internationaljournalofengineering international … › wp-content › uploads ›...

Editor-In-Chief Chair Dr. Shiv Kumar

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

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

Associated Editor-In-Chief Chair Prof. MPS Chawla

Member of IEEE, Professor-Incharge (head)-Library, Associate Professor in Electrical Engineering, G.S. Institute of Technology &

Science Indore, Madhya Pradesh, India, Chairman, IEEE MP Sub-Section, India

Dr. Vinod Kumar Singh

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

Dr. Rachana Dubey

Ph.D.(CSE), MTech(CSE), B.E(CSE)

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

Bhopal (M.P.), India

Associated Editor-In-Chief Members

Dr. Hai Shanker Hota

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

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

Dr. Gamal Abd El-Nasser Ahmed Mohamed Said

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

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

Transport, Egypt

Dr. Mayank Singh

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

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

Natal, Durban, South Africa.

Scientific Editors

Prof. (Dr.) Hamid Saremi

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

Dr. Moinuddin Sarker

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

Stamford, USA.

Prof. (Dr.) Nishakant Ojha

Principal Advisor (Information &Technology) His Excellency Ambassador Republic of Sudan& Head of Mission in New Delhi, India

Dr. Shanmugha Priya. Pon

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

Dr. Veronica Mc Gowan

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

China.

Dr. Fadiya Samson Oluwaseun

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

Cyprus, Turkey.

Dr. Robert Brian Smith

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

Centre, North Ryde, New South Wales, Australia

Dr. Durgesh Mishra

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

Special Issue Section Editor

Mr. Siddth Kumar

Founder and Managing Director, IFERP, Technoarete Groups, India

Mr. Rudra Bhanu Satpathy

Founder and Managing Director, IFERP, Technoarete Groups, India

Dr. Mahdi Esmaeilzadeh

Founder & Chairman, of Scientific Research Publishing House (SRPH), Mashhad, Iran

Executive Editor Chair

Dr. Deepak Garg

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

Executive Editor Members

Dr. Vahid Nourani

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

Dr. Saber Mohamed Abd-Allah

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

Dr. Xiaoguang Yue

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

Dr. Labib Francis Gergis Rofaiel

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

Mansoura, Egypt.

Dr. Hugo A.F.A. Santos

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

Dr. Sunandan Bhunia

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

(Bengal), India.

Dr. Awatif Mohammed Ali Elsiddieg

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

Saudi Arabia.

Technical Program Committee Chair

Dr. Mohd. Nazri Ismail

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

Technical Program Committee Members

Dr. 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.

Dr.Ch.V. Raghavendran

Professor, Department of Computer Science & Engineering, Ideal College of Arts and Sciences Kakinada (Andhra Pradesh), India.

Dr. Thanhtrung Dang

Associate Professor & Vice-Dean, Department of Vehicle and Energy Engineering, HCMC University of Technology and Education,

Hochiminh, Vietnam.

Dr. Wilson Udo Udofia

Associate Professor, Department of Technical Education, State College of Education, Afaha Nsit, Akwa Ibom, Nigeria.

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. Wameedh Riyadh Abdul-Adheem

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

Dr. T. Sheela

Associate Professor, Department of Electronics and Communication Engineering, Vinayaka Mission’s Kirupananda Variyar

Engineering College, Periyaseeragapadi (Tamil Nadu), India

Dr. Manavalan Ilakkuvan

Veteran in Engineering Industry & Academics, Influence & Educator, Tamil University, Thanjavur, India

Dr. Shivanna S.

Associate Professor, Department of Civil Engineering, Sir M.Visvesvaraya Institute of Technology, Bengaluru (Karnataka), India

Dr. H. Ravi Kumar

Associate Professor, Department of Civil Engineering, Sir M.Visvesvaraya Institute of Technology, Bengaluru (Karnataka), India

Dr. Pratik Gite

Assistant Professor, Department of Computer Science and Engineering, Institute of Engineering and Science (IES-IPS), Indore (M.P),

India

Dr. S. Murugan

Professor, Department of Computer Science and Engineering, Alagappa University, Karaikudi (Tamil Nadu), India

Dr. S. Brilly Sangeetha

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

India

Dr. P. Malyadri

Professor, ICSSR Senior Fellow Centre for Economic and Social Studies (CESS) Begumpet, Hyderabad (Telangana), India

Dr. K. Prabha

Assistant Professor, Department of English, Kongu Arts and Science College, Coimbatore (Tamil Nadu), India

Dr. Liladhar R. Rewatkar

Assistant Professor, Department of Computer Science, Prerna College of Commerce, Nagpur (Maharashtra), India

Dr. Raja Praveen.N

Assistant Professor, Department of Computer Science and Engineering, Jain University, Bengaluru (Karnataka), India

Dr. Issa Atoum

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

University, Amman- Jordan

Dr. Balachander K

Assistant Professor, Department of Electrical and Electronics Engineering, Karpagam Academy of Higher Education, Pollachi

(Coimbatore), India

Dr. Sudhan M.B

Associate Professor & HOD, Department of Electronics and Communication Engineering, Vins Christian College of Engineering,

Anna University, (Tamilnadu), India

Dr. T. Velumani

Assistant Professor, Department of Computer Science, Kongu Arts and Science College, Erode (Tamilnadu), India

Dr. Subramanya.G.Bhagwath

Professor and Coordinator, Department of Computer Science & Engineering, Anjuman Institute of Technology & Management

Bhatkal (Karnataka), India

Dr. Mohan P. Thakre

Assistant Professor, Department of Electrical Engineering, K. K. Wagh Institute of Engineering Education & Research Hirabai

Haridas Vidyanagari, Amrutdham, Panchavati, Nashik (Maharashtra), India

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

S. No

Volume-9 Issue-1S3, December 2019, ISSN: 2278-3075 (Online)

Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Page No.

1.

Authors: Roshini M, V Poompavai, K Ganesha Raj

Paper Title: Pixel based Classification of Poultry Farm using Satellite Images

Abstract: Remote sensing has emerged as a compelling tool to survey and monitor natural resources and

other features of an area due to the inherent advantages of synoptic view, repetitive nature and capability to

study inaccessible areas. Satellite data/aerial photos are interpreted using keys such as colour/tone, texture,

pattern, association, size, shape, etc., and computer-based techniques. Presently geospatial technology is used in

various sectors like agriculture, forestry, geology, marine, urban and rural planning and so on, with applications

in agriculture seeing a rise in India. This paper elaborates on the method employed for identification of poultry

farms in India, using images from satellites such as CARTOSAT and RESOURCESAT (LISS4) and also

Google Earth Images. Each poultry farm varies in the size and number of poultry sheds which further depend on

the number of chickens bred, location of vegetation and water resources nearby, temperature and humidity of

location, etc. Thus, based on these factors, training sites in Hessarghatta, Harohalli, Dommasandra near

Bengaluru City, Karnataka were identified. The paper elucidates application of vegetation and water masks

using the classification of NDVI. Two pixel-based classification techniques - Maximum Likelihood Classifier

and K-Nearest Neighbour Classifier using SNAP Application were applied. Statistics were observed for the

accuracy of classified output, and it was shown that Maximum Likelihood Classifier provided more accurate

results. The method presented in this paper can be fine-tuned and applied for poultry farms anywhere by

studying Poultry Farms in different terrains and using various associations to identify them.

Keyword: Remote Sensing, Poultry Farms, Pixel Based Classification References: 1. Poultry Farm and Manual Reference guide for central and state poultry farms 2014-15” document released by Department of Animal

Husbandry. 2. Facilitator Guide- Small Poultry Farmer” by Agriculture Skill Council of India 3. http://agritech.tnau.ac.in/expert_system/poultry/ Poultry%20House%20Construction.html 4. https://homepages.inf.ed.ac.uk/rbf/HIPR2/erode. htm 5. SNAP - ESA Sentinel Application Platform v2.0.2, http://step.esa.int

1-6

2.

Authors: Sailee Dalvi, Gilad Gressel, Krishnashree Achuthan

Paper Title: Tuning the False Positive Rate / False Negative Rate with Phishing Detection Models

Abstract: Phishing attacks have risen by 209% in the last 10 years according to the Anti Phishing Working

Group (APWG) statistics [19]. Machine learning is commonly used to detect phishing attacks. Researchers have

traditionally judged phishing detection models with either accuracy or F1-scores, however in this paper we argue

that a single metric alone will never correlate to a successful deployment of machine learning phishing detection

model. This is because every machine learning model will have an inherent trade-off between it’s False Positive

Rate (FPR) and False Negative Rate (FNR). Tuning the trade-off is important since a higher or lower FPR/FNR

will impact the user acceptance rate of any deployment of a phishing detection model. When models have high

FPR, they tend to block users from accessing legitimate webpages, whereas a model with a high FNR will allow

the users to inadvertently access phishing webpages. Either one of these extremes may cause a user base to

either complain (due to blocked pages) or fall victim to phishing attacks. Depending on the security needs of a

deployment (secure vs relaxed setting) phishing detection models should be tuned accordingly. In this paper, we

demonstrate two effective techniques to tune the trade-off between FPR and FNR: varying the class distribution

of the training data and adjusting the probabilistic prediction threshold. We demonstrate both techniques using a

data set of 50,000 phishing and 50,000 legitimate sites to perform all experiments using three common machine

learning algorithms for example, Random Forest, Logistic Regression, and Neural Networks. Using our

techniques we are able to regulate a model’s FPR/FNR. We observed that among the three algorithms we used,

Neural Networks performed best; resulting in an higher F1-score of 0.98 with corresponding FPR/FNR values of

0.0003 and 0.0198 respectively.

Keyword: Machine Learning, Phishing Detection, Model Tuning, Cyber-security References: 1. , S. and Asokan, N., 2018. On designing and evaluating phishing webpage detection techniques for the real world. In 11th USENIX

Workshop on Cyber Security Experimentation and Test (CSET 18).

