanical properties of cement – fly ash self compacting concrete incorporating high volumes of alumina based material as fine aggregate” Elsevier, Construction and building material, 95, 2015, pp. 736-747.

13. M. Fadee, R.Mirhosseini, R. Tabatabaei & M.J. Fadaee, “Investigation on using copper slag as part of cementitious materials in self

compacting concrete”, Asian Journal of Civil Engineering, Vol. 16, 2015, pp. 368-381.

14. T.H. Patel, J.M. Srihaila, Prahalada. V, “Experimental study on Durability properties of High Performance Self Compacting Concrete”

International Journal of Innovative Research in Science, Engineering and Technology” Vol. 5, 2016, pp. 14856-14863.

15. T.Adhavanathan, V.Vinoth, “Experimental Investigation of Self Compacting Concrete by using Copper Slag and Fly ash”, International

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Journal for Scientific Research & Development, Vol. 3, 2015, pp. 384-389.

16. K. S. Jhansirani and A. Jagannathan, “Studies on Effect of Mineral Admixtures on Durability of Self Compacting Concrete”, European Journal of Advances in Engineering and Technology, Vol.2, 2015, pp. 87-94.

17. V. Karthik, Dr. G. Baskar, “Study on Durability Properties of Self Compacting Concrete with Copper Slag partially replaced for fine

aggregate”, Vol. 6, 2015, pp. 20-30. 18. Iman Afshoon and Yasser Sharifi, “Ground copper slag as a supplementary cementing material and its influence on the fresh properties of self

consolidating concrete”, The IES Journal Part A: Civil & Structural Engineering, Vol 7, 2014, pp. 229-242.

19. R. Vasusmitha and Dr. P. Srinivasa Rao, “Strength and Durability study of high Strength Self Compacting Concrete”, International Journal of Mining, Metallurgy Engineering, Vol. 1, 2013, pp. 18-26.

20. N. Venkata Rao, M. Rajasekhar, Mohd Mujeeuddiahmed, “An Experimental Sudy on Durability of High Strength Self Compacting Concrete”

International Journal of Research in Engineering and Technology, Vol. 2, 2013, pp. 430-436.

12.

Authors: Telagarapu Prabhakar, S.Poonguzhali

Paper Title: Filtering and Statistical Feature based Automatic Detection of Breast Lesions in Ultrasound Images

Abstract: The objective of this paper was proposed to develop a method for a fully automatic detection of breast

tumor accurately. In this reduction of speckle noise was done by utilizing Curvelet, Shearlet. The filtered images acted

as an input of segmentation in which the contour was initially recognized by statistical features and the region was

segmented automatically. The performance analysis was done by comparing the output of the automatic segmentation

region algorithm and the ground truth (Segment by radiologist).The Shearlet filtered images gave a high performance

with the an accuracy of 91.51%, sensitivity of 92.24%, specificity of 89.44 %, Jaccard of 86.02 % and Dice Similarity

of 91.27 % when compared to other filtered images and input image.

Keywords: Breast Ultrasound Image, Speckle reduction, Statistical Features, Automatic Segmentation.

References: 1. 2012 v1.0, Cancer Fact Sheet. Available From: http://globocan.iarc.fr/Pages/fact_sheets_cancer.aspx[2012]

2. Rayudu, DKV &Murala, S 2010, ‘Denoising of Ultrasound Images using Curvelet Transform’, The 2nd International Conference on Computer and Automation Engineering (ICCAE 2010), Singapore,vol. 3, pp. 447-451.

3. Deng, Y, Wang, Y & Shen, Y 2011. ‘Speckle reduction of ultrasound Images based on Rayleigh-trimmed anisotropic diffusion filter’, Pattern

Recognition Letters, vol. 32, no. 13, pp. 1516-1525. 4. Ma, RX, Zhang, XM, Ding, MY & Liu, Q 2012, ‘Comparative Study on Filter Methods of Medical Ultrasound Images’, In Advanced

Materials Research, vol. 341, pp. 467-471.

5. Vishwa, A & Sharma, S 2012. ‘Speckle noise reduction in ultrasound Images by wavelet thresholding’, International Journal of Advanced Res arch in Computer Science & Software Engineering, vol. 2, no. 2.

6. Khusna, DA, Nugroho, HA & Soesanti, I 2015, ‘Performance analysis of Edge and detailed preserved speckle noise reduction filters for breast

Ultrasound images’. In 2nd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) IEEE, pp. 76-80.

7. Elyasi, I, Pourmina, MA &Moin, MS 2016, Speckle reduction in breast Cancer ultrasound images by using homogeneity modified bayes

shrink. Measurement. 8. Prabhakar, Telagarapu, and S. Poonguzhali. "Automatic detection and Classification of benign and malignant lesions in breast ultrasound

images using texture morphological and fractal features." Biomedical Engineering International Conference (BMEiCON), 2017 10th. IEEE,

2017. 9. Prabhakar, Telagarapu, and S. Poonguzhali. "Assessment of Texture Feature Extraction to Classify the Benign and Malignant Lesions from

Breast Ultrasound Images." Artificial Intelligence and Evolutionary Computations in Engineering Systems. Springer, Singapore, 2018. 725-

732. 10. Shan, J, Cheng, HD & Wang, Y 2008, ‘A novel automatic seed point Selection algorithm for breast ultrasound images’. IEEE-The 19th

International Conference on Pattern Recognition, ICPR, pp. 1-4.

11. Shan, J, Cheng, HD & Wang, Y 2012, ‘Completely automated Segmentation approach for breast ultrasound images using multiple-domain features’. Ultrasound in medicine & biology, vol. 38, no. 2, pp. 262-275.

12. Xian, Min, Yingtao Zhang & Cheng, HD 2015,‘Fully automatic Segmentation of breast ultrasound images based on breast characteristics in

space and frequency domains’. Pattern Recognition, vol. 48, no. 2, pp. 485-497. 13. Gómez-Flores, W & Ruiz-Ortega, BA 2016, ‘New Fully Automated Method for Segmentation of Breast Lesions on Ultrasound Based on

Texture Analysis’. Ultrasound in medicine & biology, vol. 42, no. 7, pp. 1637-1650. 14. Anant Madabhushi & Dimitris N Metaxas 2003, ‘Combining low-, high-level and empirical domain knowledge for automated segmentation

of ultrasonic breast lesion’, IEEE Transactions on Medical Imaging, vol. 22, no. 2, pp. 155-69 .

15. Liu, X, Huo, Z & Zhang, J 2005, ‘Automated segmentation of breast Lesions in ultrasound images’, 27th Annual International Conference of the IEEE in Engineering in Medicine and Biology Society, IEEE-EMBS, pp. 7433-7435.

16. Yu, Y & Acton, ST 2002, ‘Speckle reducing anisotropic diffusion’, Image Processing, IEEE Transactions, vol. 11, no. 11, pp. 1260-1270.

17. Candes, E, Demanet, L, Donoho, D & Ying, L 2006. ‘Fast discrete curvelet transforms’, Multiscale Modeling& Simulation, vol. 5, no. 3, pp.

861-899.

18. Lim, WQ 2010. ‘The discrete shearlet transform: A new directional Transform and compactly supported shearlet frames’, IEEE Transactions

on Image Processing, vol. 19, no. 5, pp. 1166-1180. 19. Easley, G, Labate, D & Lim, WQ 2008. ‘Sparse directional image Representations using the discrete shearlet transform’, Applied and

Computational Harmonic Analysis, vol. 25, no. 1, pp. 25-46.

20. Guo, K & Labate, D 2007. ‘Optimally sparse multidimensional Representation using shearlets’, SIAM journal on mathematical analysis, vol. 39, no. 1, pp. 298-318.

21. Chan, TF & Vese, LA 2001. ‘Active contours without edges’ Image Processing’, IEEE transactions, vol. 10, no. 2, pp. 266-277.

22. Zhang, K, Zhang, L, Song, H & Zhou, W 2010. ‘Active contours with Selective local or global segmentation: a new formulation and level set method’, Image and Vision computing, vol. 28, no. 4, pp. 668-676.

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13.

Authors: L. Sri Sivani, Nagi Reddy B, K. Subba Rao, A. Pandian

Paper Title: A New Single Switch AC/DC Converter with Extended Voltage Conversion Ratio for SMPS

Applications

Abstract: A single switch AC/DC converter for SMPS applications is proposed in this paper for attaining extended

voltage conversion range and unity input power factor. This AC/DC converter have a buck-boost PFC circuit that

operates in discontinuous conduction mode (DCM) to attain unity input power factor and a buck converter to achieve

tightly regulated voltage along with wide conversion range. Buck converter is also chosen to operate in discontinuous

conduction mode because of its advantages like, zero switch current at turn-ON, low switching voltage stresses,

independent voltage stresses from load variations and higher efficiencies. The proposed converter doesn’t require extra

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control technique to achieve unity power factor. The detailed operation is analysed and the essential equations are

derived. To analyze the effectiveness of the proposed converter, MATLAB/SIMULINK software is used.

Keywords: Wide voltage conversion range, AC/DC Converter, Unity input power factor, Buck-boost buck converter,

Single-switch.

References: 1. “A New Family of Single Stage AC/DC Power Factor Correction Converters with Fast Output Voltage Regulation” 0. Garcia, J.A. Cobos, P.

Alou, R. Prieto. J. Uceda and S. Ollero.

2. “A New Family of Single-Stage Isolated Power-Factor Correctors with Fast Regulation of the Output Voltage”Richard Red1 Laszlo Balogh

Nathan 0. Sokal. 3. “Integrated Buck-Flyback Converter as a High-Power-Factor Off-Line Power Supply” J. Marcos Alonso, Senior Member, IEEE, Marco A.

Dalla Costa, Student Member, IEEE, and Carlos Ordiz.

4. “Switching converters with wide dc conversion range,” D. Maksimovic and s. cuk, IEEE Trans. Power Electron., vol. 6, no. 1, pp. 151–157, Jan. 1991.

5. “Comparison of Basic converter topologies for Power-Factor Correction,” h. Wei and i. Batarseh, in Proc. IEEE SoutheastCon 1998, pp.

348–353. 6. “Analysis, Design, Modeling, Simulation and Development of Single-Switch AC-DC Converters for Power Factor and Efficiency

Improvement” Bhim Singh and Ganesh Dutt Chaturvedi.

7. “Single-stage AC-DC power-factor-corrected voltage regulator with reduced intermediate bus voltage stress”D.D.C. Lu, D.K.W. Cheng and Y.S. Lee.

8. “Improved Power Quality Switched-Mode Power Supply Using Buck–Boost Converter” Shikha Singh, Student Member, IEEE, Bhim Singh,

Fellow, IEEE, G. Bhuvaneswari, Senior Member, IEEE, and Vashist Bist, Member, IEEE.

9. “Implementation of zero-current switch turn-ON based buck-boost buck type rectifier for low power applications” Nagi Reddy.B, O.Chandra

Sekhar, M.Ramamoorty, International Journal of electronics (Accepted for publication).

10. “Reducing Storage Capacitance in Off-Line LED Power Supplies by Using Integrated Converters” J. M. Alonso1, D. Gacio1, A. J. Calleja1, F. Sichirollo2, M. F. da Silva3, M. A. Dalla Costa3, R. N. do Prado3.

11. “Analysis and Design of an Isolated Single-Stage Converter Achieving Power-Factor Correction and Fast Regulation” Tsai-Fu Wu, Senior

Member, IEEE, and Yu-Kai Chen, Student Member, IEEE.

