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This paper introduces the throughput and an energy efficient scheme for mobile access coordinated wireless sensor network (MC-WSN) for time-delicate applications. In regular sensor network with mobile access points (SENMA), the mobile access points (MAs) navigate the network to gather data specifically from individual sensors. So energy consumed in regular network structure will be more and also throughput will be low. To resolve this problem, we present the MC-WSN architecture, this provides an efficient solution for time-sensitive information exchange.

Wireless sensor networks, SENMA, MCWSN, Mobile Access Points (MAs), Throughput, Energy efficiency

[1]. Mai Abdelhakim, Member, IEEE, Yuan Liang, and Tongtong Li, SeniorMember, IEEE“Mobile Coordinated Wireless Sensor Network: An Energy Efficient Scheme for Real- Time Transmissions” IEEE Journal., Vol.34, No.5, May 2016.
[2]. M. Abdelhakim, J. Ren, and T. Li, “Throughput analysis and routing sec urity discussions of mobile access coordinated wireless sensor networks,”in Proc. IEEE Global Commun. Conf. (GLOBECOM’14), Dec 2014, pp. 4616–4621.
[3]. M. Abdelhakim, J. Ren, and T. Li, “Mobile access coordinated wireless sensor networks–Topology design and throughput analysis,” in Proc. IEEE Global Commun. Conf. (GLOBECOM’13), Dec. 2013, pp. 4627–4632.
[4]. M. Abdelhakim, L. Lightfoot, J. Ren, and T. Li, “Architecture design of mobile access coordinated wireless sensor networks,” in Proc. IEEE Int. Conf. Commun. (ICC’13), Jun. 2013, pp. 1720–1724.
[5]. P. Venkitasubramaniam, Q. Zhao, and L. Tong, “Sensor network with multiple mobile access points,” in Proc. 38th Annu. Conf. Information.
[6]. Parvathinathan Venkitasubramaniam, Student Member, IEEE, Srihari Adireddy, Student Member, IEEE, and LangTong, Senior Member, IEEE,” Sensor Networks with Mobile Access:Optimal Random Access and Coding” IEEE Journal on selected areas in communications, Vol. 22, no. 6, August 2004
[7]. A. Chandra, V. Gummalla, and J. Limb, Wireless medium access protocols, in IEEE Commun. Surveys, 2000. 2nd Quarter.
[8]. K. Sohrabi, J. Gao, V. Ail Awadhi, and G. Pottie, “Protocols for self-organization of a wireless sensor network,” IEEE Pers. Commun., vol. 7, pp. 16–27, Oct. 2000.
[9]. A. Gamal, J. Mammen, B. Prabhakar, and D. Shah, “Throughput-delay trade-off in wireless networks,” in Proc.23rd Annu. Joint Conf. IEEE.comput.Commun. Soc. (INFOCOM’04), 2004, vol. 1, pp. 464–475.
[10]. G. Mergen, Z. Qing, and L. Tong, “Sensor networks with mobile access: Energy and capacity considerations,” IEEE Trans. Commun., vol. 54, no. 11, pp. 2033–2044, Nov. 2006.
[11]. N. Kishore, S. Singh, R. Dhir, “Energy Based Evaluation of Routing Protocol for MANETs”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.14-17, 2014.
[12]. Leena Pal, Pradeep Sharma, Netram Kaurav , Shivlal Mewada, “Performance Analysis of Reactive and Proactive Routing Protocols for Mobile Ad-hoc –Networks”, International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue.5, pp.1-4, 2013.

G.Sunil Kumar Reddy, V. Sumalatha, "Implementation of Throughput and an Energy Efficient Scheme for Mobile Coordinated Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.331-340, 2018.

In today’s technological world, a majority of users across the world have access to Internet for communication via text, image, audio and video. People from diverse backgrounds exchange information on current scenarios and project their own views on them over social media. There is a need to understand and recognize the behavior of such large text information on people by analyzing their emotions. The paper focuses on data obtained from one of the most popular social media - Twitter by analyzing live as well as past feeds and getting emotions from them. The twitter data required in English language is converted into a vector of eight emotions and supervised learning techniques such as K-means, Naive Bayes and SVM is used to determine label identifying one of the basic emotion family. At the end, a comparative study of the performance of different classifiers is discussed.