2. Marchal, S., Saari, K., Singh, N. and Asokan, N., 2016, June. Know your phish: Novel techniques for detecting phishing sites and their targets. In 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS) (pp. 323-333). IEEE.

3. Tharwat, A., 2018. Classification assessment methods. Applied Com-puting and Informatics. 4. Zhang, X.D., 2010. An effective method for controlling false discovery and false nondiscovery rates in genome-scale RNAi screens.

Journal of biomolecular screening, 15(9), pp.1116-1122.

5. De Smet, F., Moreau, Y., Engelen, K., Timmerman, D., Vergote, I. and De Moor, B., 2004. Balancing false positives and false negatives for the detection of differential expression in malignancies. British Journal of Cancer, 91(6), p.1160.

6. Vazhayil, A., Harikrishnan, N.B., Vinayakumar, R., Soman, K.P. and Verma, A.D.R., 2018. PED-ML: Phishing email detection using classical machine learning techniques. In Proc. 1st AntiPhishing Shared Pilot 4th ACM Int. Workshop Secur. Privacy Anal.(IWSPA)

(pp. 1-8). Tempe, AZ, USA.

7-13

7. Whittaker, C., Ryner, B. and Nazif, M., 2010. Large-scale automatic classification of phishing pages. 8. Baader, G. and Krcmar, H., 2018. Reducing false positives in fraud detection: Combining the red flag approach with process mining.

Inter-national Journal of Accounting Information Systems, 31, pp.1-16.

9. Miyamoto, D., Hazeyama, H. and Kadobayashi, Y., 2008, November. An evaluation of machine learning-based methods for detection of phishing sites. In International Conference on Neural Information Processing (pp. 539-546). Springer, Berlin, Heidelberg.

10. Ma, J., Saul, L.K., Savage, S. and Voelker, G.M., 2009, June. Beyond blacklists: learning to detect malicious web sites from suspicious URLs. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1245-1254). ACM.

11. Fette, I., Sadeh, N. and Tomasic, A., 2007, May. Learning to detect phishing emails. In Proceedings of the 16th international conference on World Wide Web (pp. 649-656). ACM.

12. ] Darling, M., Heileman, G., Gressel, G., Ashok, A. and Poornachandran, P., 2015, July. A lexical approach for classifying malicious URLs. In 2015 international conference on high performance computing simula-tion (HPCS) (pp. 195-202). IEEE.

13. Le, A., Markopoulou, A. and Faloutsos, M., 2011, April. Phishdef: Url names say it all. In 2011 Proceedings IEEE INFOCOM (pp. 191-195). IEEE.

14. Whittaker, C., Ryner, B. and Nazif, M., 2010. Large-scale automatic classification of phishing pages. 15. Ra, V., HBa, B.G., Ma, A.K., KPa, S., Poornachandran, P. and Verma, A.D.R., 2018. DeepAnti-PhishNet: Applying deep neural

networks for phishing email detection. In Proc. 1st AntiPhishing Shared Pilot 4th ACM Int. Workshop Secur. Privacy Anal.(IWSPA)

(pp. 1-11). Tempe, AZ, USA.

16. Zhang, N. and Yuan, Y., 2013. Phishing detection using neu-ral network. Department of Computer Science, Department of Statistics, Stanford University, CA, available at: http://cs229. stan-ford. edu/proj2012/ZhangYuan-PhishingDetectionUsingNeuralNetwork. pdf

(accessed April 23, 2016).[Google Scholar].

17. Xiang, G., Hong, J., Rose, C.P. and Cranor, L., 2011. Cantina+: A feature-rich machine learning framework for detecting phishing web sites. ACM Transactions on Information and System Security (TISSEC), 14(2), p.21.

18. De Smet, F., Moreau, Y., Engelen, K., Timmerman, D., Vergote, I. and De Moor, B., 2004. Balancing false positives and false negatives for the detection of differential expression in malignancies. British Journal of Cancer, 91(6), p.1160.

19. APWG, G.A. and Manning, R., 2008-2018. APWG Phishing Reports. 20. Seleniumhq.org. (2019). Selenium WebDriver. [online] Available at: https://www.seleniumhq.org/projects/webdriver/ [Accessed 7

Jul. 2019]. 21. Phishtank.com. (2019). PhishTank — Join the fight against phishing. [online] Available at: http://phishtank.com/ [Accessed 7 Jul.

2019].

22. Majestic.com. (2019). Majestic Million - Majestic. [online] Available at: https://majestic.com/reports/majestic-million [Accessed 7 Jul. 2019].

23. Dr. Gowtham R., Gupta, J., and Gamya, P. G., Identification of phishing webpages and its target domains by analyzing the feign relationship, Journal of Information Security and Applications (Elsevier) (SNIP: 1.186) , vol. 35, pp. 75-84, 2017.

24. Chrome Driver (2019). Google Chrome WebDriver. [online] Available at: http://chromedriver.chromium.org/downloads [Accessed 7 Jul. 2019].

3.

Authors: Punna Bharghava, N. Srikanta, Pachiyaannan Muthusamy

Paper Title: MIMO Antenna for UWB with Single Tuned Frequency Notched Characteristics using Parasitic

Element Method

Abstract: A MIMO antenna with micro strip fed ultra wide band nature with characteristics of single band

notching is presented in this paper. MIMO antenna has two monopole antennas. Larger impedance bandwidth is

obtained by providing slots beside the feed line on ground plane. By using parasitic element on back side of

patch band notching characteristics cab be obtained. Here, antenna size is 44x22x1.6 mm3. This antenna

operates over the frequency band 4GHz to 11GHz with notched frequency band 5.1GHz to 5.9GHz. By keeping

two monopole antennas perpendicular to each other, isolation of less than -15dB is obtained and good value of

ECC is obtained.

Keyword: Band notched characteristics, micro strip fed monopole antennas, MIMO antennas, parasitic

element, Ultra wide band (UWB) antenna References: 1. FCC, F., “First report and order on ultra-wideband technology,” 2002 2. Jung, Jihak, Wooyoung Choi, and Jaehoon Choi. "A small wideband microstrip-fed monopole antenna." IEEE microwave and

wireless components letters 15, no. 10 (2005): 703-705.

3. Verbiest, Joeri R., and Guy AE Vandenbosch. "A novel small-size printed tapered monopole antenna for UWB WBAN." IEEE Antennas and Wireless Propagation Letters 5 (2006): 377-379. K. Elissa, “Title of paper if known,” unpublished.

4. Pan, Chien-Yuan, Tzyy-Sheng Horng, Wen-Shan Chen, and Chien-Hsiang Huang. "Dual wideband printed monopole antenna for WLAN/WiMAX applications." IEEE Antennas and Wireless Propagation Letters 6 (2007): 149-151.

5. Kim, K-H., Y-J. Cho, S-H. Hwang, and S-O. Park. "Band-notched UWB planar monopole antenna with two parasitic patches." Electronics Letters 41, no. 14 (2005): 783-785.

6. Liu, Li, S. W. Cheung, and T. I. Yuk. "Compact MIMO antenna for portable devices in UWB applications." IEEE Transactions on antennas and propagation 61, no. 8 (2013): 4257-4264.

14-16

4.

Authors: G.Naga Jyothi Sree, Suman Nelaturi

Paper Title: Design of Meandered Line MIMO Antenna at a Frequency of 3.5 Ghz for 5G Application

Abstract: The meandered line structure MIMO formation with a dimension of 40 x 23 mm2 demonstrated

here. This formation structure maintained the transmission as well as reflection coefficients ≤ -10 dB & -20 dB.

In distinction to this formation structure is described by variation distance between the patches is 2 mm to

reducing the interference. Meanwhile placing of the patches vertical and horizontal one co inside with each other

isolation at the multi-band frequencies identified that a better improvement in isolation. This formation of

system developed stable patterns of radiation, gain, diversity gain and group delay. The antenna simulations are

taken by using computer simulation technology (CST).

Keyword: ECC, group delay, meandered line MIMO , radiation efficiency

17-19

References: 1. L. Kang, H. Li, X. Wang and X. Shi, Compact offset microstrip-fed MIMO antenna for band-notched UWB applications, IEEE

Antennas Wireless Propagat. Lett. 14 (2015) 1754.

2. C.-X. Mao, Q.-X. Chu, Y.-T. Wu, and Y.-H. Qian, “Design and investigation of closely-packed diversity UWB slot-antenna with high isolation,” Progress in Electromagnetics Research C, vol. 41, pp. 13–25, 2013.

3. K.J. Babu, R.W. Aldhaheri, M.Y. Talha and I.S. Alruhaili, Design of a compact two element MIMO antenna system with improved isolation, Progr. Electromag. Res. Lett. 48 (2014) 27.

4. S. S. Jehangir and M. S. Sharawi, “A miniaturized uwb biplanar yagi-like mimo antenna system,” IEEE Antennas and Wireless Propagation Letters, vol. 16, pp. 2320–2323, 2017.

5. P. Gao, S. He, X. Wei, Z. Xu, N. Wang and Y. Zheng, Compact printed UWB diversity slot antenna with 5.5 GHz band-notched characteristics, IEEE Antennas Wireless Propagat. Lett. 13 (2014) 376.

6. J. Ren, W. Hu, Y. Yin, and R. Fan, “Compact printed MIMO antenna for UWBapplications,” IEEE Antennas and Wireless Propagation Letters, vol. 13, pp. 1517–1520, 2014.

7. Z.-H. Tu, W.-A. Li and Q.-X. Chu, Single-layer differential CPW-fed notch-band tapered-slot UWB antenna, IEEE Antennas Wireless Propagat. Lett. 13 (2014) 1296.

8. L. Liu, S. W. Cheung, and T. I. Yuk, “Compact MIMO antenna for portable devices in UWB applications,” IEEE Transactions on Antennas and Propagation, vol. 61, no. 8, pp. 4257–4264, 2013

9. R. Mathur and S. Dwari, “Compact CPW-Fed ultrawideband MIMO antenna using hexagonal ring monopole antenna elements,” AEU - International Journal of Electronics and Commu- ¨ nications, vol. 93, pp. 1–6, 2018

10. Aw, M. S., K. Ashwath, and T. Ali. "A compact two element MIMO antenna with improved isolationfor wireless applications." Journal of Instrumentation 14.06 (2019): P06014

5.

