Editor-In-Chief Chair Dr. Shiv Kumar

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

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

Additional Director, Technocrats Institute of Technology and Science, Bhopal (MP), India

Associated Editor-In-Chief Members Dr. Hitesh Kumar

Ph.D.(ME), M.E.(ME), B.E. (ME)

Professor and Head, Department of Mechanical Engineering, Technocrats Institute of Technology, Bhopal (MP), 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

Associated Editor-In-Chief Members 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. 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 (CSE) and Director, Microsoft Innovation Centre, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh India

Executive Editor Dr. Deepak Garg

Professor, 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.

Technical Program Committee Dr. Mohd. Nazri Ismail

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

Technical Program Committee Members Dr. Haw Su Cheng

Faculty of Information Technology, Multimedia University (MMU), Jalan Multimedia (Cyberjaya), Malaysia.

Dr. Hasan. A. M Al Dabbas

Chairperson, Vice Dean Faculty of Engineering, Department of Mechanical Engineering, Philadelphia University, Amman, Jordan.

Dr. Gabil Adilov

Professor, Department of Mathematics, Akdeniz University, Konyaaltı/Antalya, Turkey.

Manager Chair Mr. Jitendra Kumar Sen

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

Editorial Chair Dr. Arun Murlidhar Ingle

Director, Padmashree Dr. Vithalrao Vikhe Patil Foundation’s Institute of Business Management and Rural Development, Ahmednagar

(Maharashtra) India.

Editorial Members Dr. J. Gladson Maria Britto

Professor, Department of Computer Science & Engineering, Malla Reddy College of Engineering, Secunderabad (Telangana), India.

Dr. Wameedh Riyadh Abdul-Adheem

Academic Lecturer, Almamoon University College/Engineering of Electrical Power Techniques, Baghdad, Iraq

Dr. S. Brilly Sangeetha

Associate Professor & Principal, Department of Computer Science and Engineering, IES College of Engineering, Thrissur (Kerala),

India

Dr. Issa Atoum

Assistant Professor, Chairman of Software Engineering, Faculty of Information Technology, The World Islamic Sciences & Education University, Amman- Jordan

Dr. Umar Lawal Aliyu

Lecturer, Department of Management, Texila American University Guyana USA.

Dr. K. Kannan

Professor & Head, Department of IT, Adhiparasakthi College of Engineering, Kalavai, Vellore, (Tamilnadu), India

Dr. Mohammad Mahdi Mansouri

Associate Professor, Department of High Voltage Substation Design & Development, Yazd Regional Electric Co., Yazd Province,

Iran.

Dr. Kaushik Pal

Youngest Scientist Faculty Fellow (Independent Researcher), (Physicist & Nano Technologist), Suite.108 Wuhan University, Hubei,

Republic of China.

Dr. Wan Aezwani Wan Abu Bakar

Lecturer, Faculty of Informatics & Computing, Universiti Sultan Zainal Abidin (Uni SZA), Terengganu, Malaysia.

Dr. P. Sumitra

Professor, Vivekanandha College of Arts and Sciences for Women (Autonomous), Elayampalayam, Namakkal (DT), Tiruchengode

(Tamil Nadu), India.

Dr. S. Devikala Rameshbabu

Principal & Professor, Department of Electronics and Electrical Engineering, Bharath College of Engineering and Technology for

Women Kadapa, (Andra Pradesh), India.

Dr. V. Lakshman Narayana

Associate Professor, Department of Computer Science and Engineering, Vignan’s Nirula Institute of Technology & Science for

women, Guntur, (Andra Pradesh), India.

S. No

Volume-8 Issue-2S5, JULY 2019, ISSN: 2277-3878 (Online) Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Page

No.

1.

Authors: Ramya. M, Marykutty Cyriac

Paper Title: A Non Invasive Biomarkers for Alzheimer’s disease Detection

Abstract: Alzheimer's disease (AD) is one of the most common neurodegenerative diseases occurring in

elderly population worldwide, which usually starts slowly and worsens over time. AD is generally diagnosed too

late, when irreversible damages have been caused in the patient‘s brain region. Present need demands the

discovery of diagnostic and prognostic patient specific effective biomarkers to improve patient‘s life quality and

avoid big healthcare costs. Objective of this survey is to review the non-invasive biomarkers that could be used to

predict early onset of AD and delay cognitive impairment.

Keyword: Alzheimer's disease, biomarkers, cognitive impairment, neuro-degenerative disease. References:

1. W. Thies, L. Bleiler, ― Alzheimer‘s disease facts and figures,‖ Alzheimer‘s & Dementia, 2013; 9(2): 208-45.

2. K. Strimbu, J. A. Tavel, ―What are biomarkers? Current opinion in HIV and AIDS,‖ 2010; 5(6): 463.

3. WHO International Programme on Chemical Safety Biomarkers in Risk Assessment: Validity and Validation. 2001. http://www.inchem.org/documents/ehc/ehc/ehc222.htm. December 30, 2013.

4. Mingxia Liu, Daoqiang Zhang*, and Dinggang Shen*, Senior Member, IEEE, ―Relationship Induced Multi-Template Learning for Diagnosis of Alzheimer‘s Disease and Mild Cognitive Impairment,‖ IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL.

35, NO. 6, pp. 1463-

5. Jin Liu, Min Li, Wei Lan, Fang-Xiang Wu, Yi Pan, and Jianxin Wang, ―Classification of Alzheimer‘s Disease Using Whole Brain Hierarchical Network,‖ IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, VOL. 14,

NO. 8, 2015.

6. Robin Wolz, Dong Ping Zhang, et al, ―Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer‘s Disease,‖ PLoSONE .,www.plosone.org, Volume 6, Issue 10, e25446, 2011.

7. B. Al-Naami, N. Gharaibeh, and A. AlRazzaqKheshman, ―Automated Detection of Alzheimer Disease Using Region Growing

technique and Artificial Neural Network,‖ International Science Index, Biomedical and Biological Engineering, Vol:7, No:5, pp. 204-208, 2013 waset.org/Publication/11271.

8. Luis Javier Herrera*, Ignacio Rojas, H. Pomares, A. Guillén, O. Valenzuela, O. Baños, ―Classification of MRI images for

Alzheimer‘s disease detection,‖ SocialCom/PASSAT/Big Data/EconCom/BioMedCom, pp. 846-851, 2013, IEEE. 9. Simon Duchesne*, Member, IEEE, Anna Caroli, et al. ―MRI-Based Automated Computer Classification of Probable AD versus

Normal Controls,‖ IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol. 27, N0. 4, pp. 509-520, 2008.

10. Chenhui Hu, Xue Hua, Jun Ying, Paul M. Thompson, Georges E. Fakhri, Fellow, IEEE, and Quanzheng Li, ―Localizing Sources of Brain Disease Progression with Network Diffusion Model,‖ IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING,

VOL. 10, NO. 7, pp. 1214-1225, 2016

11. R. Armañanzas, M. Iglesias, D. A. Morales and L. Alonso-Nanclares, "Voxel-Based Diagnosis of Alzheimer's Disease Using Classifier Ensembles," in IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 3, pp. 778-784, May 2017. doi:

10.1109/JBHI.2016.2538559

12. Tianhao Zhang*, Member, IEEE, and Christos Davatzikos, Senior Member, IEEE, ―ODVBA: Optimally-Discriminative Voxel-Based Analysis,‖ IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 30, NO. 8, pp. 1441-1454, 2011.

13. Aoyan Dong*, Nicolas Honnorat, Member, IEEE, Bilwaj Gaonkar, and Christos Davatzikos, Fellow, IEEE, ―CHIMERA: Clustering

of Heterogeneous Disease Effects via Distribution Matching of Imaging Patterns,‖ IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 35, NO. 2, pp. 612-621, 2016.

14. Ching-Cheng Chuang, Pei-Ning Wang, et al, " Near-Infrared Brain Volumetric Imaging Method: A Monte Carlo Study,‖ IEEE

JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS, VOL. 18, NO. 3, pp. 1122-1129, 2012. 15. Jun Zhang, Yue Gao, Senior Member, IEEE, Yaozong Gao, Brent C. Munsell, and Dinggang Shen*, Senior Member, IEEE,

―Detecting Anatomical Landmarks for Fast Alzheimer‘s Disease Diagnosis,‖ IEEE TRANSACTIONS ON MEDICAL IMAGING,

VOL. 35, NO. 12, pp. 2524-2533, 2016. 16. G. B. Frisoni, et al, ―The clinical use of structural MRI in Alzheimer disease,‖ Nat Rev Neurol. Author manuscript; pp.67-77,

available in PMC 2011 .

17. Mingxia Liu, Daoqiang Zhang∗, Ehsan Adeli, Member, IEEE, and Dinggang Shen∗, Senior Member, IEEE,‖ Inherent Structure-

Based Multiview Learning With Multitemplate Feature Representation for Alzheimer‘s Disease Diagnosis,‖ IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 63, NO. 7,pp. 1473-1482, 2016.

18. Biao Jie, Mingxia Liu, Jun Liu, Daoqiang Zhang∗, and DinggangShen∗,‖Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer‘s Disease,‖ IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 64, NO.

1, pp. 238-249, 2017.

19. [L. Sørensen , C. Igel, N. Liv Hansen, M.Osler, M. Lauritzen, E. Rostrup, M. Nielsen, for the Alzheimer's Disease Neuroimaging Initiative and the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing (2015), ―Early detection of Alzheimer‘s

disease using MRI hippocampal texture,‖ Hum Brain Mapp, accepted, which has been published in final form at

DOI:10.1002/hbm.23091. 20. E. M. Ali, A. F. Seddik, M. H. Haggag, ―Automatic Detection and Classification of Alzheimer's Disease from MRI using TANNN,‖

1-6

International Journal of Computer Applications (0975 – 8887) Volume 148 – No.9, pp. 30-34, 2016.

21. Baiying Lei, Member, IEEE, Peng Yang, Tianfu Wang, Siping Chen, and Dong Ni, Member, IEEE, ―Relational-Regularized Discriminative Sparse Learning for Alzheimer‘s Disease Diagnosis,‖ IEEE TRANSACTIONS ON CYBERNETICS, VOL. 47, NO.

4, pp. 1102-1113, 2017.

22. Saman Sarraf, GhassemTofighi, for the Alzheimer's Disease Neuroimaging Initiative,‖DeepAD: Alzheimer's Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI,‖ bioRxiv preprint first posted online Aug. 21, 2016; doi:

http://dx.doi.org/10.1101/070441.

23. Siqi Liu∗, Student Member, IEEE, et al, ‖Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer‘s Disease,‖ IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 62, NO. 4,,pp. 1132-1140, 2015.

24. J. Shi; X. Zheng; Y. Li; Q. Zhang; S. Ying, "Multimodal Neuroimaging Feature Learning with Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's," Disease in IEEE Journal of Biomedical and Health Informatics, vol. PP, no.99, pp.1-1. doi:

10.1109/JBHI.2017.2655720

25. Qi Zhou, Mohammed Goryawala, et al, ‖An Optimal Decisional Space for the Classification of Alzheimer‘s Disease and Mild Cognitive Impairment,‖ IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 61, NO. 8, pp. 2245-2253,2014.

26. Rajesh, M., and J. M. Gnanasekar. "Path Observation Based Physical Routing Protocol for Wireless Ad Hoc Networks." Wireless

Personal Communications 97.1 (2017): 1267-1289. 27. André Santos Ribeiro, Luís Miguel Lacerda, Nuno André da Silva and Hugo Alexandre Ferreira for the Alzheimer‘s Disease

Neuroimaging Initiative, ―Multimodal Imaging of Brain Connectivity Using the MIBCA Toolbox: Preliminary Application to

Alzheimer‘s Disease,‖ IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 62, NO. 3,pp. 604-611, 2015.

28. Javier Escudero∗, Member, IEEE, Emmanuel Ifeachor, Member, IEEE, et al, ―Machine Learning-Based Method for Personalized and Cost-Effective Detection of Alzheimer‘s Disease,‖ IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 60, NO. 1,

pp. 164-168, 2013.

29. N. Nithiyanandam, K. Venkatesh, M. Rajesh, Transfer The Levels Of The Monitored Carbon, Nitrogen Gases From The Industries, International Journal of Recent Technology and Engineering, Volume-7 Issue-6S3 April, 2019.

30. Sivanesh Kumar, A., Brittoraj, S., Rajesh, M., Implementation of RFID with internet of things, Journal of Recent Technology and

Engineering, Volume-7 Issue-6S3 April, 2019. 31. Rajesh, M., Sairam, R., Big data and health care system using mlearningJournal of Recent Technology and Engineering, Volume-7

Issue-6S3 April, 2019.

2.

Authors: Sivakamasundari. J, Jesintha Rani. D, Mohanapriya. S, Raksha. G

Paper Title: Retinal Biometric System using Electromagnetism-like Optimization Algorithm

Abstract: Biometric system is the technology used for the purpose of identifying the physiological and

behavioural characteristics of an individual as input, analyzes it and identifies the individual as a genuine or

imposter. Among all biometrics, retina based identification is perceived as a robust, unforgeable and reliable form

of biometric solution. The blood vasculatures of retina are unique and used as features for retinal biometric

system. In this work, an attempt has been made to employ an Electromagnetism-like Optimization Algorithm

(EMOA) with Otsu Multilevel Thresholding (MLT) for segmentation of vascular pattern from the retinal fundus

images for retinal biometric system. Retinal images are taken from the publicly available database such as

DRIVE, STARE and HRF. The original images are subjected to preprocessing. Segmentation is carried out on the

preprocessed images using EMOA Based Otsu MLT. This method provides comparatively better segmentation

accuracy of 0.974 than other existing methods. Texture and vessel features are extracted from the segmented

image. Matching is done between query and enrolled images using Euclidian distance measure. Decision is made

using best matched image. This biometric system shows matching accuracy of 97%. Hence, this method could be

recommended for retinal biometric system.

Keyword: Biometric system, vasculature, segmentation, electromagnetism-like optimization, multilevel

thresholding, retinal fundus image, matching References:

1. I. M .Alsaadi, ―Physiological biometric authentication systems, advantages disadvantages and future development: A review,‖ Int J Sci Technol Res, vol. 12, pp. 285-289, 2015.

2. T. Sabhanayagam, V. Prasanna Venkatesan and K. Senthamaraikannan, ―A Comprehensive Survey on Various Biometric Systems,‖

Int J Applied Engg Res, vol.13, pp. 2276-2297, 2018. 3. Alfred, C. Weaver, ―Biometric Authentication,‖ IEEE Computer Society, vol.39, pp. 96-97, 2006.

4. M. Usman Akram, Anam Tariqy and A. Shoa'b Khanz, ―Retinal Recognition: Personal Identification using Blood Vessels,‖ P 6th Int C

Internet Tech and Sec Trans, pp. 180-184, 2011. 5. A. Budai, R. Bock, A. Maier, J. Hornegger and G. Michelson, G, ―Robust vessel segmentation in fundus images,‖ Int J Biomed Imag,

vol. 2013, pp. 1-11, 2013.

6. J. Staal, M. D. Abràmoff, M. Niemeijer, M.A. Viergever, and B. V. Ginneken, ―Ridge-based vessel segmentation in color images of the retina,‖ IEEE Trans Med Imag, vol. 23, pp. 501–509, 2004.

7. D. Marín, A. Aquino, M. E. Gegundez-Arias and J. M. Bravo, ―A new supervised method for blood vessel segmentation in retinal

images by using gray-level and moment invariants-based features‘, IEEE Trans Med Imag, vol. 30, pp. 146–158, 2011. 8. D. Y. Huang, T. W. Lin, and W. C. Hu, ―Automatic multilevel thresholding based on two-stage Otsu‘s method with cluster

7-12

determination by valley estimation,‖ Int. J. Innov Comput. I., vol. 7, pp. 5631-5644, 2011.

9. S. Arora, J. Acharya, A. Verma, and P.K. Panigrahi, ―Multilevel thresholding for image segmentation through a fast statistical recursive algorithm,‖ Pattern Recogn. Lett., vol.29, pp. 119-125, 2008.

10. P. S. Liao, T. S. Chen, and P. C. Chung, ―A fast algorithm for multilevel thresholding,‖ J. Inf. Sci. Eng., vol. 17, pp. 713–727, 2001.

11. T. Köhler, A. Budai, M. F. Kraus, J . Odstrcilik, G . Michelson, and J. Hornegger, ―Automatic no-reference quality assessment for retinal fundus images using vessel segmentation,‖ P IEEE 26th Int sympo comput med s, pp. 95-100, 2013.

12. J. Sivakamasundari and V. Natarajan, ―Design of content based image retrieval scheme for diabetic retinopathy images using harmony

search algorithm,‖ Biomed Sci Inst, Instrument Society of America, vol. 51, pp. 273-80, 2015. 13. D. Oliva, E. Cuevas, G. Pajares, D. Zaldivar and V. Osuna, ―A multilevel thresholding algorithm using electromagnetism

optimization,‖ Neurocomputing, vol. 139, pp. 357-381, 2014.

14. S. S. Choi, S. H. Cha and C. Tappet, ―A survey of Binary similarity and distance measures,‖ Sys, Cyber Info, vol. 8, pp. 43-48, 2010 15. P. C. Siddalingaswamy and G. K. Prabhu, ―Automatic detection of multiple oriented blood vessels in retinal images,‖ J Biomed Sci

Engg, vol. 3, pp. 101-107, 2010.

16. J. Odstrcilik, R. Kolar, A. Budai, J. Hornegger, J. Jan, J. Gazarek, T. Kubena, P. Cernosek, O. Svoboda and E. Angelopoulou, ―Retinal vessel segmentation by improved matched filtering: evaluation on a new high resolution fundus image database,‖ IET Image

Processing, vol. 7, pp. 373–383, 2013.

17. B. Dai, W. Bu, X. Wu and Y. Zheng, ―Retinal blood vessel detection using multiscale line filter and phase congruency,‖ P Int C on imag proc, comput vision, and pat recog, pp. 235-241, 2013.

18. Y. Q. Zhao, X. H. Wang, X. F. Wang and F. Y. Shih, ―Retinal vessels segmentation based on level set and region growing,‖ Pat

Recog, vol. 47, pp. 2437–2446, 2014. 19. J. V. B. Soares, J. J. G. Leandro, R. M. Cesar, H. F. Jelinek and M. J. Cree, ―Retinal vessel segmentation using the 2-D Gabor

wavelet and supervised classification‖, IEEE Trans Med Imag, vol. 25, pp. 1214-1222, 2006.

20. Rajesh, M., and J. M. Gnanasekar. "Path Observation Based Physical Routing Protocol for Wireless Ad Hoc Networks." Wireless Personal Communications 97.1 (2017): 1267-1289.

21. N. Nithiyanandam, K. Venkatesh, M. Rajesh, Transfer The Levels Of The Monitored Carbon, Nitrogen Gases From The Industries,

International Journal of Recent Technology and Engineering, Volume-7 Issue-6S3 April, 2019. 22. Sivanesh Kumar, A., Brittoraj, S., Rajesh, M., Implementation of RFID with internet of things, Journal of Recent Technology and

Engineering, Volume-7 Issue-6S3 April, 2019.

23. Rajesh, M., Sairam, R., Big data and health care system using mlearningJournal of Recent Technology and Engineering, Volume-7 Issue-6S3 April, 2019.

3.

Authors: Purnima.S, Aditya.S, Meenakshi.E, Narumugai.L, Yamini.E

Paper Title: Early Prediction of Non-Cardiac disorders From ECG Using Lab view

Abstract: The Electrocardiogram (ECG) is one of the most basic cardiological test done for any suspected

diseases related to cardiological system. Abnormalities in any other system can also be detected with change in

morphology of ECG. In this paper we note the changes in morphology of ECG for prediction of non-cardiac

diseases like Emphysema, CNS haemorrhage, Thyroidism, Hypokalemia and Hyperkalemia. ECG is used to

predict these diseases as it is a non-invasive technique and also the morphology of ECG wave is repetitive until

any abnormality manifests itself through ECG. If any of the above mentioned non-cardiac diseases occur,

significant changes appear in ECG signal and with the knowledge of these changes, early clues are provided

regarding the diseases which are lifesaving. This paper works on acquisition and segmentation of ECG for

extraction of features that are inevitable for the prediction of above mentioned diseases. The extracted features are

classified as normal or abnormal based on the comparison with the reference signal. The reference signal contains

information about the normal and abnormal morphological conditions of ECG which are segmented, extracted

and stored prior in the LabVIEW. The automatic prediction of non-cardiac diseases is carried out with LabVIEW

through which a tolerance method is used to correctly compare and predict that particular kind of disease. This

will be later extended to real-time acquisition, processing and classification. The basic motive behind this project

is to create an awareness and alert the patient before the fatal stage.

Keyword: ECG, LabVIEW References:

1. ECG in non cardiac disorders – journal – Yash lokhandwala, Pallavi lanjewar, Sameer Ambar (2005)

2. LabVIEW Based Real Time Bio-Telemetry System for Healthcare –journal-Jay A. Raval, Vivek V. Sakinala, Nitin R. Jadhav and Deepak C. Karia(April 2017)

3. An ECG Patch Combining a Customized Ultra-Low-Power ECG SoC with Bluetooth Low Energy for Long Term Ambulatory

Monitoring-journal-Marco Altini, Salvatore Polito, Julien Penders, Hyejung Kim, Nick Van Helleputte, Sunyoung Kim, Firat Yazicioglu

4. Cardiac Abnormalities in Subarachnoid Hemorrhage - A Resume- Bernard M. Weintraub and Lawrence C. McHenry (2017)

5. Thyroid Hormones and Electrocardiographic Parameters: Findings from the Third National Health and Nutrition Examination Survey- Yiyi Zhang, Wendy S. Post, Alan Cheng, Elena Blasco-Colmenares, Gordon F. Tomaselli, Eliseo Guallar (2013)

6. ECG Diagnosis: Hyperkalemia – journal – Perm J (2013)

7. Rajesh, M., and J. M. Gnanasekar. "Path Observation Based Physical Routing Protocol for Wireless Ad Hoc Networks." Wireless

13-17

Personal Communications 97.1 (2017): 1267-1289.

8. N. Nithiyanandam, K. Venkatesh, M. Rajesh, Transfer The Levels Of The Monitored Carbon, Nitrogen Gases From The Industries, International Journal of Recent Technology and Engineering, Volume-7 Issue-6S3 April, 2019.

9. Sivanesh Kumar, A., Brittoraj, S., Rajesh, M., Implementation of RFID with internet of things, Journal of Recent Technology and

Engineering, Volume-7 Issue-6S3 April, 2019. 10. Rajesh, M., Sairam, R., Big data and health care system using mlearningJournal of Recent Technology and Engineering, Volume-7

Issue-6S3 April, 2019.

11. ECG Diagnosis: Hypokalemia – journal – Perm J (2013)

4.

Authors: K.B SHOBA, P.ASHA

Paper Title: Non Destructive Studies On Engineered Cementitious Composites Using Microsilica &

Polypropylene Fibre

Abstract: This study focuses on assessing the durability property of engineered cementitious composites

using Ultrasonic pulse velocity method (direct and semi direct) to compute the compressive strength. Even the

effect of mineral admixture on the mortar properties for different curing regimes shall be determined using this

method. Mortar specimens containing microsilica in different percentages ranging from 5% to 25%, replacing

portland cement by weight and adding polypropylene fibres ranging from 0.5% to 2% are chosen for evaluation.

20% of microsilica and 2% of polypropylene fibres induced to increase the range of UPV from 3463 m/s to 3505

m/s for 7 and 28 day curing regimes and also the compressive strength significantly improved for the above

constituent. However there was a marginal decrease in the compressive strength and UPV outcomes when cement

is replaced by microsilica greater than 20%. A relationship had been framed between ultrasound pulse velocity

and compressive strength.

Keyword: Durability, Engineered cementitious composites, Ultrasonic pulse velocity, Microsilica. References:

1. K.B.Shoba and P.Asha―Study On Engineered Cementitious Composites Using Micro Silica & Polypropylene Fibre‖, International Journal of Civil Engineering and Technology (IJCIET), Volume 9, Issue 7, (July 2018)

2. Lafhaj Z., Goueygou M., Djerbi A., Kaczmarek M. (2006). Correlation between porosity,permeability and ultrasonic parameters of

mortar with variable water/cement ratio and water content. Cement and Concrete Research, Vol. 36, pp. 625 – 633.

3. Madandoust R., Ghavidel R., Nariman-zadeh N. (2010). Evolutionary design of generalized GMDH-type neural network for

prediction of concrete compressive strength using UPV. Computational Materials Science, Vol. 49, pp. 556–567.

4. Mohammed B.S., Azmi N.J., Abdullahi M. (2011). Evaluation of rubbercrete based on

ultrasonic pulse velocity and rebound hammer tests. Construction and Building

Materials, Vol. 25, pp. 1388–1397. 5. Trtnik G., Kavcic F., Turk G., (2009). Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks

Ultrasonics Vol. 49, pp. 53–60.

6. Ye, G., van Breugel, K., Fraaij, A.L.A. (2001). Experimental study on ultrasonic pulse velocity evaluation of the microstructure of cementitious material at early age. Heron, Vol.

46, No. 3, pp. 161-167 7. Zhang, J., Qin, L., Li, Z. (2009). Hydration monitoring of cement-based materials with resistivity and ultrasonic methods. Materials

and Structures, Vol. 42, pp. 15-24.

8. Zhang, J., Qin, L., Li, Z. (2009). Hydration monitoring of cement-based materials with resistivity and ultrasonic methods. Materials and Structures, Vol. 42, pp. 15-24.

9. Kheder G.F. (1999). A two stage procedure for assessment of in situ concrete strength using combined non-destructive testing.

Materials and Structures, Vol. 32, pp. 410–417. 10. Krishna Rao, M.V., Rathish Kumar, P., Khan, A.M. (2010). A sudy on the influence of curing on the strength of a standard grade

concrete mix. Facta Universitatis (Series

Architecture and Civil Engineering), Vol. 8, No. 1, pp. 23-34. 11. Trtnik G., Turk G., Kavèiè F., Bosiljkov V. B. (2008). Possibilities of using the ultrasonic wave transmission method to estimate

initial setting time of cement paste. Cement and

Concrete Research, Vol. 38, pp. 1336–1342. 12. Krautkramer J and Krautkramer H, in Ultrasonic testing of materials (Springer, Berlin), (1990), pp 522-524

13. Rajesh, M., and J. M. Gnanasekar. "Path Observation Based Physical Routing Protocol for Wireless Ad Hoc Networks." Wireless

Personal Communications 97.1 (2017): 1267-1289. 14. N. Nithiyanandam, K. Venkatesh, M. Rajesh, Transfer The Levels Of The Monitored Carbon, Nitrogen Gases From The Industries,

International Journal of Recent Technology and Engineering, Volume-7 Issue-6S3 April, 2019.

15. Sivanesh Kumar, A., Brittoraj, S., Rajesh, M., Implementation of RFID with internet of things, Journal of Recent Technology and Engineering, Volume-7 Issue-6S3 April, 2019.

16. Rajesh, M., Sairam, R., Big data and health care system using mlearningJournal of Recent Technology and Engineering, Volume-7

Issue-6S3 April, 2019.

18-21

5. Authors: S.Tamil Selvan, M.Sundararajan

Paper Title: Performance Parameters of 3 Value 8t Cntfet Based Sram Cell Design Using H-Spice

Abstract: this paper presents a design of a 3ValueLogic 9T memory cell using carbon nano-tube field-

effect transistors (CNTFETs). The carbon nano tubes with their superior transport properties, excellent current

capabilities ballistic transport operation, 3-value logic have been proposed for 8T SRAM cell implementation in

CNTFET technology. The CNTFET design to achieves the different threshold voltages. And it also avoids the

usage of additional power supplies. The channel length used here is 18nm wide. The power consumption is

reduced, as there is absence of stand-by power dissipation. Second order effects are removed by using CNTFET.

In a 3 Value Logic, it only takes log3 (2n) bits to represent an n-bit binary number. In 3Value logic 9T memory

cell based CNTFET have been developed and extensive HSPICE simulations have been performed in realistic

environments. CNTFET 9T based SRAM cell proves which is Dynamic power better than CNTFET, based

3value logic 8T SRAM cell as well as CMOS SRAM cell.

Keyword: CNTFET, 3ValueLogic, HSPICE, Multi threshold value, FINFET, QDGFET, SNM, SWCNT References:

1. SeyyedAshkan,EbrahimiPeiman,Keshavarzian,SaeidSoroui MahyarShahsavariLow ―Power CNTFETBased Ternary Full Adder Cell for NanoelectronicsInternational‖ Journal of Soft Computing and Engineering (IJSCE) ISSN: 22312307, Volume2, Issue2, May 2012

2. S.Tamil Selvan, B.PremKumar, g.laxmanaa ―Power Efficient 3VL Memory Cell Design Using Carbon Nanotube Field Effect

Transistors‖ International Journal of Advanced Research in Electrical, Electronics and Instrumentation EngineeringVol. 3 Special Issues 3, April2013

3. P. L. McEuen, M. S. Fuhrer, and H. Park : 'Single Walled Carbon Nanotube Electronics', Nanotechnology, IEEE Transactions on.,

2002, 1, (1), pp. 78-85 4. M. Mukaidono, ―Regular ternary logic functions - ternary logic functions suitable for treating ambiguity,‖ IEEE Trans. Comput., vol.