14.

Authors: Pooja Sharma, Nisheeth Joshi

Paper Title: Design and Development of a Knowledge-Based Approach for Word Sense Disambiguation by using

WordNet for Hindi

Abstract: Word Sense Disambiguation (WSD) aims at deciphering word meaning in terms of context with the help

of computers. WSD is an accessible and challenging AI-complete problem. WSD analyses word tokens and determine

the exact sense of the word being used according to the context. WSD is viewed asafundamental problem in Artificial

Intelligence (AI) and Natural Language Processing (NLP). Our problem area involves finding the appropriate sense of

a lexeme for available context and relationship between lexicons. This is done using natural language processing

techniques which involve queries, NLP specific documents or output texts from Machine Translation (MT). MT

automatically translates text from one native language into another. It can be performed on various natural languages

like Urdu, Marathi, Punjabi, Bengali, English, and Hindi etc. The different application areas for word sense

disambiguation involves SpeechProcessing, Information Retrieval (IR), lexicography, Text Processing and

MTetc.With this article, we are exploring the knowledge-based technique for WSD for Hindi. This approach uses

explicitly available lexical resources viz. lexicon and thesaurus. It involves incorporating word knowledge from

external knowledge resources to removethe equivocalness of words. In this experiment, we tried to develop a WSD

tool by considering a knowledge-based approach with WordNet of Hindi. The system uses knowledge-based LESK

Algorithm for WSD for Hindi. Our proposed system gives the accuracy of about 71.4%.

Keywords: Word Sense Disambiguation, LESK, WordNet

References: 1. Bala, P. (2013). KnKnowledge-Basedpproach for Word Sense Disambiguation using Hindi Wordnet. The International Journal Of Engineering

And Science (IJES), 2(4), 36-41.

2. Baldwin, T., Kim, S., Bond, F., Fujita, S., Martinez, D., & Tanaka, T. (2010). A reexamination of MRD-based word sense disambiguation.

ACM Transactions on Asian Language Information Processing (TALIP), 9(1), 4. 3. Bhattacharyya, D. P. (2006). Hindi WordNet Data and Associated Software License Agreement. Indian Institute of Technology, Mumbai, CSE

dept., TcTechnicaleport.

4. Chaplot, D. S., &Salakhutdinov, R. (2018). Knowledge-based word sense disambiguation using topic models. arXiv preprint arXiv:1801.01900.

5. Ekedahl, J., & Golub, K. (2004). Word sense disambiguation using Wordnet and the Lesk algorithm. Projektarbeten 2004, 17.

6. Gupta, M., Yadav, S., Sharma, S., & Surendra, Y. (2013). Word Sense Disambiguation Using Hindi WordNet and Lesk Approach. IPASJ International Journal of Computer Science (IIJCS), 1(6).

7. Kaur, A. (2010). Development of an approach for disambiguating ambiguous Hindi postposition. Department of Computer Science, Punjab

University, India. 8. Kolte, S. G., &Bhirud, S. G. (2009). WordNet: a knowledge source for word sense disambiguation. International Journal of Recent Trends in

Engineering, 2(4).

9. Mohapatra, R., &Hembram, L. (2010). Thought Boundary Detection in English Text through ththelawf Conservation of thought'for Word Sense Disambiguation. Language in India, 10(8).

10. Moldovan, D., &Novischi, A. (2004). Word sense disambiguation of WordNet glosses. Computer Speech & Language, 18(3), 301-317.

11. Montoyo, A., Suárez, A., Rigau, G., & Palomar, M. (2005). Combining knowledge-and corpus-based word-sense-disambiguation methods. Journal of Artificial Intelligence Research, 23, 299-330.

12. Moro, A., Raganato, A., &Navigli, R. (2014). Entity linking meets word sense disambiguation: a unifiedapproach. Transactions the

Association for Computational Linguistics, 2, 231-244. 13. Munusamy, M., Arasu, G. T., Palanisamy, V., &Selvarajan, S. (2011). A Design and Development of Word Sense Disambiguation Algorithm

for English Language Understanding for Database Access. Journal of Computer Science, 7(10), 1612.

14. Naseer, A., & Hussain, S. (2009). Supervised Word Sense Disambiguation for Urdu Using Bayesian Classification. Center for Research in Urdu Language Processing, Lahore, Pakistan.

15. Pal, A. R., Saha, D., & Pal, A. (2017). A KnKnowledge-basedethodology for Word Sense Disambiguation for Low Resource Language.

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Advances in Computational Sciences and Technology, 10(2), 267-283.

16. Pandey, P., & Arora, R. (2012). Cross-Lingual Word Sense Disambiguation using Wordnets and CoContext-basedapping. 17. Parameswarappa, S., & Narayana, V. N. (2011). Kannada word sense disambiguation for machine translation. International Journal of

Computer Applications, 34(10).

18. Reddy, S., &Inumella, A. (2010, July). WSD as a distributed constraint opoptimisationroblem. In Proceedings of the ACL 2010 Student Research Workshop (pp. 13-18). Association for Computational Linguistics.

19. Reddy, S., Inumella, A., McCarthy, D., & Stevenson, M. (2010, July). IIITH: DoDomain-specificord sense disambiguation. In Proceedings of

the 5th International Workshop on Semantic Evaluation (pp. 387-391). Association for Computational Linguistics. 20. Reddy, S., Inumella, A., Sangal, R., & Paul, S. (2009). All words unsupervised semantic category lalabellingorhiHindiIn Proceedings of the

International Conference RANLP-2009(pp. 365-369).

21. Sankaran, B., & Vijay-Shanker, K. (2003). Influence of morphology in word sense disambiguation for TamilAU-KBC Research Centre. 22. Sharma, N., Kumar, S., & Niranjan, S. (2012). Using Machine Learning Algorithms for Word Sense Disambiguation: A Brief Survey.

International Journal of Computer Technology and Electronics Engineering (IJCTEE) Volume, 2. 23. Sreedhar, J., Raju, S. V., Babu, A. V., Shaik, A., & Kumar, P. P. (2012). Word sense disambiguation: An empirical survey. International

Journal of Soft Computing and Engineering (IJSCE) ISSN, 2231-2307.

24. Tamilselvi, P., & Srivatsa, S. K. (2011). Word Sense Disambiguation using CaCase-basedpproach with Minimal Features Set. Indian Journal of Computer Science and Engineering (IJCSE), 2(4), 628-633.

25. Trivedi, M., Sharma, S., &Deulkar, K. (2014). Approaches To Word Sense Disambiguation. International Journal of Engineering Research &

Technology, 3(10), 645-647. 26. Yadav, P., & Husain, M. S. (2014). Study of Hindi Word Sense Disambiguation Based on Hindi Wordnet. IJRASET ISeSN.

15.

Authors: Maram ALMuhisen, HüseyinGökçekuş, Dilber Uzun Ozsahin

Paper Title: The Most Common Factors Effecting Ground Water Quality

Abstract: Ever since the existence of living organisms on the planet, water has been a vital element for survival.

Not only is it essential for existence, but also for consuming, watering, manufacturing and many other purposes in

various fields. Regardless, humans have continuously polluted and allowed the waters to be unacceptable for human

consumption by dumping waste into the environment with no care. Furthermore, increasing populations, construction

expansions, larger income expenses, both industrial and economical developments have impacted an upsurge in water

utilization. Consequently, these aspects have also escalated the pressure placed on the water ecosystem extensively and

qualitatively. The water ecosystem also faces a heighten of conceivable risks resulting from dumping household as

well as manufacturing waste water into regions adjacent to the water supplies. Evidently, utilization of unsuitable

irrigation techniques could escalate earth salinity as well as vaporization percentages. The following research

deliberates most of the ground water foundations, water difficulties, as well as the warm and mineral waters which

have vitally influenced our wellbeing as a result of its organic and harmful aspects. Furthermore, in this research, the

most influential factors affecting the quality of groundwater, will be analysed using the fuzzy method which will focus

on the five following factors; coalash, industrial storage tanks, seawater intrusion, Arsenic, agriculture.

Keywords: polluted, ecosystem, wastewater, water supplies, ground water, fuzzy

References: 1. De Chaisemartin, M.,Varady, R.G.,Megdal, S.B., Conti, K.I., van der Gun, J.,Merla, A.,Nijsten, G.J. andScheibler, F. (2017).Addressing the

groundwater governance challenge. Switzerland: Springer International Publishing. 2. Lutey, T. (2018).Cleanup of Toxic Coal Ash That Contaminated Colstrip Groundwater Begins. USA: Billings Gazette.

3. Baker, S. (2018).Contaminated Groundwater Seeping into the Trinity River from This Spot Needs Costly Fix. USA: Sandra Baker.

4. Johnson, J. (2018).Farm Bureau Declares Opposition to Proposed Salinas Valley New wells Moratorium. 5. USA: Monterey Herald.

6. Pakianathan, R. (2018).Study Measures Arsenic Contamination in Wells. UK: The Dartmouth

7. Bergquist, L. (2018). DNR Board Approves Measure to Limit Manure Pollution in Eastern Wisconsin toProtect Groundwater. USA: Milwaukee J.-Sentinel.

8. Mukherji, A.& Shah, T. (2005). Groundwater socio-ecology and governance: A review of institutions and policies inselected countries. HydrogeolJournal, 13, 328–345.

9. Varady, R. G., Weert, F., Megdal, S. B., Gerlak, A., Iskandar, C. A., & House-Peters, L. (2010). Groundwater Governance: A Global

Framework for Country Action. USA: GEF. 10. OECD. (2017).Water Governance Initiative.France: Organisation for Economic Co-operation and Development.

11. Varady, R. G., Zuniga-Teran, A. A., Gerlak, A. K., &Megdal, S. B. (2016). Modes and approaches of groundwater governance: A survey of

lessons learned from selected cases across the globe. Journal of Water (Switzerland), 8(10), 417. 12. Michaels, S.& Kenney, D.S. (2000). State approaches to watershed management: Transferring lessons between theNortheast and Southwest.

Proceedings of theWatershed Management and Operations Management Conferences. USA.

13. Megdal, S.B., Gerlak, A.K., Huang, L.Y., Delano, N., &Varady, R.G. (2017). Innovative Approaches to Collaborative Groundwater Governance in the. United States:Environ.

14. Sugg, Z.P.,Varady, R.G.&Gerlak, A.K. (2015). de Grenade, R. Transboundarygroundwater governance in theGuarani Aquifer System:

Reflections from a survey of global and regional experts. Journal of Water Int, 40,377–400. 15. Heikkila, T. (2004). Institutional boundaries and common-pool resource management: A comparative of watermanagement programs in

California. Journal ofPolicy Anal. Manag, 23, 97–117.

16. Abazid, M., &Gökçekuş, H. (2019). Application of Total Quality Management on The Construction Sector in Saudi Arabia. International Journal of Technology.

17. Wade, R. (1987). The management of common-property resources: Collective action as an alternative to privitisationor state regulation.

Cambridge Journal of Economics, 11, 95–106. 18. Ostrom, E., Burger, J., Field, C.B., Norgaard, R.B.&Policansky, D. (1999). Revisiting the Commons:Local lessons. global challenges, 284,

278–282.

19. Feeny, D.,Berkes, F.&McCay, B.J. (1990). Acheson, J.M. The tragedy of the commons: Twenty-two years later.Human Ecology Journal, 18, 1–19.

20. Schlager, E. (2007).In the Agricultural Groundwater Revolution:Opportunities and Threats to Development. UK: Wallingford.

