S. No

Volume-6 Issue-5, June 2017, ISSN:  2249-8958 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Shubhangi Pandhare, Abhishek Gautam, Sayali Chavan, Shital Sungare

Paper Title:

Co-Operative Content Downloading Framework Over Cellular Network

Abstract: The multifold advancement over wireless communication has in a way, predicted to use smartphones, laptops, and tabs vastly for downloading purpose. But due to confined data transfer capacity, the statistics of downloading quantity approximately for a distinctive person is constrained and time taking for a high precision video. The co-operative content downloading framework will permit the requested joiners inside the network to download a section of the file independently. This may aid the potential to download the document with cost effectiveness and with a reduced time consumption component. The above mentioned framework will additionally trace the real process how the transfer speed (bandwidth) will be distributed within the joiners and one requestor. The entire framework will deliver the efficient utilization of bandwidth in specific environments.

Keywords:  Segmentation, Cluster formation, Adhoc network, Sequencing.

References:
1.       Haibo Zhou, Student Member, IEEE, Bo Liu, Member, IEEE, Tom H. Luan, Member,, “ChainCluster: Engineering a Cooperative Content Distribution Framework for Highway Vehicular Communications”, IEEE transactions on intelligent transportation systems, 2014.
2.       Chao-Hsien Lee, Chung-Ming Huang, Senior Member, IEEE, Chia-Ching Yang, and Hsiao-Yu Lin,,“ The K-hop Cooperative Video Streaming Protocol Using H.264/SVC Over the Hybrid Vehicular Networks,” , IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 13, NO. 6, JUNE 2014.
3.       Aarti R. Thakur,  Prof. Jagdish Pimple, “Performing vehicle to vehicle communication based on two tier approach with high security using aodv protocol in VANET”, 1) International Journal of Emerging Research in Management &Technology ISSN: 2278-9359 (Volume-3, Issue-7),July 2014
4.       J. Luo and D. Guo, “Neighbor discovery in wireless ad-hoc networks based on group testing,” in Proc. 46th Annu. Allerton Conf.Communication, Control, Computing, Urbana-Champaign, IL, USA Sep. 2008, pp. 791–797.
5.       R. Khalili, D. L. Goeckel, D. Towsley, and A. Swami, “Neighbor discovery with reception status feedback to transmitters,” in Proc. 29th IEEE Conf. INFOCOM, San Diego, CA, USA, Mar. 2010,pp. 2375–2383
6.       C.-M. Huang, C.-C. Yang, and H.-Y. Lin, “A K-hop bandwidth aggregation scheme for member-based cooperative transmission over vehicular networks,” in Proc. 17th IEEE ICPADS, Tainan, Taiwan, 2011, pp. 436–443.
7.       Nandan, S. Das, G. Pau, M. Gerla, and M. Y. Sanadidi, “Cooperative downloading in vehicular ad-hoc wireless networks,” in Proc. 2nd Annu. Conf. WONS, Washington, DC, USA, 2005 pp. 32–41
8.       M. F. Tsai, N. Chilamkurti, J. H. Park, and C. K. Shieh, “Multi-path transmission control scheme combining bandwidth aggregation and packet scheduling for real-time streaming in multi-path environment,” Instit. Eng. Technol. Commun., vol. 4, no. 8, pp. 937–945, 2010.
9.       M. Y. Hsieh, Y. M. Huang, and T. C. Chiang, “Transmission of layered video streaming via multi-path on ad-hoc networks,” Multimedia Tools Appl., vol. 34, no. 2, pp. 155–177, 2007.
10.    D. Fan, V. Le, Z. Feng, Z. Hu, and X. Wang, “Adaptive joint session scheduling for multimedia services in heterogeneous wireless networks, in Proc. 70th IEEE VTC, Anchorage, AK, USA, Sep. 2009, pp. 1–5.
11.    M. Li, Z. Yang, and W. Lou, “Codeon: Cooperative popular content distribution for vehicular networks using symbol level network coding,” IEEE J. Sel. Areas Commun., vol. 29, no. 1, pp. 223–235, Jan. 2011.