C-35, no. 2, pp. 179–183, Feb. 1986.

5. K.Nepal: 'Dynamic circuits for Ternary computation in Carbon Nanotube based Field Effect Transistors', NEWCAS Conference (NEWCAS), 2010 8th IEEE International, June 2010: pp. 53-56

6. J. Appenzeller, ―Carbon nanotubes for high-performance electronics-progress and prospect,‖ Proc. IEEE, vol. 96, no. 2, pp.201–211, Feb.2008.

7. H. Hashempour and F. Lombardi, ―Device model for ballistic CNFETs using the first conducting band,‖ IEEE Des. Test Comput.,

vol. 25, no. 2, pp. 178–186, Mar./Apr. 2008. 8. Y. Lin, J. Appenzeller, J. Knoch, and P. Avouris, ―High-performance carbon nanotube field-effect transistor with tunable 489, Sep.

9. Rajesh, M., and J. M. Gnanasekar. "Path Observation Based Physical Routing Protocol for Wireless Ad Hoc Networks." Wireless

Personal Communications 97.1 (2017): 1267-1289. 10. Rajesh, M., and J. M. Gnanasekar. "Sector Routing Protocol (SRP) in Ad-hoc Networks." Control Network and Complex Systems 5.7

(2015): 1-4.

11. Rajesh, M. "A Review on Excellence Analysis of Relationship Spur Advance in Wireless Ad Hoc Networks." International Journal of Pure and Applied Mathematics 118.9 (2018): 407-412.

12. Rajesh, M., et al. "SENSITIVE DATA SECURITY IN CLOUD COMPUTING AID OF DIFFERENT ENCRYPTION

TECHNIQUES." Journal of Advanced Research in Dynamical and Control Systems 18. 13. Rajesh, M. "A signature based information security system for vitality proficient information accumulation in wireless sensor

systems." International Journal of Pure and Applied Mathematics 118.9 (2018): 367-387.

22-27

6.

Authors: Elizabeth Angela M, Thilagavathy K, Soundharya R, M Somasundaram

Paper Title: Ai Enabled Blind Spot Detection Using Rcnn Based Image Processing

Abstract: Due to the rapid increase in the rate of road accidents and traffic density, modern automobiles are

equipped with intelligent systems like Adaptive cruise control and Lane Departure Warning System. Therear-

view mirror can be effective to observe a limited range only and there are zones that cannot be viewed. This

region is referred to as the blind spot. Therefore, we present a method to detect the vehicles from the side and the

rear for Blind Spot Detection with vision system incorporating RCNN. Blind spot detection is a key technology

among driver aids that provides 360 degrees of electronic coverage around the car during motion.The

methodology presented in this paper uses two stereo cameras as input devices which constantly capture the

images at the blind spot area and the information is passed to the main controlling unit. Potholes are also detected

and the alert is sent to the nearby vehicle. The incorporation of Artificial Intelligence would help in enhancing the

picture quality and blur or cancel the background images probable of misreading the target image. RCNN is used

for the vehicle detection and for evaluating the relative distance between the vehicles.This technology allows us

to provide a realistic environment for commercial vehicle drivers as they can‘t monitor the side and rear-view

mirrors all the time, making the whole driving experience more comfortable.

28-30

Keyword: Blind spot, RCNN, Potholes, Artificial Intelligence References:

1. Amir Vahid Dastjerdi and Rajkumar Buyya, ―Fog Computing: Helping the Internet of Things Realize Its Potential‖, IEEE Communications Society, August 2016.

2. Aref Meddeb, ―Internet of Things Standards: Who Stands Out from the Crowd?‖, IEEE Communications Magazine -

Communications Standards Supplement, July 2016. 3. Constantinos Kolias and Angelos Stavrou, Irena Bojanova, and Richard Kuhn, ―Learning Internet of Things Security Hands-on‖,

Copublished by the IEEE Computer and Reliability Societies, January/February 2016.

4. Dusit Niyato, Dinh Thai Hoang, Nguyen Cong Luong, Ping Wang, Dong In Kim, and Zhu Han, ―Smart Data Pricing Models for the Internet of Things: A Bundling Strategy Approach‖, IEEE Network, March/April 2016.

5. David Park, ―The Quest for the Quality of Things: Can the Internet of Things deliver a promise of the quality of things?‖, IEEE

Consumer Electronics Magazine, April 2016. 6. Daqiang Zhang, Laurence Tianruo Yang, Min Chen, Shengjie Zhao, Minyi Guo, and Yin Zhang, ―Real-Time Locating Systems Using

the Active RFID for the Internet of Things‖, IEEE Systems Journal, Vol. 10, No. 3, September 2016.

7. David Metcalf, Sharlin T. J. Milliard, Melinda Gomez, and Michael Schwartz, ―Wearables and the Internet of Things for Health‖, IEEE Pulse, September / October 2016.

8. Glenn Parsons, ―The Internet of Things‖, IEEE Communications Magazine, July 2016.

9. Guiou Kobayashi, Maria Eunice Quilici-Gonzalez, Mariana Claudia Broens, and José Artur Quilici-Gonzalez, ―The Ethical Impact of

the Internet of Things in Social Relationships‖, IEEE Consumer Electronics Magazine, July 2016.

10. Huadong Ma, Liang Liu, Anfu Zhou, and Dong Zhao, ―On the Networking of Internet of Things: Explorations and Challenges‖, IEEE

Internet of Things Journal, Vol. 3, No. 4, August 2016. 11. Huadong Ma, Liang Liu, Anfu Zhou, and Dong Zhao, ―On the Networking of Internet of Things: Explorations and Challenges‖, IEEE

Internet of Things Journal, Vol. 3, No. 4, August 2016.

12. Jonathan Margulies, ―Garage Door Openers: An Internet of Things Case Study‖, IEEE Computer and Reliability Societies, July/August 2015.

13. Keshav Sood, Shui Yu, and Yong Xiang, ―Software-Defined Wireless Networking Opportunities and Challenges for Internet-of-

Things: A Review‖, IEEE Internet of Things Journal, Vol. 3, No. 4, August 2016. 14. Michele Nitti, Virginia Pilloni, Giuseppe Colistra, and Luigi Atzori, ―The Virtual Object as a Major Element of the Internet of

Things,‖ IEEE Communications Surveys & Tutorials, Vol. 18, No. 2, Second Quarter 2016.

15. Mohammad Abdur Razzaque, Marija Milojevic-Jevric, Andrei Palade, and Siobhán Clarke, ―Middleware for Internet of Things: A Survey‖, IEEE Internet of Things Journal, Vol. 3, No. 1, February 2016.

16. Maria Rita Palattella, Mischa Dohler, and Alfredo Grieco, ―Internet of Things in the 5G Era: Enablers, Architecture, and Business

Models‖, IEEE Journal of Selected Areas in Communications, Vol. 34, No. 3, March 2016. 17. Mohamed Essaid Khanouche, Yacine Amirat, Abdelghani Chibani, Moussa Kerkar, and Ali Yachir, ―Energy-Centered and QoS-

Aware Services Selection for Internet of Things‖, IEEE Transactions on Automation Science and Engineering, Vol. 13, No. 3, July

2016.

18. Oladayo Bello and Sherali Zeadally, ―Intelligent Device-to-Device Communication in the Internet of Things‖, IEEE Systems Journal,

Vol. 10, No. 3, September 2016.

19. Phillip A. Laplante and Nancy Laplante, ―The Internet of Things in Healthcare: Potential Applications and Challenges‖, IT Pro, IEEE Computer Society, May/June 2016.

20. Pawani Porambage, Mika Ylianttila, Corinna Schmitt, Pardeep Kumar, Andrei Gurtov, and Athanasios V. Vasilakos, ―The Quest for

Privacy in the Internet of Things‖, IEEE Cloud Computing, March/April 2016. 21. Phillip A. Laplante, Jefrey Voas, and Nancy Laplante, ―Standards for the Internet of Things: A Case Study in Disaster Response‖,

IEEE Computer Society, May 2016.

22. Sara Amendola, Rossella Lodato, Sabina Manzari, Cecilia Occhiuzzi, and Gaetano Marrocco, ―RFID Technology for IoT-Based Personal Healthcare in Smart Spaces‖, IEEE Internet of Things Journal, Vol. 1, No. 2, April 2014.

23. Yi Xu and Abdelsalam Helal, ―Scalable Cloud–Sensor Architecture for the Internet of Things‖, IEEE Internet of Things Journal, Vol.

3, No. 3, June 2016. 24. Yunchuan Sun, Houbing Song, Antonio J. Jara, and Rongfang Bie, ―Internet of Things and Big Data Analytics for Smart and

Connected Communities‖, Digital Object Identifier 10.1109/Access, March 2016.

25. Yuvraj Agarwal and Anind K. Dey, ―Toward Building a Safe, Secure, and Easy-to-Use Internet of Things Infrastructure‖, IEEE Computer Society, April 2016.

26. Zhangbing Zhou, Beibei Yao, Riliang Xing, Lei Shu, and Shengrong Bu, ―E-CARP: An Energy-Efficient Routing Protocol for UWSNs in the Internet of Underwater Things‖, IEEE Sensors Journal, Vol. 16, No. 11, June 2016.

27. S.P. Raja, T. Dhiliphan Rajkumar and Vivek Pandiya Raj, Internet of Things: Challenges, Issues and Applications, Journal of Circuits,

Systems and Computers, Vol. 27, No. 12, 2018. 28. S.P. Raja, T. Sampradeepraj, Internet of Things: a Research oriented Introductory, International Journal of Ad Hoc and Ubiquitous

Computing, Vol. 29, No. 1/2, 2018.

29. Rajesh, M., and J. M. Gnanasekar. "Path Observation Based Physical Routing Protocol for Wireless Ad Hoc Networks." Wireless Personal Communications 97.1 (2017): 1267-1289.

30. Rajesh, M., and J. M. Gnanasekar. "Sector Routing Protocol (SRP) in Ad-hoc Networks." Control Network and Complex Systems 5.7

(2015): 1-4. 31. Rajesh, M. "A Review on Excellence Analysis of Relationship Spur Advance in Wireless Ad Hoc Networks." International Journal of

Pure and Applied Mathematics 118.9 (2018): 407-412.

32. Rajesh, M., et al. "SENSITIVE DATA SECURITY IN CLOUD COMPUTING AID OF DIFFERENT ENCRYPTION TECHNIQUES." Journal of Advanced Research in Dynamical and Control Systems 18.

33. Rajesh, M. "A signature based information security system for vitality proficient information accumulation in wireless sensor

systems." International Journal of Pure and Applied Mathematics 118.9 (2018): 367-387. 34. Rajesh, M., K. Balasubramaniaswamy, and S. Aravindh. "MEBCK from Web using NLP Techniques." Computer Engineering and

Intelligent Systems 6.8: 24-26.

7.

Authors: S.A.Thirumalini, D.Deepika, K.Muthumeenakshi, S.SakthivelMurugan

Paper Title: Design Of Galvanic Cell Battery For Underwater Applications Using Seawater As Electrolyte

Abstract: Seawater battery is one of the green electricity sources to fulfill energy need for electrical

equipment, especially in the coastal area and fishing activity .A survey was conducted among fishermen in which

it was found that small scale fishermen uses lead acid battery and fuel cell in order to charge the mobile phone

and glow the fishing lights. But major drawback of lead acid battery and fuel cell is that the maintenance cost is

higher which is difficult for them to afford. Seawater is one most available sources all over the world and it is of

no cost, hence a seawater battery is designed.Thispaper aims to study galvanic cells using sea water as electrolyte

for energy harvesting. The electrochemical performances of Galvanic cells were carried out by measuring electric

potentials by understanding the nature of conductivity of electrodes. The effect of sea water pH on electric

potential was analyzed using sea water from different parts of Bay of Bengal with varying depths. Various

combinations of electrodes like Graphite, Zinc, Copper, Aluminium, Brass and Iron were tested. A maximum

yield of 1.1 V was obtained using the combination of Graphite–Iron as Cathode–Anode for a single cell. Further,

we developed a working prototype for 16 cell. It generates a voltage of 12 V and 20 mA. Since the output current

obtained was not as desired so we added a current amplification circuit and obtained a maximum current of

300mA from 20mA.

Keyword: Galvanic cell, seawater, Current amplification, electrolyte. References:

1. Amir Vahid Dastjerdi and Rajkumar Buyya, ―Fog Computing: Helping the Internet of Things Realize Its Potential‖, IEEE

Communications Society, August 2016. 2. Aref Meddeb, ―Internet of Things Standards: Who Stands Out from the Crowd?‖, IEEE Communications Magazine -

Communications Standards Supplement, July 2016.

3. Constantinos Kolias and Angelos Stavrou, Irena Bojanova, and Richard Kuhn, ―Learning Internet of Things Security Hands-on‖, Copublished by the IEEE Computer and Reliability Societies, January/February 2016.

4. Dusit Niyato, Dinh Thai Hoang, Nguyen Cong Luong, Ping Wang, Dong In Kim, and Zhu Han, ―Smart Data Pricing Models for the

Internet of Things: A Bundling Strategy Approach‖, IEEE Network, March/April 2016. 5. David Park, ―The Quest for the Quality of Things: Can the Internet of Things deliver a promise of the quality of things?‖, IEEE

Consumer Electronics Magazine, April 2016.

6. Daqiang Zhang, Laurence Tianruo Yang, Min Chen, Shengjie Zhao, Minyi Guo, and Yin Zhang, ―Real-Time Locating Systems Using the Active RFID for the Internet of Things‖, IEEE Systems Journal, Vol. 10, No. 3, September 2016.

7. David Metcalf, Sharlin T. J. Milliard, Melinda Gomez, and Michael Schwartz, ―Wearables and the Internet of Things for Health‖,

IEEE Pulse, September / October 2016. 8. Glenn Parsons, ―The Internet of Things‖, IEEE Communications Magazine, July 2016.

9. Guiou Kobayashi, Maria Eunice Quilici-Gonzalez, Mariana Claudia Broens, and José Artur Quilici-Gonzalez, ―The Ethical Impact of

the Internet of Things in Social Relationships‖, IEEE Consumer Electronics Magazine, July 2016. 10. Huadong Ma, Liang Liu, Anfu Zhou, and Dong Zhao, ―On the Networking of Internet of Things: Explorations and Challenges‖, IEEE

Internet of Things Journal, Vol. 3, No. 4, August 2016. 11. Huadong Ma, Liang Liu, Anfu Zhou, and Dong Zhao, ―On the Networking of Internet of Things: Explorations and Challenges‖, IEEE

Internet of Things Journal, Vol. 3, No. 4, August 2016.

12. Jonathan Margulies, ―Garage Door Openers: An Internet of Things Case Study‖, IEEE Computer and Reliability Societies, July/August 2015.

13. Keshav Sood, Shui Yu, and Yong Xiang, ―Software-Defined Wireless Networking Opportunities and Challenges for Internet-of-

Things: A Review‖, IEEE Internet of Things Journal, Vol. 3, No. 4, August 2016. 14. Michele Nitti, Virginia Pilloni, Giuseppe Colistra, and Luigi Atzori, ―The Virtual Object as a Major Element of the Internet of

Things,‖ IEEE Communications Surveys & Tutorials, Vol. 18, No. 2, Second Quarter 2016.

15. Mohammad Abdur Razzaque, Marija Milojevic-Jevric, Andrei Palade, and Siobhán Clarke, ―Middleware for Internet of Things: A Survey‖, IEEE Internet of Things Journal, Vol. 3, No. 1, February 2016.

16. Maria Rita Palattella, Mischa Dohler, and Alfredo Grieco, ―Internet of Things in the 5G Era: Enablers, Architecture, and Business

Models‖, IEEE Journal of Selected Areas in Communications, Vol. 34, No. 3, March 2016. 17. Mohamed Essaid Khanouche, Yacine Amirat, Abdelghani Chibani, Moussa Kerkar, and Ali Yachir, ―Energy-Centered and QoS-

Aware Services Selection for Internet of Things‖, IEEE Transactions on Automation Science and Engineering, Vol. 13, No. 3, July

2016. 18. Oladayo Bello and Sherali Zeadally, ―Intelligent Device-to-Device Communication in the Internet of Things‖, IEEE Systems Journal,

Vol. 10, No. 3, September 2016.

19. Phillip A. Laplante and Nancy Laplante, ―The Internet of Things in Healthcare: Potential Applications and Challenges‖, IT Pro, IEEE Computer Society, May/June 2016.

20. Pawani Porambage, Mika Ylianttila, Corinna Schmitt, Pardeep Kumar, Andrei Gurtov, and Athanasios V. Vasilakos, ―The Quest for

Privacy in the Internet of Things‖, IEEE Cloud Computing, March/April 2016. 21. Phillip A. Laplante, Jefrey Voas, and Nancy Laplante, ―Standards for the Internet of Things: A Case Study in Disaster Response‖,

31-34

IEEE Computer Society, May 2016.

22. Sara Amendola, Rossella Lodato, Sabina Manzari, Cecilia Occhiuzzi, and Gaetano Marrocco, ―RFID Technology for IoT-Based Personal Healthcare in Smart Spaces‖, IEEE Internet of Things Journal, Vol. 1, No. 2, April 2014.

23. Yi Xu and Abdelsalam Helal, ―Scalable Cloud–Sensor Architecture for the Internet of Things‖, IEEE Internet of Things Journal, Vol.

3, No. 3, June 2016. 24. Yunchuan Sun, Houbing Song, Antonio J. Jara, and Rongfang Bie, ―Internet of Things and Big Data Analytics for Smart and

Connected Communities‖, Digital Object Identifier 10.1109/Access, March 2016.

25. Yuvraj Agarwal and Anind K. Dey, ―Toward Building a Safe, Secure, and Easy-to-Use Internet of Things Infrastructure‖, IEEE Computer Society, April 2016.

26. Zhangbing Zhou, Beibei Yao, Riliang Xing, Lei Shu, and Shengrong Bu, ―E-CARP: An Energy-Efficient Routing Protocol for

UWSNs in the Internet of Underwater Things‖, IEEE Sensors Journal, Vol. 16, No. 11, June 2016. 27. S.P. Raja, T. Dhiliphan Rajkumar and Vivek Pandiya Raj, Internet of Things: Challenges, Issues and Applications, Journal of Circuits,

Systems and Computers, Vol. 27, No. 12, 2018.

28. S.P. Raja, T. Sampradeepraj, Internet of Things: a Research oriented Introductory, International Journal of Ad Hoc and Ubiquitous Computing, Vol. 29, No. 1/2, 2018.

29. Rajesh, M., and J. M. Gnanasekar. "Path Observation Based Physical Routing Protocol for Wireless Ad Hoc Networks." Wireless

Personal Communications 97.1 (2017): 1267-1289. 30. Rajesh, M., and J. M. Gnanasekar. "Sector Routing Protocol (SRP) in Ad-hoc Networks." Control Network and Complex Systems 5.7

(2015): 1-4.

31. Rajesh, M. "A Review on Excellence Analysis of Relationship Spur Advance in Wireless Ad Hoc Networks." International Journal of Pure and Applied Mathematics 118.9 (2018): 407-412.

32. Rajesh, M., et al. "SENSITIVE DATA SECURITY IN CLOUD COMPUTING AID OF DIFFERENT ENCRYPTION

TECHNIQUES." Journal of Advanced Research in Dynamical and Control Systems 18. 33. Rajesh, M. "A signature based information security system for vitality proficient information accumulation in wireless sensor

systems." International Journal of Pure and Applied Mathematics 118.9 (2018): 367-387.

34. Rajesh, M., K. Balasubramaniaswamy, and S. Aravindh. "MEBCK from Web using NLP Techniques." Computer Engineering and Intelligent Systems 6.8: 24-26.

8.

Authors: Prasath Kumar.S, Auvai Saraswathy.M, Malligeshwari.H, Nandhinni.Su

Paper Title: IoT Controlled All Terrain Rocker Bogie Robot

Abstract: In today‘s world, we concentrate mainly on newly emerging technologies for several monitoring,

surveillance and recovery operations. This paper presents combination of two emerging technologies, which are

Robotics and IoT. Most surveillance and monitoring robots does not have the ability to move on uneven surfaces

and on slopes, but the rocker bogies have these features. While the present rocker bogies are remote controlled, it

needs a human to be near it to control it. So our aim is to design a rocker bogie robot that can be controlled via

IoT from a distance, which can be done using web page controlling. The control mechanism is provided with

video transmission facility through high speed image transmission. The robot is fitted with a camera which

captures the scene and transfer the images to the server on which the user can control and watch the live feed. We

present the design of rocker bogie suspension and how to control it using commands in the further sections.

Keyword: Robotics, IoT, Rocker Bogie Suspension, Live feed, Web page controlling. References:

1. Yunchuan Sun, Houbing Song, Antonio J. Jara, and Rongfang Bie, ―Internet of Things and Big Data Analytics for Smart and

Connected Communities‖, Digital Object Identifier 10.1109/Access, March 2016. 2. Yuvraj Agarwal and Anind K. Dey, ―Toward Building a Safe, Secure, and Easy-to-Use Internet of Things Infrastructure‖, IEEE

Computer Society, April 2016.

3. Zhangbing Zhou, Beibei Yao, Riliang Xing, Lei Shu, and Shengrong Bu, ―E-CARP: An Energy-Efficient Routing Protocol for UWSNs in the Internet of Underwater Things‖, IEEE Sensors Journal, Vol. 16, No. 11, June 2016.

4. S.P. Raja, T. Dhiliphan Rajkumar and Vivek Pandiya Raj, Internet of Things: Challenges, Issues and Applications, Journal of Circuits,

Systems and Computers, Vol. 27, No. 12, 2018. 5. S.P. Raja, T. Sampradeepraj, Internet of Things: a Research oriented Introductory, International Journal of Ad Hoc and Ubiquitous

Computing, Vol. 29, No. 1/2, 2018.

6. Rajesh, M., and J. M. Gnanasekar. "Path Observation Based Physical Routing Protocol for Wireless Ad Hoc Networks." Wireless Personal Communications 97.1 (2017): 1267-1289.

7. Rajesh, M., and J. M. Gnanasekar. "Sector Routing Protocol (SRP) in Ad-hoc Networks." Control Network and Complex Systems 5.7

(2015): 1-4. 8. Rajesh, M. "A Review on Excellence Analysis of Relationship Spur Advance in Wireless Ad Hoc Networks." International Journal of

Pure and Applied Mathematics 118.9 (2018): 407-412.

9. Rajesh, M., et al. "SENSITIVE DATA SECURITY IN CLOUD COMPUTING AID OF DIFFERENT ENCRYPTION TECHNIQUES." Journal of Advanced Research in Dynamical and Control Systems 18.

10. Rajesh, M. "A signature based information security system for vitality proficient information accumulation in wireless sensor

systems." International Journal of Pure and Applied Mathematics 118.9 (2018): 367-387. 11. Rajesh, M., K. Balasubramaniaswamy, and S. Aravindh. "MEBCK from Web using NLP Techniques." Computer Engineering and

35-37

Intelligent Systems 6.8: 24-26.

9.

Authors: G.T.Bharathy, S.Bhavanisankari, T.Tamilselvi, G.Bhargavi

Paper Title: Analysis and Design of RF Filters with Lumped and Distributed Elements

Abstract: Radio frequency (RF) and microwave filters characterize a class of electronic filter, intended to

function on signals in the frequency range between megahertz to gigahertz. A Lumped element RF filters is a

passive device whose size across any dimension is much smaller than the operating wavelength so that there is

minimal change in phase of waveform between the input and output connections. RF filters can also be designed

using distributed elements in which all the inductors and the capacitors are replaced by the open and short circuit

stubs. This paper concentrates on an analysis and design of low pass filter with the help of only lumped elements

and a high pass filter with both lumped and distributed elements.

Keyword: Lumped elements, RF filters, LC components, stubs, Advanced Design System (ADS). References:

1. Defu wang,Bjorn Thorsten Thiel and Renato negra,‖Reconstruction Lumped-Elements Bandpass Filter Suitable for Lowpass Delta-

Sigma RF Transmitters‖,2014 international workshop on intregrated nonlinear microwave and millimetre-wave 2. Y.-C.Ou and G.Rebeiz,‖Lumped-elements fully tunable bandstop filters for cognitive radio application,‖IEEE Trans .Microwave

Theory Tech,vol.59, no,10,pp.2461-2468,oct 2011

3. Dimitra Psychogion,Roberto Gomez-Garcia and Dimitrios Peroulis,‖RF Design of Acoustic-Wav e –Lumped-Element- Resonator-(AWLR)-Based Bandpass Filters With Constant In-Band Group Delay‖,in 2017

4. D.Wang and Negra,‖Reconstruction filter suitable for Lowpass delta-sigma rf transmitters,‖in 2013 IEEE Radio Wireless Symp,Jan

2013,pp.322-324 5. R. Ludwig. RF and Microwave Engineering, University of San Diego. [Online]Available:

www.sandiego.edu/~ekim/e194rfs01/filterek.pdf

6. J. A. G. Malherbe. Microwave Transmission Line Filters. First Edition. Dedham: Artech House, 1979 7. George L. Matthaei, Leo Young, E. M. T. Jones. Microwaves Filters, Impedance-Matching Networks, and Coupling Structures.

Reprint of the edition by McGraw-Hill. Dedham: Artech House, 1980.

8. David M. Pozar.Microwave and RF Design ofWireless Systems. First Edition. NewYork: John Wiley & Sons, 2001.

38-42

10.

Authors: G.T.Bharathy, S.Bhavanisankari, T.Tamilselvi, G.Bhargavi

Paper Title: Design and Implementation of a Novel Microstrip Filter

Abstract: Microwave filters are circuits which perform signal processing functions, particularly to

eliminate unwanted frequency components from the signal, to enhance wanted ones, or both. Electronic filters can

be passive or active(depends on components used) Analog or digital(depends on input signal) High-pass, Low-

pass, Band-pass, Band-stop or all other pass (depends on frequency) Infinite impulse response (IIR type) or

Finite impulse response (FIR type) (Depends on response) Microstrip is a type of electrical transmission line

which can be fabricated using printed circuit board technology, and is used to convey microwave frequency

signals. Microwave components such as antennas, couplers, filters, power dividers etc can be formed using

microstrip line. This paper aims on filter design, using microstrip transmission line, with a Non-Periodic

technique especially using Defected Microstrip Structure to be operated in the C – Band frequency.

Keyword: Microstrip Filter, Defected Microstrip Structure, T - Shaped structure. References:

1. J.A.G. Malherbe, Microwave Transmission Line Filters, Norwood, Artech House Inc, 1979.

2. F.C. Chen, N.Y. Zhang, P.S. Zhang, and Q.X. Chu, ―Design of ultrawideb and bandstop filter using defected ground structure,‖ IET

Electron. Lett., vol. 49, no. 16, pp. 1010-1011, Aug. 2013. 3. S. Amari, U. Rosenberg, and R. Wu, ―In-line pseudoelliptic band-reject filters with nonresonating nodes and/or phase shifts,‖ IEEE

Trans. Microw. Theory Tech., vol. 54, no. 1, pp. 428–436, Jan. 2006.

4. M. Mandal, K. Divyabramham, and V.K. Velidi, ―Compact wideband bandstop filter with five transmission zeros,‖ IEEE Microw. Wireless Compon. Lett., vol. 22, no. 1, pp. 4-6, Jan. 2012.

5. R.J. Cameron, M. Yu, and Y. Wang, ―Direct-coupled microwave filters with single and dual stopbands,‖ IEEE Trans. Microw. Theory

Tech., vol. 53, no. 11, pp. 3288-3297, Nov. 2005. 6. M.Y. Hsieh and S.M. Wang, ―Compact and wideband microstrip bandstop filter,‖ IEEE Microw. Wireless Compon.Lett., vol. 15, no.

7, pp. 472-474, July 2005.

7. H. Shaman and J.S. Hong, ―Wideband bandstop filter with cross coupling,‖ IEEE Trans. Microw. Theory Tech., vol. 55, no.8, pp. 1780– 1785, Aug. 2007.

8. M.K. Mandal, K. Divyabramham, and S. Sanyal, ―Compact, wideband bandstop filters with sharp rejection characteristic,‖ IEEE

Microw Wireless Component. Lett., vol. 18, no. 10, pp. 665-667, Oct. 2008.

43-46

9. M.Á. Sánchez-Soriano, G. Torregrosa-Penalva, and E. Bronchalo, ―Compact wideband bandstop filter with four transmission zeros,‖

IEEE Microw. Wireless Compon. Lett., vol. 20, no. 6, pp. 313-315, June 2010. 10. K. Divyabramham, M. Mandal, and S. Sanyal, ―Sharp-rejection wideband bandstop filters,‖ IEEE Microw. Wireless Compon. Lett.,

vol. 18, no. 10, pp. 662-664, Oct. 2008.

11.

Authors: S.Bhavanisankari, G.T.Bharathy, T.Tamilselvi, G.Bhargavi

Paper Title: Fuel Management and Control in a Vehicular System

Abstract: This project simulates a working model of a vehicular system which gives a good tradeoff

between power, economy and emissions. The optimum air-fuel ratio for this system to be designed is 14.6. The

quantity of oxygen content in the exhaust gas(EGO) is obtained with the help of a sensor. The output from this

sensor is a sign of air fuel ratio and it gives the necessary feedback for closed loop control. When high oxygen

level is shown by the sensor, then as per the control law, the fuel rate will be increased. Subsequently if the sensor

indicates a fuel rich mixture, when the level of residual oxygen is very low, then this leads to reduction in fuel

rate.