21. Hou, Z.Y., Lu, W.X., Chu, H.B.& Luo, J.N. (2015). Selecting parameter-optimized surrogate models in DNAPLcontaminated aquifer remediation strategies. Journal of Environmental Engineering and Science, 32, 1016–1026.

22. Kelly, B.F., Timms, W.A., Andersen, M.S., McCallum, A.M.,Blakers, R.S., Smith, R., Rau, G.C.,Badenhop, A., Ludowici, K.& Acworth, R.I.

(2013). Aquifer heterogeneity and response time: The challenge for groundwatermanagement. Journal of Crop Pasture Science, 64, 1141–1154. 23. Prakash, O.& Datta, B. (2014). Characterization of groundwater pollution sources with unknown release time history.Journal of Water Resour,

6, 337–350.

24. Foster, S., Hirata, R.&Andreo, B. (2013). The aquifer pollution vulnerability concept: Aid or impediment in promotinggroundwater

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protection?.Journal of Hydrogeol, 21, 1389–1392.

25. Kiparsky, M.,Milman, A., Owen, D.& Fisher, A.T. (2017). The importance of institutional design for distributedlocal-level governance of groundwater: The case of California’sSustainable Groundwater Management Act.Journal of Water, 9, 755.

26. Sugg, Z.P.,Ziaja, S.& Schlager, E.C. (2016). Conjunctive groundwater management to socio-ecological disturbances:A comparison of 4

western U.S. States. Texas Water Journal, 7, 1–24. 27. Garrick, D., Lane-Miller, C.& McCoy, A.L. (2011). Institutional innovations to govern environmental water inthe Western United States:

Lessons for Australia’s Murray–Darling journal of applied economics and policy, 30, 167–184.

28. Patterson, L., Doyle, M.&Monsma, D. (2017). The Future of Groundwater: A Report from the 2017 Aspen Nicholas WaterForum. USA: The Aspen Institute.

16.

Authors: Mohammad Abazid, Hüseyin Gökçekuş, Tahir Çelik

Paper Title: Study of the Quality Concepts Implementation in the Construction of Projects in Saudi Arabia by

using Building Information Modelling (BIM)

Abstract: The concepts of Quality are closely associated with the concept of Building Information Modeling

(BIM) in managing and improving the execution of construction projects that achieve a better performance in saving

time, satisfying the customers, profits increasing, coast reduction, achieving safety requirements and maintaining the

coordination and the integrity with the project basic parameters (time, cost and quality). Therefore, the purpose of the

study is to address the importance and to assess the impact of the application of Building Information Modeling (BIM)

on the implementation of construction projects in Saudi Arabia and the extent of how it is related to the Quality

concepts over the last ten years.

In order to achieve the purpose of the study, the descriptive analytical approach was used by distributing 12

questionnaires in engineering offices and construction companies. SPSS program was used in order to process the

collected data.

The study found that the construction projects in Saudi Arabia still suffer from the weaknesses in the application of

Building Information Modeling (BIM), lack of administrative, scientific and technical competencies and poor

application of the standards of the Quality concepts in the execution of construction projects.

Keywords: Quality, BIM, Building Information Modeling, construction projects.

References: 1. Abazid, M. (2017). The Quality Control Implementation in the Construction Projects in Saudi Arabia. 2. Nouban, F. & Abazid, M. (2017). An Overview of The Total Quality Management in Construction Management. Academic Research

International, 8(4), 68-74.

3. Abazid, M., & Harb, H. (2018). An Overview of Risk Management in The Construction Projects. Academic Research International, 9(2), 73–79.

4. Abazid, M., & Gökçekus, H. (2019). Application of Total Quality Management on The Construction Sector in Saudi Arabia. International Journal of Technology.

5. American Institute of Architects (AIA). (2010). Designs for the New Decade. USA: Design Media.

6. Autodesk. (2003). Building Information Modeling. San Rafael: Autodesk Inc.

7. BSI. (2010). Constructing the Business Case: Building Information Modelling. London: British Standards Institution and BuildingSMART.

8. Buildoffsite. (2011). Building Information Modelling (BIM) Seminar. London: Buildoffsite.

9. Cabinet Office. (2011). Government Construction Strategy. London: HMSO. 10. Fatimah, F. et al. (2016). The Impact of Total Quality Management Practice on Employees’ Satisfaction and Performance: The Case of Mass

Media’s Employees. Journal of Human Resource Studies, 6(3), 182–195.

11. Haymaker, J., & Fischer, M. (2001). Challenges and Benefits of 4D Modeling of the Walt Disney Concert Hall Project. USA. Retrieved from http://cife.stanford.edu/online.publications/%0AWP064.pdf

12. Jung, Y., & Joo, M. (2010). Building information modelling (BIM) framework for practical implementation. Automation in Construction,

20(2), 126–133. 13. Lee, G., Sacks, R., & Eastman, C. (2006). Specifying parametric building object behavior (BOB) for a building information modeling system.

Automation in Construction, 15(6), 758–776.

14. NBIMS. (2010). National Building Information Modeling Standard. USA: National Institute of Building Sciences. 15. Pheng, L. S., & Teo, J. A. (2003). Implementing Total Quality Management in Construction through ISO 9001:2000. Architectural Science

Revie, 46(2), 159–165.

16. Succar, B. (2009). Building information modelling framework: a research and delivery foundation for industry stakeholders. Automation in Construction, 19(3), 357–375.

17. Underwood, J., & Isikdag, U. (2011). Emerging technologies for BIM 2.0. Emerald Group Publishing Limited, 11(3), 252–258.

18. Wheaton, B., Schrott, B. (2018). Total Quality Management, Theory and Practice. Englischsprachiger Text Mit Zweisprachigem Index. Germany: De Gruyter Oldenbourg

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17.

Authors: Priyanka J, Packiyalakshmi R, Padmapriya P, Pavithra M K S

Paper Title: Phytochemical and Anti-microbial Analysis of Punica granatum Peel and Rind extract

Abstract: Punica granatum L. (Pomegranate) is one of the imperative fruit of that has been used since olden

days for its medicinal features. It is extensively reported that that plant parts possess antiviral, antioxidant,

antiproliferative activities. The aim of the study was to validate biomolecules from Punica granatum peel and rind.

Phytochemical analyses of the metabolites were done using standard biochemical procedures. The ethanolic extract

showed higher antioxidant and antimicrobial activities. The experimental data obtained show that the Qualitative done,

forms a suitable approach to study the potential benefits of Punica granatum peel and rind.

Keywords: Punica granatum, Flavonoids, Antioxidant Activity.

References: 1. Singh, A. B. Bhatt, Comparative antibacterial study of different extract S. H. Abdollahzadeh, R. Y. Mashouf, M. H. Mogahaddam, N.

Roozbahani, M. Vahedi, Antibacterial and antifungal activities of Punica granatum extract against oral pathogens. Journal of Dent Tehran

University Medical Sciences. 2011.8:2011–2018.

A. Devi, V. of pomegranate and its wild variety, International Journal of Pharmaceutical Sciences and Research. 2011.10, 2647-2650.

B. Anibal Antifungal activity of the ethanolic extracts of Punica granatum L. and evaluation of the morphological and structural modifications of its compounds upon the cells of Candida spp. Brazilian Journal of Microbiology. 2013. 44:839–48.

88-90

2. J. Janani and D. Estherlydia. Antimicrobial Activities of Punica granatum Extracts Against Oral Microorganisms. International Journal of

PharmTech Research. 2013. 5(3), 973-977 3. Evans.W.C, “Treaseand Evans Pharmacognosy”, Harcourt Brace and company. Asia pvt.Ltd.Singapore, 1997

4. Fatma Labib AH. Effect of pomegranate (Punica granatum) peels and it's extract on obese. Pakistan J Nutr. 2009. 8:1251–1257.

5. Guo C, Yang J, Wei J, Li Y, Xu J, Jiang Y.Antioxidant activities of peel, pulp and seed fractions of common fruits as determined by FRAP assay. Nutr Res. 2003. 23:1719–1726.

6. Johanning SD, Harris GK. Pomegranate as a functional food and nutraceutical source. Annu Rev Food Sci Technol. 2011. 2:181–201

7. Li Y, Guo C, Yang J, Wei J, Xu J, Cheng S. Evaluation of antioxidant properties of pomegranate peel extract in comparison with pomegranate pulp extract. Food Chem. 2006. 96:254–260.

8. Kannan Kilavan Packiam, Poorani Thiruvenkatasamy, Venupriya Vellingiri, Sathya Rasiappan, Sivapriya Veeraiyan, Senthamarai

Manogaran, Pavithra Mettupalayam Kalliannan Sundramurthi and Madhankumar Dhakshinamoorthy, "Comparative studies on the phytoconstituents, antibacterial and pesticidal activities of blue and white varieties of Clitoriaternatea Linn", Acta Biomedica Scientia, Vol. 3,

No. 4, pp 218-213, 2016 9. Tamilselvi, S, Padma Venkatasubramanian, Kannan, KP , "Studies on Estimation of Berberine and Antimicrobial Activity of Different

Extracts of Berberis aristata DC", Asian Journal of Microbiology, Biotechnology and Environmental Science, 2014.16(3): 669-672.

10. Naqvi SA, Khan MS, Vohara SB. Antibacterial, antifungal and anthelmintic investigations on Indian medicinal plants. Fitoterapia. 1991. 62:221–228.

11. 13. Negi PS, Jayaparkasha GK, Jena BS. Antioxidant and antimutagenic activities of pomegranate peel extracts. Food Chem. 2003. 80:393–

397

18.

Authors: S. Marimuthu, M. Vigensh and N. Karthick

Paper Title: Growth, yield attributes and yield of tomato (Solanum lycopersicum L.) as influenced by application

of organic fertilization

Abstract: The organic field trial was conducted at Farm field, Department of Agriculture Engineering during rabi,

2017-18 to influence the organic source of nutrients on growth and productivity of tomato. Randomized Block Design

(RBD) was implemented and replicated thrice of treatments for this experiment. The experiment consisted of six

treatments viz., foliar spray of Panchagavya, 3G extract, Humic acid, gluconacetobactor and Neem oil. PKM 1 was

used for test variety of this experiment. From the field investigation, foliar spray of panchagavya significantly

improved the plant height, dry matter production, number of fruits per plant, fruit weight and yield per plant. However,

all the growth and yield components were comparable to application of 3G extract spray. The fruit fly incidence was

found to be significantly lower in the treatments that received 3G extract spray and it was comparable to treatments

where in panchagavya spray was given. The disease incidence was significantly reduced in the treatments applied with

panchagavya spray followed by 3G extract spray. From the study, basal application of 3 per cent panchagavya as foliar

spray twice viz. before and after flowering is viable for the production of organic tomato with higher net return through

higher yield.

Keywords: 3G extract, Panchagavya, Dry matter production, yield, tomato

References: 1. NHB, 2014. National Horticultural Board. http://nhb.gov.in/database-2014.pdf

2. Chaudhary, D.R. 2002. An over view on Organic farming. Farmers Forum, 2(4): 7-9.

3. activities on land use changes in arid Rajasthan: Retrospect and Prospects. In: Human impact on desert environment, eds. P.

Narain, S. Kathju, A. Kar, M. P. Singh and Praveen Kumar, Scientific Publishers Jo

4. Stolze, M., A. Piorr, A. Haring and S. Dabbert. 2000. The environmental impact of organic farming in Europe. Organic

Farming in Europe: Economics and Policy, University of Hohenheim, Germany.