1-5

2.

Authors:

Cini K.

Paper Title:

Value Based Reliability Evaluation of Primary Power Distribution System

Abstract: Distribution system reliability is concerned with the availability and quality of power supply at each customer’s service entrance. Analysis of customer failure statistics shows that failure in distribution system contribute as much as 90% towards the unavailability of supply to a load as compared with each part of electric systems. These statistics reinforces the need for reliability evaluation of distribution systems. In recent years with the advent of smart grids the significance of distribution system has enhanced because of the importance of co generation and distributed generation. The different causes and duration of failures are analysed season wise. The failure rate of the different feeders of the system under study was calculated and the reliable feeders were identified. Suggestions are given to improve the reliability of the feeders. This type of analysis will help the operation and maintenance engineers to maintain the quality service to the customers and schedule the maintenance services.  

Keywords: Distribution Systems, Reliability Indices, Failure Rate, Availability.

References:
1.       Biyun Chen; Qianyi Chen “The whole-process reliability evaluation  of  power  system including generation, transmission, transformation and distribution” IEEE 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), pp 482-487
2.       H. 2. Andrews, Laura, Samuel” Novel Power System Reliability Indices calculation method” 23rd International Conference on Electricity Distribution, Lyon  15-18, June .
3.       Roy Billinton and Peng Wang “ Distribution System Reliability Cost/worth analysis Using Analytical and sequential Simulation Techniques” IEEE transactions on power systems, Vol.13, No.4, November 1998,pp1245-1250.
4.       R. Billinton and J. E. Billinton, “Distribution System Reliability Indices”, IEEE Trans. on Power Delivery, Vol. 4, No. 1, Jan. 1989, pp. 561-568.
5.       Vito Longo ,Walter R. Puntel, “Evaluation of Distribution System Enhancements Using Value-Based Reliability Planning” Procedures IEEE Transactions on Power
systems, vol. 15, no. 3, august 2000.
6.       Billinton, R., and Allan, R. N., "Reliability Evaluation of Power Systems",Pitman Books, New York and London, 1984.
7.       Billinton, R., "Evaluation of Reliability Worth in an Electric Power system". Reliability Engineering and System Safety, Vol. 46, No. 1, 1994.
8.       Carlos Eduardo Paida Tenemaza “State of Art, Reliability In Electrical Distribution Systems Based On Markov Stochastic Model”  IEEE Latin America Transactions, Volume: 14, Issue: 2, pp 799-804.
9.       Farajollah Soudi and Kevin Tomsovic  “Optimal Trade-Offs in Distribution Protection Design” IEEE  transactions on power delivery, vol. 16, no. 2, April 2001.
10.    Amir Safdarian; Mohammad Farajollahi; Mahmud Fotuhi-Firuzabad “ Impacts of Remote Control Switch Malfunction on Distribution System Reliability” IEEE Transactions on Power Systems, Volume: 32, Issue: 2, 2017, pp 1572-1573.
11.    Siripha Junlakarn; Marija Ilić , “Distribution System Reliability Options and Utility Liability”  IEEE Transactions on Smart Grid , Volume: 5, Issue: 5, 2014, pp 2227-2234.

6-10

3.

Authors:

S. L. Deshpande, D S Chaudhari

Paper Title:

Wireless Nodes Assisted Micro-Irrigation System: an IoT Approach

Abstract: Irrigation systems deployed with Wireless Sensor Network (WSN) while transforming them to Micro-Irrigation systems are emerging as fruitful solution to ongoing ground water crisis. Field parameters like soil moisture, temperature and humidity can be monitored taking help of sensor array and can be fed back to decision making control system. Organized parametric results can help the optimized use of the water. By using wireless communication and environmental energy harvesting techniques, sensor network can be made totally wireless. Internet of Things (IoT) is another emerging technology that goals to extend the application of internet from complex computational machines (computer) to the stand alone devices such as consumer electronics. Integrating IoT to WSN not only can provide remote access but also allow two distinct information systems to frequently collaborate and provide common services. Also the user can be provided with flexible interface like mobile application. The miniaturization in technology and even more reliable communication are the strongest suits of such sensor network. This paper reviews for various technologies to fulfil requirement of such application and the shows some system characteristics.