sentiment, machine learning, emotion detection, twitter, SVM, Naive Bayes

[1] Bing Liu (2009) Sentiment Analysis and Subjectivity, Handbook of Natural Language Processing, Second Edition (editors: N. Indurkhya and FJ Damerau)
[2] Santhoshi Kumari, Narendra Babu, Real Time Analysis of Social Media Data to Understand People Emotions Towards National Parties, 8th ICCCNT 2017, July 3 - 5, 2017, IIT Delhi, Delhi.
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[4] Sentiment Analysis of Social Networking Sites Data using Machine Learning Approach for the Measurement of Depression, Anees Ul Hassan, Jamil Hussain, Sungyoung Lee, ICTC 2017, (Pg. - 138-140)
[5] Saima Aman and Stan Szpakowicz (2007), Identifying Expressions of Emotion in Text, V. Matouek and P. Mautner (Eds.): TSD 2007, LNAI 4629, pp. 196205, 2007. Springer-Verlag Berlin Heidelberg 2007
[6] Livia Polanyi and Annie Zaenen (2006), Contextual Valence Shifters, Computing attitude and affect in text: Theory and applications, (Pg. 1-10)
[7] Plank B, Hovy D (2015), Personality Traits on Twitter-or How to get 1,500 Personality Tests in a Week, In 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA 2015) (Pg. 92)
[8] Lionel Martin and Pearl Pu (2014), Prediction of Helpful Reviews Using Emotions Extraction. AAAI Conference on Artificial Intelligence Twenty-Eight AAAI Conference on Artificial Intelligence.
[9] Soumaya Chaffar and Diana Inkpen (2011) Using a Heterogeneous Dataset for Emotion Analysis in Text, Proceeding Canadian AI11 Proceedings of the 24th Canadian Conference on Advances in Artificial Intelligence (Pg. 62-76).
[10] Emotion and Polarity Prediction from Twitter, Rebeen A Hamad, Saeed Alqahtani Mercedes Torres, Computing Conference 2017, 18-20 July 2017, London UK, (Pg. 297-306)
[11] Exploring Human emotion via Twitter, Abu Z Riyadh, Nasif Alvi, Kamrul Hasan, 2017 20th International Conference on Computer and Information Technology (ICCIT), 22-24 December 2017.
[12] Emotion Detection from the SMS of the Internet, Uma N, Priyanka K, Aditi M, Dr. Dhananjay K., 2013 IEEE Recent Advances in Intelligent Computational Systems (RAICS). (Pg. 316 - 321)
[13] Twitter Sentiment Analysis and Opinion Mining, Ravikiran Janardhana, Dept. of CSE, University of North Carolina.
[14] Framework for Emotion Detection and Classification of English and Non-English Tweets, Anup Prasad and Mayank Jaglan, Indiana University Bloomington, School of Informatics and Computing.

Shaikh Abdul Salam, Rajkumar Gupta, "Emotion Detection and Recognition from Text using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.341-345, 2018.

The amount of fossil fuel or conventional sources of energy have forced us to think about utilization of solar energy and solar energy is going to be a best suitable option which can play a major role to meet energy demand in India. It emerges as a huge source of renewable energy both nationally and globally. The large magnitude of solar energy available makes it a highly appealing source of electricity. The amount of solar energy is several times larger than the total world energy consumption. In recent years, particularly with the adoption of the National action plan on climate change, the Jawaharlal Nehru National Solar Mission, and solar policies by several states, India has taken several steps towards increasing the share of renewable in its energy requirement by promoting the solar roof-top schemes. In this paper the residential building having load demand of 7 kW and actual installation of 2 kW solar roof-top systems is discussed.

Renewable energy, solar rooftop system, Energy demand, solar policies, National solar mission

1. http:// www.geda.gujarat.gov.in
2. Solar Electric System Design, Operation and Installation “An Overview for Builders in the Pacific”2009, Washington State University Extension, Olympia
3. “Guidelines for Grid-connected Small Scale (Rooftop) Solar PV Systems for Tamil Nadu” Version: April 2014
4. http://topsunenergy.com/
5. “Design and Simulations of Solid Oxide Fuel Cell connected to 3-phase electrical power system”, Sham Joshi and D.K. Rai, International Journal of Scientific Research in Research Paper . Computer Science and Engineering Vol.5, Issue.6, pp.75-78, December (2017) E-ISSN: 2320-7639
6.“Design of a Cascaded DC High Voltage Generator Based on Cockcroft-Walton Voltage Doubler Circuit”, Shivam Gupta , S.K. Pathak , Meena Sharma, International Journal of Scientific Research in Research Paper . Computer Science and Engineering Vol.4 , Issue.5 , pp.16-19, Oct-2016

I D Chaudhary, Nayan N. Pandya, "Grid Connected Solar Roof Top System for 3-Phase Domestic Load," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.346-351, 2018.