Keyword: Subsystems, Engine control unit, Fuel References:

1. John R. Wagner, Member, IEEE, Darren M. Dawson, Senior Member, IEEE, and Liu Zeyu (2003), Nonlinear Air-to-Fuel Ratio and

Engine Speed Control for Hybrid Vehicles. 2. Suzuki, K., Shen, T., Kako, J., & Yoshida, S. (2009). Individual A/F Estimation and Control With the Fuel–Gas Ratio for

Multicylinder IC Engines. IEEE Transactions on Vehicular Technology, 58(9), 4757–4768.

3. Zhixiang HOU, Yihu WU, IEEE (2007).The Research on Air Fuel Ratio Predictive Model of Gasoline Engine during Transient Condition

4. Shinsuke Takahashi, Teruji Sekozawa. Air-Fuel Ratio Control in Gasoline Engines Based on State Estimation and Prediction Using

Dynamic Models. 5. Yao Ju-Biao, Research on Transient Air Fuel Ratio Control of Gasoline Engines (2009).

6. Chen Linlin, Wei Minxiang On Transient Air/fuel Ratio Control for Gasoline Engine on the Basis of Model Identification (2008).

7. HOU Zhi-xiang , Predictive Control for Air Fuel Ratio of Gasoline Engine based on Neural Network (2016).

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12.

Authors: J.K Vaijayanthimala, P.Saravanan, S.Angeline Priyadarsini

Paper Title: Headway of a Program for Grading Of Fruits by using Image Handling Technique

Abstract: Agribusiness is one of the best cash related divisions and it anticipate the critical movement in

monetary movement of India. Aftereffects of the soil are fundamental for the sound life. Regular things are the

ideal hotspots for furnishing our body with all the fundamental upgrades and enhancements. There are different

mixes of normal things at any rate Apple is one of the monetarily and socially all around essential characteristic

thing adjusts and contributes all together to human well ordered use. Still to review the standard evaluation of

basic things is performed by human specialists, which is viewed as tedious, dismal, work concentrated and

extravagant. So there is an essential for robotized structure for right, energetic and quality normal things

assessing. In this, method utilized for evaluating of apple normal thing utilizing the RGB picture and apple are

surveyed dependent on their outside surface. With a definitive target to evaluating a round ordinary thing, we

clear different outside fragment of a characteristic thing like shading, shape, check and Surface. The framework

utilizes RGB photographs of the typical thing. From these picture, it subsequently evacuate the outer highlights of

the characteristic thing Based on the removed highlights it packs regular thing into two requests. The social

occasion of apple trademark thing utilizing the expelled highlights is finished with the assistance of help vector

machines (SVMs), organize is done and discovered exactness of 100%.

Keyword: Apple grading, Features , Estractions, Classifications,Support Vector Machine .

References: 1. Akira M and Renfu Lu (2013) An image segmentation method for apple sorting and grading

using support vector machine and Otsu‘s method. Comput& Electronics in Agri94:

29-37 2. .BlascoJ,AleixosN,Gómez-Sanchis J. and Moltó E (2009) Recognition And classification of

external skin damage in citrus fruits using multispectral data And morphological

features. Biosystems engg,103:137-145. 3. Banot S and Mahajan P M (2016) A Fruit Detecting and Grading System Based on Image

Processing: Review. Int J of Innovative Research in Electrical, Electronics,

Instrumentation & Control Engg4:2321-5526.

49-54

4. Uemura T, Koutaki G and Uchimura K (2011) Image Segmentation Based on Edge Detection Using Boundary Code Int J of

Innovative ComputInf andControl.7:6073-6083. 5. Xianfeng Li and Weixing Zhu (2011) Apple grading method based on features fusion of size, shape and color. Adv in control

Engg&InfSci15: 2885-91.

6. Zneit R A, Jazar A A and Ayyoub B (2012) Automatic Color Images Classification Algorithm. Int J Comput Sci.9:305-10

13.

Authors: SUBASHINI.V, .ARTHI M, ANBARASI M, RAJAVENI R

Paper Title: Irovers: Real Time Unmanned Four Wheeled Iot Vehicles for Fire Monitoring and Extinguishing

Using Sonic Waves

Abstract: The aim of the proposed system is to build an autonomous mobile robot system for measuring

the various levels of air and noise pollution as well as the fire monitoring and in case of fire, this robot is used to

extinguish the fire using SONIC WAVES. This is a IOT based robot which moves autonomously avoiding

obstacles using the IR sensor. This robot is used for temperature monitoring for the analysis of the presence of

fire. The data from the robot is sent and received using WIFI in IOT. This mobile robot is capable of avoiding

obstacles using IR sensor thereby it can be easily introduced in places of fire accidents for the process of fire

extinguishing. The fire detection are monitored by using the temperature sensor. These information from the

sensor are sent to the PIC microcontroller and then using the wi-fi the information are sent to the cloud. The fire

extinguishing process is carried out by the sonic fire extinguisher .

Keyword: IOT, sensors References:

1. Yano, T., Takahashi, K., Kuwahara, T., and M. Tanabe (2010). Influence of Acoustic Perturbations and Acoustically Induced Thermal Convection on

2. Premixed Flame Propagation. Microgravity Science and Technology (22), pp.155-161.

3. Mikedi, K., Stavrakakis, P., Agapiou, A., ... (2013). Chemical, acoustic and optical response profiling for analyzing burning patterns. Sensors and Actuators (176), pp.290-298

4. The Physics Classroom (n.d.). Sound is a Mechanical Wave. http://www.physicsclassroom.com/Class/sound/U11L1a.html [8] Vortex

ring - Wikipedia: https://en.wikipedia.org/wiki/Vortex_ring 5. Frank, Obstacle Avoidance Robot Car, robotshop.com/letsmakerobots/obstacle-avoidance-robot-car-arduino, 2014, visited on October

29,2017.

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14.

Authors: S.Gowtham, Sreenath.G, Sushanth.G,Suba.R And M Balaji

Paper Title: Switch Fault Diagnosis using S- Transform on Three Phase Inverter for BLDC Drive

Abstract: BLDC drives are highly preferred for electric vehicles application because of its less maintenance,

longer life, lower weight and reliability. Normally for electric vehicles the motors are powered by batteries, so

three phase inverter is very important in driving the motor. The closed loop system is highly efficient in

controlling the parameters of the drive but any fault occurring inside the system may lead to abnormalities which

can damage the entire system. So due to this, the fault analysis on the BLDC drive system is very important. As

three phase inverter is important in driving the motor, in this project we are going to perform fault diagnosis on

three phase inverter in BLDC drive system. The commonly occurring faults in the switches are open circuit and

short circuit faults. There are different methods for diagnosing switch open and circuit faults but S-Transform

analysis proves to be the best method in providing better results compared to the conventional methods. So in this

paper the fault diagnosis on three phase inverter is done using S-Transform method to accurately find where and

which type of fault has occurred.

Keyword: Current controller, Hall Effect, S-transform, stator current. References:

1. Y. S. Lai and Y. K. Lin, ―A unified approach to zero-crossing point detection of back EMF for brushless DC motor drives without

current and Hall sensors,‖ IEEE Trans. Power Electron., vol. 26, no. 6, pp. 1704–1713, Jun. 2011.

2. B.-G. Park, K.-J. Lee, R.-Y. Kim, T.-S. Kim, J.-S. Ryu, and D.-S. Hyun, ―Simple fault diagnosis based on operating characteristic of brushless direct-current motor drives,‖ IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1586–1593, May 2011

3. P. Duan, K. Xie, L. Zhang, and X. Rong, ―Open-switch fault diagnosis and system reconfiguration of doubly fed wind power

converter used in a microgrid,‖ IEEE Trans. Power Electron., vol. 26, no. 3, pp. 816–821, Mar. 2011. 4. M. A. Awadallah and M. M. Morcos, ―Automatic diagnosis and location of open-switch fault in brushless dc motor drives using

wavelets and neurofuzzy systems,‖ IEEE Trans. Energy Convers., vol. 21, no. 1, pp. 104–111, Mar. 2006

5. G. Scelba, G. Scarcella, G. D. Donato, F. G. Capponi, and F. Bonaccorso, ―Fault-tolerant rotor position and velocity estimation using binary Halleffect sensors for low cost vector control drives,‖ IEEE Trans. Ind. Appl., vol. 50, no. 5, pp. 3403–3413, Sep./Oct. 2014.

6. B. Yazici, G. B. Kliman, ―An adaptive statistical time-frequency methodfor detection of broken bars and bearing faults in motors

58-65

using statorcurrent,‖ IEEE Transactions on Industry Applications, 1999, 35(2),pp.442-452.

7. S. Rajagopalan, J. M. Aller, J. A. Restrepo, ―Diagnosis of rotor faults in brushless DC (BLDC) motors operating under non-stationary conditions using windowed Fourier ridges,‖ Conf. Rec. 40th IEEE IAS Annu. Meeting, Hong Kong, 2005, pp.26-33.

8. M. Blodt, M. Chabert, J. Regnier, ―Mechanical load faul detection in induction motors by stator current time-frequency analysis,‖

IEEE Transactions on Industry Applications, 2006, 42(6), pp.1454-1463. 9. S. Rajagopalan, J. A. Restrepo, J. M. Aller, ―Nonstationary motor fault detection using recent quadratic time-frequency

representations,‖ IEEE Transactions on Industry Applications 2008, 44(3), pp.735-744.

10. L. Stankovi_, ―A method for time-frequency signal analysis,‖ IEEE Transactions on Signal Processing, 1994, 42(1), pp.225-229. 11. S. Rajagopalan, J. A. Restrepo, J. M. Aller, ―Wigner-Ville distributions for detection of rotor faults in brushless DC(BLDC) motors

operation under non-stationary conditions,‖ Proc. Int. SDEMPED, Vienna, Austria, 2005, CD-ROM

12. K. Kim, A. G. Parlos, ―Induction motor fault diagnosis based on neuropredictors and wavelet signal processing,‖ IEEE/ASME Transactions on Mechartronics, 2002, 7(2), pp.201-219

15.

Authors: M.Venmathi, D.Indira

Paper Title: Design and Implementation of an Active Clamped Full Wave Quasi Resonant ZCS Boost Converter

Abstract: This paper presents a closed loop control of an active-clamped full-wave quasi-resonant boost

converter with zero-current-switching (ZCS) for power factor correction. Possibility to incorporate

higherswitching frequency and has some potency to reduce switching losses. Power factor improvement and high

efficiency is achieved with a constant output voltage and DC output voltage is regulated by using closed loop

control .The concept of the proposed switchingscheme results lesser switching loss, higher efficiency, possibility

to have higher switching frequency, and has potential to reduce converter's conducted EMI. This paper also

presents voltage regulation using closed loop system and the simulation results are verified

Keyword: Quasi ResonantBoost converter, Zerocurrent switching (ZCS), Half Bridge Rectifier, Resonant

circuit, Power factor Correction.

References: 1. D. M. Divan, ―The resonant dc link converter—A new concept in static power conversion, in Proc. Conf. Rec. IEEEIAS Annu.

Meet., 1986, pp. 648–656.

2. J. Lai, R. W. Young, G. W. Ott, J. W. McKeever, and F. Z. Peng, ―A delta-configured auxiliary resonant snubber inverter, IEEE

Trans. Ind. Appl., vol. 32, no. 3, pp. 518–525, May/Jun.1996. 3. J. S. Lai, B. M. Song, R. Zhou, A. Hefner, D.W. Berning, and C. C. Shen, ―Characteristics and utilization of a new class of low on-

resistance MOS gated power device, IEEE Trans. Ind.Appl., vol. 37, no. 5, pp. 1282–1289, Sep./Oct. 2001.

4. Z. Liang, B. Lu, J. D. van Wyk, and F. C. Lee, ―Integrated CoolMOS FET/SiC-diode module for high performance power switching, IEEE Trans. Power Electron., vol. 20, no. 3, pp. 679–686, May 2005.

5. Y. Ren, M. Xu, J. Zhou, and F. C. Lee, ―Analytical loss model of power MOSFET,‖ IEEE Trans. Power Electron., vol. 21, no. 2, pp.

310–319, Mar. 2006 6. Ned Mohan; T.M. Underland, R.J. Ferraro,"Sinusoidal Line Current Rectification With a lOOKHz B-Sit Step-Up Converter ", IEEE-

Pesc Records, 1984, pp 92-98 .

7. Kalyan K. Sen, Alexander E. Emanuel," Unity. Power Single Phase Power Conditioning 'I, IEEE Pesc Records, 1987, pp 516,524. 8. Kwang-Hwa Liu and Yung-Lin Lin, " Current Waveform Distortion in Power Factor Correction Circuits Employing Discontinuos-

Mode Boost Converters ' I , IEEE-Pesc Record, 1989, pp-516-524.

9. Barbi, I., Oliveira da Silva, S.A., "Sinusoidal line current rectification at unity power factor withboost quasi-resonant converters," Applied Power Electronics Conference and Exposition, 1990 Conference Proceedings, 1990, 11-16 Mar 1990, Page(s):553 – 562.

10. Sebastian, J., Uceda, J., Cobos, J.A., and Gil, P., "Using zero-currentswitched quasiresonant converters as power factor preregulator," International Conference on Industrial Electronics, Control and Instrumentation Proceedings IECON 1991, 28 Oct.-1 Nov. 1991

Page(s):225 - 230 vol.1

11. Sebastian, J., Martinez, J.A., Alonso, J.M., and Cobos, J.A., "Voltagefollower control in zero-current-switched quasi-resonant power

factor preregulators," IEEE Transactions on Power Electronics Volume 13, Is-sue 4, July 1998 Page(s):727 – 738.

12. B.Mammano and B.Carsten, ―Understanding and Optimizing Electromagnetic Compatibility in Switchmode Power Supplies,

Unitrode Power Supply Design Seminar, SEM1500, 2002. 13. A.Santolaria, J. Balcells, D. Gonzalez, J. Gago, ―Evaluation of Switching Frequency Modulation in EMI Emission Reduction

applied to Power Converters, Industrial Electronics Society, 2003. IECON '03. The 29th Annual Conference of the IEEE, Vol.3, 2-6

Nov. 2003. Page(s):2306 - 2311 14. F. Lin, D.Y.Chen, ―Reduction of Power Supply EMI Emission by Switching Frequency Modulation, Power Electronics, IEEE

Transactions, Volume 9, Issue 1, Jan. 1994, Page(s):132 – 137

15. C.M., Duarte, C., and Barbi, I.: ―A new ZVS-PWM activeclamping high power factor rectifier analysis design and experimentation. IEEE PESC Record, 1998, pp. 230–236

16. Costa, A.V., Treviso, C.H.G., and Freitas, L.C.: ―A new ZCS-ZVSPWM boost converter with unity power factor operation. IEEE

PESC Record, 1994, pp. 404–410 17. Xu, D.M., Yang, C.,Ma, L., Qiao, C., Qian, Z., and He, X.: ―A novel single-phase active-clamped PFC converter‖. IEEE APEC

Record, 1997, pp. 266–271

66-72

18. Gataric, S., Boroyevich, D., and Lee, F.C.: ―Soft-switched singleswitch three-phase rectifier with power factor correction‖. IEEE

APEC Record, 1994, pp. 738–744 19. Canesin, C.A., and Barbi, I.: ―A novel single-phase ZCS-PWM high power factor boost rectifier‖. IEEE PESC Record, 1997 pp.

110–114

20. Wakabayashi, F.T., and Canesin, C.A.: ―A new family of zero-currentswitching PWM converters and a novel HPF-ZCS-PWM boost rectifier‖. IEEE PESC Record, 1999, pp. 609–611

21. Souza, A.F., and Barbi, I.: ―A New ZVS-PWM unity power factor rectifier with reduced conduction losses‖, IEEE Trans. Power

Electron., 1995, 10, (6), pp. 746–752 22. Souza, A.F., and Barbi, I.: ―A new ZCS quasi-resonant unity power factor rectifier with reduced conduction losses‖. IEEE PESC

Record, 195, pp. 1171–1177

23. Chio, H.S., and Cho, B.H.: ―Zero-current-switching (ZCS) power factor pre-regulator (PFP) with reduced conduction losses‖. IEEE PESC Record, 2002, pp. 962–967.

24. E. Firmansyah, S. Tomioka, S. Abe, M. Shoyama, T. Ninomiya, ―Zero Current Switch-Quasi Resonant Boost Converter

Performance in Power Factor Correction Application Proc. of APEC 2009, pp. 1165-1169. 25. Firmansyah, E., Tomioka, S., Abe, S., Shoyama, M., Ninomiya, T., ―Steady state characteristics of active-clamped full-wave

zero-current switched quasi-resonant boost converters, IEEE 6th International 2009 Power Electronics and Motion Control

Conference-IPEMC '09, 17-20 May 2009 Page(s):556 – 560. 26. R. W. Erickson, D. Maksimovic, "Chapter 20 : Soft Switching", in Fundamentals of Power Electronics, second edition,

Massachusetts:KluwerAcademic Publishers, 2001.

27. R.D. Middlebrook and S. Cuk, ―A General Unified Approach to Modelling Switching Power Stages‖, IEEE Power Electronics Specialists Conference Rec., pp. 18-34, 1976.

16.

Authors: S.Deivanayagi¬¬, V.G.Nandhini Sri, P.Kalai Priya, G.Aarthi

Paper Title: Pupil Detection Algorithm Based on Feature Extraction for Eye Gaze

Abstract: Exact real-time pupil tracking is an essential step in a live eye gaze. Since pupil centre is a base

point‘s reference, eye centre localization is essential for many applications. In this research, we extract pupil eye

features exactly within different intensity levels of eye images, mostly with localization of determined interest

objects and where the human is looking for. Since it‘s a digital world and digital transformation, everything is

becoming virtual. Hence this concept has a huge scope in e-learning, class room training and analyzing human

behaviour. This research covers eye tracking technology to track and analyze the learners' behavior and emotion

on e-learning platform like level of attention and tiredness. Harr‘s cascade classifier was used to first locate the

eye‘s area, and once found and support vector machine (SVM) for classification with the trained datasets. We

also include the state of emotions, facial landmarks of the salient patches on face image using automated learning-

free facial landmark detection technique.Experimental results help in developing learner eye gaze detection in

system using Pycharm and hardware output using Raspberry Pi. In Raspberry Pi is given with the input image

captured using external webcam and based on the engagement level of the learner content 1 or 2 would be

displayed in the Raspbian OS environment.

Keyword: Image processing, SVM, Harr‘s Cascade.

References:

1. Parthpancholi, Jaimeet Patel, Saumil Ajmera, ―Wheel Chair movement using Eye Ball Detection‖, International Journal of Advance

Engineering and Development (IJAERD) Volume 1,Issue 5,May 2014.

2. Yasu Taka Ito,WataruOhyama, Wakabayashi,Fumitaka Kimura, ―Detection of Eyes by circular hough transform and histogram of gradient‖, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), Nov. 2012

3. Robert Gabriel Lupu& Florina ungureanu, ―Survey of Eye tracking methods and applications‖, UniversitateaTehnică ―Gheorghe

Asachi‖ Technical University of Iaşi, Faculty of Automatic Control and Computer Engineering,August 2013 4. PengWang,MathewB.Green,Qiang ji &James Wayman, ―Automatic Eye Detection and its validation‖ ,Proceedings of the 2005 IEEE

Computer Society Conference on Computer Vision and Pattern Recognition (CVPR‘05),2005

5. Michal Ciesla,―Eye Pupil location using Webcam‖, Department of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Poland, 2010.

6. Anjith George, ―Fast and accurate algorithm for Eye localization for gaze tracking in low resolution images‖, IET Computer vision,

2016. 7. D. Li, D. Parkhurst, ―Open-source software for real-time visible spectrum eye tracking‖, 2nd Conference on Communication by Gaze

Interaction (COGAIN), 2006, Sept. 4-5, 2006, Turin, Italy.

8. A. Al-Rahayfen, M. Faezipour, ―Eye Tracking and Head Movement Detection: A State-of-Art Survey‖, Rehabilitaion Devices and Systems, IEEE Journal of Translational Engineering in Health and Medicine, vol. 1, 2013, 10.1109/JTEHM.2013.2289879, 2013.

9. R. Ranguram, J.M. Frahm, M. Pollefeys, ―A comparative analysis of RANSAC techniques leading to adaptive real-time random

sample consesnsus‖, Computer Vision (ECCV), Springer Berlin Heidelberg, pp. 500-513, 2008. 10. Sheena D., Borah B., ―Compensation for some second-order effects to improve eye position measurements‖, Eye movements:

Cognition and visual perception, pp. 257-268, 1981, Hillsdale, Nj: Erlbaum.

11. A. Drumea, P. Svasta, "Designing low cost embedded systems with ethernet connectivity", 17th International Symposium for Design

73-76

and Technology in Electronic Packaging (SIITME2011), 2011, pp.217-220.

17.

Authors: Rohini Govindaraju, Saritha Natesan, Lavanya, Ganesh, Abishag Cynthia

Paper Title: Hybrid standalone system for Rural Electrification

Abstract: Renewable energy finds major application in electrification of remote areas where the access to

electrical energy from grid is not possible.Renewable energy resources also act as the most important source for

electrical energy production to overcome the energy crisis due to lapse of conventional sources and expected to

meet the large demand in power all over the world especially in developing country like India. Among the

renewable resources, wind & solar are the most popular ones because of their abundance, ease of accessibility and

which can be easily converted to the electricity. This paper presents the design and analysis of a hybrid solar-

wind system for domestic purpose in the remote areas of the country where continuous power supply from central

grid is a problem

Keyword: Boost converter, Hybrid system, off-grid region Photo Voltaic, SEPIC Converter, Wind energy References:

1. P. Thounthong and S. Rael, ―The benefits of hybridization,‖ IEEE Ind. Electron. Mag., vol. 3, no. 3, pp. 25–37, Sep. 2018.

2. S. M. Muyeen, R. Takahashi, T. Murata, and J. Tamura, ―Integration of an energy capacitor system with a variable-speed wind

generator,‖ IEEE Trans. Energy Convers., vol. 24, no. 3, pp. 740–749, Sep. 2018.

3. L. Wang and D. J. Lee and W. J. Lee and Z. Chen, ‘‘Analysis of a novel autonomous marine hybrid power generation/energy storage

system with

4. a high-voltage direct current link‖, journal of power source, Volume 185, Issue 2, Pages 1284–1292, 1 December 2017.

5. X.Yingcheng and T. Nengling, ―Review of contribution to frequency control through variable speed Wind Turbine‖, Renewable

Energy, Volume 36, Issue 6, Pages 1671-1677, June 2017

6. D. J. Lee and L. Wang, ‗‘Small-Signal Stability Analysis of an Autonomous Hybrid Renewable Energy Power Generation/Energy

Storage System Part I: Time-Domain Simulations‖, IEEE Transaction on Energy Conversion, Vol. 23, No. 1, March 2017.

7. H. M. K. Al-Masri and M. Ehsani, "Impact of wind turbine modeling on a hybrid renewable energy system," 2016 IEEE Industry

Applications Society Annual Meeting, Portland, OR, 2017, pp. 1-8.

8. J. Plaza Castillo, C. DazaMafiolis, E. Coral Escobar, A. Garcia Barrientos and R. Villafuerte Segura, "Design, Construction and

Implementation of a Low Cost Solar-Wind Hybrid Energy System," in IEEE Latin America Transactions, vol. 13, no. 10, pp. 3304-

3309, Oct. 2017.

9. F. Guérin, D. Lefebvre, A. B. Mboup, J. Y. Parédé, E. Lemains and P. A. S. Ndiaye, "Hybrid Modeling for Performance Evaluation of

Multisource Renewable Energy Systems," in IEEE Transactions on Automation Science and Engineering, vol. 8, no. 3, pp. 570-580,

July 2016.

10. G. Rohini and V. Jamuna, ―PLL Based Energy Efficient PV System with Fuzzy Logic Based Power Tracker for Smart Grid

Applications,‖ The Scientific World Journal, vol. 2016, Article ID 2708075, 20 pages, 2016 11. A. Jenifer., N. R. Newlin, G. Rohini. and V. Jamuna., "Development of Matlab Simulink model for photovoltaic arrays," 2012

International Conference on Computing, Electronics and Electrical Technologies (ICCEET), Kumaracoil, 2012, pp. 436-442

77-80

18.

Authors: S. Sweetline Shamini ,Gayathri.M, Harshini.M, Suruthi.S

Paper Title: Advanced Patient Health Monitoring System Using Power Line Communication Technology

Abstract: Open source automation system is rapidly developing towards more reliable communication

systems. In recent years for its convenient installation and low cost the power line increasingly become a popular

transmission medium in creating industrial/ resident work. PLC is a technology uses power lines as physical

media for data transmission. PLC offers a no new wires solution because the infrastructure has already been

established. PLC modems are used for transmitting data at a rapid speed through a power line in a house, an

office, a building, and a factory, etc. Due to this additional telemetry features, cost of the devices are more and all

hospital or clinic cannot afford to buy them. Hence in our work, temperature, blood pressure and heart beat

monitoring equipment based on power line communication is developed. This is cost effective equipment which

uses existing power cables as communication medium. Power Line Modem (PLM) is used for transmitting and

receiving the signals over power line cable. Signalsare modulated and demodulated using direct-sequence spread

spectrum (DSSS) technology. When compared with other communication technologies like local area network

(LAN), ZigBee, Bluetooth, the establishment cost for healthcare monitor using Power Line Communication

(PLC) was low.

Keyword: PLC Technology, PLC modem, Energy Efficiency, ZigBee, FSK.

81-84

References: 1. Jiri Misurec, Milos Orgon, ―High- speed data transfer using PLCC‖, 25th International Conference on Systems, Signals Image

Processing (IWSSIP), June 2018. 2. Sung- Guk Yoon,‖ Performance Analysis of Power Saving Strategies for Power Line Communications ‖, International

Conference on Smart Grid Communications , October 2017.

3. Yu Min Hwang, Jun Hee Jung, Jong Kwan Seo, Jae Jo Lee, Jin Young Kim,‖ Energy-Efficient Transmission Strategy with Dynamic Load for Power Line Communication ‖,2017.

4. HananeHadlach, Hamid Touijer, Mustapha Zahir, Mohamed Habibi,‖ Modeling of a Smart Grid Monitoring System Xsing Power

Line Communication‖, International Renewable and Sustainable Energy Conference, December 2017. 5. SubhraJ.Sankar ,Palash K. Kundu,‖ A Proposed Method of Load Scheduing and Generation Control Using GSM and PLCC

Technology‖, Michael Faraday IET International Summit (MFIIS), September 2015.

19.

Authors: S.Sivajothi Kavitha,K.Hemalatha V.Jamuna

Paper Title: A Real-Time Smart Dumpsters Monitoring and Garbage Collection System Using Iot

Abstract: In most of the cities the overflowed garbage dumpsters are creating an obnoxious smell and

making an unhygienic environment. The Collection of garbage is a very much needed municipal service that

requires huge expenditures and execution of this operation is high-priced. The high pricing is due to the various

factors such as man power, navigation of vehicles, fuel, maintenances and environmental costs. The above factor

necessitates the design, implementation and execution of the new Smart Intelligent Garbage Alert System

(SIGAS) for the smart cities. This paper focuses on the implementation of an IoT based embedded system which

integrates various Sensors & controllers with RF transmitter and receiver for dumpster and vehicle monitoring

system with their performance measured in real time environment.

Keyword: Dumpsters, IoT, Smart Intelligent Garbage Alert System (SIGAS). References: 1. L Saranya, P Rajeshwari, M Priyadharshini, S S Praveen, G Pradeep ―Garbage Management System For Smart City Using IOT‖

International journal of Pure and Applied Mathematics(IJPAM)

2. Dr.V.Jamuna, S.Sivajothi Kavitha, M.Karthik Sharan, C.Gopinath, M.Aswin, (2017), ―An integrated sensor network to enhance the

performance of Gully pot monitoring system‖, international journal of advanced research in management, architecture, technology and engineering. Volume 3, special issue, PgNo:48 to 52, March 2017, ISSN: 2454-9762

3. S.Sivajothi Kavitha, C.Lily sardonyx, J.L.N.Karunya, M.Harish (2017), ―Harnessing kinetic power and sound power using PZT in a

condensed zone‖. International journal of advanced research in Management, Architecture, Technology and Engineering (IJARMATE) Volume 3, Special issue 13 PgNo: 74 to 78, ISSN: 2454-9762

4. V.Jamuna, S.Sivajothi Kavitha, S,Samuel berkins, P.Nagarajan, S.Narendran(2018), ―A monotonous cyborg for an assessment of solid

waste management in multistoried buildings‖,. International journal of scientific research and innovation, volume 3, PgNo:7 to 12, ISSN: 2455-7579.