5. Balak Ram. 2003. Impact of human dhpur, India. pp: 44-59.

6. Hansen, B., H.F. Alroe and E.S. Kristensen. 2001. Approaches to assess the environmental impact of organic farming with

particular regard to Denmark. Agric. Ecosyst. Environ., 83: 668-672.

7. Bhuma, K. 2001. Studies on the impact of humic acid on soil fertility and productivity of green gram (VBNGG2). M.Sc.

Thesis, Tamil Nadu Agric. Univ., Coimbatore, India.

8. Amanullah, M. M. 1997. Influence of fertilizer levels, intercropping and organic manures on yield of cassava. Ph.D. Thesis,

Tamil Nadu Agric. Univ., Coimbatore, India.

9. Maheswari, S.K., R.K. Sharma and S.K. Gangrade. 2000. Growth and yield of ashwagandha for application organic manures

and fertilizers under rainfed shallow black soil. Indian J. Agron., 45(1): 214-216.

10. Pragatheswari, D. 2002. Impact of VAM fungi and rhizobacteria on growth of micropropagated banana. Ph.D. Thesis, Tamil

Nadu Agric. Univ., Coimbatore, India.

11. Anandajothy, B. 2010. Regulation of nutrient requirement for banana cv. Grand Naine. M.Sc. Thesis. Tamil Nadu Agric. Univ.,

Coimbatore, India.

12. Dahama, A.K. 2010. Organic farming for sustainable agriculture. Agro botanical Publisher (India), Bikaner, India. pp: 120-122.

13. Yadav, B.K. 2006. Foliar spray of panchagavya and organic manures on yield attributes, yield and economics of rice (Oryza

sativa L.). Crop Research. 31(1): 1-5.

14. Sha, K. and P. Karuppaiah. 2010. Growth, yield and quality of chilli cv.K2 as influenced by integrated nutrient management.

Plant Archives. 10(1): 371-374.

91-93

19.

Authors: R.Bharani Kumar, S.Bhuvaneswari

Paper Title: Performance Improvement of Wind Turbine Driven PMG with Multi Level Inverter

Abstract: This paper presents the performance improvement of wind turbine PMG by incorporating multilevel

inverter and maximum power point tracking controller with perturb and observation method is proposed. To reduce the

number of stages and improve the efficiency a cacade MLI with reduced number of power switches is proposed. The

proposed wind turbine driven PMSG and MLI is modelled.The proposed wind turbine, mPMG with multilevel power

converters are madelled in MATLAB and results are analysed for various input and output load conditions.

Keywords: Wind Turbine, PMG, Perturbation and Observation, MPPT, Multilevel Inverter, Power Quality

94-98

References: 1. Abi sa, NC, Smith, RF, McDermott, TE & Chidester, MB 1985, ‘Analysis of Silicon controlled rectifier controlled shunt SSR

counter measures,’ IEEE Trans. Power App. Syst., vol. PAS-104, no. 3, pp. 584–597.

2. Jing Shi 2011, ‘SMES Based Excitation System for DFIG in Wind Application’, IEEE Transactions on Applied

Superconductivity’, vol. 21, no. 3.

3. Flannery, PS & Venkataramanan, G 2009, ‘Unbalanced voltage sag FRT of a DFIG wind turbine with series grid side

converter,’ IEEE Trans. Ind. Appl., vol. 45, no. 5, pp. 1879–1887.

4. Golshannavaz, S, Mokhtari, M & Nazarpour 2009, ‘SSR Suppression via Static converter in Series Compensated Wind Farm

Integrations’, IEEE Transactions on Energy Conversion., vol. 24, no. 4, pp. 112-116.

5. Graham P, Bashar Zahawi, David Atkinson, J & Petros, 2013, ‘Evaluation of the Performance of a Chopper as a DFIG with

LVRT Device’, IEEE Transactions on Energy Conversion, vol. 25, no. 1. pp. 535-542.

6. Guiping Z 2004, ‘Transient Behavior Research on the Combined Equipment of super conducting materials, IEEE Transactions

on Applied Superconductivity, vol. 14, no. 2.

7. M. Kesraoui, N. Korichi and A. Belkadi, “Maximum power point tracker of wind energy conversion system”,

doi:10.1016/j.renene.2010.04.028.

20.

Authors: J. Priya, A.Jeevanandham, K. Rajalashmi

Paper Title: Fuzzy Logic Controller for Position Control of Servo Motor

Abstract: The Quanser servo motor is anopen-loop, unstable and nonlinear system.The standard linear techniques

cannot model the nonlinear dynamics of the system. While simulating the system, the motor shaft rotates randomly

without any control. The identification and control is a challenging task due to the characteristics of the Quanser servo

motor. Hence a real time software is needed to controlQuanser servo motor. The dSPACE real time software was

identified and found to be Suitable for the task to be done.Accurate modelling of the system is essential as the plant is

used for controlling functions. Laboratory set up is establishedto find the transfer function of the Quanser servo plant

modulecomprising of dSPACE , interface panel and Uninterrupted Control Unit. The UPM gives the nonstop input to

the SRV02.Utilizing laboratory set up the transfer function of the SRV02 is found.In Previousresearch ,Position

control was performed using PV(Proportional - Velocity), PIV(Proportional-Integral-Velocity) controller. In this

paper PIV controller is replaced with fuzzy logic controller and the response is observed.

Keywords: Quanser servo motor, Fuzzy logic controller, dSPACE, PV, PIV controller, DS1104 interface board,

SRV02, UPM

References: 1. Quanser. SRVO2-ET “SRV02 MODELLING USING QUARC” User manual.

2. Quanser. SRVO2-ET “POSITION CONTROL USING QUARC” User manual .

3. Quanser. SRVO2-ET “SPEED CONTROL USING QUARC” User manual.

4. NicanorQuijano , KevinMPassino, “Interfacing dSPACE to the Quanser Rotary Series of Experiments ”, Department of

Electrical Engineering, The Ohio State University, 2002

5. JaswandiSawant and DivyeshGinoya, “dSPACE DSP DS-1104 based State Observer Design for Position Control of DC

Servo Motor”, Department of Instrumentation and Control, College of Engineering, Pune. September 24, 2010.

6. Manikandan, R., and R. Arulmozhiyal. "Position control of DC servo drive using fuzzy logic controller." In Advances in

Electrical Engineering (ICAEE), 2014 International Conference on, pp. 1-5.IEEE, 2014.

7. Khapekar, Neha, and K. D. Joshi. "Fully digital control of AC servo motor using fuzzy logic controller." In Electrical,

Instrumentation and Communication Engineering (ICEICE), 2017 IEEE International Conference on, pp. 1-5. IEEE, 2017.

99-102

21.

Authors: Lakshmi.K, S.Lakshmipriya

Paper Title: Weed Detection Using Image Processing In Organic Farming through Aurdino

Abstract: Programmed weed control is dependably a critical issue in natural cultivating. A basic advance in taking

care of or destroying the weed pervasions among the vegetable harvests is the capacity to precisely and dependably

separate weeds from crops. As of late, high spatial determination hyper unearthly imaging information from ground-

based stages have demonstrated specific guarantee in this application. Weeds are expelled in a semi-organized

developed field by utilizing picture preparing. The principal goal is to identify and remove weeds available with the

developed plant and gather it independently. A camera is fixed for taking the pictures of a portion of the crops in the

field. A direction of the weed is identified by picture handling utilizing MATLAB. The directions of the weed

acquired from the camera are imparted through PC to the robot at last to cut the weeds.

Keywords: Aurdino, image information, MATLAB, camera, organic farming.

References: 1. AdeelArif, 2014, K.Mahmood Butt, "Computer vision-based Navigation module for feasible Broad Acre Agriculture

Robots." Sci.int ,Lahore, 1013-531;.

2. D.Slaughter, "The Biological Engineer: Sensing the Difference between Crops and Weeds, " in Automation: The Future of

Weed Control in Cropping Systems. Springer, 2014, pp. 71-95.

3. H.Okamoto, T.Murata, T.Kataoka and S.I.Hata, "Plant Classification for weed location utilizing hyperspectral imaging with

wavelet examination, " Weed Biology and Management, vol. 7, no. 1, pp.31-37,2007.

4. H.T. Sogaard, "Weed Classification by dynamic shape models," Biosystems building, vol.91, no. 3, pp.271-281,2005.

5. J. Fella, L.Damerow and P.S.Lammers," Laser-based weed control," in Proc. 68th Int. Conf. Agriculture. Eng.,2010, pp.125-

130.

6. M Norremark, and H.W. Gripentrog, "Investigation and meaning of the near harvest region in relation to automated

weeding," IN Proc. sixth Workshop EWRS Working Group 'Phys. Social Weed Control', Lillehammer, Norway

Mar.2004,pp1-15.

103-106

7. M. Chapron, P.Boissard, and L.Assemat," A technique for 3D recreation of vegetation by stereovision, " in Proc. second Eur.

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8. C. Chaisattapagon, N. Zhang,1995, "Powerful criteria for weed distinguishing proof in wheat fields utilizing machine

vision." Transaction, page no.965-974.

9. S.Lakshmipriya, ‘Modern Imaging systems- A review’, in International Journal of research management engineering

technology, Volume 1, Issue 7, December 2016 ISSN: 2456 – 2998 (Online).

10. S.Lakshmipriya, ‘Bicycle USB charger’, in International Journal of Creative Research Thoughts (IJCRT) Volume 6, Issue 1

March 2018 | ISSN: 2320-2882.

11. S.Lakshmipriya, Vairavel, ‘Raspberry Pi based industrial process monitoring and control through raspberry pi’, International

Journal of Latest Engineering Research and Applications. No.2455-7137, Vol.02, Issue – 11, November 2017.

22.

Authors: Pavithra M K S, Jayapriya A, Ismathroshan B M, Kokila S

Paper Title: Extraction of β 1,3-1,4 Glucanase from Bacillus Subtilus: Aqueous Two-Phase Extraction Versus Salt

Precipitation

Abstract: The activity of extracellular enzyme, β-1,3-1,4 glucanase extracted from Bacillus subtilis was validated

on 0.5% starch agar plate The enzyme from the crude broth was extracted using two different methods i) Ammonium

Sulphate Precipitation ii) Aqueous two-phase extraction. Ammonium sulphate precipitation was carried out using

65% of the salt. Aqueous two-phase extraction was carried out using PEG/ Ammonium Sulphate system using two

different concentration of the salt as two different trials. From the study, it was found that extraction of the enzyme

using aqueous two-phase process yields high enzyme concentration and increasing the PEG concentration increases

the yield of the enzyme.

Keywords: β-1,3-1,4 glucanase, Ammonium Sulphate Precipitation, Aqueous Two-Phase Extraction, Starch

Plate, Halo Zone

References: 1. Antoni P. 2000. Bacterial 1,3-1,4-β-Glucanases: structure, function and protein engineering. Biochimica et Biophysica Acta.

2:1. 361-382.

2. Mauch F, Brigitte MM, Thomas B. 1988. Antifungal hydrolases in pea tissue. Plant physiology. 88:3. 936-942.

3. LI, Yan H, Rui G, Qiu YY, Ming D, Si LZ, Gen JX, Fu KZ. 2005. Purification and Characterization of Glucanases from

Mollusca Ampullaria crossean. Acta Biochimica et Biophysica Sinica. 37:10. 702-708.