Keywords:  WSN, IoT, Irrigation, Moisture, Humidity, Energy Harvesting, etc.

References:
1.       Basic Botany, Physiology, and Environmental Effects on Plant Growth, AZ master gardner manual, The University of Arizona, AZ, 1998.
2.       M. Morris. (2006). Soil Moisture Monitoring: Low-Cost Tools and Methods [Online]. Available FTP: attra.ncat.org Directory: attra-pub/PDF File: soil moisture.pdf
3.       Y. Kim, R. Evans and W. Iversen, "Remote Sensing and Control of an Irrigation System Using a Distributed Wireless Sensor Network," in IEEE Transactions on Instrumentation and Measurement, vol. 57, pp. 1379ꟷ1387, July 2008.
4.       W. Wang and S. Cao, "Application Research on Remote Intelligent Monitoring System of Greenhouse Based on ZIGBEE WSN," 2nd International Congress on Image and Signal Processing, Tianjin, pp. 1-5, 2009.
5.       Yu, Y. Cui, L. Zhang and S. Yang, "ZigBee Wireless Sensor Network in Environmental Monitoring Applications," 5th International Conference on Wireless Communications, Networking and Mobile Computing, Beijing, pp. 1ꟷ5, 2009.
6.       Z. Rasin, H. Hamzah and M. Aras, "Application and evaluation of high power Zigbee based wireless sensor network in water irrigation control monitoring system," IEEE Symposium on Industrial Electronics & Applications, Kuala Lumpur, pp. 548ꟷ551, 2009.
7.       M. Zorzi, A. Gluhak, S. Lange and A. Bassi, "From today's INTRAnet of things to a future INTERnet of things: a wireless- and mobility-related view," in IEEE Wireless Communications, vol. 17, no. 6, pp. 44-51, December 2010.
8.       G. Kortuem, F. Kawsar, V. Sundramoorthy and D. Fitton, "Smart objects as building blocks for the Internet of things," in IEEE Internet Computing, vol. 14, no. 1, pp. 44-51, Jan.-Feb. 2010.
9.       K. Langendoen, A. Baggio and O. Visser, "Murphy loves potatoes: experiences from a pilot sensor network deployment in precision agriculture," Proceedings 20th IEEE International Parallel & Distributed Processing Symposium, Rhodes Island, pp. 1ꟷ8, 2006.
10.    L. Li, H. Xiaoguang, C. Ke and H. Ketai, "The applications of WiFi-based Wireless Sensor Network in Internet of Things and Smart Grid," 6th IEEE Conference on Industrial Electronics and Applications, Beijing, pp. 789-793, 2011
11.    M. Lee, J. Hwang and H. Yoe, "Agricultural Production System Based on IoT," IEEE 16th International Conference on Computational Science and Engineering, Sydney, NSW, pp. 833-837, 2013.

11-14

4.

Authors:

Sajith A.G, Hariharan S

Paper Title:

A Region based Active Contour Approach for Liver CT Image Analysis Driven by Local likelihood Image Fitting Energy

Abstract: Computer tomography images are widely used in the diagnosis of liver tumor analysis because of its faster acquisition and compatibility with most life support devices. Accurate image segmentation is very sensitive in the field of medical image analysis. Active contours plays an important role in the area of medical image analysis. It constitute a powerful energy minimization criteria for image segmentation. This paper presents a region based active contour model for liver CT image segmentation based on variational level set formulation driven by local likelihood image fitting energy. The neigh bouring intensities of image pixels are described in terms of Gaussian distribution. The mean and variances of intensities in the energy functional can be estimated during the energy minimization process. The updation of mean and variance guide the contour evolving toward tumor boundaries. Also this model has been compared with different active active contour models. Our results shows that the presented model achieves superior performance in CT liver image segmentation. 