5. Sharaaf N. A.,Hijaz A.J.M,Kiroshan T,Suresh R,S.G.S Fernando ―Easy Clean – A Smart Solution for Garbage Finding and

collecting‖InternationalJournal of Computer Applications (0975 – 8887) Volume 169 – No.3, July 2017 Colombo Sri Lanka. 6. Mohd.Talha, Raaziyah Shamim, M.Salim Beg ―A Cloud integrated wireless garbage management system for Smart Cities‖

International Conference on Multimedia, Signal Processing and Communication technologies (IMPACT) November 2017 Aligarh,

India. 7. Chandradeep Tiwariet, Smt. Nagarathna. K ―Waste Management using Solar Smart Bin‖International Conference on Energy,

Communication, Data Analytics and Soft Computing (ICECDS) 2017.

8. Bharadwaj B, M Kumudha, Gowri Chandra N, Chaithra G ―Automation of smart waste management using IoT to support Swachh Bharat Abhiyan- A practical approach‖ 2nd International Conference on Computing and Communication Technologies (ICCCT) 2017.

9. ShilanAbdullah Hassan,Noor Ghazi M.Jameel,Boran Sekeroglu ―Smart solid waste monitoring and collection system‖ International

journal of advanced research in computer science and software engineering Volume 6, 2016. 10. Jobin Francis, Melbin TL, Praveen CN ―Solar Power Smart Waste Bin‖ International journal of computer engineering in research trends

Volume 2 2015.

11. ]Naman Sharma, NikhilMishra, Paurvi Gupta ―IoT based garbage monitoring system‖ International Journal of advance reaserch, ideas and innovations in technology Volume 4

12. Najaf Ali, M. Muzammul, Ayesha Zafar ―Intelligent System for Garbage collection: IoT technology with Ultrasonic sensor and Arduino

Mega‖ IJCSNS International Journal of Computer Science and Network Security, VOL.18 No.9, September 2018 13. Priya G, Ronit Chaudhuri, Pritthish Chattopadhyay, Sreyam Dasgupta ―Smart Garbage Monitoring System‖ International Journal of

Engineering Research & Technology (IJERT)Vol. 6 Issue 05, May – 2017

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20.

Authors: T.Deepa, D. Subbulekshmi, S.Angalaeswari, Krithiga S.

Paper Title: Single tyre system design and implementation using Optimization based PI Controller

Abstract: This work described in the paper to show the implementation of Proportional Integral (PI)

controller, Genetic algorithm based PI (GAPI), and Particle swarm optimization based PI (PSO-PI) for a Quarter

Car System. The trip comfort is developed by means of the decrease of the body acceleration caused by the car

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body, due to the smoothness of the road. This paper tells about the model and controllers used in the study and

discuss the vehicle response results. In the conclusion, a comparison of PI, GA-PI, and PSO-PI is shown using

MATLAB simulations.

Keyword: Quarter car model, PI, GAPI, PSOPI. References:

1. Michiel Haemers, Stijn Derammelaere, Clara-Mihaela Ionescu, Kurt Stockman, Jasper De Viaene, Florian Verbelen, ―Proportional-

Integral State-Feedback Controller Optimization for a Full-Car Active Suspension Setup using a Genetic Algorithm‖, IFAC Conference on Advances in Proportional Integral-Derivative Control, vol 51 [4], pp.1-6, 2018.

2. Mahesh P. Nagarkar, Yogesh J. Bhalerao, Gahininath J. Vikhe Patil and Rahul N. Zaware Patil, ―GA-based multi-objective

optimization of active nonlinear quarter car suspension system—PID and fuzzy logic control‖, International Journal of Mechanical and Materials Engineering, vol 13 [10], pp. 1-20, 2018.

3. Muhammad Ibrahim Faruk, Amir Bature, Suleeiman Babani, Najib Dankadai, ―CONVENTIONAL AND INTELLIGENT

CONTROLLER FOR QUARTER CAR SUSPENSION SYSTEM‖, international Journal of Technical Research and Applications vol.2 [1], PP. 24-27, 2014.

4. M Senthil Kumar & S Vijayarangan, Design of LQR controller for active suspension system‖, Indian Journal of Engineering &

Materials Sciences Vol. 13, pp. 173-179, 2006. 5. Min Yu, Simos A.Evangelou, Daniele Dini, ― Model Identification and Control for a Car test rig of series active variable geometry

suspension‖, IFAC papers on line, vol.50[1], pp.3376-3381, 2017.

6. MP Nagarkar, YJ Bhalerao, GJ Vikhe Patil, RN Zaware Patil, ―Multi objective optimization of nonlinear quarter car suspension system – PID and LQR control‖, Procedia Manufacturing, vol 20, pp. 420-427, 2018.

7. Mahesh P.Nagarkar, Gahininath J.Vikhe Patil, Rahul N.Zaware Patil, ―Optimization of nonlinear quarter car suspension- seat- driver

model‖, Journal of advanced research, vol.7 [6], pp.991-1007,2006. 8. Nitish Katal, Sanjay Kr. Singh, ―Optimization of PID Controller for Quarter-Car Suspension System using Genetic Algorithm‖,

International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) vol. 1[7], pp 30-32, 2012.

9. Ganesh D. Shelke, Anirban C. Mitra, Vinay R.Varude, ―Validation of simulation and analytical model of nonlinear passive vehicle suspension system for quarter car‖, Materials today, vol.5, pp. 19294-19302, 2018.

10. Min Yu, Carlos Arana, Simos A. Evangelou, Daniele Dini, George D. Cleaver, ―Parallel Active Link Suspension: A Quarter-Car Experimental Study‖, IEEE/ASME TRANSACTIONS ON MECHATRONICS, vol. 23[5], pp.2066-2077,2018.

11. Hao Zhang, Qianqian Hong, Huaicheng Yan, Fuwen Yang, Ge Guo, ―Event-Based Distributed H∞ Filtering Networks of 2-DOF

Quarter-Car Suspension Systems‖, IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, vol.13 [1], pp.312-321,2017. 12. Haiping Du, Weihua Li, and Nong Zhang, ―Integrated Seat and Suspension Control for a Quarter Car With Driver Model‖, IEEE

TRANSACTIONS ON VEHICULAR TECHNOLOGY, vol.61[9], pp.3893- 3908, 2012.

13. T. Deepa and D. Subbulekshmi, ―Design and Implementation of 2 Term and 3 Term Controllers for Magnetic Levitation System‖, International Journal of Mechanical Engineering and Technology (IJMET), vol 9[6], pp. 343–350,2018.

14. T.Deepa, S.Angalaeswari and V. Balaji,Turning of PI controller parameters using genetic algorithm (GA) for quarter car

applications,International journal of Pure and Applied Mathematics, vol.120(6), pp.no.2007-2015,2018

21.

Authors: V.Athappan, M.Saravanabalaji

Paper Title: Online Control and Monitoring of Pressure Process Station using Yokogawa DCS

Abstract: In chemical process industries pressure is one of the key process variable as pressure provides a

critical condition for any process in an industry. Inaccurate pressure control will result in major safety issues,

poor quality, and productivity problems. More over high pressure inside a sealed vessel can cause an explosion.

Therefore, it is highly desirable to keep pressure in good control and maintained within its safety limits. This

paper aims for online control and monitor of pressure variable by interfacing with DCS and the same is operated

as remotely triggered unit. Many process industries will monitor and control many different manufacturing

processes at the same time. Overall monitoring and controlling of all the process at the same time instance will

lead to increase in process productivity and plant safety. Distributed control and centralized monitoring are the

key-factors to ensure the plant safety. This paper aims to enhance the flexibility in controlling and monitoring of

pressure process station by configuring and developing Field Control Station (FCS) & Human Interface Station

(HIS) using DCS. The PID controller attempts to minimize the error by adjusting the controller output. The PID

controller parameters are calculated by using Cohen and coon tuning algorithm. The field output of pressure

process station is fetched and the parameter is been sent to the distributed control system (DCS) where the

controlling and monitoring of the pressure variable in the pressure process station is performed by the DCS. Thus

the optimized control of pressure process is been achieved.

Keyword: Pressure, DCS, FCS, HIS & PID References: 1. D.Angeline Vijula, V.Thangapandi, K.Vasanth Kumar, B.Vignesh ―Controlling and Monitoring of Industrial Pressure Process using

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Distributed Control System‖, INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN ELECTRICAL, ELECTRONICS,

INSTRUMENTATION AND CONTROL ENGINEERING, Vol. 3, Issue 2, February 2015. 2. Design field controller for level, flow, temperature and networking using YOKOGAWA DCS. 5th International Conference on

Mechatronics (ICOM‘13). IOP Conf. Series: Materials Science and Engineering 53 (2013) 012094.

3. GyanRanjanBiswal, Maheshwari R.P., Dewal M.L. (2012)‘Modeling, Control and Monitoring of Hydrogen Cooling System in Thermal power plant‘IEEE Transactions on Industrial Electronics. Vol.59,No.1,pp.562-569.

4. Brendan Galloway, Gerhard Hancke,P. (2008) ‘Introduction to Industrial Control Networks‘,IEEE Transactions on Instrumentation and

Measurements Vol.56, No.1, pp.5-10 5. Francesco Adamo, Filippo Attivissiono, Giuseppe Cavone, Nicola Giaquinto (2007), ‗SCADA/HMI system in Advance Educational

Courses‘,IEEE Transactions on Instrumentation and Measurements Vol.56,No.1,pp.4-9.

6. George Stephanopoulos,S. (1984) ‗Chemical process control ‗, Prentice – Hall publication , New Jersey , Eastern Economy Edition.

22.

Authors: Rathnavel. K, Selvasundar. K, Jamuna. V

Paper Title: Government Bus Tracking With Passenger Details

Abstract: This paper proposes an alternative to the conventional public transport tracking systems which

uses GPS. The proposed model uses LoRa wireless transmission to communicate between the bus stops and a

base station. The buses are equipped with RF transmitters, which send out data regarding the bus identity,

continuously. RF receivers placed in the bus stop, detects this bus when it is in range, and relays this information

to the base station instantly through LoRa communication. The LoRa receiver in the base station collects the

transit information from all such bus stops in its range, modifies it as per requirement, and stores all necessary

information in a database. The prototype built, only cost one-seventh of the cost required to implement a

conventional tracking system, and consumed much less power as well. Such a system has minimal dependence on

the number of buses being used. Hence, the system can be scaled at minimal costs

Keyword: Public Transport, LoRa, GPS, RF, Nano. References:

1. A. Augustin, J. Yi, T. Clausen, and W. Townsley, ―A Study of LoRa: Long Range & Low Power Networks for the Internet of

Things,‖ Sensors, vol. 16, no. 9, p. 1466, 2016.

2. A. Note, ―Gateway to Server Interface LoRaWAN Network Server Demonstration : Gateway to Server Interface Definition Gateway to Server Interface,‖ no. March, pp. 1–17, 2015.

3. L. Mainetti, L. Patrono, A. Secco, and I. Sergi, ―An IoT-aware AAL system for elderly people,‖ 2016 Int. Multidiscip. Conf. Comput.

Energy Sci. Split. 2016, 2016. 4. Bankov, D.; Khorov, E.; Lyakhov, A. (November 2016). "On the Limits of LoRaWAN Channel Access". 2016 International

Conference on Engineering and Telecommunication (EnT): 10–14. doi:10.1109/ent.2016.011

5. Matthew Knight; Balint Seeber (2016). "Decoding LoRa: Realizing a Modern LPWAN with SDR" 6. "Semtech Selected for Smart Irrigation System | San Fernando Valley Business Journal". sfvbj.com..

7. Mu-Hyun, Cho. "SK Telecom launches LoRa-based fire detection solution". ZDNet.

8. K.Rathnavel ―Accident Alert and Intercommunication System using Arduino.‖ In IJARMATE, Vol 3, Special Issue 13, March 2017. 9. K.Rathnavel ―Blind User Wearable Audio Assistance for Outdoor Navigation using PI based Image Processing‖ in IJSRR

Journal, Vol 7, Issue 4, April 2018.

10. K.Selvasundar, ―Industrial Intelligent line follower vehicle with colour track detection‖, International journal of advanced research in management architecture, Technology and Engineering .Volume 3, Special issue 13, March 2017.

11. K.Selvasundar, ―Realization of Control Strategy for a Non-linear System using FGPA‖, International Journal of Scientific Research

and Review, Volume 7, Issue 4, 2018.

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23.

Authors: I.Jeya Daisy, B.Vinoth Kumar

Paper Title: Static, Portable and Smart Detection Of Water Quality In Panchayath Distribution Tank

Abstract: The significant crunch in the Current world is Water pollution. It has created an abundant

influence on the Environment. With the intention of the non-toxic distribution of the water and its eminence

should be monitored at real time. This paper suggested the smart detection with low cost real time system which

is used to monitor the quality of water through IOT(internet of things). The system entail of different sensors

which are used to measure the physical and chemical parameters of the water. The quality parameters are

temperature, pH, turbidity, conductivity and Total dissolved solids of the water are measured. Commercially

available products capable of monitoring such parameters are usually somewhat expensive and the data‘s are

collected by mobile van. Using Sensor technology provides a cost-effective and pre-eminent reliable as they can

provide real time output. The measured values from the sensors can be observed by the core controller. The

controller was programmed to monitor the distribution tank on a daily basis to hour basis monitoring. The TIVA

C series is used as a core controller. The Controller is mounted on the side of the distribution tank.

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Finally, the sensor data from the controller is sent to Wi-Fi module through UART protocol. Wi-fi Module is

connected to a public Wi-Fi system through which data is seen by the locals who are all connected to that Wi-Fi

network.

Keyword: Water pollution, Quality testing,Wi-Fi module References:

1. Nikhil Kedia, Water Quality Monitoring for Rural Areas- A Sensor Cloud Based Economical Project, in 1st International Conference

on Next Generation Computing Technologies (NGCT-

2. 2015) Dehradun, India, 4-5 September 3. 2015. 978-1-4673-6809-4/15/$31.00

4. ©2015 IEEE

5. Jayti Bhatt, JigneshPatoliya, Iot Based Water Quality Monitoring System, IRFIC, 21feb,2016. 6. Michal lom, ondrejpriby&miroslavsvitek, Internet 4.0 as a part of smart cities, 978-1-5090-1116-2/16/$31.00 ©2016 IEEE

7. Zhanwei Sun, Chi Harold Liu, ChatschikBisdikia_, Joel W. Branch and Bo Yang, 2012 9th Annual IEEE Communications Society

Conference on Sensor, Mesh and Ad Hoc Communications and Networks 8. SokratisKartakis, Weiren Yu, Reza Akhavan, and Julie A. McCann, 2016 IEEE First International Conference on Internet-of-Things

Design and Implementation, 978-1-4673-9948-7/16 © 2016IEEE

9. MithailaBarabde, shrutiDanve, Real Time Water Quality Monitoring System, IJIRCCE, vol 3, June 2015. AkankshaPurohit, UlhaskumarGokhale, Real Time Water Quality Measurement System based on GSM , IOSR (IOSR-JECE) Volume 9, Issue 3, Ver. V

(May - Jun. 2014)

10. Niel Andre cloete, Reza Malekian and Lakshmi Nair, Design of Smart Sensors for Real-Time Water Quality monitoring, ©2016 IEEE conference.

11. Tiva C-Series

a. http://www.ti.com/lit/ds/symlink/tm4c123gh6pm.pdf 12. Energia Guide

a. http://energia.nu/ 13. NodeMcu interface and programming

14. http://www.nodemcu.com/index_en.html

15. HTML Guide 16. https://www.w3schools.com

24.

Authors: S. Vinodha, K. Thiruppathi, P. Lakshmi

Paper Title: Design and Implementation of Controller for Ph Process at Elevated Pressure

Abstract: This paper describes the modeling and control of a pH neutralization process and compares the

traditional, fuzzy logic and Genetic Algorithm (GA) optimization methods for the novel deep sea microbial

instrument at elevated pressure. National Institute of Ocean Technology (NIOT) has designed, developed and

patented a novel instrument to mimic deep sea conditions in laboratory for deep sea microbial exploration.

Controlling pH in the novel deep sea conditions mimicking laboratory system is complicated, because of high

salinity, temperature stimulus, high pressure operation, and its non-linearity. To address the pH control issues a

systematic real time experimental model was designed developed, implemented and analyzed. The simulation

results shows that the proposed controller technique is effective in tracking set point and has resulted in a

minimum value of the Integral Square Error, peak overshoot and minimum settling time as compared to

conventional methods. The experimental results show that the model accuracy and the GA and fuzzy logic

controller performance is superior then the other control methods and it matches favorably with the simulation

results.

Keyword: Fuzzy logic control, Genetic algorithm, Novel deep sea microbial system, Real time pH control. References: 1. Ziebis, W, James, M, Ferdelman, T, Schmidt, SF, Wolfgang, B, Muratli, J, Katrina, J, Edwards, H 2012, ‗Interstitial fluid chemistry of

sediments underlying the North Atlantic gyre and the influence of subsurface fluid flow‘, Earth and Planetary Science Letters, 323-324,

pp. 79–91.

2. Owens, JD, Nielsen, GS, Horner, TJ, Ostrander, CM, Peterson, LC 2017, ‗Thallium-isotopic compositions of euxinic sediments as a proxy for global manganese-oxide burial‘, Geochimica et Cosmochimica Acta 213, pp. 291–307.

3. Kroeker, KJ, Kordas, RL, Crim, R, Hendriks, IE, Ramajo,L, Singh ,GS, Duarte, CM & Gattuso, JP 2013,‘ Impacts of ocean acidification

on marine organisms: quantifying sensitivities and interaction with warming‘, Global Change Biology, vol. 19, pp. 1884–1896. 4. Yokogawa 2009, Technical note, Analytical, pH temperature compensation.

5. Niedrach, LW 1980, ‗A new membrane type pH sensor for use in high temperature-high pressure water‘, Journal of Electrochemical

Society, vol. 127, pp. 2122-2130. 6. Ziegler, G & Nichols, NB 1942, ‗Optimum settings for automatic controllers‘, Transaction of the ASME, vol. 64, pp. 759-768.

7. Cohen, GH & Coon, GA 1953, ‗Theoretical consideration of retarded control‘, Transactions ASME, vol. 75, pp. 827-834.

109-113

8. Aidan, OD 2000, ‗PID compensation of time delayed processes: a survey‘, Proceedings of the Irish Signals and Systems Conference-

Dublin, pp. 5-12.

25.

Authors: USHA.D, ANSLIN.J

Paper Title: Grey Water Treatment For Smart Cities Using Iot

Abstract: The paper proposes a grey water recycling system that will provide water to meet the needs of

the house and irrigation purpose around the house. The water can be used for cleaning and flushing purposes. The

grey water recycling system components were designed and they consist of piping system, diversion system,

Filtration and storing system. The project includes collection tank, filtration tanks and storage tank. The filtering

media used are Alum and Biosand.The filtered water is stored in storage tank and the overall process will be

controlled by Arduino Mega, monitored by IoT Technology.

Keyword: filtration, activated charcoal, Solenoid valve, Grey Water, recycle. References:

1. Niel Andre Cloete, Reza Malekian And Lakshmi Nair―Design of Smart Sensors for Real-Time Water Quality Monitoring ,‖IEEE

Access, volume 4,August 2016. 2. AlifSyarafi Mohamad Nor, Mahdi Faramarzi, MohdAmri Md Yunus, and SallehuddinIbrahim―Nitrate and Sulfate Estimations in

Water Sources Using a Planar Electromagnetic Sensor Array and Artificial Neural Network Method,‖ IEEE SENSORS JOURNAL,

VOL. 15, NO. 1, JANUARY 2015. 3. Theofanis P. Lambrou,Christos C. Anastasiou, Christos G. Panayiotou, andMarios M. Polycarpou―A Low-Cost Sensor Network for

Real-Time Monitoring and Contamination Detection in Drinking Water Distribution Systems , ‖ IEEE SENSORS JOURNAL,VOL.

14, NO. 8, AUGUST 2014 4. T. P. Lambrou, C. G. Panayiotou, and C. C.Anastasiou, ―A low-cost system for real time monitoring and assessment of potable water

quality at consumer sites,‖ in Proc. IEEE Sensors, Oct. 2012, pp1–4.

5. Pedro M. Ramos, J. M. Dias Pereira, Helena M. Geirinhas Ramos and A. Lopes Ribeiro―A Four-Terminal Water-Quality-Monitoring Conductivity Sensor‖,IEEE TRANSACTION SON INSTRUMENTATION AND MEASUREMENT, VOL. 57, NO. 3, MARCH

2008.

6. Louis COETZEE, Johan EKSTEEN, ―The Internet of Things – Promise for the Future? An Introduction,‖ ISTAfrica2011 Conference Proceedings, Paul Cunninghamand Miriam Cunningham (Eds), IIMC International Information Management Corporation, 2011, pp.

1-9.

7. E. Fleisch, What is the Internet of Things: An Economics Perspective, Auto ID Labs White Paper,WP-BIZAPP-053, Jan. 2010. 8. Zaigham Mahmood, ―Cloud Computing: Characteristics and Deployment Approaches,‖ 11th IEEE International Conference on

Computer and Information Technology, UK, 2011, pp. 121-126.

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26.

Authors: Dinesh Kumar. V, Manimekalai. V, Saravanakumar. S

Paper Title: PLC Based Fault Identification in Conveyors

Abstract: In today‘s world, the assembly rate has accrued staggeringly. Commonly, engineering industries

keep producing same models with slight distinction tall, colour, weight and form. And here fault identification

plays a major half. In such circumstances industries can‘t vacant golem errors. Therefore it's necessary to develop

mechanism for characteristic faults in these product in actual manner. Industrial automation primarily focuses on

developing automations having low value, low maintenance, long sturdiness and to create systems user friendly

as potential. Finally, here we tend to have developed a system for sorting the light-weight weight objects on the

basis of height variation mistreatment proximity sensors that is controlled by Programmable Logic Controller

(PLC) and the conveyor within the system passes the object in front of sensors and therefore fault identification is

completed.

Keyword: Automation, Programmable Logic Controller, Low value Automation, producing, Fault

identification. References:

1. Manjunatha ―Postal Automation System for Mail Sorting‖ International Journal of Emerging Technology and Advanced

Engineering (ISSN 2250-2459) Volume 5, Issue 3, March 2015)

2. Albert T. Jones, Charles R. McLean,‖A proposed hierarchical control model for automated manufacturing systems‖, National Bureau of Standards, Gaithersburg, Maryland, USA

3. Y V Aruna, Beena S ―Automatic convey or System with In–Process Sorting Mechanism using PLC and HMI System‖, Int.

Journal of Engineering Research and Applications ISSN: 2248-9622, Vol. 5, Issue 11, (Part - 3) November 2015, pp.37-4

4. Saurin Sheth,Rahul Kher, Rushabh Shah, Parth Dudhat, Pratyush Jani ―Automatic Sorting System Using Machine

vision‖, DOI: 10.13140/2.1.1432.1448 Conference: Multi-Disciplinary International Symposium on Control, Automation &

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Robotics, At DDIT, Nadiad, Volume: 1.

5. D. A. Wahab, A. Hussain, E. Scavino, M.M. Mustafa and H. Basri ―Development of a Prototype Automated Sorting System for Plastic Recycling‖ American Journal of Applied Sciences 3 (7): 1924-1928, 2006 ISSN 1546-9239 © 2006 Science

Publications

27.

Authors: S.Tamil Selvan, M.Sundararajan

Paper Title: Six Transistor Carbon Nanotube Field Effect Transistors Based RAM Design and

Hardware Description Language Code Development

Abstract: The goal of this thesis is to develop carbon nanotube field effect transistors (CNFETs) based

static random-access memory (SRAM) and implement it into a Very-highspeed integrated circuit Hardware

Description Language Analog and Mixed-Signal (VHDLAMS). To achieve this objective, a compact model of

the transistor known as enhancementmode MOSFET-like SWCNT-CNFET is used. This circuit-compatible

model of CNFET is described using VHDL-AMS and tested for basic electrical characteristics. This model is

valid for CNFETs with channel lengths greater than 20 nm. Based on the CNFETs a new SRAM is designed, and

implemented in VHDL-AMS. The performance of the proposed SRAM cell is investigated and compared with

SRAMs from conventional metal-oxide semiconductor field effect transistors (MOSFETs). The effect of substrate

biasing a CNFET is also demonstrated and implemented in designing the SRAM cell. The VHDL-AMS codes of

the CNFET and the SRAM are simulated in software known as Ansoft Simplorer. The compact model of the

CNFET is organized hierarchically in three main levels. The first level models the intrinsic channel just beneath

the gate of the transistor. The second level builds upon the first level and models the doped source and drain

regions of the CNFET. The last level represents the complete trans-capacitance model of the transistor and

accounts for multiple CNTs. The proposed SRAM cell is composed of four CNFETs and two load resistors. The

driver CNFETs of the proposed SRAM cell are substrate biased. Besides, 8-bit complete SRAM architecture

based on this cell is indicated. The performance analysis of the SRAM shows that it has better writing and reading

speed as well as better stability when compared with SRAM from conventional MOSFETs. Specifically, the

newly proposed SRAM cell has read time of twenty five pico seconds, write time of twenty pico seconds and can

tolerate a noise of 120 mV at 32 nm node technology.

Keyword: SRAM, 3 VL, CNTFET, CMOS, low power, highly stable. References:

1. R. Chau, S. Datta, M. Doczy, B. Doyle, B. Jin, J. Kavalieros, A. Majumdar, and M. Radosavlijevic, ―Benchmarking nanotechnology

for high-perfor-mance and low-power logic transistor ap-plications,‖ IEEE Trans. Nanotechnol., vol. 4, no. 2, pp. 153-158, Mar. 2005.

2. Jie Deng, ―Device modeling and circuit perfor-mance evaluation for nanoscale devices: silicon technology beyond 45 nm node and carbon nan-otube field effect transistors,‖ Dissertation, June. 2007.

3. J. Deng and H.-S. P. Wong, "A Circuit-Compatible SPICE model for Enhancement Mode Carbon Nanotube Field Effect Transistors,"

Proc. Intl. Conf. Simulation of Semiconductor Processes and Devices, pp. 166 - 169, Sept., 2006. 4. Amlani, et al., "First Demonstration of AC Gain From a Single-walled Carbon Nanotube Com-mon-Source Amplifier," Proc. Intl.

Electron De-vices Meeting, Paper 20.7, Dec., 2006.

5. Berkeley Predictive Technology Model website [Online]. Available: http://www eas.asu.edul-ptm/latest.html 6. Stanford University CNFET Model website [On-line]. Available: http://nano.stanford.edu/mod-el.php?id=23

7. J. Appenzeller, ―Carbon Nanotubes for High-Performance

8. Zaigham Mahmood, ―Cloud Computing: Characteristics and Deployment Approaches,‖ 11th IEEE International Conference on Computer and Information Technology, UK, 2011, pp. 121-126.

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28.

Authors: A Abisha, R Beulah Jayakumari, D Doreen Hephzibah Miriam

Paper Title: Drought Prediction using Geo-Spatial Big Data

Abstract: The digital world with digital processing, requires large storage space. The continuous explosion

of the data such as text, image, audio, video, data centers and backup data lead to several problem in both storage

and retrieval process. In this paper drought analysis and prediction is done using big data processing tools such as

Hadoop and hive which can increase high. Previously to analyze and predict drought, traditional techniques such

as AVISO model is used which is complex to process, requires more processing time, cannot process huge data

and also has more security issues like malware in the database, abuse of privileges, etc. The system proposed in

this paper can process huge data and has more processing speed. Here, drought analysis and prediction is carried

out. To analyze drought dataset with more than ten lakhs are processed and drought type is found using map-

reduce algorithm which maps and reduces the data using numerical summarization. Drought types such as D0,

D1, D2,D3 D4 are analyzed to obtain reduced output. The obtained drought type are clustered using hive. To

predict drought, random forest algorithm acts as an predictor which creates multiple decision trees and finds the

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best split among them. Finally, the predicted output is visualized using the time series model. The tools used in

this paper include Hadoop and hive which can process huge data and it is the solution of Big Data. Hadoop is an

open-source software framework for storing data and processing them efficiently, even if the data size is very

huge. Hadoop uses Hadoop Distributed File System(HDFS) for storage and MapReduce for processing the data.

Hive is a query processing tool which is built on top of Hadoop. It is a Structured query language(SQL)-like

language called HiveQL (HQL). In this paper hive is used to cluster the data obtained from MapReduce. Thus

using Big Data improves performance more than 50% compared to traditional system.

Keyword: Big Data, Hadoop, Hive, Random Forest. References:

1. Jinyoung Rheea, Jungho Imb, 2017. Meteorological drought forecasting for ungauged areas based on machine learning: Using

long-range climate forecast and remote sensing data.

2. Sruthi.S, M.A.Mohammed Aslam , 2015. Agricultural Drought Analysis Using the NDVI and Land Surface Temperature Data; a Case Study of Raichur District.

3. Muhammad Bilal ,Muhammad Usman Liaqat , Muhammad Jehanzeb Masud Cheema, Talha Mahmood and Qasim Khan ., 2017.

Spatial Drought Monitoring in Thar Desert Using Satellite- Based Drought Indices and Geo-Informatics Techniques. 4. Zengchao Hao , Fanghua Hao , Vijay P. Singh , Wei Ouyang , Hongguang Cheng 2017. An integrated package for drought

monitoring, prediction and analysis to aid drought modeling and assessment.