4. Silva ME, Franco TT. 2000. Liquid liquid extraction of biomolecules in downstream processing. Brazilian Journal of

Chemical Engineering. 17:1. 1-17.

5. Jiang CH, Fan ZH, Xie P, Guo JH. 2016. Bacillus cereus AR156 Extracellular Polysaccharides Served as a Novel Micro

associated Molecular Pattern to Induced Systemic Immunity to PST DC3000 in Arabidopsis. Frontiers in Microbiology. 7.

664.

6. Selvaraj R, Vytla RM, Varadavenkatesan T, Vinayagam R, Vinayagam R. 2011. Aqueous Two Phase Systems for the

Recovery of Biomolecules. Science and Technology. 1:1. 7 -16.

7. He GQ, Zhang XY, Tang XJ, Chen QH, Ruan H. 2005. Partitioning and purification of extracellular Beta-1,3-1,4-glucanase

in aqueous two phase systems. J Zhejiang Univ Sci B. 6:8. 825-831.

8. Magda P, Urszula S, Wiesław B, Edyta ZZ. 2011. Acta Physiologiae Plantarum. 33. 609.

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clinical and MTCC isolates. Int J Curr Microbiol App Sci. 4:1. 330-339.

10. Rike TKD, Nisa RM, Maggy TS. 2016. Medium optimization of β-glucanase production by Bacillus subtilis SAHA 32.6

used as biological control of oil palm pathogen. Emirates Journal of Food and Agriculture. 28:2. 116-125.

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Trichoderma harzianum. Journal of Bacteriology. 1864–1871.

13. Iqbal M, Tao Y, Xie S, Zhu Y, Chen D, Wang X, Yuan Z. 2016. Aqueous two-phase system. Biological Procedures Online.

18. 18.

14. Blattel V, Larisika M, Pfeiffer P, Nowak C, Eich A, Eckelt J, Konig H. 2011. β-1,3-Glucanase from Delftia tsuruhatensis

Strain MV01 and Its Potential Application in Vinification. Appl Environ Microbiol. 77:3. 983–990.

15. Dimas AMZ, Fabio RM, Cassia TBV. 2005. Spectrophotometric Determination of Total Proteins in Blood Plasma. Brazilian

Archives of Biology and Technology. 48:3. 385-388.

16. Andrew BN, William JW, David HP. 2014. High Resolution Electrophoretic Separation of Proteins with Retention of Native Properties Including Bound Metal Ions. Metallomics. 6:5. 1068–1078.

17. Eliane FN, Ulhoa CJ. 2000. Characterization of a 29-kDa β-1,3-glucanase from Trichoderma harzianum. Microbiology Letters. 183:1. 119–

123.

107-109

23.

Authors: Alex Stanley Raja T, Senthil Kumar R, Santhosh Kumar K V, Nandhakumar A

Paper Title: DC Micro Grid Using Boost Converter With Perturb and Observe Method for Maximum Power

Point Tracking

Abstract: This paper deals with the DC microgrid that can be controlled wirelessly and smart that can be used for

the optimal usage of power that is taken from the Distributed Renewable Energy Generators (DREGs). The usage of

the solar PV array that provides the power for commercial lighting, Direct Current loads inclusive of LED lighting

and ceiling fans that are incorporated into the given system here for microgrid without any problem is portrayed in

this paper. Here we use perturb and observe technique of Maximum energy point tracker to obtain the most

electricity and the DC to DC boost converter is used to enhance the voltage from the solar array. They are simulated

in MATLAB/Simulink.

Keywords: Boost, Micro grid, MPPT Tracking .

110-112

References: 1. Vision and Roadmap by National Electric Delivery Technologies, Energy Department, US, November 2003.

2. White Paper-EPRI, Utilization and Delivery, DC Power Production, May 2006.

3. Department of Energy, U.S. / NETL - National Energy Technology Laboratory, Building Technologies with Energy

Efficiency, DE - PS26 -04NT 42114, WV, Morgantown, 2004.

4. Barriers and Benefits of DC Microgrid, Savage P, Yale School of Forestry and Environmental Study, 2010.

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and Maksimovic

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24.

Authors: P.Selvabharathi, V.Kamatchi Kannan, S.Sathish kumar

Paper Title: FPGA based Speed Control of Three Phase Induction Motor

Abstract: Three phase induction motor is controlled using three phase sinusoidal FPGA controller with constant

V/F ratio. The controller is designed in a single integrated circuit to control both voltage and frequency

simultaneously. The single control IC is implemented using FPGA and controller outputs like voltage, frequency are

controlled by clock frequency. Single edge sinusoidal PWM technology is proposed for this controller and it operates

up to 2 kHz of fundamental frequency and 30 kHz of switching frequency. Delay time is provided for inverter

switches to avoid shoot through fault in the inverter legs. IGBT switches are used in inverters for variable voltage

variable frequency drives and switching power supplies. Dead time for the IGBT switches is adjustable. FPGA

controller is used to generate Sinusoidal Pulse Width Modulation signal to operate induction motor at variable

voltage and variable frequency.

Keywords: FPGA, speed control, induction motor, PWM technology.

References: 1. K. Bose, “Modern power electronics and ac drives,” Upper saddle River, NJ: Prentice – Hall, 2002.

2. J. Holtz, “Pulse Width Modulation for Electronic Power Conversion,” Proceeding of the IEEE, vol. 82, no. 8, 1994, pp.1194

– 1214

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AC servo motors”, IECON 97, 23rd International Conference on Industrial Electronics, Control and Instrumentation, vol.2,

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5. Z. Zhou, G. Yang, and Tiecai Li, “Design and implementation of FPGA based 3- phase Sinusoidal PWM VVVF controller,”

in Proc. IEEE- APEC’04, 2004, vol. 3, pp. 1703-1708

6. R.K.Pongiannan, P.Selvabharathi, N.Yadaiah, “FPGA based three phase sinusoidal PWM VVVF controller” International

conference on Electrical Energy Systems (ICEES 2011) pp.34-39, Jan. 2011.

113-117

25.

Authors: Nivaashini.M, Thangaraj.P

Paper Title: State-Of-The-Art Machine Learning and Deep Learning: Evolution of Intelligent Intrusion Detection

System against Wireless Network (Wi-Fi) Attacks In Internet of Things (Iot)

Abstract: With the quick technical developments of devices and sensors, Wi-Fi have turned out to be a

widespread technology for the Internet of Things (IoT). However, the benefit of wireless networks and IoT comes

with a cost, which is mainly due to the concern of security and privacy. The distributed nature, multihop data

forwarding, and open wireless medium are the factors that make wireless network highly vulnerable to security

attacks at various levels. For more than two decades, Intrusion Detection Systems (IDSs) plays an important role in

detecting and preventing such security attacks. Still, applying traditional IDS techniques to wireless network of IoT

is difficult due to its particular characteristics such as constrained-resource devices, specific protocol stacks, and

standards. Thus, a wide look at relating Intrusion Detection System (IDS) with machine-learning procedures in

scholastic world and in business field have been done commonly. Yet, massive information and complications to

acquire data occurrences in machine-learning-based IDS are sizzling challenges and correspondingly not

sophisticated enough to handle persistently erratic wireless network conditions rising from the incredible network

traffic evolution. Therefore, Deep learning, the modern revolution in the machine learning & intelligence zone,

seems to be a feasible method in scheming Intellectual IDS. In this paper, a review on IDS research efforts by means

of machine learning and deep learning practices in wireless networks of IoT has be offered along with the summary

of upcoming research guidelines in IDS using deep learning procedures to overwhelm the limits of earlier typical

machine learning based IDSs.

Keywords: Intrusion Detection System, Wireless Networks, Internet of Things, Machine Learning Techniques,

Deep Learning Techniques.

References: 1. V. N. Index, "The zettabyte era–trends and analysis," Cisco white paper, 2013. 2. Mahmoud Elkhodr, Seyed Shahrestani and Hon Cheung, “Emerging Wireless Technologies in The Internet of Things: A Comparative

Study”, International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 5, October 2016.

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4. Sanou, "The World in 2013: ICT facts and figures," International Telecommunications Union, 2013.

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Applications (0975 - 8887), Volume 154 - No.7, November 2016.

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26.

Authors: A.Nandha kumar, R.Senthilkumar, T.Alex Stanley Raja, K.V.Santhoshkumar

Paper Title: Photo Voltaic Powered Voltage Source Inverter for Single Phase Induction motor with MPPT

Algorithm

Abstract: With fast decaying of fossil fuels and increased demand of electrical energy, the utilization of renewable

energy sources have been increased in large extent during the recent years. Among various renewable energy sources

solar energy plays a vital role, because it is used assolar powered street lights, solar water heater,and solar cooker and

to derive induction motor using inverter circuit. Due to this reason, this paper is proposed to develop a model which

consists of 1 ϕ induction motor driven by multi stages of power conversion circuit. The first stage is DC – DC boost

converter where the duty cycle is controlled by Maximum power point tracking algorithm. The second stage is

voltage source inverter, with open loop sub synchronous speed control method to achieve high performance during

various operating speeds. A pulsewidth modulation strategy is developed to supply the pulses to the switches in the

inverter so that the output voltage can be controlled based on the V/F method. The speed torque performance

characteristics of the 1ϕ induction motor based on the developed control method is determined by using MATLAB

simulation and also hardware results are obtained using PIC microcontroller.

Keywords: Pulse Width Modulation, Perturb and Observe Algorithm, maximum solar power point tracking

algorithm

References: 1. Hayakwong Ekkavid, Kinnares Vijit, “Induction motor controlled by a Solar Photo Voltaic fed three leg VSI”, International Conference on

Electrical drives and Systems,October. 22-25, 2014.

2. Saravana IlangoK., Rajasekar N , “An new hybrid energy saving technique for single-phase induction motor fed from solar power

converter”, Renewable Energy saving and applications, Volume. 50, paper page.2913-2918, August. 2009. 3. Ms. Sunitha U, Srinivasa Rao. M, Uday Kiran L, “Photovoltaic fed sinusoidal pulse width modulation for Inverter Driven-Single-Phase

Induction Motor”, International Journal of Engineering Research and Applications (IJERA), Volume.2, Issue 4, July-August 2012.

4. Soto De W, Klein S A, and Beckman W A, New Improvement converter for solar photovoltaic module and its performance Solar Energy, Volume. 80, No.1,paper page.78-88, January. 2006.

5. Kumsuwan Y, Premrudeepreechacharn S, and Kinnares V, “A carrier based pulse width modulation for four-leg voltage source inverter fed

two-phase induction motor,” IEEE Transaction. on Industrial Electronics, Volume. 60, No. 5, paper page .2031-2041. May 2013. 6. Holmes D G, and Kotsopoulos A, “Wide range of speed operation of single phase induction motors using voltage source converter,”

Proceeding. IEEE IAS-93.Conference., Volume. 1, paper page. 613-620. May 1993.

7. Blaabjerg F, Lungeanu F, Skaug K, and Tonnes M, “Evaluation of hybrid least expensive converter topologies for single phase induction motor drives,” Proceedings IEEE. PCC-2002 Conference.paper page.2358-2365. June 2002.

8. Krishnan R, “Electric motor drives modelling, analysis and control”, Prentice Hall, India 2001.

9. Sinthusonthichat S , Kinnares V, “A new modulation strategy for unbalanced single phase induction motor drives using a three phase voltage source inverter,” IEEJ Transaction. India. Volume.125, No. 6, paper page.482-491. 2005.