Authors: Shaik. Jakeer Hussain, Gudapati Ramyasri

Paper Title: Security Enhancement for Devices using REST-API, Middleware & IoT Gateway

Abstract: Internet of things plays the major innovative role in the enhancement and optimization of habitual

behavior by the collaborative usage of smart objects and smart sensors. One of the major challenges that is being

faced is secure interconnection of iot devices, sensors, actuators to the cloud. As most of the IoT devices and

cloud usage is done using the third party, it is required to provide IoT security such that the attackers cant disturb

the communication path through the devies and also to provide secure data transmission from devices to cloud.

This paper mainly displays the use of middleware along with Gateway and REST API Technologies for the

secure interface between the devices, sensors and storage applications.

Keyword: Internet of Things, security, Gateway, REST API, Middleware. References:

1. da Cruz, Mauro AA, et al. "A reference model for internet of things middleware." IEEE Internet of Things Journal 5.2 (2018): 871-883.

2. Kodali, Ravi Kishore, and SreeRamya Soratkal. "MQTT based home automation system using ESP8266." 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC). IEEE, 2016.

3. Fremantle, Paul, and Philip Scott. "A survey of secure middleware for the Internet of Things." PeerJ Computer Science 3 (2017): e114.

4. Luoto, Antti, and Kari Systä. "Fighting network restrictions of request-response pattern with MQTT." IET Software 12.5 (2018): 410-417.

5. M. Wazid, A. K. Das, V. Odelu, N. Kumar, M. Conti and M. Jo, "Design of Secure User Authenticated Key Management Protocol for Generic IoT Networks," in IEEE Internet of Things Journal, vol. 5, no. 1, pp. 269-282, Feb. 2018.

6. Joseph, Tintu, et al. "IoT middleware for smart city:(An integrated and centrally managed IoT middleware for smart city)." 2017 IEEE Region 10 Symposium (TENSYMP). IEEE, 2017.

7. Garg, Hittu, and Mayank Dave. "Securing IoT Devices and SecurelyConnecting the Dots Using REST API and Middleware." 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU). IEEE, 2019.

8. Fang, Shifeng, et al. "An integrated system for regional environmental monitoring and management based on internet of things." IEEE Transactions on Industrial Informatics 10.2 (2014): 1596-1605.

9. Cheng, Bo, et al. "Lightweight service mashup middleware with REST style architecture for IoT applications." IEEE Transactions on Network and Service Management 15.3 (2018): 1063-1075.

10. Tiburski, Ramao Tiago, et al. "The role of lightweight approaches towards the standardization of a security architecture for IoT middleware systems." IEEE Communications Magazine 54.12 (2016): 56-62.

11. Ngu, Anne H., et al. "IoT middleware: A survey on issues and enabling technologies." IEEE Internet of Things Journal 4.1 (2016): 1-20.

12. Kodali, Ravi Kishore, and Venkata Sundeep Kumar Gorantla. "Weather tracking system using MQTT and SQLite." 2017 3rd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT). IEEE, 2017.

13. Kodali, Ravi Kishore, and Kopulwar Shishir Mahesh. "Low cost implementation of smart home automation." 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2017.

14. F. D. Hudson, "Enabling Trust and Security: TIPPSS for IoT," in IT Professional, vol. 20, no. 2, pp. 15-18, Mar./Apr. 2018. doi: 10.1109/MITP.2018.021921646.

15. R. T. Tiburski, L. A. Amaral, E. de Matos, D. F. G. de Azevedo and F. Hessel, "The Role of Lightweight Approaches Towards the Standardization of a Security Architecture for IoT Middleware Systems," in IEEE Communications Magazine, vol. 54, no.

12, pp. 56-62, December 2016.

20-23

6.

Authors: V. Vijayaraghavan, M. Laavanya

Paper Title: Vehicle Classification and Detection using Deep Learning

Abstract: Intelligent transportation systems have acknowledged a ration of attention in the last decades. In

this area vehicle classification and localization is the key task. In this task the biggest challenge is to

discriminate the features of different vehicles. Further, vehicle classification and detection is a hard problem to

identify and locate because wide variety of vehicles don’t follow the lane discipline. In this article, to identify

and locate, we have created a convolution neural network from scratch to classify and detect objects using a

modern convolution neural network based on fast regions. In this work we have considered three types of

vehicles like bus, car and bike for classification and detection. Our approach will use the entire image as input

24-28

and create a bounding box with probability estimates of the feature classes as output. The results of the

experiment have shown that the projected system can considerably improve the accuracy of the detection.

Keyword: Convolutional neural network, Object detection, Deep learning, Image classification.

References: 1. PF Felzenszwalb, RB Girshick, D Mcallester, and D Ramanan, “Object detection with discriminatively trained part-based models,”

IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 9, pp. 1627-1645, 2010.

2. Y Jia, E Shelhamer, J Donahue, S Karayev, J Long, R Girshick, S Guadarrama, and T Darrell, “Caffe: Convolutional architecture for fast feature embedding,” Proceedings of the 22nd ACM international conference on Multimedia, Orlando, Florida, USA, 03-07 November, 2014, pp 675-678.

3. A Krizhevsky, I Sutskever, and GE Hinton, “Imagenet classification with deep convolutional neural networks,” Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, 03-06 December, 2012, Vol. 1, pp. 1097-1105.

4. Z Cao, T Simon, S-E Wei, and Y Sheikh, “Real time multi-person 2D pose estimation using part affinity fields,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21-26 July 2017.

5. Z Yang, and R Nevatia, “A multi-scale cascade fully convolutional network face detector,” Proceedings of the 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico, 4-8 December 2016.

6. GE Hinton, and RR Salakhutdinov, “Reducing the dimensionality of data with neural networks”, Science Journal, Vol. 313, No. 5786, pp. 504-507, 2006.

7. Y Bengio, Learning deep architectures for AI. Journal Foundations and Trends in Machine Learning, Vol. 2, No. 1, pp. 1-127, January 2009.

8. J Redmon, S Divvala, R Girshick, and A Farhadi, “You only look once: Unified, real-time object detection,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27-30 June 2016.

9. S Ren, K He, R Girshick, and J Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 6, pp. 1137-1149, 2017.

10. R Girshick, J Donahue, T Darrell, and J Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation”, Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 23-28 June 2014, pp. 580-587.

11. JRR Uijlings, KEA Van De Sande, T Gevers, and AWM Smeulders, “Selective search for object recognition”, International Journal of Computer Vision, Vol. 104, No. 2, pp. 154-171, 2013.

12. CL Zitnick, and P Dollár, “Edge boxes: Locating object proposals from edges”, Proceedings of the European Conference on Computer Vision, 391-405, 2014.

13. R Girshick, “Fast R-CNN”, Proceedings of the IEEE International Conference on Computer Vision (ICCV), Washington, DC, USA, 07-13 December 2015, pp. 1440-1448.

14. K Lenc, and A Vedaldi, “R-CNN minus R”, Computer Vision and Pattern Recognition, pp. 1-12, 2015. 15. H Jiang, and EL Miller, “Face detection with the faster R-CNN”, Proceedings of the 12th IEEE International Conference on Automatic

Face and Gesture Recognition, Washington, DC, USA, 30 May-3 June 2017, pp. 650-657. 16. YH Byeon, and KC Kwak, “A performance comparison of pedestrian detection using faster RCNN and ACF”, Proceedings of the 2017

6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017, Hamamatsu, Japan, 9-13 July 2017, pp. 858-863. 17. X Zhao, W Li, Y Zhang, TA Gulliver, S Chang, and Z Feng, “A faster RCNN-based pedestrian detection system”, Proceedings of the

IEEE Vehicular Technology Conference, Montreal, QC, Canada, 18-21 September 2016.

18. MC Roh, and JY Lee, “Refining faster-RCNN for accurate object detection”, Proceedings of the 15th IAPR International Conference on Machine Vision Applications, Nagoya, Japan, 8-12 May 2017, pp. 514-517.

19. M Laavanya, and V Vijayaraghavan, “A sub-band adaptive visushrink in wavelet domain for image denoising”, International Journal of Recent Technology and Engineering, Vol. 7, No. 5S4, pp. 289-291, 2019.

20. M Laavanya, and M Karthikeyan, “Dual tree complex wavelet transform incorporating SVD and bilateral filter for image denoising”, International Journal of Biomedical Engineering and Technology (Inderscience), Vol. 26, No. 3-4, pp. 266-278, 2018.

21. V Vijayaraghavan, M Laavanya, and M Karthikeyan, “Real oriented 2-D dual tree wavelet transform with non-local means filter for image denoising”, Journal of Electrical Engineering, Vol. 17, No.2, pp. 106-111, 2017.

7.

Authors: Avvaru Srinivasulu, Y Dileep Kumar

Paper Title: Effect of PCA Feature Reduction on Ventricular Ectopic Beat Classification

Abstract: Due to the cardiac diseases called Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF)

causes sudden cardiac death, it is crucial to recognize Ventricular Ectopic Beats (VEB) in Electrocardio- gram

for the early diagnosis. There are many algorithms proposed earlier to classify the VEBs. Even though those

algorithms achieved good accuracy, the size of the feature set is large and not precise. In addition, earlier

algorithms used feature reduction methods for reducing the feature set. Therefore, in this paper, we extracted

only five features namely, Pre RR interval, post RR interval, QRS duration, QR slope, and RS slope. Later, we

applied the Principle Component Analysis (PCA) for reducing the size of the feature set to observe the effect of

Feature Reduction (FR) on the accuracy of VEB Classification. We applied different classifiers for classifying

the cardiac beats in to normal and VEBs. Finally, using K-means Nearest Neighborhood (KNN) classifier and

cubic Support Vec- tor Machine (SVM) classifier, we achieved 97.4% classification accuracy, 98.38%, 88.89%

& 98.37% sensitivity, specificity & positive predictivity respectively. In addition, it is observed that by applying

PCA-FR, the classification accuracy was reduced by a maximum of 3.7%.