Keywords: Active Contours, Chan-Vese model, Level sets

References:
1.       Kass, M., Witkin, A., and Terzopoulos, D.: ‘Snakes: Active contour models’, International journal of computer vision, 1988, 1, (4), pp. 321-331
2.       Osher, S., and Sethian, J.A.: ‘Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations’, Journal of computational
physics, 1988, 79, (1), pp. 12-49
3.       Caselles, V., Kimmel, R., and Sapiro, G.: ‘Geodesic active contours’, International journal of computer vision, 1997, 22, (1), pp. 61-79
4.       Kimmel, R., Amir, A., and Bruckstein, A.M.: ‘Finding shortest paths on surfaces using level sets propagation’, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17, (6), pp. 635-640
5.       Li, C., Xu, C., Gui, C., and Fox, M.D.: ‘Distance regularized level set evolution and its application to image segmentation’, IEEE transactions on image processing, 2010, 19, (12), pp. 3243-3254
6.       Malladi, R., Sethian, J.A., and Vemuri, B.C.: ‘Shape modeling with front propagation: A level set approach’, IEEE transactions on pattern analysis and machine intelligence, 1995, 17, (2), pp. 158-175
7.       Vasilevskiy, A., and Siddiqi, K.: ‘Flux maximizing geometric flows’, IEEE transactions on pattern analysis and machine intelligence, 2002, 24, (12), pp. 1565-1578
8.       Xu, C., and Prince, J.L.: ‘Snakes, shapes, and gradient vector flow’, IEEE Transactions on image processing, 1998, 7, (3), pp. 359-369
9.       Chan, T.F., and Vese, L.A.: ‘Active contours without edges’, IEEE Transactions on image processing, 2001, 10, (2), pp. 266-277
10.    Cremers, D., Rousson, M., and Deriche, R.: ‘A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape’, International journal of computer vision, 2007, 72, (2), pp. 195-215
11.    He, L., Peng, Z., Everding, B., Wang, X., Han, C.Y., Weiss, K.L., and Wee, W.G.: ‘A comparative study of deformable contour methods on medical image segmentation’, Image and Vision Computing, 2008, 26, (2), pp. 141-163
12.    Li, C., Huang, R., Ding, Z., Gatenby, J.C., Metaxas, D.N., and Gore, J.C.: ‘A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI’, IEEE Transactions on Image Processing, 2011, 20, (7), pp. 2007-2016
13.    Li, C., Kao, C.-Y., Gore, J.C., and Ding, Z.: ‘Minimization of region-scalable fitting energy for image segmentation’, IEEE transactions on image processing, 2008, 17, (10), pp. 1940-1949
14.    Paragios, N., and Deriche, R.: ‘Geodesic active regions and level set methods for supervised texture segmentation’, International Journal of Computer Vision, 2002, 46, (3), pp. 223-247
15.    Ronfard, R.: ‘Region-based strategies for active contour models’, International journal of computer vision, 1994, 13, (2), pp. 229-251
16.    Samson, C., Blanc-Féraud, L., Aubert, G., and Zerubia, J.: ‘A variational model for image classification and restoration’, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22, (5), pp. 460-472
17.    Tsai, A., Yezzi, A., and Willsky, A.S.: ‘Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification’, IEEE transactions on Image Processing, 2001, 10, (8), pp. 1169-1186
18.    Vese, L.A., and Chan, T.F.: ‘A multiphase level set framework for image segmentation using the Mumford and Shah model’, International journal of computer vision, 2002, 50, (3), pp. 271-293
19.    Li, C., Kao, C.-Y., Gore, J.C., and Ding, Z.: ‘Implicit active contours driven by local binary fitting energy’, in Editor (Ed.)^(Eds.): ‘Book Implicit active contours
driven by local binary fitting energy’ (IEEE, 2007, edn.), pp. 1-7
20.    Wang, L., He, L., Mishra, A., and Li, C.: ‘Active contours driven by local Gaussian distribution fitting energy’, Signal Processing, 2009, 89, (12), pp. 2435-2447
21.    Zhang, K., Song, H., and Zhang, L.: ‘Active contours driven by local image fitting energy’, Pattern recognition, 2010, 43, (4), pp. 1199-1206
22.    Mumford, D., and Shah, J.: ‘Optimal approximations by piecewise smooth functions and associated variational problems’, Communications on pure and applied mathematics, 1989, 42, (5), pp. 577-685

15-23

5.