Regression testing becomes a tedious task while validating the Functional and Non-Functional aspects of a software system at maintenance. It is frequently performed activity on a legacy system for every set of change requests at a moment. Hence, it costs a lot for software maintenance in terms of effort and computing resources. The existing approaches like model driven migration, mutual collaboration, test case-optimization, prioritization, code based test generation, ontological classifications and use case driven approaches are not adequate to handle the above mentioned problem in a cost-effective way. This paper presenting a holistic approach to handle the difficulties during software maintenance and for deriving regression suite from the behavioral models preceded by the Grounded Theory (GT).The grounded theory is used for classifying the change requests to the existing system. And it is used for separating of Functional Requirements (FR’s) and Non-Functional Requirements (NFR’s) of change request to the existing system. This approach will validate the functional and non-functional aspects of the system, it leads to low maintenance cost, early detection of requirements errors and maximizes the test coverage.

Grounded Theory, Regression Testing, Software Maintenance, Test Case, Test Suite

[1] Prabhakar K. et al, “Cost Effective Model Based Regression Testing”, “IAENG- World Congress on Engineering”, Vol. I-WCE 2017, pp241-246, July 5-7, 2017, London, U.K., 2017.
[2] David Würfel, Rainer Lutz, and Stephan Diehl, Grounded Requirement Engineering: An Approach to Use Case Driven Requirements Engineering, Journals of System and Software Vol. 117, Pp 645-657, Germany, 2016.
[3] Suranjan Chakraborty, Christoph Rosenkranz, Josh Dehlinger, “A Grounded Theoretical and Linguistic Analysis Approach for Non-Functional Requirements Analysis”, Thirty Third International Conference on Information Systems, Orlando, 2012.
[4] H.M. Sneed. Risks involved in reengineering pro je ts. In WCRE`99 (Working Conference on Reverse Engineering), pages 204-211, Atlanta, GA, USA, 1999.
[5] Fran k Fleurey, Benoit Baudry, Alain Ni olas, Erwan Breton, and Jean Mar Jézéquel “ Model-driven engineering for software migration in a large industrial on text”. In Proceedings of MODELS/UML`2007, LNCS, pages 482-497, Nashville, USA, October 2007. Springer.
[6] L. Erlikh, “Leveraging legacy system dollars for e-business”, IEEE, IT Professional, Volume: 2, Issue: 3, May/Jun 2000.
[7] Dr. Kiran Kumar J et al “An Approach to Cost Effective Regression Testing in Black-Box Testing Environment”, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 3, No. 1, ISSN (Online): 1694-0814,May 2011.
[8] Prabhakar K. et al, “A Model Driven Approach to Regression Testing of Reusable Software”, International Conference on Artificial Intelligence & Cognitive Computing, AICC-18., 02-03 Feb 2018, Hyderabad, India, 2018.
[9] Adtha Lawanna, “test case design based technique for the improvement of test case selection in software maintenance”, IEEE Xplore: Electronic ISBN: 978-4-907764-50-0, 21 November 2016.
[10] M. Gopi Chand et al, “Four Layered Approach to Non-Functional Requirements Analysis” International Journal of Computer Science and Issues (IJCSI Volume 8, Issue 6, pp 371-379, November 2011.
[11] A.AnandaRao et al, “Layered Approach for performance requirements elicitation” International Journal of Electrical Electronics and Computer Systems(IJEECS),Volume 9,Issue1, pp 568-575, July 2012.
[12] Suresh Nageswaran, “Test Effort Estimation Using Use Case Points”, Copyright(c) 2001, Cognizant Technology Solutions, Quality Week 2001, San Francisco, California, USA, June 2001.
[13] Jim Heumann, “Generating Test Cases from Use Cases” Rational edge, Copyright Rational Software 2001 | Privacy/Legal Information, 2001.