Keyword: Electrocardiogram (ECG), ventricular ectopic beats (VEB), Feature Reduction (FR), K-means

Nearest Neighborhood (KNN) classifier, Support Vector Machine (SVM) classifier, Principle Component

Analysis (PCA). References: 1. Ng, G. Andŕe,: Treating patients with ventricular ectopic beats. Heart 92.11, pp 1707-1712 (2006) 2. www.cardionetics.com/ventricular-ectopic-beats 3. James E Keany, et.al.,: Premature Ventricular Contraction. Medscape, Article, 761148, Updated: Jan 13 (2017) 4. [De Chazal, Philip: Detection of supraventricular and ventricular ectopic beats using a single lead ECG. In Engineering in

Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, IEEE, pp. 45-48 (2013)

5. De Chazal, Philip, et.al.: Automatic classification of heartbeats using ECG morphology and heartbeat interval features.

29-32

https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34https://ieeexplore.ieee.org/xpl/conhome/8097368/proceedinghttps://ieeexplore.ieee.org/xpl/conhome/8097368/proceedinghttps://ieeexplore.ieee.org/xpl/conhome/7893644/proceedinghttps://ieeexplore.ieee.org/xpl/conhome/7893644/proceedinghttps://www.researchgate.net/journal/0162-8828_IEEE_Transactions_on_Pattern_Analysis_and_Machine_Intelligencehttps://www.researchgate.net/journal/0162-8828_IEEE_Transactions_on_Pattern_Analysis_and_Machine_Intelligencehttps://link.springer.com/journal/11263https://link.springer.com/journal/11263https://link.springer.com/journal/11263/104/2/page/1https://link.springer.com/conference/eccvhttps://link.springer.com/conference/eccvhttp://www.cardionetics.com/ventricular-ectopic-beats

IEEE Transactions on Biomedical Engi- neering 51.7, pp. 1196-1206 (2004)

6. Sadiq, Ismail, and Shoab Ahmad Khan.: Heartbeat classification of ECGs using morphology and beat intervals. In Bioinformatics and Biomedical Engineer- ing,(iCBBE) 2011 5th International Conference on, IEEE, pp. 1-5 (2011)

7. Hammed, Norhan S., and Mohamed I. Owis.: Patient adaptable ventricular ar- rhythmia classifier using template matching. In Biomedical Circuits and Systems Conference (BioCAS), IEEE, pp. 1-4 (2015)

8. Chow, H. S., Moody, G. B., & Mark, R. G.: Detection of ventricular ectopic beats using neural networks. In Computers in Cardiology, Proceedings of IEEE, pp. 659- 662 (1992)

9. . Wiens, J., and John V. Guttag.: Patient-adaptive ectopic beat classification using active learning. In Computing in Cardiology, IEEE, pp. 109-112 (2010)

10. Dotsinsky, Ivan A., and Todor V. Stoyanov.: Ventricular beat detection in single channel electrocardiograms. Biomedical engineering online 3, no. 1, pp. 3 (2004)

11. . Wiens, Jenna, and John V. Guttag.: Patient-specific ventricular beat classification without patient-specific expert knowledge: A transfer learning approach. In Engineering in Medicine and Biology Society, EMBC, 2011 Annual

International Conference of the IEEE, pp. 5876-5879 (2011)

12. Baali, Hamza, and Mostefa Mesbah.: Ventricular ectopic beats classification using sparse representation and Gini Index. In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, pp. 5821-

5824 (2015)

13. Yeh Yun-Chi, et.al.: QRS complexes detection for ECG signal: The Difference Operation Method. Computer methods and programs in biomedicine 91.3, pp 245- 254 (2008)

14. Vasan, et.al.: Dimensionality reduction using Principal Component Analysis for network intrusion detection. Perspectives in Science 8, pp 510-512. (2016)

8.

Authors: Amairullah Khan Lodhi, M.S.S Rukmini, Syed Abdulsattar

Paper Title: Efficient Energy Routing Protocol based on Energy & Buffer Residual Status (EBRS) for Wireless

Sensor Networks

Abstract: Wireless networks consist of nodes, having the ability that, they can sense and collect the

information from the nearby surroundings. It has the responsibility of designed protocol to send this collected

information by data gathering and forward it to the outside network via a sink node. Furthermore, WSNs doesn’t

need any predetermined network structure; all the nodes used in WSN can operate as a router as well as the host.

It uses multiple hops to send information to the node outside the communication range through different

neighbor nodes. All the sensor nodes in WSN have their range of communication and can send and collect

messages straight to each other until they were in the communication range. Moreover, the Self-organizing

property of nodes in the network made WSN outstanding amongst the major applications. Nevertheless, the

wireless nodes there in the network have a battery with restricted energy and can’t be recharge or change once

deployed. Hence, the node energy must be utilized efficiently for various functions as sensing the information,

processing the sensed information, and transmitting the processed information to another node. With the

enhancements of the innovation and cost-effective hardware, our visualization presents a tremendous life

enhancement of WSN into several new applications. To modify following such background, the energy-efficient

routing protocol is extremely desirable and can be achieved by clustering in WSN. In the literature survey,

various energy-efficient routing techniques based on cluster have been given to attain the energy-efficiency and

enhance the lifetime of the network. However, these protocols were suffering from the bottleneck node issue. It

is the situation in the network where the router node subjected to heavy traffic due to its presence in energy-

efficient routing path or high remaining energy. This paper aims to moderate the possibility of the node to

become a bottleneck node throughout the application. Thus, we attain the objective by design and develop the

cluster-based efficient-routing protocol by selecting the head nodes of the cluster based on their residual energy

and buffer status. Performance outcome shows that the projected work out-performs in contrast with present

cluster-based routing protocols.

Keyword: Wireless Sensor Network; Sink Node, Cluster Head, Energy efficiency, Buffer, Routing, Network

Lifetime, Mobile Sink Node, and Control Packets. References:

1. Mahgoub, Imad, and Mohammad Ilyas. Sensor network protocols. CRC Press, 2018. 2. Modieginyane, Kgotlaetsile Mathews, Babedi Betty Letswamotse, Reza Malekian, and Adnan M. Abu-Mahfouz. "Software defined wireless sensor

networks application opportunities for efficient network management: A survey." Computers & Electrical Engineering 66 (2018): 274-287. 3. Cerchecci, Matteo, Francesco Luti, Alessandro Mecocci, Stefano Parrino, Giacomo Peruzzi, and Alessandro Pozzebon. "A low power IoT sensor

node architecture for waste management within smart cities context." Sensors 18, no. 4 (2018): 1282.

4. Bedi, Guneet, Ganesh Kumar Venayagamoorthy, Rajendra Singh, Richard R. Brooks, and Kuang-Ching Wang. "Review of Internet of Things (IoT) in electric power and energy systems." IEEE Internet of Things Journal 5, no. 2 (2018): 847-870.

5. Rahmani, Amir M., Tuan Nguyen Gia, Behailu Negash, Arman Anzanpour, Iman Azimi, Mingzhe Jiang, and Pasi Liljeberg. "Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach." Future Generation Computer Systems 78 (2018): 641-658.

6. Casado, Martin, Keith E. Amidon, Peter J. Balland III, Natasha Gude, Justin Pettit, Benjamin L. Pfaff, Scott J. Shenker, and Daniel J. Wendlandt. "Network operating system for managing and securing networks." U.S. Patent Application 15/838,317, filed April 12, 2018.

7. Modieginyane, Kgotlaetsile Mathews, Babedi Betty Letswamotse, Reza Malekian, and Adnan M. Abu-Mahfouz. "Software defined wireless sensor networks application opportunities for efficient network management: A survey." Computers & Electrical Engineering 66 (2018): 274-287.

8. Sánchez-Álvarez, David, Marino Linaje, and Francisco-Javier Rodríguez-Pérez. "A Framework to Design the Computational Load Distribution of Wireless Sensor Networks in Power Consumption Constrained Environments." Sensors 18, no. 4 (2018): 954.

9. Garcia-Luna-Aceves, Jose J. "System and method for efficient name-based content routing using link-state information in information-centric networks." U.S. Patent Application 10/003,520, filed June 19, 2018.

10. Sundararaj, Vinu, Selvi Muthukumar, and R. S. Kumar. "An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks." Computers & Security 77 (2018): 277-288.

11. Bhargavi, Peyakunta, and Singaraju Jyothi. "Big Data and Internet of Things for Analysing and Designing Systems Based on Hyperspectral Images." In Environmental Information Systems: Concepts, Methodologies, Tools, and Applications, pp. 621-641. IGI Global, 2019.

12. Aalsalem, Mohammed Y., Wazir Zada Khan, Wajeb Gharibi, Muhammad Khurram Khan, and Quratulain Arshad. "Wireless Sensor Networks in oil and gas industry: Recent advances, taxonomy, requirements, and open challenges." Journal of Network and Computer Applications 113 (2018):

33-37

87-97. 13. Iqbal, Rahat, Faiyaz Doctor, Brian More, Shahid Mahmud, and Usman Yousuf. "Big data analytics: Computational intelligence techniques and

application areas." Technological Forecasting and Social Change (2018).

14. Ezhilarasi, M., and V. Krishnaveni. "A survey on wireless sensor network: energy and lifetime perspective." Taga Journal of Graphic Technology 14 (2018).

15. Zhang, Zeyu, Amjad Mehmood, Lei Shu, Zhiqiang Huo, Yu Zhang, and Mithun Mukherjee. "A survey on fault diagnosis in wireless sensor networks." IEEE Access 6 (2018): 11349-11364.