5. Brahim Habibia, Mohamed Meddib, Paul J.J.F. Torfsc, Mohamed Remaound, Henny A.J. Van Lanenc ., 2018. Characterisation and prediction of meteorological drought using stochastic models in the semi-arid Chéliff–Zahrez basin (Algeria).

6. Petr Št ˇepánek, Miroslav Trnka, Filip Chuchma , Pavel Zahradníˇcek, Petr Skalák, Aleš Farda , Rostislav Fiala , Petr Hlavinka , Jan

Balek , Daniela Semerádová and Martin Možný , 2018. Drought Prediction System for Central Europe and Its Validation. 7. Vicente-Serrano, S. M., S. Begueria, and J. I. Lopez-Moreno,2010. A multiscalar drought index sensitive to global warming: The

Standardized Precipitation Evapotranspiration Index. Journal of Climate, 23, pp. 1696-1718.

8. D. A. Wilhite, Drought: A Global Assessment, Natural Hazards and Disasters Series, Routledge, London, UK, 2000. 9. A. K. Mishra and V. R. Desai, ―Drought forecasting using stochastic models,‖ Stochastic Environmental Research and Risk

Assessment, vol. 19, no. 5, pp. 326–339, 2005. 10. Mishra AK, Singh VP. A review of drought concepts. Journal of Hydrology 2010; 391(1-2): 202–16.

doi:10.1016/j.jhydrol.2010.07.012.

11. Vernimmen RRE, Hooijer A, Aldrian E, van Dijk AIJM. Evaluation and bias correction of satellite rainfall data for drought monitoring in Indonesia. Hydrology and Earth System Sciences 2012; 16 (1): 133–46. doi:10.5194/hess-16-133-2012.

12. Alam, A.T.M.J., Rahman, M.S., Saadat, A.H.M., 2013. Monitoring meteorological and agricultural drought dynamics in Barind

region Bangladesh using standard precipitation index and Markov chain model. Int. J. Geomech. 3, 511e524. 13. Condra, G.E. Drought, Its Effects and Measures of Control in Nebraska; Nebraska Conservation Bulletin 25: Lincoln, NE, USA,

1944; 43p. 2.

14. Wilhite, D.A.; Buchanan, M. Drought as hazard: Understanding the natural and social context. In Drought and Water Crisis: Science, Technology and Management Issues; Wilhite, D.A., Ed.; CRC Press: New York, NY, USA, 2005; pp. 3–29.

15. Sholihah, R.I.; Bambang, H.; Shiddiq, D.; Panuju, D.R. Identification of agricultural drought extent based on vegetation health indices

of Landsat data: Case of Subang and Karawang, Indonesia. Procedia Environ. Sci. 2016, 33, 14–20. 16. Huang, C.J.; Zhao, S.Y.; Wang, L.C.; Shakeel, A.A.; Chen, M.; Zhou, H.F. Alteration in chlorophyll fluorescence, lipid peroxidation

and antioxidant enzymes activities in hybrid ramie (Boehmeria nivea L.) under drought stress. Aust. J. Crop Sci. 2010, 7, 594–599.

17. Sruthi, S.; Aslam, M.A.M. Agricultural Drought Analysis Using the NDVI and Land Surface Temperature Data; a Case Study of Raichur District. In Proceedings of the International Conference on Water Resources, Coastal and Ocean Engineering (Icwrcoe 2015),

Mangalore, Karnataka, India, 12–16 March 2015.

18. Bachmair, S., Stahl, K., Collins, K., et al., 2016. Drought indicators revisited: the need for a wider consideration of environment and society. Wiley Interdiscip. Rev. water 3 (4), 516e536.

19. Dutra, E., Magnusson, L., Wetterhall, F., et al., 2013. The 2010e2011 drought in the Horn of Africa in ECMWF reanalysis and

seasonal forecast products. Int. J. Climatol. 33 (7), 1720e1729. 20. Hao, Z., Hao, F., Singh, V.P., 2016c. A general framework for the multivariate multiindex drought prediction based on multivariate

Ensemble Streamflow Predictions (ESP). J. Hydrol. 539, 1e10.

29.

Authors: Anbumani M, Thenmalar S

Paper Title: A Robust Method to Detect Coverage Holes and Network Re-Establishment for Nb-Iot Enabled

Networks

Abstract: Sensing coverage with respect to WSN (Wireless Sensor Network) research has gained immense

consideration. Implementation of WSNs in the domain of IoT (Internet of Things) ensures consideration of IoT

features when thinking of sensing coverage. Both IoT and WSN are with respect to coverage hole metrics that

includes network re-establishment, link establishing, throughput etc. Implementing sensor nodes may result in the

issue of coverage holes if there is improper deployment of nodes. A coverage hole can exist in any region that is

being monitored at any instance due to various purposes. As a result detecting coverage hole stand as a prime

concern for gaining absolute coverage. Detecting coverage hole pose a big question for gaining high coverage in

the Wireless Sensor Networks. The problem of coverage and connectivity particularly in coverage deployment

strategy is analyzed for the weaker nodes and in case network design fails. The proposed algorithm referred to as

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‗Store and Forward technique' aids in identifying the coverage holes and network re-establishing in WSNs and

IoT. Hence with the help of Store and Forward technique, network re-establishing and network stability can be

performed in case of a coverage hole issue. The proposed technique is mostly employed in networks having

irregular connectivity, specifically within environments where mobility is high. It‘s also beneficial in conditions

with long transmission and variable delays, maximum error rates, or cases where there is no direct, end-to-end

link. The experiment output represents that the proposed healing method magnifies coverage rate with only a few

additional sensors in contrast to the related methods.

Keyword: wireless sensor network (wsn), internet of things (iot), quality of service (qos), coverage hole

detection, store and forward technique, network reestablishment, network reconfiguration References: 1. B. Pannetier, J. Dezert, and G. Sella, ―Multiple target tracking with wireless sensor network for ground battlefield surveillance,‖ in

Proceedings of the 17th International Conference on Information Fusion (FUSION ‘14), pp. 1–8, IEEE, 2014. 2. P. K. Sahoo, J.-P. Sheu, and K.-Y. Hsieh, ―Target tracking and boundary node selection algorithms of wireless sensor networks for

internet services,‖ Information Sciences, vol. 230, pp. 21–38, 2013.

3. C. Alippi, R. Camplani, C. Galperti, and M. Roveri, ―A robust, adaptive, solar-powered WSN framework for aquatic environmental monitoring,‖ IEEE Sensors Journal, vol. 11, no. 1, pp. 45– 55, 2011.

4. J. M. L. P. Caldeira, J. J. P. C. Rodrigues, and P. Lorenz, ―Toward ubiquitous mobility solutions for body sensor networks on

healthcare,‖ IEEE Communications Magazine, vol. 50, no. 5, pp. 108–115, 2012. 5. Li-Hui Zhao, Wenyi Liu, Haiwei Lei, Ruixia Zhang, and Qiulin Tan ―Detecting Boundary Nodes and Coverage Holes in Wireless

Sensor Networks‖,Hindawi Publishing Corporation, Mobile Information Systems, 2016, p.p. 1-16.

6. B. Huang,W.Wu, G. Gao, and T. Zhang, ―Recognizing boundaries in wireless sensor networks based on local connectivity information,‖ International Journal of Distributed Sensor Networks,2014, p.p.1-12.

7. Abdullahi B. Kunya, Gaddafi S. Shehu, Adamu Y. Ilyasu, Sunusi G. Mohammed ―Distribution Network Reconfiguration for Loss

Reduction and Voltage Profile Improvement using B-PSO‖, © Research gate, 2016. 8. Ma, H. C., Sahoo, P. K., & Chen, Y. W. ―Computational geometry based distributed coverage hole detection protocol for the wireless

sensor networks‖, Journal of Network and Computer Applications, 2011, p.p. 1743–1756.

9. Ammari, H. M., & Das, S. ―A study of k-coverage and measures of connectivity in 3D wireless sensor networks‖, IEEE Transactions on Computers, 59(2), 2010, p.p. 243–257.

10. Babaie, S., &SajadPirahesh, S. ―Hole detection for increasing coverage in wireless sensor network using triangular structure‖,

International Journal of Computer Science Issues, 9(1), 2012, p.p. 213–218. 11. Kang, Z., Yu, H., &Xiong, Q. ―Detection and recovery of coverage holes in wireless sensor networks‖, Journal of Networks, 8(4), 2013,

p.p. 822–828.

12. Kamran Latif, Nadeem Javaid, Ashfaq Ahmad, Zahoor Ali Khan, Nabil Alrajeh, Majid Iqbal Khan ―On Energy Hole and Coverage Hole Avoidance in Underwater Wireless Sensor Networks‖, © IEEE SENSORS JOURNAL, VOL. 16, NO. 11, 2016, P.P. 4431 – 4442.

13. C. Ma, J. He, H.-H. Chen, and Z. Tang, ―Coverage overlapping problems in applications of IEEE 802.15.4 wireless sensor networks,‖ in

Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), Apr. 2013, pp. 4364–4369. 14. P. K. Sahoo and W.-C. Liao, ―HORA: A distributed coverage hole repair algorithm for wireless sensor networks,‖ IEEE Trans. Mobile

Comput., vol. 14, no. 7, pp. 1397–1410, Jul. 2015.

15. Yunzhou Zhang, Xiaohua Zhang, Wenyan Fu, Zeyu Wang, and Honglei Liu ―HDRE: Coverage Hole Detection with Residual Energy in Wireless Sensor Networks‖, © JOURNAL OF COMMUNICATIONS AND NETWORKS, VOL. 16, NO. 5, 2014, p.p. 1-9.

16. P. Martins, F. Yan, and L. Decreusefond, ―Connectivitybased distributed coverage hole detection in wireless sensor networks,‖ in IEEE

Global Telecommunications Conference (GLOBECOM ‘11), pp. 1–6, 2011 17. S. Zeadally, N. Jabeur, and I. M. Khan, ―Hop-based approach for holes and boundary detection in wireless sensor networks,‖ IET

Wireless Sensor Systems, vol. 2, no. 4, pp. 328–337, 2012.

18. W.-C. Chu and K.-F. Ssu, ―Location free boundary recognition in mobile wireless sensor networks with a distributed approach,‖ Science Direct Computer Network, vol. 70, pp. 96–112, 2014.

19. Pearl Antil and Amita Malik ―Hole Detection for Quantifying Connectivity in Wireless Sensor Networks: A Survey‖, Hole Detection for

Quantifying Connectivity in Wireless Sensor Networks: A Survey, Hindawi Publishing Corporation Journal of Computer Networks and Communications, 2014, p.p. 1-11.

20. Wei Li, Wei Zhang ―Coverage hole and boundary nodes detection in wireless sensor networks‖, © Elsevier Ltd, Journal of Network and Computer Applications, 2014, p.p. 1-9.

21. Anju Sangwan, Rishi Pal Singh ―Coverage Hole Detection and Healing to Enhance Coverage and Connectivity in 3D Spaces for WSNs:

A Mathematical Analysis‖, Springer, Wireless PersCommun, 2017.

30.

Authors: K.Anuratha, M.Parvathy, S.Sujeetha, J.Ghayathri

Paper Title: Role of Social Sentiment Analysis in Stock Trends Forecasting

Abstract: Social media like Face book, Twitter have attracted attention from various sectors of study in

recent years. Most of the people share ideas, opinions on various topics such as Stock Market Prediction, Digital

marketing, Movie review, Election Results Prediction and Product reviews etc,. Forecasting Financial Market is

considered to be one of the significant applications of Sentiment Analysis on Social Data like Face book, Twitter.

It is essential to accurately predict the movements in stock trends, as the stock market trends are volatile. In the

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past few years several researches have been carried out for predicting the future trends of stock market through

sentiment analysis on social media comments. This paper gives the survey on the various techniques, tools and

methodologies adopted by several researchers on Stock Market Prediction based on sentiment analysis of Social

networks.

Keyword: Stock Prediction, Twitter, Sentiment Analysis, Classifiers, Accuracy, Deep Learning References:

1. W.Long, Z. Lu and L. Cui, Deep learning-based feature engineering for stock price movement prediction, Knowledge- Based Systems (2018)

2. Dang Lein Minh, Abolghasem Sadeghi Niaraki , Huynh Duc Huy, Kyungbok Min and Hyeonjoon Moon,Deep Learning Approach

For Short-Term Stock Trends Prediction Based On Two-Stream Gated Recurrent Unit Network (2018) 3. Jiahong Li,Hui Bu,Junjie Wu,‖Sentiment-aware stock market prediction:A deep Learning method‖14th InternationaLConference on

Service Systems and Service Management,June 2017

4. X. Zhang, S. Qu, J. Huang, B. Fang and P. Yu, "Stock Market Prediction via Multi-Source Multiple Instance Learning," in IEEE Access, vol. 6, pp. 50720-50728, 2018.

5. Xiaodong Li, Haoran Xie, Raymond Y. K. Lau, Tak- Lam Wong, Fu-Lee Wang, "Stock Prediction via Sentimental Transfer

Learning", Access IEEE, vol. 6, pp. 73110-73118, 2018. 6. Jiahong Li, Hui Bu and Junjie Wu, "Sentiment-aware stock market prediction: A deep learning method," 2017 International

Conference on Service Systems and Service Management, Dalian, 2017, pp. 1-6.

7. Oliveira, Nuno et al. ―Stock market sentiment lexicon acquisition using microblogging data and statistical measures.‖ Decision Support Systems 85 (2016): 62-73.

8. L. Zhang, L. Zhang, K. Xiao and Q. Liu, "Forecasting price shocks with social attention and sentiment analysis," 2016 IEEE/ACM

International Conference on Advances in Social Networks Analysis and Mining 9. Qian Li, Bing Zhou and Qingzhong Liu, "Can twitter posts predict stock behavior?: A study of stock market with twitter social

emotion," 2016 IEEE InternationalConference on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, 2016, pp. 359-

364. 10. Nguyen T.H and K.Shirai, 2015 ,Topic Modeling Based Sentiment Analysis on social media for stock market prediction .Proceedings

of the 7th International Joint Conference on Natural Language Processing and 53rd Annual Meeting on Association for Computational

Linguistics Vol.1, July 26 – 31, 2015, Association for Computational Linguistics,Vancouver,Canada,pp:154- 1364 11. Nofer, M. & Hinz, O. Bus Inf Syst Eng (2015) 57: 229.

12. Y. E. Cakra and B. Distiawan Trisedya, "Stock price prediction using linear regression based on sentiment analysis," 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS), Depok, 2015, pp. 147-154.

13. C. Chen, W. Dongxing, H. Chunyan and Y. Xiaojie, "Exploiting Social Media for Stock Market Prediction with Factorization Machine," 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT),

Warsaw, 2014, pp. 142-149.

14. F. Xu and V. Keelj, "Collective Sentiment Mining of Microblogs in 24-Hour Stock Price Movement Prediction," 2014 IEEE 16th Conference on Business Informatics, Geneva, 2014, pp. 60-67.

15. Porshnev .A,I.Redkin and A.Shevchenko,2013.Machine Learning in prediction of stock market indicators based on historical data and

data from Twitter Sentiment Analysis. Proceedings of the 2013 IEEE 13TH International Conference on Data Mining Workshops (ICDMW,2013),December 7-10,2013 IEEE,Dallas,Texas,pp:440 – 444

16. Z. Chen and X. Du, "Study of Stock Prediction Based on Social Network," 2013 International Conference on Social Computing,

Alexandria, VA, 2013, pp. 913-916. 17. Al Augby,S.H.,2015 Text Mining method in evaluation of media‘s impact on market value ratios.Ph.D thesis,University of Szczecin,

Szczecin ,Poland

18. D.Duong ,T.Nguyen,M.Dang, ―,Stock Market Prediction using Financial News Articles on Ho Chi Minh Stock Exchange‖-Proceedings of 10th International Conference on Ubiquitous Information management and communication, 2016 ,Article No

71.

19. Al-Augby,Noor Al-Musawi and Abdul Hussein Mezher,Stock Market Prediction Using Sentiment Analysis Based on Social Network:Analytical Study,Journal of Engineering and Applied Sciences,2018,pp:2388 - 2402

31.

Authors: Aruljothi R, Maya Eapen

Paper Title: Booster In High Dimensional Data Classification Using Cnn And Decision Tree Algorithm

Abstract: Classification problems in high dimensional data with small number of observations are

becoming more common especially in microarray data. The performance in terms of accuracy is essential while

handling sensitive data particularly in medical field. For this the stability of the selected features must be

evaluated. Therefore, this paper proposes a new evaluation measure that incorporates the stability of the selected

feature subsets and accuracy of the prediction. Booster in feature selection algorithm helps to achieve the same.

The proposed work resolves both structured and unstructured data using convolution neural network based

multimodal disease prediction and decision tree algorithm respectively. The algorithm is tested on heart disease

dataset retrieved from UCI repository and the analysis shows the improved prediction accuracy.

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Keyword: Feature Selection, Micro Array, Structured Data, Un-structured Data. References:

1. BissanGhaddar, JoeNaoum-Sawaya, ―High dimensional data classification and feature selection using support vector machines‖. 2. HyunJi Kim, Byong Su Choi and Moon Yul Huh, ―Booster in high dimensional data classification.‖.

3. E. Kokilamani, Dr. R. Gunavathi, ―A Survey on Boosting High dimensional Feature Selection Classification‖.

4. K. Sai Sravani, Dr. P. Kiran Sree, ―Efficient Technique for Classifying High-Dimensional Data‖. 5. Shubhangi N. Katole; Swapnili P. Karmore, ―A new approach of microarray data dimension reduction for medical applications‖

6. Vikas Chaurasia, Saurabh Pal, ―Data Mining Approach to Detect Heart Diseases‖.

7. https://archive.ics.uci.edu/ml/datasets/%20heart+Disease 8. Saad Albawi ; Tareq Abed Mohammed ; Saad Al-Zawi,Understanding of a convolutional neural network

9. Linna Li ; Xuemin Zhang,Study of data mining algorithm based on decision tree

32.

Authors: Manoj Sai, E. Bijolin Edwin, Mani Arun, Santhosh Paul, Sudarshan

Paper Title: Train Collision Avoidance & Crack Detection Using GPS

Abstract: In this day‘s people are using different transport systems from one place to another place.

Among all the transportation people prefer public transport as their choice for safer journey. So the transportation

department takes several checks to measure the safety of the people. The proposed system is for identify and

avoid the cracks in railway tracks and collision in-order to prevent the accidents. In this paper to use crack

detection and ZIGBEE sensor this will be placed in the train engine. By this if any train comes in the same track

or if some crack is detected on the crack the rain starts to slow down and stop at respective point automatically

and the place where the crack would be given to control room. In the train collision avoidance system if both the

trains are in the same track the both ZIGBEE sensor senses the same signal from opposite train then it

automatically applies break and stop the train at certain distance. The proposed system introduces trans receiver

based technology, to prevent the trains accident. The ZIGBEE device is installed at each front-end of the

locomotive. The main idea of the work is to avoid the train accidents and reduce the manual power.

Keyword: GSM modem, GPS, Train positioning, Microcontroller, IR crack sensors, ZIGBEE. References: 1. Aamir Shaikh,Siraj Pathan " Research on Wireless Sensor Network Technology " in International Journal of Information and Education

Technology, October 2012.

2. Chengbo YU, Yanfei LIU, Cheng WANG "Research on ZigBee Wireless Sensors Network Based on ModBus Protocol " Published Online April 2009 in SciRes.

3. Dr.S.S.Riaz Ahamed "THE ROLE OF ZIGBEE TECHNOLOGY IN FUTURE DATA COMMUNICATION SYSTEM " in Journal of

Theoretical and Applied Information Technology 2009. 4. Arun.P, Saritha.S, K.M.Martin, Madhukumar.S ―Simulation of zigbee based TACS for collision detection and avoidance for railway

traffic., ―in International conference on advanced computing & communication technologies for high performance application, paper ID

51,June 2012. 5. ―Communication Systems‖ by Simon Hawkins.

6. Jennic, JN-AN-1059 Deployment guidelines for IEEE 802.15.4/ZigBee wireless networks, 37-38, 2007

7. D.Roychoudary and Sail Jain‖L.I.C‖, New Age International. 8. Kenneth.J.Ayala‖The 89C51 Microcontroller Architecture programming and Applications‖, Pen ram International.

9. Ramesh.S, ―Detection of Cracks and Railway Collision Avoidance System,‖ International Journal of Electronic and Electrical

Engineering, Volume 4, Number 3, pp. 321-327, 2011. 10. Richard j.Greene ,john R.Yates and Eann A.Patterson,‖Crack detection in rail using infrared methods‖,Opt.Eng.46,051013,May 2007.

11. Sonia Shah, Ravi Mishra ―Train Positioning and Crack Detecting System‖ in International Journal of Scientific Research in Science,

Engineering and Technology March-April 2016. 12. Parneet Dhillon ,Dr. Harsh Sadawarti "A Review Paper on Zigbee Standard" in International Journal of Engineering Research &

Technology , April 2014.

13. Christeena Joseph ,A.D.Ayyappan , A.R.Aswini, B.Dhivya Bharathy "GPS/GSM Based Bus Tracking System " in International Journal of Scientific & Engineering Research,December-2013.

153-157

33.

Authors: Lekshmipriya S B, G Geetha

Paper Title: Insider threats and Insider Intrusion Detection

Abstract: this survey paper narrates insider threats and their detection types and methods. Insider threats are

emerging nowadays, it is important to identify these threats as they are generating critical problems to the system.

This paper pays particular attention to the categories of threats and different types of detection methods. Based on

different strategies, statistical and machine learning methods for detecting these threats, are identified and

summarized here.

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Keyword: Security, Insider threats, IDS. References:

1. Jennifer U Mills, Steven M F Stuban, Jason Dever ―Predict insider threats Using Human behaviours‖ in IEEE Engineering Management Review, Vol-45, No.1 First quarter 2017

2. Anton, S., I. SimonaTUTUIANU. 2015. The complex and dynamic nature of the security environment. Proc. International Scientific

Conf. Strategies XXI. 3. Bensing, R. G. 2009. An Assessment of Vulnerabilities for Ship-based Control Systems Naval Postgraduate School, Monterey, CA.

4. M. Bhuyan, D. Bhattacharyya, and J. Kalita, ―Network anomaly detection: Methods, systems and tools,‖ IEEE Commun. Surv. Tuts.,

vol. 16, no. 1, pp. 303–336, First Quart. 2014. 5. M. Bishop, D. Gollmann, J. Hunker, and C. W. Probst, ―Countering insider threats,‖ in Dagstuhl Seminar Proceedings 08302, 2008,

pp. 1–18

6. Mouna Jouini, Latifa Ben Arfa Rabai, Anis Ben Aissa,‖Classification of insider threats in information security system‖, 5th International Conference on Ambient systems, Networks and Technologies.Procedea Computer science 32(2014) 489-496

7. ―Threat Modeling in Security Architecture – The Nature of Threats‖, Lukas Ruf, Consecom AG, Anthony Thorn, ATSS GmbH,

Tobias Christen, Zürich Financial Services AG, Beatrice Gruber, Credit Suisse AG, Roland Portmann, Hochschule Luzern ISSS working group of society architecture

8. Swiderski F, Snyder W,‖Threat modeling‖, Microsoft press, 2004

9. Liu Liu1, Olivier De Vel2, Qing-Long Han1, Jun Zhang1, and Yang Xiang1,‖ Detecting and Preventing Cyber Insider Threats: A

Survey‖, IEEE COMMUNICATIONS SURVEY & TUTORIALS-2018

10. Chandola, A. Banerjee, and V. Kumar, ―Anomaly detection: A survey,‖ ACM computing surveys (CSUR), vol. 41, no. 3, p. 15, 2009.

11. Kandias, A. Mylonas, N. Virvilis, M. Theoharidou, and D. Gritzalis, ―An insider threat prediction model,‖ in International Conference on Trust, Privacy and Security in Digital Business. Springer, 2010, pp. 26–37.

12. M. Hutchins, M. J. Cloppert, and R. M. Amin, ―Intelligencedriven computer network defense informed by analysis of adversary

campaigns and intrusion kill chains,‖ Leading Issues in Information Warfare & Security Research, vol. 1, p. 80, 2011. 13. Dinil Mon Divakaran, ―Insider threat detection and its future directions‖, Article in International Journal of Security and Networks ·

December 2016

14. Cappelli, A. Moore, R. Trzeciak, and T. J. Shimeall, ―Common sense guide to prevention and detection of insider threats 3rd edition–version 3.1,‖ CERT, Software Engineering Institute, Carnegie Mellon University, Tech. Rep., 2009

15. Cappelli, A. P. Moore, and R. Trzeciak, ―The CERT Guide to Insider Threats: How to Prevent, Detect, and Respond to Information

Technology Crimes, 1st ed. Addison-Wesley Professional, 2012. 16. CERT Insider Threat Team, ―Unintentional Insider Threats: Social Engineering,‖ Carnegie Mellon Univ, vol. CMU/SEI-20, no.

January, 2014.

17. Duncan, S. Creese, and M. Goldsmith, ―An overview of insider attacks in cloud computing,‖ Concurr. Comput. Pract. Exp., 2014 18. Antonia Nisioti, Alexios Mylonas, Member, IEEE, Paul D.Yoo, Senior Member, IEEE, Vasilios Katos, Member, IEEE From

Intrusion Detection to Attacker Attribution: A Comprehensive Survey of Unsupervised MethodsIEEE survey and tutorials,2018

19. DETECTION, I. (2002). Intrusion detection: a brief history and overview.

20. Laskov, P., Düssel, P., Schäfer, C., & Rieck, K. (2005, September). Learning intrusion detection: supervised or unsupervised?. In

International Conference on Image Analysis and Processing (pp. 50-57). Springer Berlin Heidelberg.

21. ―Insider Threat Detection Using Graph-Based Approaches‖, William Eberle Lawrence Holder, Cyber security Applications & Technology Conference For Homeland Security,IEEE2009

22. Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2016). A multi-step outlier-based anomaly detection approach to network-wide

traffic. Information Sciences, 348, 243-271. 23. Kim, Gisung, Seungmin Lee, and Sehun Kim. "A novel hybrid intrusion detection method integrating anomaly detection with misuse

detection." Expert Systems with Applications 41.4 (2014): 1690-1700.

24. ―A Hybrid Network Intrusion Detection Technique Using Random Forests‖, Jiong Zhang, Mohammad Zulkernine,ARES ‘06 Proceedings of the First international conference on Availability, Reliability and security,IEEE Computer security Society

Washington,DC,USA,2006

25. N. T. Nguyen, P. L. Reiher, and G. H. Kuenning, ―Detecting insider threats by monitoring system call activity,‖ in IAW. Citeseer, 2003, pp. 45–52

26. A. E. Ahmed and I. Traore, ―Anomaly intrusion detection based on biometrics,‖ in Proceedings from the Sixth Annual IEEE SMC

Information Assurance Workshop. IEEE, 2005, pp. 452–453. 27. Y. Song, M. B. Salem, S. Hershkop, and S. J. Stolfo, ―System level user behavior biometrics using fisher features and Gaussian

mixture models,‖ in Security and Privacy Workshops (SPW). IEEE, 2013, pp. 52–59. 28. J. Zhang, Y. Xiang, Y. Wang, W. Zhou, Y. Xiang, and Y. Guan, ―Network traffic classification using correlation information,‖ IEEE

Transactions on Parallel and Distributed Systems, vol. 24, no. 1, pp. 104–117, 2013

29. A. Tuor, S. Kaplan, B. Hutchinson, N. Nichols, and S. Robinson, ―Deep learning for unsupervised insider threat detection in structured cyber security data streams,‖ in AI for Cyber security Workshop at AAAI, 2017

30. M. Hanley and J. Montelibano, ―Insider threat control: Using centralized logging to detect data exfiltration near insider termination,‖

DTIC Document, Tech. Rep., 2011 31. IElise T Axelrad,Paul J.Sticha,Oliver Brdiczka,Jianqiang Shen―A Bayesian network model for predicting insider threats,‖IEEE

security and privacy workshop,2013

32. Ameya Zanzgiri, DipankarDasgupta, ―Classification of Insider Threat Detection Technique‖ Conference‘CISR‘16, April 5–7, 2016, Oak Ridge, TN, USA. Copyright 2016 ACM

33. Hastie, T., Tibshirani, R., and Friedman, J. 2008, The Elements of Statistical Learning (2nd edition). SpringerVerlag

34. Ted E. Senator, Henry G. Goldberg, Alex Memory‖ Detecting Insider Threats in a Real Corporate Database of Computer Usage Activity‖ACM, 2013

35. L. Spitzner, ―Honeypots: catching the insider threat,‖ 19th Annu. Comput. Secur. Appl. Conf., no. Acsac, 2003

36. Abdul Aziz Almehmadi, Khalil-El-Khatib, ―On the Possibility of Insider Threat Detection Using Physiological Signal Monitoring‖SIN‘14 Proceedings of the 7th International Conference on Security of Information and Networks

34.

Authors: P.Mahalakshmi, G.Geetha

Paper Title: IOT Security Threats and Block chain based solutions

Abstract: The Internet of Things (IoT) is one of the important technologies that has taken the attention of

researchers. It is the interconnection of things connected with each other and to also with humans, to achieve

some goals. In future IoT is expected to be effortlessly integrated into our environment and human will be solely

dependent on this technology for comfort and easy life style. Any security concern of the system will directly

affect human life. So security and privacy of this technology is primarily important issue to resolve. In this paper,

we discuss the threats and vulnerabilities to the security of IoT devices in different domains, different layers, its

deployment architecture and provides possible Block chain solution to overcome these issues. The paper also

analyzes how the Block chain technology can be used to provide security and privacy in IoT.