Prabhakar Kandukuri, A. Ananda Rao, K. Venugopala Rao, "An Approach to Regression Testing based on Grounded Theory Specifications," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.352-361, 2018.

Productivity of the crops is affected due to diseases. Traditional disease diagnosis system is very time consuming in which pathologist carried out experimentation in the laboratory. Hence it is needed to produce the system which diagnosis the disease accurately and fast with the help of the technology. Identifying disease of crops in its early stage is a major challenge in front of researchers. Many machine learning algorithms and image processing techniques are applied to efficiently identify the disease based on the symptoms that appeared on the leaves. In this paper, the infected leaf is segmented using the Kmeans clustering algorithm and further the 12 texture features are extracted from the segmented image. The backpropagation (BP) algorithm is used for identifying the disease. Here two versions of the backpropagation i.e. online BP and batch BP are used. The Pomegranate infected leaf image database is used for the experimentation purpose. It is observed that online BP performance is better as compared to the batch BP.

Leaf disease, Agriculture,Backpropagation neural network

[1] S Sankaran, A Mishra, R Ehsani, C Davis “A review of advanced techniques for detecting plant diseases” Computers and Electronics in Agriculture, Vol.72, Issue. 1, pp.1-13, 2010
[2] V Singh, AK Misra. “Detection of plant leaf diseases using image segmentation and soft computing techniques”. Information Processing in Agriculture, Vol.4, Issue.1, pp.41-49, 2017.
[3] S. Vijayalakshmi, D. Murugan, "Comparative Analysis on Segmentation Approaches for Plant Leaf Disease Detection", International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.412-418, 2018.
[4] D Al Bashish, M Braik, S Bani-Ahmad “Detection and classification of leaf diseases using K-means-based segmentation and Neural-Networks-Based Classification”, Information Technology Journal, Vol.10, Issue.2, pp.267-275, 2011.
[5] M Islam, A Dinh, K Wahid, “Detection of potato diseases using image segmentation and multiclass support vector machine”, In the Proceedings of the 2017 30th Canadian Conference on Electrical and Computer Engineering (CCECE 2017), Canada, pp. 1-4, 2017.
[6] VA Gulhane & AA Gurjar, “Detection of diseases on cotton leaves and its possible diagnosis”. International Journal of Image Processing (IJIP), Vol. 5, Issue. 5, pp. 590-598, 2011.
[7] YC Zhang, HP Mao, B Hu, MX Li., “Features selection of cotton disease leaves image based on fuzzy feature selection techniques” In the Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, (ICWAPR`07), Beijing, China, Vol. 1, pp.124-129, 2007.
[8] SP Mohanty, DP Hughes, M Salathé “Using deep learning for image-based plant disease detection”, Frontiers in plant science, Vol.7, pp.14-19, 2016.
[9] P Balamurugan, R Rajesh, “Neural network based system for the classification of leaf rot disease in cocos nucifera tree leaves” European Journal of Scientific Research, Vol.88, Issue.1, pp. 137-145, 2012.
[10] MF Kazerouni, J Schlemper, “Comparison of modern description methods for the recognition of 32 plant species”. Signal & Image Processing,Vol. 6, Issue.2, pp.1, 2015.
[11] JK Patil, R Kumar, “Color feature extraction of tomato leaf disease”,International Journal of Engineering Trends and Technology, Vol.2, Issue 2, pp. 72-74, 2011.
[12] S Arivazhagan, RN Shebiah, S Ananthi, “Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features”. Agricultural Engineering International: CIGR Journal, Vol. 5, Issue. 1, pp. 211-217,2013.
[13] Q Yao, Z Guan, Y Zhou, J Tang, Y Hu, “Application of support vector machine for detecting rice diseases using shape and color texture features”. In the Proceedings of the 2009 International Conference on Engineering Computation, 2009. ICEC`09. Hong Kong, China, pp. 79-83,2009.
[14] SS Sannakki, VS Rajpurohit , VB Nargund, &, P Kulkarni, “Diagnosis and classification of grape leaf diseases using neural networks”. In the Proceedings of the 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), USA, pp. 1-5. 2013.
[15] T Rumpf, AK Mahlein, U Steiner, EC Oerke "Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance", Computers and Electronics in Agriculture Vol.74, Issue.1, pp. 91-99, 2010.
[16] SD Bauer, F Korč, W Förstner. “The potential of automatic methods of classification to identify leaf diseases from multispectral images”.Precision Agriculture, Vol.12, Issue.3, pp.361-77. 2011
[17] Priyanka PT, SA Angadi, "Classification of normal and affected (Decayed) fruit images", International Journal of Computer Sciences and Engineering, Vol.2, Issue.7, pp.31-19, 2014.
[18] A Patil, K Patil, K Lad, ”Leaf Disease detection using image processing techniques”, Journal of Scientific Research in Computer Science and Engineering, Vol. 6, Issue.1, pp. 33-36, 2018
[19] G Patil, ”Digital image processing-An Elegant Technology to perceive disease in plantss”, Journal of Scientific Research in Computer Science and Engineering, Vol. 6, Issue.1, pp. 43-47, 2018