16. Wang, Xu, Xuan Zha, Wei Ni, Ren Ping Liu, Y. Jay Guo, Xinxin Niu, and Kangfeng Zheng. "Survey on blockchain for Internet of Things." Computer Communications (2019).

9.

Authors: Krishna Chennakesava Rao M, Pachiyaannan M

Paper Title: Dual Frequency CPW Fractal Antenna for Wireless Applications

Abstract: A Co-planar wave guide fed rectangular ring antenna for WiFi and 5G applications is proposed in this paper. The operating frequency of the antenna is centered at two frequencies i.e 2.52GHz and 3.65GHz which are in S-band

frequency spectrum. The main radiator is rectangular ring for whose inner corners are smoothed. The similar structure of the

main radiator is etched from the ground plane making the proposed technique a fractal. For the proposed antenna FR4

laminate is used as substrate and a line feed is used to provide excitation. To achieve the high wide bandwidth we have

implemented the fractal technique. The antenna size is 60mm×60mm×1.6mm and is radiating in the frequency range of

2.27GHz to 2.67GHz covering 400MHz of bandwidth and 3.5GHz to 3.82GHz covering 320MHz of bandwidth.

Keyword: WiFi, 5G, Fractal, Dual band Applications. References:

1. E. Kusuma Kumari, A.N.V.Ravi Kumar. (2017). Wideband High-Gain Circularly Polarized Planar Antenna Array for L Band Radar. IEEE International Conference on Computational Intelligence And Computing Research, Tamilnadu College of Engineering. Tamil Nadu.

2. E. Kusuma Kumari, A.N.V.Ravi Kumar. (2017). Development of an L Band Beam Steering Cuboid Antenna Array. IEEE International Conference on Computational Intelligence And Computing Research, Tamilnadu College of Engineering. Tamil Nadu.

3. Sunkaraboina Sreenu, Vadde Seetharama Rao. (2017). Stacked Microstrip Antenna For Global Positioning System. IEEE International Conference on Computational Intelligence And Computing Research, Tamilnadu College of Engineering. Tamil Nadu.

4. Rao N.A, Kanapala S. (2018). Wideband Circular Polarized Binomial Antenna Array for L-Band Radar. Panda G., Satapathy S., Biswal B., Bansal R. (eds) Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering, vol 521. Springer, Singapore.

5. Kanapala S, Rao N.A. (2018). Beam Steering Cuboid Antenna Array for L Band RADAR. Panda G., Satapathy S., Biswal B., Bansal R. (eds) Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering, vol 521. Springer, Singapore.

6. Sunkaraboina Sreenu, P. Gnanasivam, M. Sekhar (2018). Circular polarised Antenna Array for C Band Applications. Journal of Advanced Research in Dynamical & Control Systems, Vol. 10, 14-Special Issue.

7. K. Ashwini, M. Sekhar, Sunkaraboina Sreenu. (2018). Mutual Coupling Reduction Using Meander Square EBG Structures for C-Band Radars. Journal of Advanced Research in Dynamical & Control Systems, Vol. 10, 12-Special Issue.

8. Sekhar M, S Naga Kishore B, Siddaiah P. (2014). Triple Frequency Circular Patch Antenna. IEEE International Conference on Computational Intelligence And Computing Research, Park College Of Engineering And Tekhnology. Tamil Nadu.

9. J Lavanya, S Nagakishore Bhavanam, Vasujadevi Midasala “Design of Spiral Antenna for Multiband Applications” International Journal of Innovative Technology and Exploring Engineering, Volume-8 Issue-5 March, 2019.

38-40

10.

Authors: Nadendla Bindu Priya, Muralidharan Jayabhalan

Paper Title: A 5 Bit 600MS/S Asynchronous Digital Slope ADC with Modified Strong Arm Comparator

Abstract: Strong arm comparator has some characteristics like it devours zero static power and yields rail to

rail swing. It acquires a positive feedback allowed by two cross coupled pairs of comparators and results a low

offset voltage in input differential stage. We modified a strong arm Comparator for high speed without relying

on complex calibration Schemes. a 5-bit 600MS/s asynchronous digital slope analog to digital converter (ADS-

ADC) with modified strong arm comparator designed in cadence virtuoso at 180nm CMOS technology. The

design of SR-Latch using Pseudo NMOS NOR Gate optimizes the speed. Thus delay reduced in select signal

generation block. Power dissipation is minimized with lesser transistor count in Strong arm comparator and SR-

Latch with maximum sampling speed. The speed of the converter can be improved by resolution. The proposed

circuit is 5-bit ADC containing a delay cell, Sample and hold, continuous time comparator, strong arm

comparator, Pseudo NMOS SR-Latch and Multiplexer. This 5-bit ADC operates voltage at 1.8 volts and

consumes an average power.

Keyword: Pseudo NMOS, strong arm comparator, asynchronous digital slope analog to digital converter

(ADS-ADC) References: 1. Shu, Fengyi Mei, youling yu, and jiangfeng wu, member, ieee A 5-bit 500-MS/s Asynchronous digital slope ADC with two

comparators. ieee transactions on circuits and systems—ii: express briefs, vol. 65, no. 4, april 2018. 2. B. Razavi, “The strong ARM latch,” IEEE Solid State Circuits Mag., vol. 7, no. 2, pp. 7–12, Jun. 2015. 3. B. P. Ginsburg and A. P. Chandrakasan, “Highly interleaved 5-bit, 250-Msample/s, 1.2-mW ADC with redundant channels in 65-nm

CMOS,” IEEE J. Solid-State Circuits, vol. 43, no. 12, pp. 2641–2650, Dec. 2008. 4. P. J. A. Harpe, C. Zhou, K. Philips, and H. de Groot, “A 0.8-mW5-bit 250-MS/s time-interleaved asynchronous digital slope ADC,”

IEEEJ. Solid-State Circuits, vol. 46, no. 11, pp. 2450–2457, Nov. 2011.

5. C.-C. Liu, “27.4 A 0.35mW 12b 100MS/s SAR-assisted digital slopeADC in 28nm CMOS,” in Proc. ISSCC, San Francisco, CA, USA, 2016,pp. 462–463.

6. M. Ding et al., “A 5bit 1GS/s 2.7mW 0.05mm2 asynchronous digital slope ADC in 90nm CMOS for IR UWB radio,” in Proc. IEEE Radio Frequency Integr. Circuits Symp., Montreal, QC, Canada, 2012, pp. 487–490.

7. N. B. Patel, and K. N. Muralidhara, "Design of a 5-bit, 4.87GS/s, 240uW flasf ADC using a MUX based decoder with regenerative buffer in 45nm CMOS," in proc. ICIIECS, Coimbature, India, 2015pp. 1–7.

8. C. Vudadha et al., “Low-power self reconfigurable multiplexer based decoder for adaptive resolution flash ADCs,” in Proc. 25th Int. Conf. VLSI Design, Hyderabad, India, 2012, pp. 280–285.

41-44

11.

Authors: Yatavakilla Amarendra Nath, Vuyyuru Rachana, Aryasomayajula Sri Teja, Talasila Srujan

Kumar, Alaka Chandan

Paper Title: Development of a Reflectance Photo Plethysmograph for the Estimation of Pulse Transit Time

Abstract: Pulse transit time is considered as a vital physiological parameter for assessing several cardiac

disorders. It was proven that pulse transit time provides accurate diagnosis in estimation of blood pressure. In

this paper, we demonstrate an instrumented hardware for the development of reflectance photoplethysmograph.

The designed system uses two reflectance sensors at two different arterial locations for the estimation of pulse

transit Time. Developed hardware is used for the calculation or estimation of the pulse transit time from the two

photoplethysmograph signals obtained, one from the wrist and the other from the finger respectively, estimated

pulse transit time is compared against the gold standard equipment, the results indicate the estimated values are

within the limits of medical diagnostic values.

Keyword: References: 1. Asada H, et al., “Mobile Monitoring with Wearable Photoplethysmogrphic Biosensors,” IEEE Engineering in Medicine and Biology

Magazine, 22(3), pp. 28-40, 2003.

2. Cheah Kim Wei, “Photoplethysmography Blood Pressure Measurement” Department of Mechanical Engineering, National University of Singapore.

3. Devin B. McCombie,et al; "Adaptive blood pressure estimation from wearable PPG sensors using peripheral artery pulse wave velocity measurements and multi-channel blind identification of local arterial dynamics"28th IEEE EMBS Annual International Conference New York City, USA, Aug 30-Sept 3, 2006

4. Fredrik Gustafsson and Niclas Bergman, “MATLAB for Engineer Explained”, Published by Springer-Verlag London Limited. 5. K. Ashoka Reddy; “Novel Method for Performance Enhancement of Pulse Oximeters” PhD Thesis, Dept of Electrical Engineering, IIT

Madras 2008.

6. Kejia Li “Wireless Reflectance Pulse Oximeter Design and Photoplethysmographic Signal Processing “B.S., Zhejiang University, 2008.

7. Lass J, Meigas K, Karai D, Kattai R, Kaik J, Rossmann M. Continuous Blood Pressure Monitoring During Exercise Using Pulse Wave Transit Time Measurement. Proceedings of the 26th Annual International Conference of the IEEE EMBS 2004; September: 2239-

2242. 8. Michael Maguire and Tomas Ward; “The Photoplethysmograph as an instrument for physiological measurement” Department of

Electronic Engineering, NUI Maynooth 2002

9. Ramakant A. Gayakwad, “OP AMP and Linear Integrated Circuits and their applications” 4th Edition 10. Rasmus G. Haahr; “Reflectance Pulse Oximetry Sensor for the Electronic Patch” MSc Thesis, MIC - Department of Micro and

Nanotechnology Technical University of Denmark, November 2006.

11. Rudrapratap, “MATLAB7 A Quick Introduction for Scientist and Engineers”, Published by Oxford University Press, New Delhi. 12. Russell Dresher, “Wearable Forehead Pulse Oximetry: Minimization of Motion and Pressure Artifacts”, MSc Thesis, Worcester

Polytechnic Institute

45-49

12.