Keyword: Block chain, Internet of Things, Security attacks

References: 1. MehiarDabbagh and AmmarRayes, ―Internet of Things Security and Privacy‖ Oct 2017 2. Minhaj Ahmad Khana, KhaledSalah ,―IoT security: Review, blockchain solutions, and openchallenges‖ Zakariya University

Multan, Pakistan Khalifa University of Science, Technology & Research, Sharjah, United Arab Emirates,2018 in IEEE Access.

3. Ali Dorri, Salil S. Kanhere, and Raja Jurdak―Block chain in Internet of Things: Challenges and Solutions "

4. Internet of Things Security, Device Authentication and Access Control: A Review

5. G.Noubir, G.Lin,Low-power DoS attacks in data wireless LANs and countermeasures, SIGMOBILE Mob.Comput. Commun Rev. 7(3) (2003).

6. S.H.Chae, W.Choi,J.H.Lee, T.Q.S.Quek, Enhanced secrecy in stochastic wireless networks: Artificial noise with secrecy

protected zone, Trans.Info for. Sec .9(10)(2014)1617 1628.http://dx.doi.org/10.1109/ TIFS.2014.2341453. 7. W. Xu, T. Wood, W. Trappe, Y. Zhang, Channel surfing and spatial retreats: Defenses against wireless denial of service,

in:Proceedings of the 3rd ACM Workshop on Wireless Security, WiSe ‘04, ACM, New York, NY, USA, 2004, pp.80–89.

http://dx. doi.org/ 10.1145/ 1023646. 1023661.

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Advanced Computer Research (IJACR) 3, no. 2 (2013): 10. 16. A. Mitrokotsa, M. R. Rieback, and A. S.Tanenbaum,"Classification of RFID attacks." Gen 15693 (2010): 14443.

17. D. Wu, and G. Hu, "Research and improve on secure routing protocols in wireless sensor networks." In Circuits and Systems for

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Nanjing University of Posts and Telecommunications (Natural Science), vol. 30, no. 4, Aug 2010.

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35. Authors: J Shiny Duela, M Roshni Thanka, Bijolin Edwin

Paper Title: Improving the Reliability of Web Based Result Query Systems during High Traffic Periods

Abstract: Displaying of examination results by a single central entity, for lakhs of students becomes a

tedious task, and sometimes may also result in server crashing. These servers typically rely on heavy and often

unrestricted threads spawned to handle each incoming request which is the reason why the server resources are

used up quickly. We propose a solution that is three fold: First, multiple Volunteer entities are brought in to hold

the data and donate a portion of their computing power to offload the enormous work placed on the central entity.

Second, the central entity is changed to play the role of dispatcher that generates monitors and assigns extremely

lightweight, independent processes (called agents) to each user request without requiring any additional hardware

upgrade. Each agent will be responsible to satisfy their assigned user requests. Third, we introduce a load

balancing technique derived from the ideas of autonomous agents load balancing techniques in cloud to provide

load balancing among the Volunteer entities and the central entity such that the Volunteer entities can continue

with its own tasks and not be overwhelmed by its Volunteer work while ensuring fast response time and better

reliability and response to the user.

Keyword: servers, concurrency, threading, actors, event-driven References:

1. Singh, Aarti, Dimple Juneja, and Manisha Malhotra. "Autonomous agent based load balancing algorithm in cloud computing."

Procedia Computer Science 45 (2015): 832-841.

2. Lightner, Scott, and Franz Weckesser. "Fault tolerant architecture for distributed computing systems." U.S. Patent No. 9,201,744. 1 Dec. 2015.

3. Chechina, Natalia, et al. "Evaluating scalable distributed Erlang for scalability and reliability." IEEE Transactions on Parallel and

Distributed Systems (2017). 4. Vernon, Vaughn. Reactive Messaging Patterns with the Actor Model: Applications and Integration in Scala and Akka. Addison-

Wesley Professional, 2015.

5. Dobale, Ms Radha G., and R. P. Sonar. "Load balancing in cloud." International Journal of Engineering Research and General Science 3.3 (2015): 160-167.

6. Ariharan, V., and Sheeja S. Manakattu. "Neighbour Aware Random Sampling (NARS) algorithm for load balancing in Cloud computing." Electrical, 7. Computer and Communication Technologies (ICECCT), 2015 IEEE International Conference on. IEEE,

2015.

7. Rajan, Hridesh. "Capsule-oriented programming." Proceedings of the 37th International Conference on Software Engineering-Volume 2. IEEE Press, 2015.

8. Hao, Benjamin Tan Wei. "The Little Elixir & OTP Guidebook." (2016).

9. Agha, Gul A. Actors: A model of concurrent computation in distributed systems. No. AI-TR-844. MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB, 1985.

10. Ritson, Carl G., Adam T. Sampson, and Frederick RM Barnes. "Multicore scheduling for lightweight communicating processes."

Science of Computer Programming 77.6 (2012): 727-740. 11. Leung, Joseph Y-T., and M. L. Merrill. "A note on preemptive scheduling of periodic, real-time tasks."Information processing letters

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36.

Authors: N Vanitha, C R Rene Robin

Paper Title: Segmentation of Tropical Cyclone Eye Using Satellite Infrared Images

Abstract: The tropical cyclones are destructive weather systems and are known for their devastating

effects during landfall. Cyclone tracking is one of the important tasks for the meteorologist. The eye of the

tropical cyclone is the most remarkable feature. The eye of the cyclone is the roughly circular area extending over

30 - 65 km in diameter. The deepest convection is found around the eyewall for some tens of kilometers. The eye

grows deeper when the cyclone becomes heavy and the winds speed grows high. In this study, the data from the

1995 - 2016 of the CIRA imagery for the tropical cyclone of the Bay of Bengal basin is analyzed and the model is

developed to determine the eye of the cyclone. The segmented eye features are fed into the Rule Based Classifier

which classifies the tropical cyclone images based on the presence and absence of the eye.

Keyword: segmentation, image processing, classification References:

1. U C Mohanty, ―Tropical cyclone in the Bay of Bengal and deterministic Method for Prediction of their trajectories‖, Springer,

Sadhana, vol. 19, pp. 567 - 582, 1994. 2. Ka Yan Wong, Chi Lap Yip and Ping Wah Li, ―A novel algorithm for automatic tropical cyclone eye fix using Doppler radar data‖ ,

RMets, Meteorological Applications, vol. 14, pp. 49-59, 2007.

3. Ka Yan Wong, Chi Lap Yip and Ping Wah Li, ―Automatic tropical cyclone eye fix using genetic algorithm‖, Elsevier, Expert Systems

181-185

with Applications, vol. 34, pp.643 - 656, 2008.

4. Xuezhu Lv ; Xiaofeng Li ; Xiaofeng Yang ; William Pichel ; Xuan Zhou ; Yuguang Liu, ―The impact of vertical wind shear on the hurricane eye tilt at the sea and cloud levels‖ , 2013 - IEEE International Geoscience and Remote Sensing Symposium - IGARSS,

IEEE, pp. 566 - 569.

5. Y. Cheng, S. Huang, A.K. Liu, C. Ho, and N. Kuo, ―Observation of typhoon eyes on the sea surface using multi-sensors‖, Remote Sensing of Environment, 123, 434-442, 2012.

6. Jyotshna, Dongardive, Agnes Xavier, Kavita Jain and Siby Abraham, ―Classification and Rule-Based Approach to Diagnose

Pulmonary Tuberculosis‖, International Conference on Advances in Computing and Communications, Springer, pp. 328 -339.

37.

Authors: J. Vijayalakshmi, K. PandiMeena

Paper Title: Agriculture TalkBot Using AI

Abstract: Artificial Intelligence and Machine Learning are driving IT industry to new landscape. This

system ―The TalkBot‖ overcomes this problem and provides farmers the better opportunity to obtain the desired

information and to scale up with upcoming market trends and technologies in a user friendly manner. TalkBot is

actually a chatbot, which is a virtual conversational assistant, through which the users can communicate with the

bot as if they are conversing with humans. The focus is on developing the bot in a more intellectual way, that it

can even recognize not so well grammatically defined sentences, misspelled words, incomplete phrases, etc.,.

This can help people to converse easily with the bot, since this system uses the Natural Language Processing

technique to parse the user queries, identify the key words, match them with Knowledge Base and respond with

the accurate results. To make the responses more understandable, the responses are generated using classification

algorithms and produce non textual responses so that it can be easily perceived by the users. Bot also has an

ability to provide voice oriented responses using text to speech techniques..

Keyword: Classification algorithms, Knowledge Base, Machine Learning, Natural Language Processing,

TalkBot. References:

1. J. Bang, H. Noh, Y. Kim and G. G. Lee, "Example-based chat oriented dialogue system with personalized long-term memory," 2015

International Conference on Big Data and Smart Computing (BIGCOMP), Jeju, 2015. 2. E. Haller and T. Rebedea, "Designing a Chat-bot that Simulates an Historical Figure," 2013 19th International Conference on Control

Systems and Computer Science, Bucharest, 2013.

3. S. J. du Preez, M. Lall and S. Sinha, "An intelligent web-based voice chat bot," EUROCON 2009, EUROCON '09. IEEE, St.-Petersburg, 2009.

4. Y. Chen, W. Wang and Z. Liu, "Keyword-based search and exploration on databases," 2011 IEEE 27th International Conference on

Data Engineering, Hannover, 2011. 5. B. K. Kim, J. Roh, S. Y. Dong, and S. Y. Lee, ―Hierarchical committee of deep convolutional neural networks for robust facial

expression recognition,‖ Journal on Multimodal User Interfaces, pp. 1-17, 2016.

6. L. Chao, J. Tao, M. Yang, Y. Li, and Z. Wen, ―Audio Visual Emotion Recognition with Temporal Alignment and Perception Attention,‖ arXiv preprint arXiv:1603.08321, 2016.

7. H. Lee, Y. S. Choi, S. Lee, and I. P. Park, ―Towards unobtrusive emotion recognition for affective social communication,‖ In proc. Of 2012 IEEE Consumer Communications and Networking Conference, pp. 260-264, 2012.

8. M. Wöllmer, F. Weninger, T. Knaup, B. Schuller, C. Sun, K. Sagae, and L. P. Morency, ―Youtube movie reviews: Sentiment analysis

in an audio-visual context,‖ IEEE Intelligent Systems 28(3), pp. 46-53, 2013. 9. A. Hommersom, P. J. Lucas, M. Velikova, G. Dal, J. Bastos, J. Rodriguez, M. Germs, and H. Schwietert, ―Moshca-my mobile and

smart healthcare assistant,‖ In proc. of e-Health Networking, Applications & Services (Healthcom), pp. 188-192, 2013.

10. R. J. Vidmar. (1992, August). On the use of atmospheric plasmas as electromagnetic reflectors. IEEE Trans. Plasma Sci. [Online].

186-190

38.

Authors: P.Karthikeyan, S.Yuvaraja, R.Kumarasubramanian, G. Sanjeeth, K. Sai Karthik

Paper Title: Characteristics Of Poultry Litter Biodiesel With Magnalium And Cobalt Oxide As A Additives

Abstract: The importance of bio diesel in CI engine has substantiated, the recent research has been

motivated on the use of different Nano materials as additives in diesel engines. The present investigation is to

study the performance and emission characteristics of a single cylinder direct injection CI engine using

transesterified Poultry litter(PL) biodiesel blend with and without Cobalt oxide and Magnalium nanoparticles as

additives. This biodiesel blends with diesel, and biodiesel-diesel-nanoparticles with each and both the nano

additives are tested in CI engine with constant speed of 1600 rpm with variation loads low to high. The

performance parameters like Brake power, Brake specific energy consumption, Brake specific fuel consumption

and efficiency of both Mechanical and volumetric are measured by VCR engine setup, emission characteristics

like NO2, CO, UBHC are measured by GAS ANALYSER these results are compared with pure diesel or neat

diesel. The NO emissions gradually reduced for B20 Co3O4 Al-mg test fuel with percentage of 7.5% to the diesel

191-195

and B20 to 11% for the diesel. Under maximum load of 10%, observed that there is improvement in Brake

thermal efficiency for B20Al-Mg 30ppm Co3O4 30ppm and followed by 9% improvement in Brake thermal

efficiency for B20 Co3O4 30ppm and followed by 4% increase in biodiesel test fuel, compared to diesel.

Keyword: Mechanical and volumetric are measured by VCR engine setup, emission characteristics like NO2,

CO References:

1. R.D. Misra, Straight vegetable oils usage in a compression ignition engine, Renew. Sustain. Energy Rev. 13 (2010). 2. Agarwal AK, Gupta JG, Dhar A. Potential and challenges for large-scale application of biodiesel in automotive sector. Prog Energy

Combust Sci 2017;61:113–49.

3. M. N. Nabi, M. M. Rahman, M. S. Akhter, Biodiesel from cotton seed oil and its effect on engine performance and exhaust emissions, Applied Thermal Engineering. 29 (2009) 2265–2270

4. K. Nantha Gopal, Karupparaj R. Thundil, Effect of pongamia biodiesel on emission and combustion characteristics of DI compression

ignition engine, Ain Shanms Eng. J. (2014). 5. Yang H, et al. Performance and emissions analysis of a diesel engine directly fuelled with waste cooking oil biodiesel. Int J Ambient

Energy 2016;38(4):428–34.

6. Suresh S, Sinha D, Murugavelh S. Biodiesel production from waste cotton seed oil: engine performance and emission characteristics. Biofuels 2016;7(6):689–98.

7. Al-Hasan MI. Biodiesel production from waste frying oil and its application to a diesel engine. Transport 2013;28(3):276–89.

8. Shaafi T, et al. Effect of dispersion of various nanoadditives on the performance and emission characteristics of a CI engine fuelled with diesel, biodiesel and blends. Renewable and Sustainable Energy Reviews, 49(2015)563–573.

9. Vijay Kumar M, et al. The impacts on combustion, performance and emissions of biodiesel by using additives in direct injection

diesel engine, Alexandria Engineering Journal (2018) 57, 509–516. 10. G. Karavalakis, D. Hilari, L. Givalou, D. Karonis, S. Stournas, Storage stability and ageing effect of biodiesel blends treated with

different antioxidants, Energy 36 (2011) 369–374.

11. Selvan V A M, Anand R B, Udayakumar M. Effectsof cerium oxide nanoparticle addition in diesel and diesel–biodiesel–ethanol blends on the performance and emission characteristics of a CI engine.ARPNJEngApplSci2009;4:1–6.

12. Basha JS,AnandRB.AnexperimentalstudyinaCIengineusingnanoadditive blended water–diesel

emulsionfuel.IntJGreenEnergy2011;8:332–48. 13. FangsuwannarakK,TriratanasirichaiK.Improvementsofpalmbiodiesel properties byusingnano-TIO2 additive,

exhaustemissionandengineperfor- mance. TheRomanianreviewprecisionmechanics.OptMechatron 2013;43:111–8.

14. Ors I,et al. The effects on performance, combustion and emission characteristics of DICI engine fuelled with TiO2 nanoparticles addition in diesel/biodiesel/nbutanol blends. Fuel 234 (2018) 177–188.

15. Felipe Santos Dalólio, et al. Poultry litter as biomass energy: A review and future perspectives. Renewable and Sustainable Energy

Reviews 76 (2017) 941–949

39.

Authors: S.Yuvaraja, S.Venkatesh, P.karthikeyan, R.kumarasubramanian

Paper Title: Emission Control in Diesel Engine Using Magnesium as a Catalyst

Abstract: Most cities in the world are subjected to rapid urbanization and a majority of the country‘s

population is estimated to move towards the cities within a span of the two decades. Majority of Population in all

over country expects to live in a city, Such that most cities face rapid Urbanization. The fast growth in urban

cities has stemmed in an incredible growth in the quantity of motor vehicles. In some major cities, the motor

vehicles are doubled in the last decade. The numbers of vehicles used in cities have almost tripled its usage in a

number of Automobiles by few decades. The majority of environmental pollution is caused by two-wheelers in

large number. This results in a massive environmental pollution. A serious prerequisite for most effective

precautionary measures has to be carried out. The emission controlled by dual techniques to control the pollution

specifically, pre-pollution and post pollution control. Here the investigation is conceded out by establishing the

post pollution control method for the two-wheeler automobiles via magnesium as a catalyst. To accomplish this

objective, a pioneering proposal of catalytic converter is offered using magnesium as a catalyst for two-wheeler

automobiles. The projected technique is precise operational in the deterrence of conservational pollution

contributed from two-wheeler automobiles. It encompasses the consumption of magnesium which is economical

than the, palladium, platinum and rhodium nano-particles used in automobiles.

Keyword: To accomplish this objective, a pioneering proposal of catalytic converter is offered using

magnesium as a catalyst for two-wheeler automobiles References:

1. Angelidis T N, Papadakis P G 1997 ―Partial regeneration of an aged commercial automotive catalyst ―, Environmental 12: 193–

206.

2. Bonnel, Martini, Krasenbrink, ―EURO 3 Stage for motorcycles: Derivation of equivalent limits for the WMTC driving cyclel, Joint

196-199

Research Centre, Ispra, Italy, p.40 (Moto_103)

3. Forzatti P, Lietti L 1999 ―Catalyst deactivation, Catalysis Today 52: 165– 181. 4. Heywood J B 1989 ―Internal combustion engine fundamentals, (New York: McGraw-Hill).

5. KALAM, H MASJUKI, M REDZUAN, ―Development and test of a new catalytic converter for natural gas fuelled engine.

S¯adhan¯a Vol. 34, Part 3, June 2009, pp. 467– 481. 6. Kaspar J, Fornasiero P, Graziani M ―Use of CeO2-based oxides in the three-way catalysis, Catalysis Today 50: 285–298.

7. Kenji Yamauchi, Norihiro Murayama and Junji Shibata , ―Absorption and Release of Carbon Dioxide with Various Metal Oxides

and Hydroxides Materials Transactions, Vol. 48, No. 10 (2007) pp. 2739 to 2742. 8. Me i-Rong Songa,b,c, Miao Chena, Zhi-Jun Zhangb , ―Preparation and characterization of Mg nanoparticles. MATERIALS

CHARACTERIZATION 59 (2008) 514 – 518

9. Mordike, Ebert ,Magnesium Properties — applications — potential‖,Materials Science and Engineering A302 (2001) 37–45. 10. Murali Krishna, Kishor , Murthy, Gupta and Narasimha Kumar,―Comparative Studies on Emissions from Two Stroke Copper

Coated Spark Ignition Engine with Alcohols with Catalytic Converter, International Journal of Scientific & Technology Research

Volume 1, Issue 2, March 2012 ISSN 2277-8616 11. Pranav Raghav Sood ―Air Pollution Through Vehicular Emissions in Urban India and Preventive Measures IPCBEE vol.33 (2012)

© (2012) IACSIT Press, Singapore

12. Taku Iwaoka and Mitsuru Nakamura, ―Effect of Compaction Temperature on Sinterability of Magnesium and Aluminum Powder Mixtures by Warm 41 Compaction Method Materials Transactions‖, Vol. 52, No. 5 (2011) pp. 943 to 947

13. Thakur Mukesh and Saikhedkar N.K.,‖Reduction of Pollutant Emission from Two-wheeler Automobiles using Nano-particle as a

Catalyst Res. J. Engineering Sci.Vol. 1(3), 32-37, Sept. (2012) 14. Zissis Samaras, Karl-Heinz Zierock, ― Emission Inventory Guidebook, Chapter 0706, Gasoline Evaporation from Vehicles‖

Technical report No 16/2007, European Environment Agency, Copenhagen, Denmark, p. 21

40.

Authors: N. VasanthaGowri, C.Harish, D.Harsha

Paper Title: Modeling of Solar Electric Propulsion System for UAVs

Abstract: UAVs are growing their importance in both civil and military applications. The endurance of UAVs

are related to their on board fuel carrying capacity which is limited by the weight class of aircraft. There is a need

for long endurance UAVs for persistent Intelligence, Surveillance, Target Acquisition, and

Reconnaissance(ISTAR) missions. One of the solutions to overcome the endurance limitations for usage of UAV

is the renewable energy. Among all renewable energy, solar energy is found more economical. Electrical powered

aircraft/(UAV) propulsion system uses electrical energy to change the velocity of UAV. Electric propulsion

system is now mature and widely used technology on spacecraft. In this work, UAV with solar cells on the

surface of the wings as well as on board energy storage is discussed. This paper quantifies the requirement for

perpetual endurance in solar-powered flight.

Keyword: UAV, Solar electric propulsion system, Perpetual Endurance References:

1. W. Thies, L. Bleiler, ― Alzheimer‘s disease facts and figures,‖ Alzheimer‘s & Dementia, 2013; 9(2): 208-45.

2. K. Strimbu, J. A. Tavel, ―What are biomarkers? Current opinion in HIV and AIDS,‖ 2010; 5(6): 463.

3. WHO International Programme on Chemical Safety Biomarkers in Risk Assessment: Validity and Validation. 2001. http://www.inchem.org/documents/ehc/ehc/ehc222.htm. December 30, 2013.

4. Mingxia Liu, Daoqiang Zhang*, and Dinggang Shen*, Senior Member, IEEE, ―Relationship Induced Multi-Template Learning for

Diagnosis of Alzheimer‘s Disease and Mild Cognitive Impairment,‖ IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 35, NO. 6, pp. 1463-

5. Jin Liu, Min Li, Wei Lan, Fang-Xiang Wu, Yi Pan, and Jianxin Wang, ―Classification of Alzheimer‘s Disease Using Whole Brain

Hierarchical Network,‖ IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, VOL. 14, NO. 8, 2015.

6. Robin Wolz, Dong Ping Zhang, et al, ―Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer‘s Disease,‖

PLoSONE .,www.plosone.org, Volume 6, Issue 10, e25446, 2011. 7. B. Al-Naami, N. Gharaibeh, and A. AlRazzaqKheshman, ―Automated Detection of Alzheimer Disease Using Region Growing

technique and Artificial Neural Network,‖ International Science Index, Biomedical and Biological Engineering, Vol:7, No:5, pp.

204-208, 2013 waset.org/Publication/11271. 8. Luis Javier Herrera*, Ignacio Rojas, H. Pomares, A. Guillén, O. Valenzuela, O. Baños, ―Classification of MRI images for

Alzheimer‘s disease detection,‖ SocialCom/PASSAT/Big Data/EconCom/BioMedCom, pp. 846-851, 2013, IEEE.

9. Simon Duchesne*, Member, IEEE, Anna Caroli, et al. ―MRI-Based Automated Computer Classification of Probable AD versus Normal Controls,‖ IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol. 27, N0. 4, pp. 509-520, 2008.

10. Chenhui Hu, Xue Hua, Jun Ying, Paul M. Thompson, Georges E. Fakhri, Fellow, IEEE, and Quanzheng Li, ―Localizing Sources of

Brain Disease Progression with Network Diffusion Model,‖ IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 10, NO. 7, pp. 1214-1225, 2016

11. R. Armañanzas, M. Iglesias, D. A. Morales and L. Alonso-Nanclares, "Voxel-Based Diagnosis of Alzheimer's Disease Using

Classifier Ensembles," in IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 3, pp. 778-784, May 2017. doi: 10.1109/JBHI.2016.2538559

12. Tianhao Zhang*, Member, IEEE, and Christos Davatzikos, Senior Member, IEEE, ―ODVBA: Optimally-Discriminative Voxel-Based

Analysis,‖ IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 30, NO. 8, pp. 1441-1454, 2011.

200-202

13. Aoyan Dong*, Nicolas Honnorat, Member, IEEE, Bilwaj Gaonkar, and Christos Davatzikos, Fellow, IEEE, ―CHIMERA: Clustering

of Heterogeneous Disease Effects via Distribution Matching of Imaging Patterns,‖ IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 35, NO. 2, pp. 612-621, 2016.

14. Ching-Cheng Chuang, Pei-Ning Wang, et al, " Near-Infrared Brain Volumetric Imaging Method: A Monte Carlo Study,‖ IEEE

JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS, VOL. 18, NO. 3, pp. 1122-1129, 2012. 15. Jun Zhang, Yue Gao, Senior Member, IEEE, Yaozong Gao, Brent C. Munsell, and Dinggang Shen*, Senior Member, IEEE,

―Detecting Anatomical Landmarks for Fast Alzheimer‘s Disease Diagnosis,‖ IEEE TRANSACTIONS ON MEDICAL IMAGING,

VOL. 35, NO. 12, pp. 2524-2533, 2016. 16. G. B. Frisoni, et al, ―The clinical use of structural MRI in Alzheimer disease,‖ Nat Rev Neurol. Author manuscript; pp.67-77,

available in PMC 2011 .

17. Mingxia Liu, Daoqiang Zhang∗, Ehsan Adeli, Member, IEEE, and Dinggang Shen∗, Senior Member, IEEE,‖ Inherent Structure-Based Multiview Learning With Multitemplate Feature Representation for Alzheimer‘s Disease Diagnosis,‖ IEEE TRANSACTIONS

ON BIOMEDICAL ENGINEERING, VOL. 63, NO. 7,pp. 1473-1482, 2016.

18. Biao Jie, Mingxia Liu, Jun Liu, Daoqiang Zhang∗, and DinggangShen∗,‖Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer‘s Disease,‖ IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 64, NO.

1, pp. 238-249, 2017.

19. [L. Sørensen , C. Igel, N. Liv Hansen, M.Osler, M. Lauritzen, E. Rostrup, M. Nielsen, for the Alzheimer's Disease Neuroimaging Initiative and the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing (2015), ―Early detection of Alzheimer‘s

disease using MRI hippocampal texture,‖ Hum Brain Mapp, accepted, which has been published in final form at

DOI:10.1002/hbm.23091. 20. E. M. Ali, A. F. Seddik, M. H. Haggag, ―Automatic Detection and Classification of Alzheimer's Disease from MRI using TANNN,‖

International Journal of Computer Applications (0975 – 8887) Volume 148 – No.9, pp. 30-34, 2016.

21. Baiying Lei, Member, IEEE, Peng Yang, Tianfu Wang, Siping Chen, and Dong Ni, Member, IEEE, ―Relational-Regularized Discriminative Sparse Learning for Alzheimer‘s Disease Diagnosis,‖ IEEE TRANSACTIONS ON CYBERNETICS, VOL. 47, NO.

4, pp. 1102-1113, 2017.

22. Saman Sarraf, GhassemTofighi, for the Alzheimer's Disease Neuroimaging Initiative,‖DeepAD: Alzheimer's Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI,‖ bioRxiv preprint first posted online Aug. 21, 2016; doi:

http://dx.doi.org/10.1101/070441.

23. Siqi Liu∗, Student Member, IEEE, et al, ‖Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer‘s Disease,‖ IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 62, NO. 4,,pp. 1132-1140, 2015.

24. J. Shi; X. Zheng; Y. Li; Q. Zhang; S. Ying, "Multimodal Neuroimaging Feature Learning with Multimodal Stacked Deep Polynomial

Networks for Diagnosis of Alzheimer's," Disease in IEEE Journal of Biomedical and Health Informatics, vol. PP, no.99, pp.1-1. doi:

10.1109/JBHI.2017.2655720 25. Qi Zhou, Mohammed Goryawala, et al, ‖An Optimal Decisional Space for the Classification of Alzheimer‘s Disease and Mild

Cognitive Impairment,‖ IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 61, NO. 8, pp. 2245-2253,2014.

26. Rajesh, M., and J. M. Gnanasekar. "Path Observation Based Physical Routing Protocol for Wireless Ad Hoc Networks." Wireless Personal Communications 97.1 (2017): 1267-1289.

27. André Santos Ribeiro, Luís Miguel Lacerda, Nuno André da Silva and Hugo Alexandre Ferreira for the Alzheimer‘s Disease

Neuroimaging Initiative, ―Multimodal Imaging of Brain Connectivity Using the MIBCA Toolbox: Preliminary Application to Alzheimer‘s Disease,‖ IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 62, NO. 3,pp. 604-611, 2015.

28. Javier Escudero∗, Member, IEEE, Emmanuel Ifeachor, Member, IEEE, et al, ―Machine Learning-Based Method for Personalized and Cost-Effective Detection of Alzheimer‘s Disease,‖ IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 60, NO. 1,

pp. 164-168, 2013.

29. N. Nithiyanandam, K. Venkatesh, M. Rajesh, Transfer The Levels Of The Monitored Carbon, Nitrogen Gases From The Industries, International Journal of Recent Technology and Engineering, Volume-7 Issue-6S3 April, 2019.

30. Sivanesh Kumar, A., Brittoraj, S., Rajesh, M., Implementation of RFID with internet of things, Journal of Recent Technology and

Engineering, Volume-7 Issue-6S3 April, 2019. 31. Rajesh, M., Sairam, R., Big data and health care system using mlearningJournal of Recent Technology and Engineering, Volume-7

Issue6S3 April, 2019.

41.