A.T. Sapkal, U.V. Kulkarni, "Leaf Disease Diagnosis using Online and Batch Backpropagation neural network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.362-366, 2018.

Automatic segmentation and detection of brain disease is a disreputably complicated issue in Magnetic Resonance Image (MRI). The similar state-of-art segmentation methods and techniques are limited for the detection of brain disease in multimodal brain MRI. Thus this research work deals with the accurate segmentation and detection of brain diseases in multimodal brain MRI and this research work is focused on improve automatic segmentation results. This work analyses the segmentation performance of existing state-of-art method improved Fuzzy C-Means Clustering (FCMC) method and marker controlled Watershed method .In our research work the proposed method is to compound segmentation results of improved Fuzzy C-Means Clustering (FCMC) method and marker controlled Watershed method to carry out accurate brain tumor detection and improved the segmentation results. The performance of proposed method is evaluated with the assorted performance metric, viz., Segmentation accuracy, Sensitivity and Specificity. The comparative performance of our Proposed Method, FCMC Method and Watershed method is demonstrated on real and benchmark multimodal brain MRI datasets, viz. FLAIR (Fluid Attenuated Inversion Recovery) MRI, T1 MRI, MRI and T2 MRI and the experimental results of the proposed method exhibits better results for segmentation and detection of brain diseases in multimodal brain MR images.

Brain diseases, FCMC Method, Watershed Method, Proposed Method, Bilateral Filter, Brain MRI, Multimodal

[1] N Van . Porz, "Multi-modalodal glioblastoma segmentation: Man versus machine", PLOS ONE, Vol. 9, pp. e96873, 2014.
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[8].Meenakshi, R & Anandhakumar, P 2012, ‘Brain Tumor identification in MRI with BPN Classifier and Orthonormal Operators’, European Journal of Scientific Research, vol.85, no.4, pp.559-569.
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[10] Jin Liu, Min Li, Jianxin Wang, Fangxiang Wu, Tianming Liu, and Yi Pan, “A Survey of MRI-Based Brain Tumor Segmentation Methods”, TSINGHUA SCIENCE AND TECHNOLOGY,Volume 19, Number 6, December 2014.
[11] J. B. T. M. Roerdink and A. Meijster, “The watershed transform: Definitions, Algorithms and parallelization strategies,” Fundamenta Informaticae, Vol. 41, Pp. 187–228, 2000.
[12] Gang Li , “Improved watershed segmentation with optimal scale based on ordered dither halftone and mutual information”,Page(s)296-300, Computer Science and Information Technology (ICCSIT), 2010, 3rd IEEE International Conference, 9-11 July 2011.
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[14] L´aszl´o Szil´agyi,L´aszl´o Lefkovits and Bal´azs Beny´o, "Automatic Brain Tumor
Segmentation in Multispectral MRI Volumes Using a Fuzzy c-Means Cascade Algorithm",12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD),2015.
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[16]NageswaraReddy P, C.P.V.N.J.Mohan Rao, Ch.Satyanarayana, “Optimal Segmentation Framework for Detection of Brain Anomalies”, I.J. Engineering and Manufacturing, 2016, 6, 26-37, November 2016 in MECS.
[17] Devanand Bhonsle, Vivek Chandra , G.R. Sinha3 , “Medical Image Denoising Using Bilateral Filter”, I.J. Image, Graphics and Signal Processing, 2012, 6, 36-43. July 2012 in MECS.
[18] Sanjay Kumar, Santosh Kumar Ray, Peeyush Tewari,, A “Hybrid Approach for Image Segmentation Using Fuzzy Clustering and Level Set Method”, I.J. Image, Graphics and Signal Processing, 2012, 6, 1-7, July 2012 in MECS.
[19] Kalaiselvi T and Nagaraja P, “An Automatic Segmentation of Brain Tumor from MRI Scans through Wavelet Transformations”, I.J. Image, Graphics and Signal Processing, 2016, 11, 59- 65, November 2016 in MECS.
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[21] B.Jyothia, Y.MadhaveeLathab, P.G.Krishna Mohanc ,V.S.K.Reddy, ”Integrated Multiple Features for Tumor Image Retrieval Using Classifier and Feedback Methods”, International Conference on Computational Modeling and Security (CMS 2016), Science direct, 2016, PP141-148.
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[25] B.Jyothi, Y.MadhaveeLatha, P.G.Krishna Mohan,” Multidimentional Feature Vector Space for an Effective Content Based Medical Image Retrieval”, 5th IEEE International Advance Computing Conference(IACC- 2015),BMS College of Engineering Bangalore, June 12 to 13
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[26] Nitin Jain & Dr. S. S. Salankar,” Color & Texture Feature Extraction for Content Based Image Retrieval”, IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-ISSN: 2278-1676, p-ISSN: 2320-3331 PP 53-58.
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V. Murugesh, V. Sivakumar, P. Janarthanan, "An Ingenious Segmentation Application for Brain Lesion Detection in Multimodal MR Images," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.367-374, 2018.