Authors: T. Ganga Prasad, MSS. Rukmini

Paper Title: QoS Aware Optimal Confederation based Radio Resource Management Scheme for LTE Networks

Abstract: In this paper proposed for future generation Long Term Evolution (LTE) networks a radio

resource management using QoS with aware QOC-RRM method. In QOC-RRM scheme we present the hybrid

Recurrent Deep Neural Network (RDNN) technique to differentiate the operators by priority wise based on

multiple constraints and it control the allocated resource bybase stations. For routing share queuing criterion data

with other schaotic weed optimization (CWO) algorithm are proposed. Once information received each BS

schedules the resources for priority user first. The proposed QOC-RRM scheme is implemented in Network

Simulator (NS3) tool and performance can better than conventional RRM schemes in terms of minimum date

rate requirement, maximum number of active users and utilization of the radio spectrums.

Keyword: Quality of Service, Radio Resource Management, Long Term Evolution, Recurrent Deep Neural

Network, chaotic weed optimization. References: 1. Deb, S., & Monogioudis, P. (2015). Learning-based uplink interference management in 4G LTE cellular systems. IEEE/ACM

Transactions on Networking (TON), 23(2), 398-411.

2. Militano, L., Niyato, D., Condoluci, M., Araniti, G., Iera, A., & Bisci, G. M. (2014). Radio resource management for group-oriented services in LTE-A. IEEE Transactions on Vehicular Technology, 64(8), 3725-3739.

3. El Essaili, A., Schroeder, D., Steinbach, E., Staehle, D., & Shehada, M. (2014). QoE-based traffic and resource management for adaptive HTTP video delivery in LTE. IEEE Transactions on Circuits and Systems for Video Technology, 25(6), 988-1001.

4. Malandrino, F., Limani, Z., Casetti, C., & Chiasserini, C. F. (2015). Interference-aware downlink and uplink resource allocation in HetNets with D2D support. IEEE Transactions on Wireless Communications, 14(5), 2729-2741.

5. Asheralieva, A., & Miyanaga, Y. (2016). QoS-oriented mode, spectrum, and power allocation for D2D communication underlaying LTE-A network. IEEE transactions on vehicular technology, 65(12), 9787-9800.

6. Su, S. T., Huang, B. Y., Wang, C. Y., Yeh, C. W., & Wei, H. Y. (2016). Protocol design and game theoretic solutions for device-to-device radio resource allocation. IEEE Transactions on Vehicular Technology, 66(5), 4271-4286.

7. Liu, J. S. (2018). Joint downlink resource allocation in LTE-Advanced heterogeneous networks. Computer Networks, 146, 85-103. 8. Liu, T. C., Wang, K., Ku, C. Y., & Hsu, Y. H. (2016). QoS-aware resource management for multimedia traffic report systems over

LTE-A. Computer Networks, 94, 375-389. 9. Kong, C., & Peng, I. H. (2017). Tradeoff design of radio resource scheduling for power and spectrum utilizations in LTE uplink

systems. Journal of Network and Computer Applications, 78, 116-124.

10. Lucas-Estañ, M. C., & Gozálvez, J. (2017). Distributed radio resource allocation for device-to-device communications underlaying cellular networks. Journal of Network and Computer Applications, 99, 120-130.

11. Jiang, M., Xenakis, D., Costanzo, S., Passas, N., & Mahmoodi, T. (2017). Radio resource sharing as a service in 5G: a software-

50-54

defined networking approach. Computer Communications, 107, 13-29. 12. Pramudito, W., & Alsusa, E. (2014). Confederation based RRM with proportional fairness for soft frequency reuse LTE

networks. IEEE Transactions on Wireless Communications, 13(3), 1703-1715.

13. Novlan, T. D., Ganti, R. K., Ghosh, A., & Andrews, J. G. (2012). Analytical evaluation of fractional frequency reuse for heterogeneous cellular networks. IEEE Transactions on Communications, 60(7), 2029-2039.

14. Park, S., Seo, W., Kim, Y., Lim, S., & Hong, D. (2010). Beam subset selection strategy for interference reduction in two-tier femtocell networks. IEEE Transactions on Wireless Communications, 9(11), 3440-3449.

15. Andrews, J. G. (2005). Interference cancellation for cellular systems: a contemporary overview. IEEE Wireless Communications, 12(2), 19-29.

16. Kim, J., & Cho, D. H. (2010). A joint power and subchannel allocation scheme maximizing system capacity in indoor dense mobile communication systems. IEEE Transactions on Vehicular Technology, 59(9), 4340-4353.

17. Park, D., Seo, H., Kwon, H., & Lee, B. G. (2005). Wireless packet scheduling based on the cumulative distribution function of user transmission rates. IEEE Transactions on Communications, 53(11), 1919-1929

18. Huang, Y., & Rao, B. D. (2012). An analytical framework for heterogeneous partial feedback design in heterogeneous multicell OFDMA networks. IEEE Transactions on Signal Processing, 61(3), 753-769.

19. Chandrasekhar, V., & Andrews, J. G. (2009). Spectrum allocation in tiered cellular networks. IEEE Transactions on Communications, 57(10), 3059-3068.

20. Qiu, X., & Chawla, K. (1999). On the performance of adaptive modulation in cellular systems. IEEE transactions on Communications, 47(6), 884-895.

21. Seetharam, A., Dutta, P., Arya, V., Kurose, J., Chetlur, M., & Kalyanaraman, S. (2014). On managing quality of experience of multiple video streams in wireless networks. IEEE Transactions on Mobile Computing, 14(3), 619-631.

22. Wong, I. C., Oteri, O., & McCoy, W. (2009). Optimal resource allocation in uplink SC-FDMA systems. IEEE Transactions on Wireless communications, 8(5), 2161-2165.

13.

Authors: P Krishna Chaitanya, M. Pachiyannan

Paper Title: Design of Dual Frequency Stacked Patch Antenna for L Band Applications

Abstract: This article presents a thorough explanation on stacked patch antenna (SPA) for dual frequency

applications. The strip antenna operates in L-Band at 1.34GHz and 1.579 GHz. The SPA designed and simulated

on a Rogers RT Duroid 5880 substrate using dielectric constant of 2.2 and depth of 3.77mm for each layer. In

Finite Element Method the proposed design is based Ansys electromagnetic suit (HFSS version 19.1.0), the

proposed antenna simulated results provides good performance for dual band frequency applications in term of

return loss and radiation pattern for dual frequency applications

Keyword: Microstrip antenna, Dual Frequency SPA, HFSS, Return Loss.

References: 1. N. P. Yadav, et al., “High Gain Electromagnetically Coupled Stacked Circular Disk Patch Antenna for Wideband Application,”

Progress In Electromagnetics Research Symposium Proceedings, Guangzhou, China, Aug. 25–28, 2014.

2. Md. Maruf Ahamed, et al., “Rectangular Microstrip Patch Antenna at 2GHZ on Different Dielectric Constant for Pervasive Wireless Communication,” International Journal of Electrical and Computer Engineering, Vol.2, No.3, June 2012, pp. 417 ~ 424.

3. M.I. Jais, et al., " High gain 2.45 GHz 2×2 patch array stacked antenna," Computer, Communications, and Control Technology, 2015 International Conference on 21-23 April 2015.

4. Shiqiang Fu, et al., “Low-cost single-fed circularly polarized stacked patch antenna for UHF RFID reader applications,” Progress in Electromagnetic Research Symposium, 8-11 Aug. 2016.

5. R. Mishra, et al., “Effect of Height of the Substrate and Width of the Patch on the Performance Characteristics of Microstrip Antenna,” International Journal of Electrical and Computer Engineering, Vol.5, No.6, December 2015, pp. 1441-1445.

6. Sekhar M, Siddaiah P “Performance of Feed on Dual Frequency Antenna in Ka-Band” International Journal Of Innovative Research In Electrical, Electronics, Instrumentation And Control Engineering Vol. 2, Issue 5, May 2014. ISSN 2321 – 2004.

7. Sekhar M, Siddaiah P “Comparison of Dual Frequency Antenna in Ka-Band with and without Shorting pin (IJMCTR), Volume-2, Issue-8, August 2014.

8. Sekhar M, Siddaiah P “UWB ANTENNA FOR KA-BAND” Global Journal of Advanced Engineering Technologies Volume 4, Issue 1- 2015.

9. Sekhar M, Siddaiah P “Quad Band Triangular Ring Slot Antenna” International Journal of Scientific & Engineering Research, Volume 6, Issue 4, April-2015 1637

55-57

14.

Authors: M. Kirnamai, M. Sekhar

Paper Title: A Compact Circular Polarized Antenna for Deep Space Applications

Abstract: A compact Circular polarized rectangular patch antenna has been designed for the Deep Space applications in the Ku band with an operating frequency of 13.15GHz. The patch has been truncated at the diagonal corners and two L

shaped slots are been etched in the patch to get the circular polarization. A 50Ω coaxial cable has been used to excite the

antenna. The overall dimension of the antenna is 13mm×13mm×0.508mm which is 0.65λ×0.65λ making the proposed

antenna a compact one. The Proposed antenna is having an impedance bandwidth of 25GHz ranging from 13GHz to

13.25GHz with a gain of 7.82dB at the operating frequency of 13.15GHz. The axial ratio of the antenna at the operating

frequency is 2.94dB which indicate the circular polarization. Low cost FR4 material is been used as the laminate base for the

antenna which will act as the dielectric material.

Keyword: Compact, Circular Polarization, Deep Space Applications. References:

1. E. Kusuma Kumari, A.N.V.Ravi Kumar. (2017). Wideband High-Gain Circularly Polarized Planar Antenna Array for L Band Radar. IEEE International Conference on Computational Intelligence And Computing Research, Tamilnadu College of

Engineering. Tamil Nadu. 2. E. Kusuma Kumari, A.N.V.Ravi Kumar. (2017). Development of an L Band Beam Steering Cuboid Antenna Array. IEEE

International Conference on Computational Intelligence And Computing Research, Tamilnadu College of Engineering. Tamil

Nadu. 3. Sunkaraboina Sreenu, Vadde Seetharama Rao. (2017). Stacked Microstrip Antenna For Global Positioning System. IEEE

58-61

International Conference on Computational Intelligence And Computing Research, Tamilnadu College of Engineering. Tamil Nadu.