10. Walker G, “Evaluating of Power Point Tracking Algorithm with converter topologies using a simulation model in Matlab,” Journal of

Electrical & Electronics Engineering, Australia, Volume. 21, No. 1, pp.49-56, May 2001

11. Villalva M G, Gazoli J R, and Filho E R, “Modelling and simulation of photovoltaic arrays,” IEEE Transactions on Power Electronics,

Volume. 24, No.5, paper page.1198-1208, June 2009. 12. Xiao W, Dunford W G , “A modified Power point tracking Algorithm for solar photovoltaic system used for power systems applications,”

131-136

Power Electronics and Application Specialists Conference, Volume. 3, paper page.1957-1963, June. 2004.

13. Villalva M G, Gazoli J R, and Filho E R, “Modelling and simulation of photo voltaic arrays using various circuit topologies” Brazilian Power Electronics Conference, paper page.1244-1254, October. 2009.

14. Holmes, and Lipo T A, “Pulse width modulation for power converters principles and practice,” John Wiley & Sons, 2003.

15. Charumit, and Kinnares V, “Carrier wave based unbalanced phase voltage SPWM method for 1 phase induction motor and drives,” Electric power systems research, Volume. 79, No. 7, pp.1127-1135, July. 2009.

27.

Authors: T.Nagamani, S.Logeswari, B.Gomathy

Paper Title: Heart Disease Prediction using Data Mining with Mapreduce Algorithm

Abstract: The World Health Organization (WHO) estimated that cardiovascular diseases (CVD) are the major

cause of mortality globally, as well as in India. They are caused by disorders of the heart and blood vessels, and

includes coronary heart disease (heart attacks), Data mining acts as a major role in the construction of an intellectual

prediction model for healthcare systems to detect Heart Disease (HD) using patient data sets, which support doctors

in diminishing mortality rate due to heart disease. Several researches have been carried out for building model using

individually or by combining the Data Mining with computational techniques involving Decision tree (DT), Naïve

bayes (NB) along with Meta-heuristics approach, Trained Neural Network (NN), Machine intelligence or AI and

unsupervised learningalgorithms like KNN and Support vector machine (SVM). In the proposed system, large set of

medical instances are taken as input. From this medical dataset, it is aimed to extract the needed information from the

record of heart patients using Mapreduce technique. The performance of the proposed Mapreduce Algorithm’s

implementation in parallel and distributed systems was evaluated by using Cleveland dataset and compared with that

of the predictable ANN method. The trial results verify that the projected method could achieve an average

prediction accuracy of 98%, which is greater than the conventional recurrent fuzzy neural network. In addition, this

Mapreduce technique also had better performance than previous methods that reported prediction accuracies in the

range of 95–98%. These findings suggest that the Mapreduce technique could be used to accurately predict HD risks

in the clinic.

Keywords: Data Mining, cardiovascular diseases (CVD), accuracy, prediction, heart disease (HD), recurrent fuzzy

neural network(RFNN), Mapreduce, world health organization (WHO)

References: 1. WHOCardiovascularDiseases.http://www.who.int/cardiovascular_diseases.

2. A Statistical Update 2018 Report from the American Heart Association (AHA),http://circ.ahajournals.org/content/137/12/e67,(https://doi.org/10.1161/CIR.0000000000000558), March 2018.

3. AnkitaDewan, Meghna Sharma, ”Prediction of Heart Diseases using a hybrid technique in Data Mining Classifiscation”, 2nd International

Conference on Computing for Sustainable Global Development (INDIACom),2015. 4. S.Bhagavathy, V.Gomathy ,S.Sheeba Rani, Sujatha.K, “Early Heart Disease Detection Using Data Mining Techniques with

HadoopMapreduce”, International Journal of Pure and Applied Mathematics, 119(12), 1915-1920, 2018.

5. Syed Umar Amin, Agarwal, K., & Beg, R., “Genetic neural network based data mining in prediction of heart disease using risk factors”,

IEEE Conference in Information & Communication Technologies (ICT), 1227-1231, 2013.

6. S. Shilaskar and A. Ghatol, “Feature selection for medical diagnosis : Evaluation for cardiovascular diseases,” Expert Syst. Appl., vol. 40,

no. 10, pp. 4146–4153, Aug. 2013. 7. Purusothaman, G., &Krishnakumari, P. “A survey of data mining techniques on risk prediction: Heart disease”, Indian Journal of Science

and Technology, 8(12), 1, 2015.

8. Miao, K. H., Miao, J. H., & Miao, G. J. (2016), “Diagnosing Coronary Heart Disease Using Ensemble Machine Learning”, International Journal of Advanced Computer Science and Applications, 7(10), 30-39, 2016.

9. Dbritto, R., Srinivasaraghavan, A., & Joseph, V., ”Comparative Analysis of Accuracy on Heart Disease Prediction using Classification

Methods”, International Journal of Applied Information Systems (IJAIS)–ISSN, 2249-0868, 11(2), 2016. 10. KaanUyar ,Ahmetİlhan, “ Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks “, 9 th

International Conference on Theory and Application of Soft Computing, Procedia 11. UCI Machine learning Repository fromhttp://archive.ics.uci.edu/ml/datasets/Heart+Disease.

12. Mapreduce,https://hadoop.apache.org/docs/r1.2.1/mapred_tutorial.html

13. Samuel, O.W., Asogbon , G.M., Sangaiah, A.K., Fang, P. , Li, G., “An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction”, Expert Systems with Applications 68. 163–172, 2017.

137-140

28.

Authors: Aniket Srivastava, Parul Verma, AtulPati Tripath

Paper Title: Study and Identification of ADHAYYAN Framework in CBSE Schools of India and UAE

Abstract: Digital Learning has become the key rationale behind any student learning process success today. M-

learning(market is still untouched and niche. Digital pedagogy based content has bridged the demand and supply gap

in M-learning market, through shifting interactive board & projector to hand held device model, which is

conceptualized as a personalized learn-ing strategy. Primarily due to the exponential growth of ICT tools in India(8)

and UAE, the ICT tools has made the balance between e-learning and M-learning solutions to more Students.

Integration and synergy between E-learning and M-learning model cover many schools of India and UAE. Tier I by

M-learning and Tier II/ Tier III by E-learning model. Making of the affordable pricing system for both ICT Solutions

makes this more acceptable to schools towards both spaces. This paper study provides a detailed view of digital

pedagogy foundation concept, integration of e-learning to M-learning, unique digital pedagogy, innovative. The

Information and Communication Technology (ICT) sector has exponential growth in the last two decade. India and

UAE are now consider as a global leader in ICT industry by its low operational cost, availability of high intelligence

pool and remote delivery model. ICT can be divided into two categories; one is Information Technology, while other

is Communication Technology. It boasts of a rapidly growing global IT market. The dramatic boom of the IT sector

in India and UAE has played an important role in transforming the country education system with the identification

of Adhayyan Framework for informal education setup.

Keywords: ICT,E-learning ,M-learning ,Digital pedagogy

141-144

References: 1. Ally, M. (Ed.). (2009). Mobile learning: Transforming the delivery of education and training. Athabasca University Press. 2. Arimoto, M. M., Barbosa, E. F., &Barroca, L. (2015, October). An agile learning design method for open educa-tionalresources.In Frontiers in

Education Conference (FIE), 2015 IEEE (pp. 1-9). IEEE

3. Bindu, C. N. (2017). Attitude towards, and Awareness of Using ICT in Classrooms: A Case of Expatriate Indian Teachers in UAE. Journal of

Education and Practice, 8(1), 10-17.

4. Bhuasiri, W., Xaymoungkhoun, O., Zo, H., Rho, J. J., &Ciganek, A. P. (2012). Critical success factors for e-learning in developing countries: A

comparative analysis between ICT experts and faculty. Computers & Education, 58(2), 843-855. 5. Howell, J. (2012). Teaching with ICT: Digital pedagogies for collaboration and creativity. Oxford University Press.

6. Jones, A. C., Scanlon, E., & Clough, G. (2013). Mobile learning: Two case studies of supporting inquiry .learning in informal and semiformal

settings. Computers & Education, 61, 21-32. 7. Kearney, M., Schuck, S., Burden, K., &Aubusson, P. (2012). Viewing mobile learning from a pedagogical per-spective.Research in learning

technology, 20(1), n1. 8. Mallik, U. (2009). National policies and practices on ICT in education: India. CrossNational Information and Com-munication Technology

Policies and Practices in Educa-tion:(Revised Second Edition), 18(16), 295.

9. Prensky, M. (2001). Digital natives, digital immigrants part 1. On the horizon, 9(5), 1-6. 10. Senjov-Makohon, N. (2004, January). How do Digital Immigrant Teachers (DITs) learn ICT for the Information Age?. In AARE (Australian

Association of Research in Ed-ucation) Conference, Doing the Public Good: Positioning Education Research, Melbourne, Austalia.

11. Tomlinson, C. A., &McTighe, J. (2006). Integrating differentiated instruction & understanding by design: Connecting content and kids. ASCD. 12. Web links:-

a)www.negt.gov.in

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d)www.khda.gov.ae./en

e)www.mhrd.gov.in

29.

Authors: P.N. Jayanthi, S. Ravishankar

Paper Title: Optimization Driven Beam Forming in 2D Linear Array for MIMO Systems

Abstract: The advancements in the multimedia applications gained the attention of users and are considered as an

important technique for wireless networks. Multiple Input Multiple Output (MIMO) systems depend on the wireless

channels propagation. This work proposes a technique, named Exponential-Monarch Butterfly Optimization

(Exponential-MBO), for initiating beamforming in 2D linear array under a MIMO system. The proposed

Exponential-MBO algorithm is responsible for computing the optimal weights for the beamforming in the MIMO

systems. At first, the preferred beam pattern attributes for the 2D array in the MIMO system are described using the

initial beam patterns. Then, the fitness function of Exponential-MBO is derived to determine the pattern attributes in

the required direction using the azimuth and the elevation beam pattern. The proposed Exponential-MBO algorithm

determines the optimal weights for generating the patterns for beamforming in the MIMO system. The channel is

estimated using the Least Square (LS) channel estimation method along with Quadrature Amplitude Modulation

(QAM) multi-carrier modulation. The implementation of the proposed Exponential-MBO is in MATLAB, and its

performance is evaluated by varying the channel. The proposed Exponential-MBO shows superior performance in

terms of power gain and Bit Error Rate (BER).

Keywords: MIMO-OFDM, Beamforming, Elevation beam pattern, Azimuth, QAM-multi-carrier

References: 1. PrabinaPattanayak and Preetam Kumar,”Quantized feedback scheduling for MIMO-OFDM broadcast networks with subcarrier

clustering”,Ad Hoc Networks,Vol :65,pp :26-37, October 2017.

2. Xin Xiong, Bin Jiang, Xiqi Gao, and Xiaohu You, “QoS Guaranteed User Scheduling and Pilot Assignment for Large-Scale MIMO-OFDM

Systems”, IEEE Transactions on Vehicular Technology “,Vol: 65, No: 8,pp: 6275 - 6289, August 2016. 3. Kianoush Hosseini, Wei Yuand Raviraj S. Adve,”Large-Scale MIMO versus Network MIMO forMulticell Interference Mitigation”,IEEE

Journal of Selected Topics in Signal Processing, Vol:8,no:5, pp:930-941.

4. Arin Minasian,Shahram Shahbaz Panahi and Raviraj S.,”Distributed Massive MIMO Systems withNon-Reciprocal Channels: Impacts and RobustBeamforming”,IEEE Transactions on Communications,pp: 1-1,July 2018.