The presence of textual components in images is of specific interest which can be extracted using several extraction methods. These components can be helpful for many applications like assisting visually impaired, translator tourists, and robotic navigation in urban areas. The text extraction methods can be classified into three categories: region based, texture based and hybrid method. Extraction based on a region can be further divided into connected component based and edge based method. In spite of numerous scene text detection methods available, ‘text extraction’ remains unsuccessful. Many issues like different fonts, size, colors, and background noise due to the presence of trees, bricks which are similar to text like objects make text detection difficult. In this paper, the scene text extraction is performed by detecting the edges using canny edge detection algorithm. Then stroke width transform is applied on an edge image with a small yet effective modification in second pass followed by connected component labelling algorithm. The labelled components are then clustered based on the number of pixels available in a particular label. And finally the extracted text is recognized using Google’s open source optical character recognition (OCR) engine ‘Tesseract’.

Text extraction, stoke width, connected component, textual components

[1] B. Epshtein, E. Ofek, and Y. Wexler, “Detecting text in natural scenes with stroke width transform,” IEEE conference on Computer Vision and Pattern Recognition, pp. 2963-2970, June 2010.
[2] John H Canny, “A Computational approach to Edge Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL. PAMI-8, NO. 6, NOVEMBER 1986
[3] Pooja Chavre, Archana Ghotkar, “Scene Text Extraction using Stroke Width Transform for Tourist Translator on Android Platform,” 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT), pp. 301-306, 2016.
[4] Lifeng He, Yuyan Chao, and Kenji Suzuki,“Two Efficient Label-Equivalence-Based Connected-Component Labeling Algorithms for 3-D Binary Images,” IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 20, NO. 8, AUGUST 2011
[5] K. Jung, K. Kim, A. K. Jain, “Text information extraction in images and video: a survey”, Pattern Recognition, p. 977 – 997,Vol 5. 2004.
[6] Adrian Canedo, Jung H. Kim, ,Soohyung and Yolanda Blanco-Fernández “English to Spanish Translation of Signboard Images from Mobile Phone Camera,” IEEE conference, Southeastcon, pp. 356-361, Mar. 2009.
[7] Sarwar Khan and Somying Thainimit, “Text Detection and Recognition on traffic panel in roadside imagery," International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES), pp. 1-6, 2017
[8] Najwa-Maria Chidiac, Pascal Damien, Charles Yaacoub, “A Robust Algorithm for Text Extraction from Images,” International Conference on Telecommunications and Signal Processing (TSP), pp. 493- 497, 2016.
[9] ZhuoyaoZhong, LianwenJin, Shuangping Huang, “DeepText: A new approach for text proposal generation and text detection in natural images,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1208-1212, 2017.
[10] Pooja Kumari, Mamta Yadav, "Detection and Recognition for Reading Text in Images", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.980-984, May-June.2018

K. Esther Amulya, P. Sanoop Kumar, "Scene Text Extraction using Stroke Width Transform," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.375-379, 2018.