Authors: N. Sardar Basha, A. Rajesh

Paper Title: Video compression based on Multiwavelet and Multi Stage Vector Quantization using Adaptive

Diamond Refinement Search Algorithm

Abstract: Due to the advances in the digital technology, multimedia processing has become the essential

requirement in many applications. These applications find wide use in mobile, personal computer(PC), TV,

surveillance and satellite broadcast. Also it is necessary that the video coding algorithms to be updated in order to

meet the requirements of latest hardware devices. The processing speed and bandwidth are essential parameters in

these applications. A good video compression standard can achieve these parameters adequately. In the proposed

system, the video coding standard is implemented using the three important stages. In which the first sage uses

multiwavelets to achieve good compression rate. Also it reduces the memory and bandwidth requirement. Second

stage is the Multi Stage Vector Quantization(MVSQ) which reduces the complexity of searching process and the

size of codebook. Third stage uses Adaptive Diamond Refinement Search(ADRS) algorithm for the motion

estimation which has better performance than the Adaptive Diamond Orthogonal Search(ADOS) and Diamond

Refinement Search(DRS) algorithms. The combination of multiwavelet, Multi Stage Vector

Quantization(MVSQ) and Adaptive Diamond Refinement Search(ADRS) algorithm gives the high compression

203-207

ratios. Preliminary results indicate that the proposed method has good performance in terms of average number of

search points, PSNR values and compression rates.

Keyword: Video coding, video compression, multiwavelets, Multi Stage Vector Quantization, Adaptive

Diamond Refinement Search algorithm and MATLAB. References:

1. S. Liu, ― Performance comparison of MPEG1 and MPEG2 video compression standards,‖ COMPCON 96. Technologies for the

Information Superhighway Digest of Papers, p.p. 0199 - 0203. 2. Lehtoranta, and Sarinen, ― Real-time H.263 encoding of QCIF-images on TMS320C6201 fixed point DSP,‖ IEEE International

Symposium on Circuits & Systems. Emerging Technologies for the 21st Century, 2000, p.p. 0583- 0586, vol. 01.

3. P. K. Srivastava and Jakkani, ― FPGA Implementation of Pipelined 8×8 2-D DCT and IDCT Structure for H.264 4. Protocol,‖ 3rd International Conference for Convergence in Technology, 2018, p.p. 01- 06.

5. Ying-Jui Chen, Soontorn Oraintara and Truong Nguyen, ―Video compression using integer DCT,‖ Proceedings International

Conference on Image Processing, 2000, p.p. 0844-0845, vol. 02. 6. Zixiang Xiong, Ramchandran, ―A comparative study of DCT- and wavelet-based image coding,‖ IEEE Transactions on Circuits &

Systems for Video Technology, vol. 09, no. 05, p.p. 0692-0695, 1999.

7. Bystrov, Gryzov, ―Wavelet-Based Video Coding: Modem Implementations and Prospects of Coding Efficiency Increase,‖ IVth International Conference on Engineering & Telecommunication, 2017, p.p. 038 -041.

8. Sudhakar, Jayaraman, ― A New Video Coder using Multiwavelets,‖ 2007 International Conference on Signal Processing,

Communications and Networking, Chennai, 2007, p.p. 0259- 0264. 9. Priya and Ananthi, ―Image compression using multiwavelet transform for medical image,‖ International Conference on Innovations

in Green Energy and Healthcare Technologies, 2017, p.p. 01- 03

10. Rajakumar and Arivoli, ―Lossy image compression using multiwavelet transform coding,‖ International Conference on Information Communication and Embedded Systems, 2014, p.p. 01- 06.

11. G. Jeong and Lee, ―Wavelet-based ECG compression using dynamic multi-stage vector quantization,‖ 4th IEEE Conference on

Industrial Electronics and Applications, 2009, p.p. 02100- 02105. 12. M. Wang, Zhou, ―An Improved Multi-stage Vector Quantization for Image Coding,‖ 3rd International Conference on Intelligent

Information Hiding & Multimedia Signal Processing, 2007, p.p. 0415- 0420. 13. N. A. Hamid and Sulaiman, ―Adaptive Diamond Orthogonal Search Algorithm for Motion Estimation,‖ International Conference on

Computer, Communications, and Control Technology, 2015, p.p. 0498- 0501.

14. Y. Lai and Lien, ―Fast Motion Estimation Based on Diamond Refinement Search for High Efficiency Video Coding,‖ IEEE International Conference on Consumer Electronics, 2019, p.p. 01- 02.

15. A. A. Devi, Sumalatha, Priya, Sukruthi and Minisha, ― Modified diamond-square search technique for efficient motion estimation,‖

International Conference on Recent Trends in Information Technology, 2011, p.p. 01149- 01153. 16. Hong Chae and Barnwell, ― Fast codeword search for vector quantization using a multi-stage approach,‖ IEEE International

Conference on Acoustics, Speech & Signal Processing. Proceedings, 2000, p.p. 02629- 02632, vol. 05.

42.

Authors: R. Sivasubramnian, K. Arthi

Paper Title: A Security Architecture for Reallocation in Data Grid Environment

Abstract: The grid is one of the eminent technologies which are used for the efficient storing of the data,

whereas the data processing in the grid remained difficult until the reallocation strategy was proposed and

designated. The data reallocation process which remains an ultimate methodology for the reusability of the grid

server, but the grid servers remains unstable and vulnerable in the point of the security architecture. This paper

proposes a unique methodology to provide the security to the data that is available in the grid and also to the grid

environment. The Sec-Grid algorithm which is proposed for the providing of the efficient security to the grid

environment which achieves the greater security for the environment. The two distinct homomorphic algorithms

which maintains the proper way of security to the system and the environment. The Experimental Simulation

shows the higher achievement of the security to the grid.

Keyword: Grid Environment, Grid Security, Sec-Grid, Homomorphic Algorithm References: 1. C. Castillo, G. Rouskas, and K. Harfoush, ―On the Design of Online Scheduling Algorithms for Advance Reservations and QoSin

Grids‖ Proc. IEEE Int‘l Conf. Parallel and Distributed Processing Symp. (PDP),pp. 1-10, Mar. 2007

2. N. Doulamis, A. Doulamis, A. Panagakis, K Dolkas, T. Varvarigou,and E. Varvarigos, ―A Combined Fuzzy -Neural Network

ModelforNonLinear Prediction of 3D Rendering Workload in Grid Computing,‖ IEEE Trans. Systems, Man, and Cybernetics (SMC)-Part-B, vol. 34, no. 2, pp. 1235- 1247, Apr. 2004.

3. E. Arkin and E. Silverberg, ―Scheduling Tasks with Fixed Startand End Times, ―Discrete Applied Math.,vol. 18, no. 1, pp. 1-8, 1987.

4. R.W. Lucky, ―Cloud Computing,‖IEEE Spectrum, vol. 46, no. 5,p. 27, May 2009. 5. K. Singh, E._Ipek, S.A. McKee, B.R. de Supinski, M. Schulz, and R.Caruana, ―Predicting Parallel Application Performance via

Machine Learning Approaches,‖ Concurrency and Computation:Practice & Experience, vol. 19, no. 17, pp. 2219-2235, Dec. 2007.

6. M. Maheswaran, K. Krauter, and R. Buyya, ―A Taxonomy and Survey of Grid Resource Management Systems for Distributed

208-211

Computing,‖ Software: Practice and Experience, vol. 32, no. 2,pp. 135-164, Feb. 2002.

7. R.J. Al-Ali et al., ―Analysis and Provision of QoS for Distributed Grid Applications,‖ J. Grid Computing,vol. 2, pp. 163-182, 2004. 8. M.S. Fineberg and O. Serlin, ―Multiprogramming for Hybrid Computation,‖ Proc. IFIPS Fall Joint Computer Conf.,1967.

9. A. Stankovic et al., ―Implications of Classical Scheduling Results for Real Time Systems,‖ Computer,vol. 28, no. 6, pp. 16-25, June

1995. 10. P. Kokkinos and E. Varvarigos, ―A Framework for Providing Hard Delay Guarantees and User Fairness in Grid Computing,‖ Future

Generation Computer Systems,vol. 25, no. 6, pp. 674-686, 2009.

11. D. Jackson, Q. Snell, and M. Clement, ―Core Algorithms of the Maui Scheduler,‖ Proc. Seventh Int‘l Workshop Job Scheduling Strategies for Parallel Processing (JSSPP),pp. 87-102, 2001.

12. B. Bode et al., ―The Portable Batch Scheduler and the Maui Scheduler on Linux Clusters,‖ Proc. Usenix Conf.,2000. ―Platform

Computing Corporation,‖ http://www.platform.com, 2013. 13. H. Casanova, A. Legrand, D. Zagorodnov, and F. Berman,―Heuristics for Scheduling Parameter Sweep Applications in Grid

Environments,‖ Proc. Ninth Heterogeneous Computing Workshop, pp. 349- 363, 2000.

14. R. Buyya, M. Murshed, D. Abramson, and S. Venugopal,―Scheduling Parameter Sweep Applications on Global Grids: A Deadline and Budget Constrained Cost-Time Optimisation Algorithm,‖ Software: Practice and Experience, vol. 35, pp. 491-512, 2005.

15. N. Doulamis, A. Doulamis, E. Varvarigos, and T. Varvarigou, ―Fair Scheduling Algorithms in Grids,‖ IEEE Trans. Parallel and

Distributed Systems,vol. 18, no. 11, pp. 1630-1648, Nov. 2007. 16. K. Rzadca, D. Trystram, and A. Wierzbicki, ―Fair Game-Theoretic Resource Management in Dedicated Grids,‖ Proc. IEEE Seventh

Int‘l Symp. Cluster Computing and the Grid (CCGrid), pp. 343-

350,2007. 17. . S. Kim and J. Weissman, ―A Genetic Algorithm Based Approach for Scheduling Decomposable Data Grid Applications‖ Proc. IEEE

Int‘l Conf. Parallel Processing (ICPP),pp. 406-413, Aug. 2004.

18. S. Kim and J. Weissman, ―A Genetic Algorithm Based Approach for Scheduling Decomposable Data Grid Applications, ‖Proc. IEEE Int‘l Conf. Parallel Processing (ICPP),pp. 406-413, Aug. 2004.

19. G. Ye, R. Rao, and M. Li, ―A Multiobjective Resources Scheduling Approach Based on Genetic Algorithms in Grid Environment,‖

Proc. Fifth Int‘l Conf. Grid and Cooperative Computing Workshops (GCCW ‘06),pp. 504-509, Oct. 2006. 20. W. Smith, I. Foster, and V. Taylor, ―Scheduling with Advanced Reservations,‖ Proc.14th Int‘l Parallel and Distributed Symp.

(IPDPS),pp.127-132,2000.

21. E. Varvarigos, N. Doulamis, A. Doulamis, and T. Varvarigou, ―Timed/Advance Reservation Schemes and Scheduling Algorithms for QoS Resource Management in Grids,‖ Engineering the Grid,pp. 355-378, Am. Scientific Publishers, 2006.

22. I. Foster, C. Kesselman, C. Lee, R. Lindell, K.Nahrstedt, and A. Roy, ―A Distributed Resource Management Architecture that Supports

Advance Reservation and Co-Allocation,‖Proc. Seventh Int‘l Workshop Quality of Service (IWQOS), pp. 27-36, 1999

43.

Authors: S. Lokesh, Akash G, Gangadharan S, Prabhu A C, Vivek kumar sharma P

Paper Title: Raspberry Pi Based Battle Field Robot Using Dijkstra’s Algorithm

Abstract: The robot is intended to follow protests by turning left and ideal to keep the article in sight and

driving forward and in reverse to keep up a consistent separation between the robot and the item. Pictures are

obtained through the camera of a Raspberry-pi gadget which is appended to the robot. The camera is appended to

servos on the robot which enable the camera to skillet and tilt. A few picture handling methods are utilized to

distinguish the area of the article being followed in the pictures. The vehicle route utilizing the information

transmission time is expanded with the convention standard. Correspondences between two hubs (equipment and

application) are practiced through IEEE 802.15.4. The client can give the source and goal hub address to the

server segment. Utilizing QR code by executing DIJSKTRA calculation the most brief way can be effectively

decided. Metal discovery should be possible by utilizing inductive closeness sensor. Ultrasonic sensor is utilized

for finding the separation between the robot and article to be picked.

Keyword: Dijkstra algorithm, Raspberry-pi device, Analogue-to-digital converter (ADC) References:

1. J. Pan, R. Jain, S. Paul, T. Vu, A. Saifullah and M. Sha, "An Internet of Things Framework for Smart Energy in Buildings: Designs, Prototype, and Experiments," IEEE Internet of Things Journal, vol. 2, no. 6, pp. 527-537, 2015.

2. R. Piyare, ―Internet of Things: Ubiquitous Home Control and Monitoring System using Android based Smart Phone,‖ International

Journal of Internet of Things, vol. 2 no. 1, pp. 5-11, 2013. 3. J. Wan, M.J. O‘Grady, G.M.P. O‘Hare, "Dynamic Sensor Event Segmentation for Real-Time Activity Recognition in a Smart Home

Context," Personal and Ubiquitous Computing, vol. 19, no. 2, pp. 287-301, 2015.

4. K. Afifah, S. Fuada, R.V.W. Putra, T. Adiono, M.Y. Fathany, ―Design of Low Power Mobile Application for Smart Home,‖ Proc. of Int. Symposium on Electronics and Smart Devices (ISESD), pp. 127-131, November 2016.

5. Videla and J.J.W. Williams, RabbitMQ in Action Distributed Messaging for Everyone, New York: Manning Publication Co., 2012.

6. Z. R. Lai, D. Q. Dai, C. X. Ren, and K. K. Huang, ―Discriminative and compact coding for robust face recognition,‖ IEEE Transactions on Cybernetics, vol. 45, pp. 1900–1912, 2015.

7. Dr. AntoBennet, M, Sankar Babu G, Natarajan S, ―Reverse Room Techniques for Irreversible Data Hiding‖, Journal of Chemical and

Pharmaceutical Sciences 08(03): 469-475, September 2015. 8. Dr. AntoBennet, M , Sankaranarayanan S, Sankar Babu G, ― Performance & Analysis of Effective Iris Recognition System Using

212-215

Independent Component Analysis‖, Journal of Chemical and Pharmaceutical Sciences 08(03): 571-576, August 2015.

9. Dr. AntoBennet, M, Suresh R, Mohamed Sulaiman S, ―Performance &analysis of automated removal of head movement artifacts in EEG using brain computer interface‖, Journal of Chemical and Pharmaceutical Research 07(08): 291-299, August 2015.

10. Dr. AntoBennet, M ―A Novel Effective Refined Histogram For Supervised Texure Classification‖, International Journal of Computer

& Modern Technology , Issue 01 ,Volume02 ,pp 67-73, June 2015. 11. Dr. AntoBennet, M, Srinath R,Raisha Banu A,―Development of Deblocking Architectures for block artifact reduction in videos‖,

International Journal of Applied Engineering Research,Volume 10, Number 09 (2015) pp. 6985-6991, April 2015.

12. AntoBennet, M & JacobRaglend, ―Performance Analysis Of Filtering Schedule Using Deblocking Filter For The Reduction Of Block Artifacts From MPEQ Compressed Document Images‖, Journal of Computer Science, vol. 8, no. 9, pp. 1447-1454, 2012.

13. AntoBennet, M & JacobRaglend, ―Performance Analysis of Block Artifact Reduction Scheme Using Pseudo Random Noise Mask

Filtering‖, European Journal of Scientific Research, vol. 66 no.1, pp.120-129, 2011.

44.

Authors: V.Nehru , N.D.Bobby, K.S.Rani, Giridhar Reddy, M.Anto Bennet S.

Paper Title: Automatic Detection and Classification Techniques of Acute Myelogenous Leukemia (AML) Using

SVM and Generic Algorithm

Abstract: Majority of youngsters‘ having connected online through internet either through computers or

by smart phones. After the entry of Jio in the field of internet, the competition began and the cost of internet

service became much cheaper andnoweveryone can afford the cost. Latest ―Times of India‖ statistics shows

around 59% of internet users are college students/young men. The trend of going to the physical stores to buy the

product is in the decline stage where as the trend of surfing product specification as well as its cost and alternates

through online marketing sites is increasing among youngsters. Since, it is more convenient for them to shop

anywhere and anytime. Shopping can be done 24 x 7 and before buying; review of product performance through

social media and compare its price through varies alternate sites. There is no compulsion to buy the product while

surfingoreven if visited the siteforany number of times. The payment can be made through online transition and

products will be delivered to doorstep.Hence shopping through online become a joyful experience and preferred

by youngsters.

Keyword: Marketing, Segmentation, Technology and Buying Behaviour. References:

1. Bruno Direito, Francisco Ventura, Cesar Teixeira and Antonio Dourado, ― Optimized feature subsets for epileptic seizure prediction

studies,‖ in Proc.Int.Conf.EMBC., pp. 1636-1639, 2011.

2. Chunni Dai and Jingao Liu, ―Spectral feature extraction of blood cells based on hyperspectral data,‖ in Proc.Int.Conf.Natural Computation (ICNC)., pp. 1439-1443, 2013.

3. Ilea. D and Whelan. P, ―Image segmentation based on the integration of colour-texture descriptors—A review,‖ Pattern Recognit.,

vol. 44,no. 10/11, pp. 2479–2501, Oct./Nov. 2011. 4. Jing Zhou, Omaru Maruatona. O, and Wei Wang, ―Parameter optimization for support vector machine classifier with IO-GA,‖ in

Proc. IWCDM, pp. 117-120, 2011.

5. Mohapatra. S and Patra. D, ―Automated leukemia detection using hausdorff dimension in blood microscopic images,‖ in Proc.Int.Emerg.Trends Robot Commun.Technol., pp. 64-68, 2010.

6. Dr. AntoBennet, M, Sankar Babu G, Natarajan S, ―Reverse Room Techniques for Irreversible Data Hiding‖, Journal of Chemical and

Pharmaceutical Sciences 08(03): 469-475, September 2015. 7. Dr. AntoBennet, M , Sankaranarayanan S, Sankar Babu G, ― Performance & Analysis of Effective Iris Recognition System Using

Independent Component Analysis‖, Journal of Chemical and Pharmaceutical Sciences 08(03): 571-576, August 2015.

8. Dr. AntoBennet, M, Suresh R, Mohamed Sulaiman S, ―Performance &analysis of automated removal of head movement artifacts in EEG using brain computer interface‖, Journal of Chemical and Pharmaceutical Research 07(08): 291-299, August 2015.

9. Dr. AntoBennet, M ―A Novel Effective Refined Histogram For Supervised Texure Classification‖, International Journal of Computer

& Modern Technology , Issue 01 ,Volume02 ,pp 67-73, June 2015. 10. Dr. AntoBennet, M, Srinath R,Raisha Banu A,―Development of Deblocking Architectures for block artifact reduction in videos‖,

International Journal of Applied Engineering Research,Volume 10, Number 09 (2015) pp. 6985-6991, April 2015.

11. AntoBennet, M & JacobRaglend, ―Performance Analysis Of Filtering Schedule Using Deblocking Filter For The Reduction Of Block

Artifacts From MPEQ Compressed Document Images‖, Journal of Computer Science, vol. 8, no. 9, pp. 1447-1454, 2012.

12. AntoBennet, M & JacobRaglend, ―Performance Analysis of Block Artifact Reduction Scheme Using Pseudo Random Noise Mask

Filtering‖, European Journal of Scientific Research, vol. 66 no.1, pp.120-129, 2011.

216-221

45.

Authors: K. Ramesh , M.Anto Bennet, S.Prasanth, M A .Anand , V.Sujith Kumar, R.Ragavendran

M.Thirumana sambantham

Paper Title: Dynamic Fingerprint Pattern Lock Mobile Application Using Android

Abstract: In this paper we are examining about information security in mobile. Numerous cell phone

creators currently fuse biometric security highlights into their products. Furthermore, some gadget makers

presently enable application designers to utilize these highlights through their product advancement packs

(SDKs). In this investigation, we use fingerprint recognition with a pattern, to build up a security for mobile

222-225

application. Before, application had the single time finger press. Here we have utilized various time check and

long-term hold confirmation techniques. Inside a constrained time, outline, the unique fingerprint image can be

utilized to open the app which has classified information identified with government, banking, training, and so on

which must be verified. As the generation of cell phones with fingerprint recognition keeps on expanding, this

type of authentication system, the one we present in this paper, will turn into a great safety measure.

Keyword: Marketing, Segmentation, Technology and Buying Behaviour. References:

1. I. Akyldiz, W.Su, Y. Sankarasubramanian and E. Cayirci, ―A survey on sensor networks,‖ IEEE Communication Mag., vol. 40, no. 8, Aug. 2002, pp. 102-14.

2. C. Shen, C. Srisathapornphat, and C. Jaikaeo, ―Sensor information networking architecture and applications,‖ IEEE Personnel

Communications, Aug. 2001, pp.52-59. 3. Luis Javier García Villalba, Ana Lucila Sandoval Orozco, Alicia Triviño Cabrera and Cláudia Jacy Barenco Abbas "Routing

Protocols in Wireless Sensor Networks" Sensors 2009,9,8399-8421;doi:10.3390/s91108399.

4. S. Tilak, N. Abhu-Gazhaleh, W. R. Heinzelman, ―A taxanomy of wireless micro-sensor network models,‖ ACM SIGMOBILE Mobile Comp. Commun. Rev., vol. 6, no. 2, Apr. 2002, pp. 28- 36.

5. P.N.Renjith, E. Baburaj, ―Analysis on Ad Hoc routing protocols in wireless sensor networks‖ International Journal of Ad hoc, Sensor

& Ubiquitous Computing (IJASUC) Vol.3, No.6, December 2012. 6. Dr. AntoBennet, M, Sankar Babu G, Natarajan S, ―Reverse Room Techniques for Irreversible Data Hiding‖, Journal of Chemical and

Pharmaceutical Sciences 08(03): 469-475, September 2015.

7. Dr. AntoBennet, M , Sankaranarayanan S, Sankar Babu G, ― Performance & Analysis of Effective Iris Recognition System Using Independent Component Analysis‖, Journal of Chemical and Pharmaceutical Sciences 08(03): 571-576, August 2015.

8. Dr. AntoBennet, M, Suresh R, Mohamed Sulaiman S, ―Performance &analysis of automated removal of head movement artifacts in

EEG using brain computer interface‖, Journal of Chemical and Pharmaceutical Research 07(08): 291-299, August 2015. 9. .Dr. AntoBennet, M ―A Novel Effective Refined Histogram For Supervised Texure Classification‖, International Journal of Computer

& Modern Technology , Issue 01 ,Volume02 ,pp 67-73, June 2015. 10. Dr. AntoBennet, M, Srinath R,Raisha Banu A,―Development of Deblocking Architectures for block artifact reduction in videos‖,

International Journal of Applied Engineering Research,Volume 10, Number 09 (2015) pp. 6985-6991, April 2015.

11. AntoBennet, M & JacobRaglend, ―Performance Analysis Of Filtering Schedule Using Deblocking Filter For The Reduction Of Block Artifacts From MPEQ Compressed Document Images‖, Journal of Computer Science, vol. 8, no. 9, pp. 1447-1454, 2012.

12. AntoBennet, M & JacobRaglend, ―Performance Analysis of Block Artifact Reduction Scheme Using Pseudo Random Noise Mask

Filtering‖, European Journal of Scientific Research, vol. 66 no.1, pp.120-129, 2011.

46.

Authors: T.R.Dinesh Kumar, M.Anto Bennet, R.Aishwarya, S.Elamathy, M.Kowsalya, C.Ranti Bownisha

Paper Title: Design of 13T SRAM Bitcell in 22nm Technology using FinFET for Space Applications

Abstract: Majority of youngsters‘ having connected online through internet either through computers or

by smart phones. After the entry of Jio in the field of internet, the competition began and the cost of internet

service became much cheaper andnoweveryone SRAM can be found in the cache memory which is a part of the

RAM digital to analog converter. SRAM is used for high speed register and some of the small memory banks.

The risk of these circuits and memory arrays which are capable to radiation effects than circuits powered at

minimal supply voltages. when an high energy particle hits a sensitive node in a circuit soft errors like Single

Event Upsets(SEUs)occurs. The attainment of radiation hardening of memory blocks is executing large bit cells

or single Error Correcting Codes(ECCs). But ECC may require notable area, performance and leakage power

penalties. The favorable device characteristic of FinFET avails them as a popular contender for the replacement

of CMOS technologies. An optimal approach to reduce the leakage power of a 13T SRAM cell based on 22nm

FinFET technology is proposed in this work . The circuit contains a dual- driven separated feedback mechanism

to tolerate the upset with charge of deposits . Better immunity is supplied by this cell to soft errors when

compared to 6T SRAM cell.

Keyword: Radiation hardening, low voltage, critical charge, Single Event Upset(SEU), FinFET, Static

Random Access Memory(SRAM), Memory array, soft errors, Ultra Low Power (ULP). References:

1. Kishore Kumar K, Radha B. L, ―Design and Implementation of 12T SRAM Cell in 32nm FinFET Technology‖ (IJERECE)Vol 4, Issue 8, August 2017 .

2. Challabotla Shalini,S. Rajendar, ―Design of CMOS Schmitt Trigger Inverter based SRAM Cell for Low

PowerApplications‖(ICECDS),2017. 3. sundeep kaur,Mandeep kaur ‗‗Performance Evalution Of 6T FinFET SRAM and 6T CMOS SRAM Cell‘‘(IJRASET)volume5 Issue

VII, July 2017.

4. Priya Thakare, Sanjay Tembhurne ―A Power Analysis of SRAM Cell Using 12T Topology for FasterData 2016.

226-230

5. Pasandi G. Fakhraie S.M. ―A 256-kb 9T near-threshold SRAM with 1k cells per bitline and enhanced write and read operations‖,

IEEE Trans. VLSI Syst., 2015, pp. 2438–2446. 6. Yuan, J.S., Bi, Y.: ―Process and temperature robust voltage multiplier design for RF energy harvesting‖,

Microelectron.Reliab,2015,Pp. 107–113.

7. Rahaman M. and Mahapatra, R. (2014), ―Design of a 32nm Independent Gate FinFET based SRAM Cell with Improved Noise Margin for Low Power Application‖ (ICECS), , pp. 1–5, February2014.

8. G.BoopathiRaja, M.Madheswaran, ―DesignandPerformance Comparison of 6-T SRAM Cell in 32nmCMOS, FinFET and

CNTFETTechnologies‖. International Journal of Computer Applications, 2013. 9. Satyendra Kumar, Vinay Anand Tikkiwal, Rariom Gupta ―Read SNM Free SRAM Cell Design in Deep Sub micron Technology‖.

2013 International Conference on Signal Processing and Communication (Icsc).

10. J. S. Shah, D. Nairn, and M.Sachdev, ―A soft error robust 32 kb SRAM macro featuring access transistor-less 8T cell in 65-nm,‖ in Proc. IEEE/IFIP Int. Conf. VLSI Syst.-Chip (VLSI-SoC), Oct. 2012, pp. 275–278.

11. J. Mezhibovsky, A. Teman, and A. Fish, ―Low voltage SRAMs and the scalability of the 9T supply feedback SRAM,‖ in Proc. IEEE

Int. Syst.-Chip Conf. (SOCC), Sep. 2011, pp. 136–141. 12. Raj.B, Dasgupta, S. ‗Nanoscale FINFET based SRAM cell design analysis of performance metric process variation, under lapped

FINFET and temperature effect‘, IEEE Circuits Syst. Mag., Third Quarter,2011, pp. 38–50.

13. Wang, J., Calhoun, B.H.: ‗Minimum supply voltage and yield estimation for large SRAMs under parametric variations‘, IEEE Trans. VLSI Syst., 2011, 19, (11), pp. 2120– 2125.

47.

Authors: S. Kalaivani, R.Anitha, S.Anusooya, Jean Shilpa

Paper Title: Iot Based Physical Condition Screening System For Animals

Abstract: Animal husbandry and its health monitoring is one of the prime facts for the socio-economic

development of a nation. This necessitates monitoring the wellbeing of the animals and thereby increasing the

food supply of the country. Advancement in wearable biosensors and wireless communication technologies help

in ubiquitous E-health monitoring of animals. The proposed system is build up of recent technologies to screen

the physical condition of the animals continuously from anywhere and at anytime. The vital parameters like body

temperature, heart rate and position tracking are acquired using respective sensors. The collected data are

transmitted wirelessly over internet and are stored in a database using IoT technology. The system also alerts the

farmers/care takers at the critical conditions. The records that reflect the physical condition of animals collected

in the database helps veterinary doctors to provide effective treatment.

Keyword: IoT; Arduino; Animal Health monitoring; Sensors References:

1. International Journal of Advancements in Research & Technology, Volum

2. A.Kumar and G.P.Hancke, ―Zigbee based Animal health monitoring system‖ IEEE Sensors Journal, 2013. 3. Greg Byrd, North Carolina State University, ―Tracking Cows Wirelessly‖, IEEE Journals, June 2015

4. Amrutha Helwatkar, Daniel Riordan and Joseph Walsh, ―Sensor Technology for animal haelth monitoring‖, proceedings on 8th

international conference on sensing technology, September 2 -4, 2014, Liverpool UK. 5. R. N. Handcock et al., "Monitoring animal behaviour and Environmental interactions using wireless sensor Networks, GPS collars

and satellite remote sensing," Sensors 2009, vol. 9,no. 5, pp. 3586.