5. Said E. El-Khamy, Ahmed S. Eltrass and Huda F. El-Sayed,”Design of thinned fractal antenna arrays foradaptive beam forming and

sidelobereduction”, IET Microwaves, Antennas & Propagation :Vol: 12, no: 3, pp: 435 – 441,2018. 6. Boufeldja Kadri, MiloudBoussahla and Fethi Tarik Bendimerad,”Phase-Only Planar Antenna Array Synthesis with Fuzzy Genetic

Algorithms”,International Journal of Computer Science Issues, IJCSI, Vol:7, no:2, January 2010.

7. Ju-Hong Lee and Yung-Han Lee,” Two-Dimensional Adaptive Array Beamforming With Multiple Beam Constraints Using a Generalized Sidelobe Canceller”, IEEE Transactions on Signal Processing, Vol: 53, No: 9, pp: 3517 - 3529,Sept. 2005.

8. Gopi Ram, P. S. Pal, D. Mandal, R. Kar and Sakti Prasad Ghosal,”Social Emotional Optimization Algorithm forBeamforming of Linear

Antenna Arrays”,In proceedings of TENCON 2014 - 2014 Region 10 Conference,pp:1-5,IEEE. 9. Lei Wang,”Array Signal Processing Algorithmsfor Beamforming and Direction Finding”, Doctoral dissertation, University of York.

10. IoannisTzanidis, Yang Li, Gary Xu,Ji-Yun Seol and JianZhong (Charlie) Zhang,”2D Active Antenna Array Design for FD-MIMO

Systemand Antenna Virtualization Techniques”, International Journal of Antennas and Propagation, 2015. 11. Junhui Qian , Zishu He, Wei Zhang, Yulong Huang, Ning Fu and Jonathon Chambers, ”Robust adaptive beamforming for multiple-input

multiple-output radar with spatial filtering techniques”, Signal Processing,Vol:143, pp:152-160, February 2018.

12. Xue, C., He, S., Huang, Y., Wu, Y. and Yang, L., "An efficient beam-training scheme for the optimally designed subarray structure in mmWave LoS MIMO systems," EURASIP Journal on Wireless Communications and Networking, vol.1, pp.31, 2017.

13. Lee, S.J., "On the training of MIMO-OFDM channels with least square channel estimation and linear interpolation," IEEE Communications

Letters, vol.12, no.2, 2008. 14. Seyman, M.N. and Taşpınar, N., "Pilot tones optimization using artificial bee colony algorithm for MIMO–OFDM systems," Wireless

personal communications, vol.71, no.1, pp.151-163, 2013. 15. Gai-Ge Wang, Suash Deb, Zhihua Cui, "Monarch butterfly optimization", Neural Computing and Applications, pp. 1–20, May 2015.

16. Michael S. Saccucci , Raid W. Amin, and James M. Lucas,Exponentially weighted moving average control schemes with variable sampling

intervals," Communications in Statistics - Simulation and Computation, vol. 21, no.3, pp. 627-657, 1992.

145-152

30. Authors: Nandini K. S, S.A. Hariprasad

Paper Title: Shortest-Path Routing in Wireless Mesh Networks under Time Varying Link-Cost Metric

Abstract: In a wireless Mesh Network, with cognitive radios, the cost of the available communication links can

vary with respect to time due to the schedules of the primary users. Under this condition, to find the time dependent

shortest paths for the secondary users is a challenging task. At a particular time slot, if no forward links are available

at a node, the data packet which has just arrived have to halt (or wait) until the availability of one or more forward

paths. Then the data packet suffers halting delay. Thus the overall arrival time could be more than that of an alternate

path. Therefore, in this paper we provide an innovative method of reducing the halts along the path. We also have to

minimize the length of the topological (physical) distance along the selected path. Thus, in our paper, two objectives,

the earliest arrival time as well as minimum shortest topological path, are realized. The weight assigned to each

objective can be adjusted according to the requirements. To solve this bi-objective optimization problem, we propose

a new modification to the well-known Dijkstra shortest path algorithm which takes care of the actual link cost and

also the halting cost.

Keywords: Cognitive Radio Network, Spectrum sensing, Secondary users, Time dependent cost Matrices,

Probability of Interference, Optimal Assignment

References: 1. M. Amjad, M. H. Rehmani and S. Mao, "Wireless Multimedia Cognitive Radio Networks: A Comprehensive Survey," in IEEE

Communications Surveys & Tutorials, vol. PP, no. 99, pp. 1-1, 2018. (Early Access article). 2. D. M. Alias and R. G. K, “Cognitive radio networks: A survey”,International Conference on Wireless Communications, Signal Processing

and Networking (WiSPNET), 2016, pp. 1981–1986.

3. N. Bouabdallah, B. Ishibashi and R. Boutaba, "Performance of Cognitive Radio-Based Wireless Mesh Networks," IEEE Transactions on

Mobile Computing, vol. 10, no. 1, 2011,pp. 122-135.

4. K. R. Chowdhury and I. F. Akyildiz, "Cognitive Wireless Mesh Networks with Dynamic Spectrum Access," in IEEE Journal on Selected

Areas in Communications, vol. 26, no. 1, 2008,pp. 168-181. 5. R. B. Battula, M. S. Gaur, D. Gopalani, K. Radhika and S. Shiwani, "A dynamic medium access mechanism for cognitive radio wireless

mesh networks," International Conference on Signal Processing and Communication (ICSC), Noida,2015, pp. 434-438.

6. K. M. F. Rabbi, D. B. Rawat, M. A. Ahad and T. Amin, "Analysis of multi-hop opportunistic communications in cognitive radio network," SoutheastCon 2015, Fort Lauderdale, FL,2015, pp. 1-8.

7. Akyildiz, I., Wang, X., & Wang, W, “Wireless mesh networks: A survey. Elsevier Computer Networks”, 47(4), 2005, pp 445–487.

8. Deo, N., and pang, C. Y, “Shortest path algorithms: Taxonomy and annotation.”, Networks, volume 14, Issue 2,1984, pp 275-323. 9. L. Cooke and E. Halsey, "The shortest route through a network with time dependent internodal transit times", Journal of Mathematical

Analysis and Applications, 1966,pp 492-498.

10. Klafszky, E. Determination of shortest path in a network with time-dependent edge-lengths. Math. Oper. Stat, 1972,pp 255-257. 11. Dreyfus, S. E. An appraisal of some shortest path algorithms. Oper. Rex I7, 1969,pp 395-412.

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pp117-124. 13. D. E. Kaufman, R. L. Smith (1993). "Fastest paths in time-dependent networks for intelligent-vehicle-highway systems application". IVHS

Journal 1, 1-11.

14. Daniel Delling and Dorothea Wagner. Time-dependent route planning. In Robust and Online Large-Scale Optimization, volume 5868 of Lecture Notes in Computer Science,2009, pp 207–230.

15. A. Orda and R. Rom. Shortest-path and minimum-delay algorithms in networks with time-dependent edge-length. J.ACM, 37(3), 1990,pp

607–625. 16. B. Ding, J. Yu, and L. Qin. Finding time-dependent shortest paths over large graphs. In Proc. EDBT, pages 205–216. ACM, 2008.

17. B. C. Dean. Continuous-time dynamic shortest path algorithms. Master’s thesis, Massachusetts Institute of Technology, 1999.

18. W. Zhan-yu and G. p. Liu, "Earliest-arrival path: A global optimized transmission for networked control systems," 2017 36th Chinese Control Conference (CCC), 2017, pp. 7909-7914.

19. Abdelfattah Idri, Mariyem Oukarfi, Azedine Boulmakoul, Karine Zeitouni and AliMasri “A new time-dependent shortest path algorithm for

multimodal transportation network,” Procedia Computer Science Volume 109, 2017, pp 692-697. 20. https://ahmedhanibrahim.wordpress.com/2016/04/15/solving-time-dependent-graph-using-modified-dijkstra-algorithm/.

21. Shylashree N, “Non-crossing rectilinear shortest minimum bend paths in the presence of rectilinear obstacles”, Journal of

Telecommunication and Information Technology, no. 3, 2018, pp 82-91.

153-165

31.

Authors: Vinay P, Shylashree N

Paper Title: Verification of JESD204BTX Soft IP using Universal Verification Methodology

Abstract: JESD204B transmitter is a part of the serialized data interface between logic devices and data

converters bases on the JESD204B standards. The organization, where this project is currently executed, is currently

developing the JESD204B Tx and Rx IP for the avionics spacecraft applications where the fail proof and function

safe and reliable data communication is essential. The verification of this IP is an important phase in the development

wherein it is extremely important to perform rigorous tests on the design to confirm its acclaimed functionality and

performance. The verification of an IP of this complexity is done in a systematic and efficient way using the

Universal Verification Methodology which is basically constructed using the SystemVerilog. A verification

environment is built using the UVM to verify the functionality of the IP. The test cases are written to verify each

functionality of the design and the randomized stimuli are applied to cover all the possible input scenarios. The code

coverage and the functional coverage is determined and further stimuli are applied to achieve the target coverage.

The verification of the JESD204B Transmitter IP is completed with a functional coverage of around 39.17% for each

test instance and an overall functional coverage of 100% and code coverage of 94.25%. The verification environment

can be reused with minor changes to verify the JESD204B Receiver IP.

Keywords: Coverage, IP verification, JESD204B, Universal Verification Methodology.

References: 1. H. Saheb and S. Haider, "Scalable high speed serial interface for data converters: Using the JESD204B industry standard," 9th International

Design and Test Symposium (IDT), Algiers, pp. 6-11, 2014

2. Y. N. Yun, J. B. Kim, N. D. Kim and B. Min, "Beyond UVM for practical SoC verification," International SoC Design Conference, Jeju, pp.

158-162, 2011

3. Universal Verification Methodology(UVM) 1.2 Class Reference, Accellera, 2014

166-175

4. Universal Verification Methodology(UVM) 1.2 User’s Guide, Accellera, 2015

5. IEEE Standard for Universal Verification Methodology Language Reference Manual," in IEEE Std 1800.2-2017, vol., no., pp.1-472, May 26 2017

6. JEDEC Standard JESD204B (July 2011), JEDEC Solid State Technology Association.

7. N.Shylashree, “Two Stage Block Truncation Coding for Lower Mean Square Error”, IJRTE, 2018.

32.

Authors: Sudhamani M J, M K Venkatesha

Paper Title: Fusion of Iris Texture with Finger vein Geometry for Authentication

Abstract: A framework for feature level fusion-based authentication system is proposed in this paper. Feature level

fusion improves the robustness and classification accuracy compared to unimodal authentication system. This work

is carried in two-folds. The first experiment fuses the translation, rotation and scale (TRS) invariant statistical

features of iris with finger vein. The second experiment contemplates geometrical, structural and statistical properties

of finger vein with local and statistical features of iris.

Keywords: This work is carried in two-folds. The first experiment fuses the translation, rotation and scale (TRS)

invariant statistical features of iris with finger vein.

References: 1. A. A. Ross, K. Nandakumar, A. K. Jain, Handbook of Multibiometrics , vol. 6. ,Springer, online, 2006

2. Ross, Arun, and Norman Poh. "Multibiometric systems: Overview, case studies, and open issues." Handbook of Remote Biometrics.Springer,

London, 2009.273-292. 3. A. Ross, K. Nandakumar, and A. K. Jain, ‘‘Introduction to multibiometrics,’’ in Handbook of Biometrics, Springer, 2008, pp. 271–292

4. Long TB, Hanh T. Multimodal biometric person authentication using fingerprint, face features. In Pacific Rim International Conference on

Artificial Intelligence 2012, pp. 613-624 5. R. Gayathri and P. Ramamoorthy, ‘‘Feature level fusion of palmprint and iris,’’ IJCSI Int. J. Comput.Sci. Issues, vol. 9, no. 4, pp. 194–203,

2012.