The fly-back converter is used in both AC/DC and DC/DC conversion with galvanic isolation between the input and outputs. More precisely, the fly-back converter is a buck boost converter with the inductor split to form a transformer, so that the voltage ratios are multiplied with an additional advantage of isolation. In the on-state the energy is transferred from the input voltage source to the transformer while the output capacitor supplies energy to the output load. In the off-state, the energy is transferred from the transformer to the output load and the output capacitor. By controlling the duty ratio of switch used in the primary of transformer a DC supply of zero to a desired maximum value is obtained. A fly-back transformer is designed to meet the output requirement. In this project the aim is to design a pure DC supply that can be used to obtain the output of 48V.

Flyback converter , IC UC3843, MOSFET,Transformer core

[1] Marty brown,“ Power Supply Cookbook,EDN series for Design Engineers,pp.93-220,2001
[2] A Preeman., K Billings., T morey., “switching Power Supply Design”, 3nd ed, Mc Grawhill, 2009
[3] H. C. H. Chung, S. Y. R. Hui and W. H. Wang, ``A zero current switching pwm flyback converter with a simple auxiliary switch,`` IEEE Transactions power electronics, vol. 14, no. 2, pp. 329-342, Mar. 1999.
[4] E. Adib and H. Farzaneh fard, ``Family of zero current transition pwm converters,`` IEEE Transactions industrial electronics, vol. 55, no. 8, pp. 3055-3063, Aug. 2008.
E. Adib and H. Farzaneh fard, ``Family of zero current zero voltage transition pwm converters,`` IET power electron, vol. 1, no. 2, pp. 214-223, 2008.
[5] G. Hua, E. X. Yang, Y. Jiang and F. C. Lee, ``Zero current transition pwm converter,`` IEEE Transactions power electronics, vol. 9, no. 6, pp. 601-606, Mar. 1994
[6] H.S. Kim, J.H. Jung, J.W. Baek, and H.J. Kim, “Analysis and Design of a Multioutput Converter Using Asymmetrical PWM Half-Bridge FlybackConverter Employing a Parallel–Series Transformer,” IEEE Trans. on Industrial Electronics, vol. 60, pp.3115-3125, Aug 2013
[7] L. K. Kaushik, and M. K. Pathak, “An improved multiple output forward Converter topology,” Int. Conference on Power Electronics and Drives Energy Syst., 2010, pp. 1–6
[8] O.Garcia, J.A. cobos, P. Alou, R.Prieto, and J.Uceda, “A simple single switch single-stage ac/dc converter with fast output voltage regulation,” IEEE Trans. Power Electron., vol 17, pp. 163– 171, 2002
[9] C.A.canesin., and I.Barbi, “Novel zero-current-switching PWM converters,” IEEE Trans. Ind. Electron., vol 44, pp. 372– 381, 1997.
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[11] T. M. Chen and C.-L. Chen, “Analysis and design of asymmetrical half bridge Fly-back converter,” IEE Proc. in Elect. Power Appl, vol. 1, pp. 433–440. Nov. 2002
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Rashmi Sharma , "Design of Low Cost DC-DC Flyback Converter," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.380-385, 2018.

Flexi multiradio-10 Base Transceiver Station developed by Nokia Networks is a BTS capable of resource sharing by multiple operators, supporting 2G, 3G and LTE technologies either in concurrent mode or dedicated mode as well as is world’s smallest high capacity BTS. Depending upon customer requirement, resource availability, environment of installation etc. its System module(s) and Radio Frequency module(s)can be configured in many possible ways. Intelligent Downlink Diversity is one of those configurability feature of FMR-10 that increases its gain by 5 dB. To check for functionalities supported by the BTS several test cases are designed which can be tested manually as well as by automation. Since manual execution can be time consuming, tedious and not efficient automation is preferred. RIDE is one such open source software that has easy-to-use tabular data syntax and open source libraries and tools to be used by user.