6. M. Sveda and R. Vrba, "Integrated smart sensor Networking framework for sensor-based appliances," IEEE Sensors J., vol. 3, no. 5,pp. 579- 586, Oct. 2003.

7. D.Wobschall, ―Networked sensor monitoring using the universal IEEE 1451 Standard‖, IEEE instrum. Measr. Magazine. 18 – 22,

April 2008. 8. Leena Narayan, Dr. T. Muthumanickam and Dr. A. Nagappan, ―Animal Health Monitoring System using Raspberry Pi and Wireless

Sensor‖,International Journal of Scientific Research and Education (IJSRE), Volume 3 Issue 5, May 2015.

9. Anushka Patil et al., ― Smart animal health monitoring‖, IEEE Explore, 2016. 10. Sajjad Hussain Shah and Ilyas Yaqoob, ―A survey: Internet of Things (IOT) technologies, applications and challenges‖, IEEE

Explore, 2016.

11. Pallavi Sethi and Smruti R. Sarangi, ―Internet of Things: Architectures, Protocols, and Applications‖, Journal of Electrical and Computer Engineering, 2017.

12. SureshNeethirajan, ―Recent advances in wearable sensors for animal health management‖, Sensing and Bio-Sensing Research,

Elsevier, Volume 12, February 2017, Pages 15-29. 13. Amruta Awasthi et al., ―Non-Invasive Sensor Technology for the Development of a Dairy Cattle Health Monitoring System‖,

Computers, MDPI, 2016.

14. http://www.biotrack.co.uk/pdf/sensetemp.pdf 15. https://www.sparkfun.com/products/245

16. Rita Brugarolas et al., ―Wearable Heart Rate Sensor Systems for Wireless Canine Health Monitoring‖ , IEEE Journal, 2015.

17. http://acid.uclan.ac.uk/doguino 18. https://www.arduino.cc/en/Main/arduinoBoardUno

19. sensorembedded.com/product_extra_files/SIM808.pdf

20. https://thingspeak.com

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48.

Authors: T.R.Dinesh Kumar, M.Anto Bennet, J.Kishan, Kishore P, Sanjeet Kumar, Vidhur B Praman S

Paper Title: Path Delay Optimized Booth Radix-8 Architecture

Abstract: In this paper, a quick and low power marked MAC Unit is proposed with reconfigurable

Modified stall calculation (MBE). The proposed engineering depends on adjusted corner radix-8 with

consolidated 2's supplement and MUX units with a low basic way postponement and low equipment multifaceted

nature. Here corner-based methodology for incomplete items ages and Tree based methodology for halfway items

decrease in increases. The new decreases design the equipment intricacy of the summation arrange utilizing

consolidated swell convey viper and convey look forward (CLA), along these lines lessens the general power and

multifaceted nature. Expanding the speed of activity can be broadened utilizing marked corner radix-8 based

MAC unit with noteworthy execution enhancement. We can stall augmentation into two sections, incomplete item

age and halfway item amassing. Accelerating augmentation, hence, must point (I) accelerating fractional item

age, (ii) decreasing the quantity of incomplete items, (iii) accelerating halfway item summation or (iv) a

combination of the above.

Keyword: Marketing, Segmentation, Technology and Buying Behaviour. References:

1. Jiang, H., Han, J., Qiao, F., et al.: ‗Approximate radix-8 booth multipliers for low-power and high-performance operation‘, Trans. Comput., 2016, 65, (8), pp. 2638–2644, doi: 10.1109/ TC.2015.2493547

2. Xue, H., and Ren, S.: ‗Low power-delay-product dynamic CMOS circuit design techniques‘, Electron. Lett., 2017, 53, (5), pp.

302–304, doi: 10.1049/el.2016.4173 3. Chattopadhyay, T., and Gayen, D.: ‗All-optical 2‘s complement number conversion scheme without binary addition‘, Optoelectronics,

2017, 11, (1), pp. 1–7, doi: 10.1049/iet-opt.2015.0087

4. Qian, L., Wang, C., Liu, W., et al.: ‗Design and evaluation of an approximate wallace-booth multiplier‘. IEEE Int. Symp. Circuits and Systems (ISCAS), Montreal, QC, Canada, May 2016, pp. 1974–1977

5. Chuang, P., Sachdev, M., and Gaudet, V.: ‗A 167-ps 2.34-mW singlecycle 64-bit binary tree comparator with

6. constant-delay logic in 65-nm CMOS‘, Trans. Circuits Syst., 2014, 61, (1), pp. 160–171, doi: 10.1109/TCSI.2013.2268591 7. B. Bross, W.-J. Han, J.-R. Ohm, G. J. Sullivan, Y.-K. Wang, and T. Wiegand, High Efficiency Video Coding (HEVC) Text

Specification Draft 10, document Rec. JCTVC-L1003, 2013.

8. G. A. RUIZ, J. A. MICHELL AND A. BURO´ N ―High Throughput Parallel- Pipeline 2-D DCT/IDCT Processor Chip‖ Journal of VLSI Signal Processing 45, 161–175, 2006

9. JarmoTakala, Jari Nikara, David Akopian, Jaakko Astola', and Jukka Saarinen' ―PIPELINE ARCHITECTURE FOR 8 x 8

DISCRETE COSINE TRANSFORM‖ 0-7803-6293-4/00/$10.00 0 2000IEE 10. Dr. AntoBennet, M, Sankar Babu G, Suresh R, Mohammed Sulaiman S, Sheriff M, Janakiraman G ,Natarajan S, ―Design & Testing

of Tcam Faults Using TH Algorithm‖, Middle-East Journal of Scientific Research 23(08): 1921-1929, August 2015 .

11. Dr. AntoBennet, M ―Power Optimization Techniques for sequential elements using pulse triggered flipflops‖, International Journal of Computer & Modern Technology , Issue 01 ,Volume01 ,pp 29-40, June 2015.

12. Dr. AntoBennet, M,Manimaraboopathy M,P. Maragathavalli P,Dinesh Kumar T R, ―Low Complexity Multiplier For Gf(2m) Based

All One Polynomial‖, Middle-East Journal of Scientific Research 21 (11): 2064-2071, October 2014.

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49.

Authors: P.Kalyana Sundaram, M.Anto Bennet, Giridhar Reddy

Paper Title: Lifi Based Audio Communication for Coal Mine Parameter Monitoring and Automatic Control

System

Abstract: Li-Fi (Light Fidelity) is a quick and shoddy optical rendition of correspondence. The principle

segments of this correspondence framework are a high power white LED which goes about as a correspondence

source and a silicon Photo diode which demonstrates great reaction to noticeable wavelength locale filling in as

the getting component. An essential factor while planning Li-Fi is Line of Sight (LoS). The LED can be turned on

and off to create advanced series of 0s. Information is coded in the light which changes into new information by

shifting the glinting rate of the LED. Since the speed of the light is exceptionally quick, the transmitted yield is

gotten as voice signs to the specialists at the coal mineshaft. Coal mining and oring includes the disclosure of coal

and its troublesome works of extraction, notwithstanding its evacuation and deal in the generation of concrete

industry. Most wounds occurring in the underground mines incorporates the falling of rocks, slips and blasts.

Harmful gas is produced amid the season of mining and oring forms. The mining laborers are influenced by lung

illness by breathing in residue and lethal gas in Coal mining condition. This data is implied to the specialists at

the coal mineshaft from the higher authorities through Li-Fi as a voice motions in this work.

Keyword: Li-Fi (Light Fidelity), Line of Sight (LoS), optical wireless communications (OWC), Visible light

238-241

communications (VLC). References:

1. Revathi.S, Aishwarya. N J, Parana Sinha, Illakiya .K,(2018), ―Audio Transmission Using LIFI‖, ISSN:2454-132X, VOLUME.4, ISSUE.2

2. Rajesh H S, Punith D Gowda, Pallavi N.S, Shilpa. R, Kavitha B C,(2018), ―Mining Environment System Using LIFI‖, ISSN:2278-

0181 3. Sabditha Gauni, Manimegalai C.T, Kalimuthu K, Muralu Krishnan,(2018), ―Voice Guidance System Using LIFI Technology‖,

ISSN:1314-3395, Volume.118, No.20

4. S.R.Deokar, J.S. Wakode, (2017), ―Coal Mine Safety Monitoring And Alerting Systems‖, E-ISSN: n ,V. 2395-0056, Volume.4, Issue.3

5. K.H Shakthi Muruga Jacintha, Judysimon,(2017), ―Safety System For Gold Mining Process Using Visible Light Communication‖ ,

ISSN:1311-8080, Volume.117, No.16 6. Humank, Kumawat, Shivam Verma, Prof.Subhabharathi .S, (2017), ―Audio Transmission Through Visible Light Communication‘‘,

ISSN: 2278-7798, Volume.6, Issue.5

7. Dr. AntoBennet, M, Sankar Babu G, Natarajan S, ―Reverse Room Techniques for Irreversible Data Hiding‖, Journal of Chemical and Pharmaceutical Sciences 08(03): 469-475, September 2015.

8. Dr. AntoBennet, M , Sankaranarayanan S, Sankar Babu G, ― Performance & Analysis of Effective Iris Recognition System Using

Independent Component Analysis‖, Journal of Chemical and Pharmaceutical Sciences 08(03): 571-576, August 2015.

9. Dr. AntoBennet, M, Suresh R, Mohamed Sulaiman S, ―Performance &analysis of automated removal of head movement artifacts in

EEG using brain computer interface‖, Journal of Chemical and Pharmaceutical Research 07(08): 291-299, August 2015.

10. Dr. AntoBennet, M ―A Novel Effective Refined Histogram For Supervised Texure Classification‖, International Journal of Computer & Modern Technology , Issue 01 ,Volume02 ,pp 67-73, June 2015.

11. Dr. AntoBennet, M, Srinath R,Raisha Banu A,―Development of Deblocking Architectures for block artifact reduction in videos‖,

International Journal of Applied Engineering Research,Volume 10, Number 09 (2015) pp. 6985-6991, April 2015. 12. AntoBennet, M & JacobRaglend, ―Performance Analysis Of Filtering Schedule Using Deblocking Filter For The Reduction Of Block

Artifacts From MPEQ Compressed Document Images‖, Journal of Computer Science, vol. 8, no. 9, pp. 1447-1454, 2012.

13. AntoBennet, M & JacobRaglend, ―Performance Analysis of Block Artifact Reduction Scheme Using Pseudo Random Noise Mask Filtering‖, European Journal of Scientific Research, vol. 66 no.1, pp.120-129, 2011.

50.

Authors: M. SHERIFF, R. GAYATHRI

Paper Title: Attention Deficit Hyperactivity Disorder (Adhd) Detection Methods

Abstract: Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common mental-health

disorders, affecting around 5%-10% of school-age children. This paper details about various methodologies for

detecting and diagnosing the ADHD disease in patients using different soft computing and deep learning

techniques. The limitations of advantages of each ADHD method were discussed in detail with its corresponding

simulation results. The feature extraction method and its training with classification procedure for each

conventional ADHD method were illustrated in detail.

Keyword: ADHD, Disorder, Features, Classifications, Diagnosing. References:

1. Miguel Ángel Bautista, Antonio Hernández-Vela, Sergio Escalera, ―A Gesture Recognition System for Detecting Behavioral Patterns

of ADHD‖, IEEE Transactions on Cybernetics, Vol. 46, no. 1, 2016,pp.136-148.

2. Liang zou , Jiannan zheng, Chunyan miao, Martin J. mckeown, and Z. Jane wang, ―3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI‖, IEEE Access, 2017.

3. Sudha D, M. Pushpa Rani, ―Gait Classification for ADHD Children Using Modified Dual Tree Complex Wavelet Transform‖, Gait

Classification for ADHD Children Using Modified Dual Tree Complex Wavelet Transform, 2017. 4. Alaa Eddin Alchalabi , Shervin Shirmohammadi, ―FOCUS: Detecting ADHD Patients by an EEG-Based Serious Game‖, IEEE

Transactions on Instrumentation and Measurement, Vol. 67, no. 7, 2018, pp.1512-1520.

5. Gulay CICEK, Aydin AKAN, Baris METIN, ―Detection of Attention Deficit Hyperactivity Disorder Using Local and Global

Features‖, PLOS ONE, 2018.

6. Eloyan, A., Muschelli J., Nebel, H, Han, F., Zhao T., Barber, A., Joel S., James J., Mostofsky, S., & Brian Caffo ―Automated

diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging‖, Frontiers in Systems Neuroscience, vol.6, No.61, 2012.

7. Matthew, R., Sidhu, G., Greiner, R., Asgarian, N., Bastani, M., Silverstone, P., Greenshaw, A., & Serdar M., ―ADHD-200 Global

Competition: diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements‖, Frontiers in Systems Neuroscience, vol.6, no.69, 2012.

8. H.-I. Suk, S.-W. Lee, D. Shen, and The Alzheimer's Disease Neuroimaging Initiative, ``Hierarchical feature representation and

multimodal fusion with deep learning for AD/MCI diagnosis,'' NeuroImage, vol. 101, pp. 569582, Nov. 2014. 9. C.-W. Chang, C.-C. Ho, and J.-H. Chen, ``ADHD classification by a texture analysis of anatomical brain MRI data,'' Frontiers Syst.

Neuroscience., vol. 6, p. 66, Sep. 2012.

10. Q.-H. Zou et al., ―An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF,'' J. Neuroscience. Methods, vol. 172, no. 1, pp. 137_141, 2008.

242-244

51.

Authors: S. Ramachandran,s. Rabiyathul basariya

Paper Title: Implementation of Virtual Reality in Serious Gaming - War Simulation

Abstract: In this work, the researcher presents War Simulation through virtual reality. Through the work,

the researcher tries to create a tactical war kind off a simulation that will not only be beneficial to train the

military personnel but also combine with the Virtual Reality in order to give an experience that will help the

Indian personnel to be trained in a better way. The researcher takes into account the simulation process that is

being used throughout the world and follows the steps of various already constructed simulations with added

advantages and upgrades. This work shall discuss the technical aspects of the construction of the simulation while

also taking into account the amount that will be used in building the simulation. By combining the elements of a

serious game and combine them with Virtual reality, the simulation provides a very immersive experience that

will be used to help the soldiers in a more virtual way. While starting to create the simulation the researcher takes

into account the various necessities that will be used to create the simulation. There will be different multiplayer

aspects that will be used in this simulation. A detailed explanation will be provided for various topics.

Keyword: Virtual Reality (VR), Photon Unity Network (PUN), Remote Procedure Calls (RPCs) References:

1. D.W.F. Van Krevelen, R. Poelman. A Survey of Augmented Reality Technologies, Applications and Limitations. In The International Journal of Virtual Reality, 2010, 9(2), pp1-20.

2. F. Zhou, H.B.L. Duh, M. Billinghurst. Trends in Augmented reality Tracking, Interaction, and Display: A Review of Ten Years of

ISMAR. In International Symposium on Mixed and Augmented Reality (ISMAR‘08) 3. J. Carmigniani, B. Furht. Augmented Reality: An Overview. In Handbook of Augmented Reality. ISBN: 978-1-4614- 0063-9.

Springer Science, London

4. M. Billinghurst. Augmented Reality in Education. New Horizons for Learning. Retrieved from: http://www.it.civil.aau.dk/it/education /reports/ar_edu.pdf.2002.

5. M. Billinghurst, A. Henrysson. Research Direction in Handheld AR. In The International Journal of Virtual Reality, 2006, 5(2), pp51-

58 6. M. Haller, M. Billinghurst, B. Thomas. Augmented Reality: Interfaces and Design. ISBN: 1-59904-066-2. Idea Group Publishing

7. P. Milgram, F.Kishino. A Taxonomy of Mixed Reality Visual Displays. In IEICE Transactions on Information Systems, VolE77D,

No. 12, December 1994. 8. R.T. Azuma. A Survey of Augmented Reality. In Presence:Teleoperators and Virtual Environment, 6:4, 355-385, August 1997.

9. S. Markus, F.Y. Wang, B.G. Lee. Development of Edutainment Content for Elementary School Using Mobile Augmented Reality.

Proceeding of ICCRD‘12 (Chengdu, China, May 5-6, 2012). 10. Dr. AntoBennet, M ,Resmi R. Nair, Mahalakshmi V,Janakiraman G ―Performance and Analysis of Ground-Glass Pattern Detection in

Lung Disease based on High-Resolution Computed Tomography‖,Indian Journal of Science and Technology, Volume09 (Issue02):01-

07, January 2016 11. Dr. AntoBennet, M , Sankaranarayanan S, Ashokram S ,Dinesh Kumar T R,―Testing of Error Containment Capability in can

Network‖, International Journal of Applied Engineering Research, Volume 9, Number 19 (2014) pp. 6045-6054.

12. Dr. AntoBennet, M, Sankar Babu G, Natarajan S, ―Reverse Room Techniques for Irreversible Data Hiding ‖, Journal of Chemical and Pharmaceutical Sciences 08(03): 469-475, September 2015.

13. Dr. AntoBennet, M , Sankaranarayanan S, Sankar Babu G, ― Performance & Analysis of Effective Iris Recognition System Using Independent Component Analysis ‖, Journal of Chemical and Pharmaceutical Sciences 08(03): 571-576, August 2015.

14. Dr. AntoBennet, M, Suresh R, Mohamed Sulaiman S, ―Performance &analysis of automated removal of head movement artifacts in

EEG using brain computer interface‖, Journal of Chemical and Pharmaceutical Research 07(08): 291-299, August 2015. 15. Dr. AntoBennet, M ―A Novel Effective Refined Histogram For Supervised Texure Classification‖, International Journal of Computer

& Modern Technology , Issue 01 Volume02 ,pp 67-73, June 2015.

16. Dr. AntoBennet, M, Srinath R,Raisha Banu A,―Development of Deblocking Architectures for block artifact reduction in videos‖, International Journal of Applied Engineering Research, Volume 10, Number 09 (2015) pp. 6985-6991, April 2015.

245-248

52.

Authors: S.Venkatraman, M.Sundhararajan

Paper Title: A Metaheuristic Algorithm for VLSI Floorplanning Problem

Abstract: Floorplanning plays an important role within the physical design method of very large Scale

Integrated (VLSI) chips. It‘s a necessary design step to estimate the chip area before the optimized placement of

digital blocks and their interconnections. Since VLSI floorplanning is an NP-hard problem, several improvement

techniques were adopted to find optimal solution. In this paper, a hybrid algorithm which is genetic algorithm

combined with music-inspired Harmony Search (HS) algorithm is employed for the fixed die outline constrained

floorplanning, with the ultimate aim of reducing the full chip area. Initially, B*-tree is employed to come up with

the first floorplan for the given rectangular hard modules and so Harmony Search algorithm is applied in any

stages in genetic algorithm to get an optimum solution for the economical floorplan. The experimental results of

249-254

the HGA algorithm are obtained for the MCNC benchmark circuits.

Keyword: Genetic algorithm (GA), Harmony Search algorithm (HAS), Hybrid Genetic algorithm (HGA),

slicing floorplan References:

1. Tung-Chieh Chen, and Yao-Wen Chang, ―Modern floorplanning based on fast simulated annealing,‖ Proceedings of the 2005

international symposium on Physical design, April 03-06, 2005, San Francisco, California, USA. 2. Chang-Tzu Lin, De-Sheng Chen, Yi-Wen Wang, Hsin-Hsien Ho ―Modern Floorplanning with Abutment and Fixed-Outline

Constraints ‖IEEE transactions on systems, man, and cybernetics—part b: cybernetics, vol. 39, no. 1, February 2002.

3. Gracia Nirmala Rani., Rajaram .S ―Analysis & Design of VLSI Floorplanning Algorithms for Nano-Circuits‖ IntJAdv Engg Tech/Vol. VII/Issue I/Jan.March.,2016/527-532.

4. Fernando, Pradeep Ruben, "Genetic algorithm based design and optimization of VLSI ASICs and reconfigurable hardware" (2009).

Graduate Theses and Dissertations. 5. http://scholarcommons.usf.edu/etd/1963 N. Sherwani, Algorithms for VLSI Physical Design Automation, Kluwer Academic

Publishers, Boston, Mass, USA, 1999.

6. J. J. Grefenstette, R. Gopal, B. J. Rosmaita, and D. Van Gucht, ―Genetic Algorithms for the Traveling Salesman Problem,‖ Proceedings of the 1st International Conference on Genetic Algorithms, pp. 160-168, 1998.

7. Hameem Shanavas and Ramaswamy Kannan Gnanamurthy, ―Wirelength Minimization in Partitioning and Floorplanning Using

Evolutionary Algorithms,‖ VLSI Design, vol. 2011, Article ID 896241, 9 pages, 2011. doi:10.1155/2011/896241. 8. T.C.Chen and Y.W.Chang ―Modern floorplanning based on fast simulated annealing ―In Proc. ACM Int. Symp. Physical Design, San

Francisco, CA, Apr. 2005, pp. 104–112.

9. J.-M. Lin and Y.-W. Chang, ―TCG: A Transitive Closure Graph-Based Representation for Non-Slicing Floorplans‖, in Design Automation Conference, 2001.

10. J.P. Cohoon, S. Hegde, W. Martin, and D. Richards, ―Distributed genetic algorithms for the floorplan design problem,‖ IEEE

transactions on Computer-Aided Design of Integrated Circuits and Systems, vol: 10(4), pp. 483-492, 1991. 11. K. Hatta, S. Wakabayashi, and T. Koide, ―Solving the rectangular packing problem by an adaptive GA based on sequence-pair‖, in

Proceedings of ASPDAC, pp. 181-184, 1999.

12. S. N. Adya and I. L. Markov, "Fixed-outline Floorplanning: Enabling Hierarchical Design", IEEE Trans. on VLSI Systems, vol: 11(6), pp. 1120-1135, December 2003.

13. Venkatraman.S, Dr.M.Sundhararajan, ―Optimization of VLSI floorplanning using genetic algorithm‖ Journal of Chemical and

Pharmaceutical Sciences, JCPS Volume 10 Issue 1, January - March 2017,pp 311-316. 14. Chang-Tzu Lin, De-Sheng Chen, Yi-Wen Wang, Hsin-Hsien Ho ―Modern Floorplanning with Abutment and Fixed-Outline

Constraints ‖IEEE transactions on systems, man, and cybernetics—part b: cybernetics, vol. 39, no. 1, February 2002.

15. Venkatraman.S, Dr.M.Sundhararajan, ‖Particle swarm optimization algorithm for VLSI floorplanning problem‖ Journal of Chemical and Pharmaceutical Sciences, JCPS Volume 10 Issue 1, January - March 2017,pp 311-316.

16. Z. W. Geem, ―Harmony search algorithm for solving Sudoku‖, in Proceedings of the 11th international conference, KES 2007 and

XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I. 2007, Springer Verlag: Vietrisul Mare, Italy.

17. J. Fourie, S. Mills, and R. Green, ―Harmony filter: a robust visual tracking system using the improved harmony search algorithm,‖

Image and Vision Computing, vol. 28, no. 12, pp. 1702–1716, 2010.

53.

Authors: D.Sugumaran, C. R. Bharathi

Paper Title: Scheduling to maximize the data transfer rate for big data Applications in Cloud System

Abstract: In cloud platform, parallel computing is precisely one of the methods to handle various

computational tasks which need to perform fast on a large dataset. In a system each job was run by the respective

processors. Jobs may need to be accompanying through nodes and it will share resources. So scheduling is

important to share the resources and path diversity is very much of important in order to get the data within least

retrieval time. The existing scheduling algorithms should not efficiently find the optimum solution. In this paper

we make a survey to provide the better transfer scheduling algorithm for transfer the data within stipulated time,

to maximize the data transfer rate and to choose cost effective paths.

Keyword: Data center networks, parallel computing, maximizing data transfer, Cloud System References:

1. ―Scheduling Jobs across Geo-Distributed Datacenters with Max-Min Fairness‖, IEEE Transactions on Network Science and Engineering,2018, Chen, L., Liu, S., Li, B., Li, B.

2. ―Bandwidth-Aware Scheduling With SDN in Hadoop: A New Trend for Big Data‖, IEEE SYSTEMS JOURNAL, VOL. 11, NO. 4,

DECEMBER 2017, Peng Qin, Bin Dai, Benxiong Huang, and Guan Xu 3. ―Storage-Tag-Aware Scheduler for Hadoop Cluster‖,IEEE Access,2017, Qureshi, N.M.F., Shin, D.R., Siddiqui, I.F., Chowdhry, B.S.

4. ―Energy-Aware Scheduling of MapReduce Jobs for Big Data Applications‖, IEEE TRANSACTIONS ONPARALLEL AND

DISTRIBUTED SYSTEMS, VOL. 26, NO. 10, OCTOBER 2015, Mashayekhy, L., Nejad, M.M., Grosu, D., Zhang, Q., Shi, W. 5. ―Workflow scheduling in cloud computing environment using Cat Swarm Optimization‘, Souvenir of the 2014 IEEE International

255-258

Advance Computing Conference, IACC 2014, Saurabh Bilgaiyan, Santwana Sagnika, Madhabananda Das

6. A distributed data management system to support large-scale data analysis‖, Journal of Systems and Software,148, pp. 105-115, February 2019, Emara, T.Z.aEmail Author,Huang, J.Z.a,b

7. ―A Dynamical and Load-Balanced Flow Scheduling Approach for Big Data Centers in Clouds‖ IEEE TRANSACTIONS ON

CLOUD COMPUTING 2016, Feilong Tang Member, IEEE, Laurence T. Yang Senior Member, IEEE, Can Tang, Jie Li Senior Member, IEEE, and Minyi Guo Senior Member, IEEE

8. ―A Resource Co-Allocation method for load-balance scheduling over big data platforms‖, Future Generation Computer Systems

Volume 86, September 2018, Wanchun Dou a, *, Xiaolong Xu b , Xiang Liu a , Laurence T. Yang c,d , Yiping Wen a 9. ―Achieving Load Balance for Parallel Data Access on Distributed File Systems‖, IEEE Transactions on ComputersVolume 67, Issue

3, 1 March 2018, Dan Huang†, Dezhi Han∗‡, Jun Wang†, Jiangling Yin †, Xuhong Zhang†, Xunchao Chen†, Jian Zhou†, Mao Ye†, 10. ―Adaptive Workflow Scheduling on Cloud Computing Platforms with Iterative Ordinal Optimization‖, IEEE Transactions on Cloud

Computing, 2015, Zhang, F., Cao, J., Hwang, K., Li, K., Khan, S.U.

11. ―Concurrent Bandwidth Reservation Strategies for Big Data Transfers in High-Performance Networks‖, IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, VOL. 12, NO. 2, JUNE 2015, Liudong Zuo, Student Member, IEEE, and Michelle

M. Zhu, Member, IEEE

12. ―Enhancement of Task Scheduling Technique of Big Data Cloud Computing‘,. 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems, icABCD 20188465422, Abed, S., Shubair, D.S.

13. ―Improving Data Center Network Utilization Using Near-Optimal Traffic Engineering‖, IEEE TRANSACTIONS ON PARALLEL

AND DISTRIBUTED SYSTEMS, VOL. 24, NO. 6, JUNE 2013, Tso, F.P., Pezaros, D.P.

14. ―Locality-aware Scheduling for Containers in Cloud Computing‖, IEEE Transactions on Cloud Computing,2018, Zhao,

D., Mohamed, M., Ludwig, H.

15. ―Profile-based power-aware workflow scheduling frame work for energy-efficient data centers‖, Future Generation Computer Systems,2019, Qureshi, B.

16. ‖objective Cat Swarm Optimization Algorithm for Workflow Scheduling in Cloud Computing Environment‖, Advances in Intelligent

Systems and Computing Volume 308 AISC, Issue VOLUME 1, 2015, Saurabh Bilgaiyan, Santwana Sagnika and Madhabananda Das 17. ―PRISM: Fine-Grained Resource-Aware Scheduling for Map Reduce‖, IEEE Transactions on Cloud Computing,2015, Zhang,

Q., Zhani, M.F., Yang, Y., Boutaba, R., Wong, B. 18. ―Joint Static and Dynamic Traffic Scheduling in Data Center Networks‖, IEEE/ACMTRANSACTIONSONNETWORKING,2016,

Cao, Z., Kodialam, M., Lakshman, T.V.

19. ―Intelligent Scheduling for Parallel Jobs in Big Data Processing Systems‖, 2019 International Conference on Computing, Networking and Communications, 2019, Xu.,M, Wu, C.Q., Hou, A., Wang, Y.

20. ―Cross-platform Resource Scheduling for Spark and Map Reduce on YARN‖, IEEE TRANSACTIONS ON COMPUTERS, VOL. ,

NO. , NOVEMBER 2016, Dazhao Cheng, Xiaobo Zhou, Palden Lama, Jun Wu and Changjun Jiang 21. ―DyScale: a MapReduce Job Scheduler for Heterogeneous Multicore Processors‖, IEEE Transactions on Cloud Computing 5(2), pp.

317-330, 2017, Yan, F., Cherkasova, L., Zhang, Z., Smirni, E.

22. ―Flutter: Scheduling Tasks Closer to Data Across Geo-Distributed Datacenters‖, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, Zhiming Hu1, Baochun Li2, and Jun Luo1