4. Rao N.A, Kanapala S. (2018). Wideband Circular Polarized Binomial Antenna Array for L-Band Radar. Panda G., Satapathy S., Biswal B., Bansal R. (eds) Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering, vol 521. Springer, Singapore

5. Kanapala S, Rao N.A. (2018). Beam Steering Cuboid Antenna Array for L Band RADAR. Panda G., Satapathy S., Biswal B., Bansal R. (eds) Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering, vol 521. Springer, Singapore.





15.

Authors: K. Satish, M.Sekhar

Paper Title: A 2×2 Antenna Array for L Band Phased Array RADAR Applications

Abstract: A Four element antenna array for the Phase array RADAR applications in the L Band with an

operating frequency of 1.32GHz is presented. The four patches are been connected to four different transmitter

circuit with which we can control the phase of the input signal. The antenna is having a size of

212mm×212mm×1.6mm with a gain of 8.78dB and directivity of 10.31dB at the operating frequency of

1.32GHz.. Low cost FR4 material is been used as the laminate base for the antenna which will act as the

dielectric material.

Keyword: Coax Feed, Phased Array, RADAR Applications. References:

1. E. Kusuma Kumari, A.N.V.Ravi Kumar. (2017). Wideband High-Gain Circularly Polarized Planar Antenna Array for L Band Radar. IEEE International Conference on Computational Intelligence And Computing Research, Tamilnadu College

of Engineering. Tamil Nadu.

2. E. Kusuma Kumari, A.N.V.Ravi Kumar. (2017). Development of an L Band Beam Steering Cuboid Antenna Array. IEEE International Conference on Computational Intelligence And Computing Research, Tamilnadu College of Engineering.

Tamil Nadu.

3. Sunkaraboina Sreenu, Vadde Seetharama Rao. (2017). Stacked Microstrip Antenna For Global Positioning System. IEEE International Conference on Computational Intelligence And Computing Research, Tamilnadu College of Engineering.

Tamil Nadu.

4. Rao N.A, Kanapala S. (2018). Wideband Circular Polarized Binomial Antenna Array for L-Band Radar. Panda G., Satapathy S., Biswal B., Bansal R. (eds) Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in

Electrical Engineering, vol 521. Springer, Singapore

5. Kanapala S, Rao N.A. (2018). Beam Steering Cuboid Antenna Array for L Band RADAR. Panda G., Satapathy S., Biswal B., Bansal R. (eds) Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering,

vol 521. Springer, Singapore.





62-65

16.

Authors: S. Praveena, Sunakar Prusty

Paper Title: Frequency Reconfigurable Antenna using PIN Diodes

Abstract: With the increase in wireless applications, there is a need for compact antennas that adapt their

behavior with changing system requirements or environmental conditions. Here adapt implies the antenna should

be able to alter operating frequencies, impedance bandwidths, polarizations, radiation patterns. These all features

are provided by the “Reconfigurable antenna”. The important feature of reconfigurable antenna is that, they

provide the same throughput as a multi-antenna system. A compact frequency reconfigurable antenna is

designed with the aid of Ansoft HFSS that provides multiple frequency bands. This is achieved by using

electrical switches such as PIN diodes. Depending on state of switches different operating frequencies are

obtained. The switches placed on the antenna elements are powered wirelessly by the antenna itself. The design,

geometries and simulation results of a frequency reconfigurable antenna are presented in this report. Further

advancements are to be done for this structure to achieve polarization and radiation pattern re-configurability.

Keyword: Wireless Communication system, Frequency Reconfigurable, Microstrip feed line, PIN diode References: 1. D. Orban and G.J.K. Moernaut, “The basics of patch antennas”, Orban microwave products, White Paper, 2005. 2. Keysight technologies, “Understanding RF/Microwave solid state switches and their applications”, Literature number 5989-7618EN,

October 2007.

3. IzniHusna Idris, Mohamad rijal Hamid, MohdHaizal Jamaluddin, Mohamad kamal A. Rahim, James R. Kelly and Huda A. Majid, “Single, dual, and triple band frequency reconfigurable antenna”, Radio Engineering 23(3): 805-811, Vol. 23, No. 3, September 2014.

4. Babu Lal Sharma, Girish Parmar and Mithilesh Kumar, “Design of frequency reconfigurable Microstrip patch antenna for S-band and

66-70

C-band applications”, 2015 4th International Conference onReliability, Infocom Technologies and Optimization, IEEE Conferences, pages 1-3, 2015.

17.

Authors: Pagadala Pavan Kumar, Thokala Santhosh Naidu, S.Vishnu

Paper Title: Remote Ecg Monitoring System by using Iot

Abstract: In this paper, we proposed a prototype to monitor the heartbeat of a patient perpetually through

the cloud. The AD8232 sensor is utilized to quantify the heartbeat. Nodemcu ESP 8266 microcontroller is

utilized to process the data like transmitting and receiving the data. The data from the heartbeat sensor is

accumulated through the microcontroller and the data is posted into the cloud with the avail of Thingspeak. This

model is very secure in terms of data because the target person is connected to the medico predicated on the

unique IP address which provides us a secure transmission of data. This model consumes less power and

withhalf the precision is more.

Keyword: Microcontroller; Thingspeak; (key words) References: 1. Kozlovszky M, Kovacs L, Karoczkai K (2015) Cardiovascular and diabetes focused remote patient monitoring. In: Braidot A, Hadad A

(eds) VI Latin American congress on biomedical engineering CLAIB 2014, Paran{á}, Argentina 29, 30 {&} 31 October 2014.

Springer International Publishing, Cham, pp 568–71. 2. Szydlo T, Koneiczny M (2015) Mobile devices in the open and universal system for remote patient monitoring. IFAC-PapersOnLine

48(4):296–301

3. Otoom AF et al (2015) Effective diagnosis and monitoring of heart disease. Int J Softw Eng Appl 9(1): 143–156. 4. Thelen S et al (2015) Using off-the-shelf medical devices for biomedical signal monitoring in a telemedicine system for emergency

medical services. IEEE J Biomed Health Inf 19(1):117–123

5. Pinheiro EC, Postolache OA, Girão PS (2013) Dual architecture platform for unobtrusive wheelchair user monitoring. In: Medical measurements and applications proceedings (MeMeA), 2013 IEEE international symposium on, pp 124–29

6. Lanata A et al (2015) Complexity index from a personalized wearable monitoring system for assessing remission in mental health. IEEE J Biomed Health Inf 19(1):132–139

7. Ramesh MV, Anand S, Rekha P (2012) A mobile software for health professionals to monitor remote patients. In: 2012 Ninth international conference on wireless

8. Ganesan M et al (2015) A novel based algorithm for the prediction of abnormal heart rate using bayesian algorithm in the wireless sensor network. In: Proceedings of the 2015 international conference on advanced research in computer science engineering

technology (ICARCSET 2015), ICARCSET’15, New York, NY, USA: ACM, 53:1–53:5

9. Tanantong T, Nantajeewarawat E, Thiemjarus S (2015) False alarm reduction in bsn-based cardiac monitoring using signal quality and activity type information. Sensors 15(2): 3952.

10. Nguyen HH, Silva JNA (2016) Use of smartphone technology in cardiology.Trends Cardiovasc Med 26(4):376–386

71-73

18.

Authors: Srinath.Yasam, S Anu H Nair

Paper Title: Precision Farming and Predictive Analytics in Agriculture Context

Abstract: The scope of sensor networks and the Internet of Things spanning rapidly to diversified domains

but not limited to sports, health, and business trading. In recent past, the sensors and MEMS integrated Internet

of Things are playing crucial role in diversified farming strategies like dairy farming, animal farming, and

agriculture farming. The usage of sensors and IoT technologies in farming are coined in contemporary literature

as smart farming or precision farming. At its early stage of smart farming, the practices applying in agriculture

farming are limited to collect the data related to the context of farming, such as soil state, weather state, weed

state, crop quality, and seed quality. These collections are to help the farmers, scientists to conclude the positive

and negative factors of crop to initiate the required agricultural practices. However, the impact of these practices

taken by the agriculturists depends on their experience. In this regard, the computer-aided predictive analytics by

machine learning and big data strategies are having inevitable scope. The emphasis of this manuscript is

reviewing the existing set of computer-aided methods of predictive analytics defined in related to precision

farming, gaining insights into how distinct set of precision farming inputs are supporting the predictive analytics

to help farming communities towards improvisation. It is imperative from the review of the literature that right

from the farming process and techniques to usage of distinct sets of farming precision models like the machine

learning solutions and other such factors indicate that there are potential ways in which the precision farming

solutions can be resourceful for the farming groups. Optical sensing, soil analysis, imagery processing based

analysis, machine learning models that can support in effective prediction are some of the key areas wherein the

numbers of solutions that have offered from the market are high. From the compiled sources of literature in the

study, there must be many techniques, tools, and available solutions, but one of the key areas wherein the

solutions are turning complex for the companies is about usage of the comprehensive kind of machine learning

models used in the precision farming which is currently a major gap and is potential scope for the future research

process. This contemporary review indicating that both supervised and unsupervised machine learning models

are yielding results, still in terms of improvements that are essential in precision farming. The overall efforts of

this review portraying that, there is a need for developing a system that can self-train on the critical features

based on the loop model of features gathered from the process and make use of such inputs for analysis. If such

clustered solution is gathered, it can help in improving the quality of analysis based on the learning practices and

the historical data captured from the systems aligned.

Keyword: Precision agriculture, optical sensors, Variable Rate Fertilizers, Global Positioning System,

Geographic Information System.