6. Raghavendra R., Dorizzi B., Rao A., Hemantha K.G. ‘‘PSO versus AdaBoost for feature selection in multimodal biometrics,’’ in Proc. IEEE 3rd Int. Conference on Biometrics, Theory, Applications and Systems (BTAS’09), 2009, pp. 1–7.

7. Roy K., Kamel M.S. (2012) Multibiometric System Using Level Set Method and Particle Swarm Optimization. In: Campilho A., Kamel M.

(eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7325. Springer,pp 20-29 8. OmidSharifi, Maryam Eskandari, Optimal Face-Iris Multimodal Fusion Scheme, Symmetry 2016, Volume 8, issue 6, 48,

https://doi.org/10.3390/sym8060048

9. Y. Huang, D. Xu, and F. Nie, ‘‘Patch distribution compatible semisu- pervised dimension reduction for face and human gait recognition,’’ IEEE Trans. Circuits Syst. Video Technol., vol. 22, no. 3, pp. 479–488, Mar. 2012.

10. E.-S. M. El-Alfy and G. M. BinMakhashen, ‘‘Improved personal identification using face and hand geometry fusion and support vector

machines,’’ in Networked Digital Technologies (Communications in Computer and Information Science), vol. 294, R. Benlamri, Ed. Berlin, Germany: Springer, Apr. 2012, pp. 253–261.

11. Y. Huang, D. Xu, and F. Nie, ‘‘Patch distribution compatible semi supervised dimension reduction for face and human gait recognition,’’

IEEE Trans. Circuits Syst. Video Technol., vol. 22, no. 3, pp. 479–488, Mar. 2012. 12. Sungjun Hong, Heesung Lee, Sung Je An, Euntai Kim, “Fusion of multiple gait features for human identification”, in International

Conference on Control, Automation and Systems, ,pp 2121 – 2125, 2008.

13. Michael A. O’brien, John M I, “Information fusion for feature extraction & the development of Geospatial information”, in Proceedings of the 7th International Conference on Information Fusion, pp 976—982, 2008.

14. Wenchao Zhang, Shiguang Shan, Wen Gao, Yizheng Chang, Bo Cao, Peng Yang, “Information fusion in Face identification”, Proceedings of

the 17th International Conference on Pattern Recognition, 2004, Vol 3, pp 950 – 953 ,2004. 15. DakshinaRanjanKisku, Phalguni Gupta, JamunaKanta Sing, “Multi biometrics feature level fusion by graph clustering”, in international

Journal of Security and Its Applications, Vol. 5, No. 2, pp. 61-74, 2011.

16. DakshinaRanjanKisku, Phalguni Gupta, JamunaKanta Sing, “Feature Level Fusion of Face and Palmprint Biometrics by Isomorphic Graph-based Improved K-Medoids Partitioning”, in proc. of Advances in Computer Science and Information Technology, pp 70-81 , June 2010.

17. Chetty G, Wagner M, “Multilevel liveness verification for Face-voice biometric authentication”, in Biometrics Symposium: Special Session

on Research at the Biometric Consortium Conference, pp 1 – 6, 2006. 18. Yong Jian Chin, Thian Song Ong, Teoh A.B.J, Goh M.K.O, “Multimodal Biometrics based bit extraction method for template security” in 6th

IEEE Conference on Industrial Electronics and Applications, pp 1971 – 1976,2011. 19. Yan Yan, Yu-Jin Zhang, “Multimodal Biometric fusion using correlation filter bank”, in 19th International Conference on Pattern

Recognition, pp 1-4, 2008

20. Yan Hui, Long Duo, “Personal identification based on multi-modal hand-print features”, in proc. of the 7th World Congress Intelligent Control and Automation, pp 7649 - 7652 ,2008.

21. Raghavendra R, Dorizzi B, Rao A; Hemantha G.K, “PSO VsAdaBoost for Feature Selection in Multimodal Biometrics”, in proc. of IEEE 3rd

International Conference on Biometrics Theory, Applications and Systems, pp 1 - 7, 2009.

176-180

33.

Authors: A.Nandha Kumar, R. Senthilkumar, T. Alex Stanley Raja, K. V. Santhoshkumar

Paper Title: Optimization of Maximum Power Point Tracking Algorithm using Artificial Intelligence

Abstract: This paper highlights the maximum power point tracking algorithm for a photovoltaic array operating

under different levels of solar irradiation using Particle Swarm Optimization (PSO). Further the photovoltaic array

produces two or more maximum power points for different temperature conditions. Thus it is difficult to find the

correct MPP using conventional method. In order to reduce this difficulty the MPP is obtained using Particle Swarm

Optimization Technique. The feasibility of the new system is verified using MATLAB/SIMULINK simulation and

its performance is analyzed.

Keywords: Photovoltaic Array, MPPT, Particle Swarm Optimization, Boost Converter, Photo voltalic Inverter

References: 1. M. Veerachary: “Power Tracking for Non-linear PV sources with Coupled Inductor SEPIC Converter”, IEEE Trans. on Aerospace &

Electronic Systems, 41(3),1019-1029, 2005.

2. Shantharama.Rai: “A Novel Technique for Photovoltaic Maximum Power Point Tracking System, in Proc. of EPE, 286, Dresden, 2005.

3. J.Rodriguez, J.S.Lai, F.Z.peng, “Multilevel inverter: A survey of controls, and applications,” IEEE Trans. Ind. Electron..49, 4, 724-

738..2002.

4. Joung-Hu Park: “Dual-Module-Based Maximum Power Point Tracking Control of Photovoltaic Systems” IEEE Trans. Ind. Electron., 53,

4,1036-1047, 2006.

181-184

5. N. Kasa, T. Iida, L. Chen, “Flyback Inverter Controlled by Sensorless Current MPPT for Photovoltaic Power System,” IEEE Trans. Ind.

Electron., 52, 4, 1145-1152, 2005. 6. M. Matsui : “A Simple Maximum Photovoltaic Power Tracking Technique Utilizing System Inherent Limit Cycle Phenomena” , in Proc. of

IEEE 38th IAS Meeting,3,2041-2047, 2003.

7. N. Femia : “Optimization of perturb and observe maximum power point tracking method”, IEEE Transactions on Power Electronics, 20, 4, 963-973, 2005.

34.

Authors: M.Rajendran, N.Lavanya

Paper Title: Influence of Perlite and Glass Fiber on the Compresive, Split Tensile and Flexural Strength of

Concrete Incorproting Glass Fiber

Abstract: Concrete is used most asconstructionmaterial than any material in the world. Concrete is used because it

can be casted by anyone even in site itself. 60to80 percent aggregate occupies concrete volume and influence the

concrete properties, mixproportions and economy. The properties likecompressive strength &fireresistance can be

increased by adding perlite in concrete and it reduces weight crack resistance & environmental impact.The

researchintention istoinvestigate the structural properties of of perlite-glassfiber concrete. 20%, 35% &50% of fine

aggregate by weight is replaced by perlite. In additiontothat glass fiber material is added by 0.2%, 0.4% and

0.6%withrespect to the volume of the concrete in order to increasethetransverse rupture strength and the compressive

strength considerably. Glass fiber is very good insulation to electricity andbetterresistance to chemical impact. The

intentionof this study istoincrease strength and create environment-friendlyconcrete. Test result indicated that

replacing fine aggregate of quantity 35% with perlite results in higher compressive strength and addition of 0.6%

glass fiber has yielded higher flexural strength of M30 grade concrete.

Keywords: Perlite, Glass fiber, Compressive strength test,Split tensile test, Flexural strengthtest.

References: 1. Shah, SurendraandRangan (1994), “Effect ofFiber addition on concrete strength”, Indian Concrete Journal, Vol. 5, Issue 2-6, PP.13-21,

1994. 2. ACI Committee 544. 1982. “State-of-the-Art Report onFiber Reinforced Concrete (ACI544.1R-82)”. ConcreteInternational, May, Vol. 4,

No. 5, PP. 9-30.

3. Mohanraj A, Rajendran M, Ramesh A S, Mahalakshmi M, ManojPrabhakar S. “An Experimental Investigation of Eco-Friendly Self-Curing Concrete Incorporated with Polyenthylene Glycol” International Advanced Research Journal in Science, Engineering andTechnology,

Volume 1, Issue 2, October 2014.

4. S.J.Sonule, G P Deskmukh “Study of Conventional Concrete, Fiber Reinforced Concrete, and Self Curing Concrete” International journal for research Trends and Innovation ISSN 2456-3315 Volume 2, Issue 8, 2017.

5. S. Shankarananth, B. Jaivignesh. “ExperimentalStudyon the use of Glass Powder, GGBS,& Perlite in Fly ash Brick”. ISSN 2320-5407

International Journal of AdvancedResearch (2016), Volume 4, Issue 4, 1381-1387. 6. R.Yuvaraja, S.P.Palanisamy “Investigation on partialreplacement of fine aggregate with unexpanded perlite in concrete” International

Journal of Civil Engineering andMaterial & Construction, Volume 3, Number 1(2015) PP. 29-43. 7. Nagarkar P, Tambe S, Pazare D, Study of fiberreinforced concrete; 1987.

8. SamanKhanPP, Roohul Abad KhanPP, Amadur Rahman KhanPP, MisbahulIslamPP, SamanNayal, “Mechanical properties of

Polypropylene Fiber reinforced concrete for M 25 & M 30 mixes: A Comparative study” International Journal of ScientificEngineering and Applied Science (IJSEAS) - Volume-1, Issue-6, September 2015 ISSN: 2395-3470 .

9. Amit Rai et al Int. “Applications and Properties of Fiber Reinforced Concrete” Journal of EngineeringResearch and Applications

www.ijera.com ISSN: 2248-9622, Vol. 4, Issue 5 (Version 1), May 2014, PP.123-131. 10. K.Srinivasa Rao, S.Rakeshkumar, A.Laxmi Narayana “Comparison of Performance of Standard ConcreteAnd Faber Reinforced

Standard Concrete Exposed To Elevated Temperatures” American Journal of Engineering Research, Vol.2, No.3, PP.20-26.

11. Kavita SKene, Vikrant S Vairagade and Satish Sathawane “Experimental Study on Behavior of Steeland Glass Fiber Reinforced Concrete Composites” Bonfring International Journal of Industrial Engineering and Management Science, Vol. 2, No. 4, December 2012.

12. IS 383, “Indian Standard Specification for Coarse and Fine Aggregate for Natural Sources for Concrete”, Bureau of Indian Standards, New

Delhi, Second revision, Feb. 1997. 13. IS: 10262-2009, “Recommended guidelines for concrete mix design, Bureau of Indian standards”, New Delhi, India.

14. IS: 2386 (part-III and IV)-1963, “Methods of testing for aggregate for concrete”.

15. IS: 516-1959, “Indian standard methods of tests for strength of concrete”, Bureau of Indian Standards, New Delhi, India. 16. M.S Shetty ,Concrete Technology: Theory and Practice, Third eidtion, 2008, S.Chand Publications.

17. A.M.Neville, J.J.Broke, Concrete Technology, Second edition, 2010.

185-188