Flexi Multiradio-10, Intelligent Downlink Diversity with 4-way Uplink Diversity, Robot Integrated Development Environment

[1] Andreas F. Molisch. “GSM Global System for Mobile Communications”2011.
[2] Flexi Multiradio 10 in https://networks.nokia.com/products/flexi-multiradio-10-base-station
[3] WCDMA RAN, Rel. RU50 and RU50 EP1, Operating Documentation, Issue 04, Flexi Multiradio-10 Base Station Product Description
[4] https://en.wikipedia.org/wiki/GSM
[5] https://en.wikipedia.org/wiki/Antenna_diversity
[6] http://robotframework.org/#introduction
[7] https://en.wikipedia.org/wiki/Diversity_scheme
[8] Liu Jian-Ping, Liu Juan-Juan, Wang Dong-Long, “Application Analysis of Automated Testing Framework Based on Robot”, 2012.
[9] Qiu Na and Du Huaichang, “Extension and Application Based on Robot Testing Framework”, 2016

Shaifali, Shylaja B. S, "Automation of Nokia Flexi Multiradio-10 BTS in Idd-4ud Configuration," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.386-389, 2018.

Technology driven life become inseparable living system in present day world. The rate of introduction and adoption of technology happens very rapidly in all utility services. The focus of technology providers aims to enhance sophisticated life pattern by end users through investing less time and cost. The expediting of innovation technology happens day by day according to end user preference. But at the same time, the correlation between expanding of technology and awareness about its updation need to be study among youngsters in present day scenario(techno savvy) through a comprehensive exploratory study. This paper attempts to address the research gap of awareness of youngster’s preferably professional graduates about emerging technologies with respect to testing of hypotheses and clustering technique. In order to address this gap, the researcher has employed questionnaire method for collecting the data and deployed SPSS tool for interpretation.

Technology driven, Enhance sophisticated, Awareness, Emerging Technologies

[1]. Mohanad Halaweh, “Emerging Technology: What is it?”, J. Technol. Manag. Innov. 2013, Volume 8, Issue 3, pp.108-115, 2013.
[2]. Göran Kindvall, Anna Lindberg, Camilla Trané, Jonatan Westman, “Exploring future technology development”, ISSN NO 1650-1942, pp.1-72, 2017.
[3]. Kasey Panetta, Gartner Top 10 Strategic Technology Trends for 2018, 2017.
[4]. Kelvin Claveria, “Blockchain, the Internet of Things and other top tech trends for 2018”, 2017.
[5]. Dr.M.Kannan & Dr.N.Ashok Kumar, “Future Technology Growth and its Awareness to the Present Youth”, Seventh National Conference of Recent Enhancement in Advanced Computing Technologies (NCREACT’18), RRASE College of Engineering, Padappai, 2018.
[6]. Nic Newman, Journalism, “Media and Technology Trends and Predictions”, ISBN 978-1-907384-42-4, pp.1-52, 2018.
[7]. Jani Suomalainen, Kimmo Ahola, Mikko Majanen, Olli Mämmelä and Pekka Ruuska, “Security Awareness in Software-Defined Multi-Domain 5G Networks”, Future Internet, doi:10.3390/fi10030027, 10,27, 2018.
[8]. Dr.S.Amutha and S.John Kennedy, “Awareness on Technology Based Education by the Student Teachers”, International Journal of Scientific and Research Publications, ISSN 2250-3153, Vol.5, No.9, pp.1-4, 2015.
[9]. Yunos Zahri, Ab Hamid R. Susanty, Ahmad Mustaffa, Cyber Security Situational Awareness among Students: A Case Study in Malaysia, International Journal of Educational and Pedagogical Sciences, Vol.11, No.7, pp.1699-1705, 2017.
[10]. Aisar Salihu Musa, Mohd Nazri Latiff Azmi and Nur Salina Ismail, “Awareness and Usage of Social Media: A Study of Mass Communication Students of Kano State Polytechnic”, International Conference on Languages, ICL, pp.1-15, 2015.
[11]. Nilamadhab Mishra , “Internet of Everything Advancement Study in Data Science and Knowledge Analytic Streams”, International Journal of Scientific Research in Computer Science and Engineering, E-ISSN: 2320-7639, Vol.6, Issue.1, pp.30-36, 2018.
[12]. Marie Fernandes , “Data Mining: A Comparative Study of its Various Techniques and its Process”, International Journal of Scientific Research in Computer Science and Engineering, E-ISSN: 2320-7639, Vol.5 , Issue.1, pp.19-23, 2017.

M. Kannan, "An Awareness Study about Future Technology among Youngsters with Special Reference to Professional Graduates – Technical Approach," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.390-393, 2018.