74-80

References: 1. http://en.wikipedia.org/wiki/Agriculture_in_India 2. R. D. Grisso, M. M. Alley, P. McClellan, D. E. Brann, and S. J. Donohue, “Precision farming. a comprehensive approach,” 2009. 3. R. D. Ludena, A. Ahrary et al., “Big data approach in an ict agriculture project,” in Awareness Science and Technology and Ubi-

Media Computing (iCAST-UMEDIA), 2013 International Joint Conference on. IEEE, 2013, pp. 261–265.

4. AP Ag-Tech Summit 2017: Progressive Farmer, Smart Farming, 15-17 Nov, 2017, Vizag, www.apagtechsummit2017.in/. 5. ARS within USDA - Annual Report on Science, http://www.ars.usda.gov/. 6. Orts, E., Spigonardo, J., 2014. Sustainability in the Age of Big Data. IGEL/Wharton, University of Pennsylvania, Pennsylvania, US,

p. 16.

7. Sonka, S., 2015. Big Data: from hype to agricultural tool. Farm Policy Journal 12, 1–9. 8. Lesser, A., 2014. Big Data and Big Agriculture. Gigaom Research, p. 11. 9. Kshetri, N., 2014. The emerging role of Big Data in key development issues: opportunities, challenges, and concerns. Big Data &

Society 1 10. Van 't Spijker, A., 2014. The New Oil - Using Innovative Business Models to Turn Data into Profit. Technics Publications, Basking

Ridge.

11. Poppe, K.J., Wolfert, J., Verdouw, C.N., Renwick, A., 2015. A European perspective on the economics of Big Data. Farm Policy Journal 12, 11–19

12. Gilpin, L., 2015b. How Big Data Is Going to Help Feed Nine Billion People by 2050. TechRepublic. http://www.techrepublic.com/article/how-big-data-is-going-to-helpfeed-9-billion-people-by-2050/ (Accessed: 7 May 2015).

13. Yang, C., 2014. Big Data and its potential applications on agricultural production. Crop, Environment & Bioinformatics 11, 51–56 14. Bennett, J.M., 2015. Agricultural Big Data: utilization to discover the unknown and instigate practice change. Farm Policy Journal 12,

43–50.

15. Wolfert, Sjaak, Lan Ge, Cor Verdouw, and Marc-Jeroen Bogaardt. "Big data in smart farming–a review." Agricultural Systems 153 (2017): 69-80.

16. Zhang, Naiqian, Maohua Wang, and Ning Wang. "Precision agriculture a worldwide overview." Computers and electronics in agriculture 36, no. 2-3 (2002): 113-132.

17. Tiwari, Akhilesh, and Praveen Kumar Jaga. "Precision farming in India A review." Outlook on Agriculture 41, no. 2 (2012): 139-143. 18. Singh, N. P. "Application of Data Warehouse and Big Data Technology in Agriculture in India." In Proceedings of VII Seventh

International Conference on Agricultural Statistics, Rome, October, pp. 24-26. 2017.

19. Takeshima, Hiroyuki, and Pramod Kumar Joshi. Protected agriculture, precision agriculture, and vertical farming: Brief reviews of issues in the literature focusing on the developing region in Asia. Vol. 1814. Intl Food Policy Res Inst, 2019.

20. Bendre, M. R., R. C. Thool, and V. R. Thool. "Big data in precision agriculture: Weather forecasting for future farming." In 2015 1st International Conference on Next Generation Computing Technologies (NGCT), pp. 744-750. IEEE, 2015.

21. Morota, Gota, Ricardo V. Ventura, Fabyano F. Silva, Masanori Koyama, and Samodha C. Fernando. "Big Data Analytics and Precision Animal Agriculture Symposium: Machine learning and data mining advance predictive big data analysis in precision animal

agriculture." Journal of animal science 96, no. 4 (2018): 1540-1550.

19.

Authors: Sivaji Satrasupalli, S Vishnu

Paper Title: An Encryption Algorithm for Enhanced Image Security

Abstract: Image security finds its applications in many areas such as bio-metric, digital signatures,

watermarking, confidentiality of medical and data in transit. It has become necessary to secure the images in

order to protect the confidential data. Cryptography is one of the methodologies through which image data can

be secured for storing or transmission purposes by encrypting the same and decrypt it when needed. The main

challenge in image encryption is generating cipher which takes less computational without compromising the

security. In this paper, we present a symmetric encryption algorithm which outperforms the existing algorithms

such as AES, DES, Blowfish and etc which require more computational time on bulk amounts of data.

Keyword: Cryptography, image security, computational time References: 1. Chen, T.S. and Chang, C.C., 1997. A new image coding algorithm using variable-rate side-match finite-state vector quantization. IEEE

Transactions on Image Processing, 6(8), pp.1185-1187. 2. Kuo, C.J., 1993. Novel image encryption technique and its application in progressive transmission. Journal of Electronic Imaging, 2(4),

pp.345-352. 3. Chuang, C.H., Yen, Z.Y., Lin, G.S. and Hong, Z.W., 2011. A Virtual Optical Encryption Software System for Image Security. Journal

of Convergence Information Technology, 6(2). 4. Ding, W., Yan, W.Q. and Qi, D.X., 2000. A novel digital image hiding technology based on tangram and Conways' game.

In Proceedings 2000 International Conference on Image Processing (Cat. No. 00CH37101) (Vol. 1, pp. 601-604). IEEE. 5. Chang, H.K. and Liou, J.L., 1994, December. An image encryption scheme based on quadtree compression scheme. In Proceedings of

the international computer symposium, Taiwan (pp. 230-237). 6. Diffie, W. and Hellman, M., 1976. New directions in cryptography. IEEE transactions on Information Theory, 22(6), pp.644-654. 7. Chen, T.S. and Chang, C.C., 1997. Diagonal axes method (DAM): a fast search algorithm for vector quantization. IEEE Transactions

on Circuits and Systems for Video Technology, 7(3), pp.555-559. 8. Zhao, X.F., 2003. Digital image scrambling based on baker's transformation. Journal of Northwest Normal University (Natural

Science), 39(2), pp.26-29. 9. Li, C.G., Han, Z.Z. and Zhang, H.R., 2002. Image encryption techniques: A survey [j]. Journal of Computer Research and

Development, 10(023), p.15. 10. Behnia, S., Akhshani, A., Mahmodi, H. and Akhavan, A., 2008. A novel algorithm for image encryption based on mixture of chaotic

maps. Chaos, Solitons & Fractals, 35(2), pp.408-419. 11. Wong, K.W., Kwok, B.S.H. and Yuen, C.H., 2009. An efficient diffusion approach for chaos-based image encryption. Chaos, Solitons

& Fractals, 41(5), pp.2652-2663. 12. Patidar, V., Pareek, N.K. and Sud, K.K., 2009. A new substitution–diffusion based image cipher using chaotic standard and logistic

maps. Communications in Nonlinear Science and Numerical Simulation, 14(7), pp.3056-3075. 13. Chen, T.S. and Chang, C.C., 1997. A new image coding algorithm using variable-rate side-match finite-state vector quantization. IEEE

Transactions on Image Processing, 6(8), pp.1185-1187. 14. Kuo, C.J., 1993. Novel image encryption technique and its application in progressive transmission. Journal of Electronic Imaging, 2(4),

pp.345-352. 15. Chuang, C.H., Yen, Z.Y., Lin, G.S. and Hong, Z.W., 2011. A Virtual Optical Encryption Software System for Image Security. Journal

of Convergence Information Technology, 6(2). 16. Ding, W., Yan, W.Q. and Qi, D.X., 2000. A novel digital image hiding technology based on tangram and Conways' game.

81-84

In Proceedings 2000 International Conference on Image Processing (Cat. No. 00CH37101) (Vol. 1, pp. 601-604). IEEE. 17. Chang, H.K. and Liou, J.L., 1994, December. An image encryption scheme based on quadtree compression scheme. In Proceedings of

the international computer symposium, Taiwan (pp. 230-237). 18. Diffie, W. and Hellman, M., 1976. New directions in cryptography. IEEE transactions on Information Theory, 22(6), pp.644-654. 19. Chen, T.S. and Chang, C.C., 1997. Diagonal axes method (DAM): a fast search algorithm for vector quantization. IEEE Transactions

on Circuits and Systems for Video Technology, 7(3), pp.555-559. 20. Zhao, X.F., 2003. Digital image scrambling based on baker's transformation. Journal of Northwest Normal University (Natural

Science), 39(2), pp.26-29. 21. Li, C.G., Han, Z.Z. and Zhang, H.R., 2002. Image encryption techniques: A survey [j]. Journal of Computer Research and

Development, 10(023), p.15. 22. Behnia, S., Akhshani, A., Mahmodi, H. and Akhavan, A., 2008. A novel algorithm for image encryption based on mixture of chaotic

maps. Chaos, Solitons & Fractals, 35(2), pp.408-419. 23. Wong, K.W., Kwok, B.S.H. and Yuen, C.H., 2009. An efficient diffusion approach for chaos-based image encryption. Chaos, Solitons

& Fractals, 41(5), pp.2652-2663. 24. Patidar, V., Pareek, N.K. and Sud, K.K., 2009. A new substitution–diffusion based image cipher using chaotic standard and logistic

maps. Communications in Nonlinear Science and Numerical Simulation, 14(7), pp.3056-3075.

20.

Authors: S Shabana Begum, N. Kasiviswanath

Paper Title: Recommending System for Penny Stock Trading

Abstract: Penny stocks at times makes the investors wealthy by turning to be a multi-bagger stocks or erode

the wealth of the investors with poor performance in volatile conditions. While there are many machine

learning-based prediction models that are used for stock price evaluation, very few studies have focused on the

dynamics to be considered in penny stock conditions. Though the pattern might remain the same for normal

stocks and the penny stock classification, still some of the parameters to be evaluated in the process needs

changes. The model discussed in this report is a comprehensive solution discussed as

internationaljournalofengineering international … › wp-content › uploads ›...

Documents