Authors:

Ogundare A.B, Ihiovi M.M

Paper Title:

Design of a 3 Phase Automatic Change-Over Switch using a PIC Microcontroller (PIC16F877A)

Abstract: Change over process involves switching electrical load from one power source to another, when the load is powered by two alternative sources (main utility and stand by generator). The process can be complex if it involves starting and stopping of source like generator and monitoring of mains. This paper presents a method to ease this rigorous process. A 3 phase automatic change over which uses generator control mechanism is designed to select between two available sources of power in this case, generator and utility with preference to the utility. The system monitors the utility mains supply and checks for complete failure as well as phase failure upon which it automatically start the generator, run it on idle for a minute, then switch the load to it. The system keeps monitoring the utility source for power restoration, it also monitor the generator output for failure upon any of which it switches back the load to utility supply and automatically switch off the generator. Once power is restored, the system delays for two minute before transferring the load to the utility supply. Success was recorded as the above processes were automated. This was achieved with the combination of discrete electrical and electronics components

Keywords:  Electrical Load, Utility, Generator, Electrical and Electronics Components.

References:
1.       Ahmed M.S., Mohammed A.S. and Agusiobo O.B. (2006) ‘’Development of a Single Phase Automatic Change-Over Switch’’. AU J.T. 10(1): 68-74. Federal University of Technology Minna, Nigeria. (Jul. 2006)
2.       Amos, S.W. and James, M. (1981). Principles of transistor circuit: Introduction to the design of amplifiers, receivers and digital circuits. 6th ed., Hartnolls ltd., bodmin.UK.
3.       Atser A. Roy et-al, (2014). Design and Implementation of a 3-Phase Automatic Power Change-over Switch. e-ISSN : 2320-0847 p-ISSN : 2320-0936 Volume-3, Issue-9, pp-07-14
4.       Ezema L.S., Peter B.U., Harris O.O. (2012). Design of automatic change over switch with Generator control mechanism. SAVAP international.
5.       L.S. Ezema et-al, (2012). Design of Automatic Change Over Switch with Generator Control Mechanism. ISSN-L: 2223-9944. Vol.3, No.3, November 2012.
6.       Faissler, W.L. (1991). An introduction to modern Electronics, Willey, New York, NY, USA.
7.       Horowitz, P. and Winfield, H. (2002). The Art of Electronics, 2nd ed. Cambridge Univ. Press, Cambridge, UK
8.       Owen, B. (1995). Beginner’s Guide to Electronics 4th Ed. A Newness Technical Book, McGraw-Hill Companies Inc. New York, N.Y, USA.
9.       Oduobuk, E. J. et-al (2014). Design and Implementation of Automatic Three Phase Changer over Using LM324 Quad Integrated Circuit. International Journal of
Engineering and Technology Research Vol. 2, No. 4, April 2014, pp. 1 - 15, ISSN: 2327 – 0349.
10.    Rocks G. and Mazur G., (1993). Electrical motor controls. American Technical Publication, New-York, N.Y, USA.
11.    Ragnar, H. (1958). Electric Contacts Handbook. 3rd Edition, Springer-Verlag, Berlin/ Göttingen /Heidelberg. pp. 331-342.
12.    Theraja, B.L.; and Theraja, A.K. 2002. Electrical Technology, 21st ed. Ranjendra Ravida, New Delhi, India.

24-27

6.

Authors:

Pooja C.S, K. R. Prassana Kumar 

Paper Title:

Survey on Load Balancing and Auto Scaling techniques for cloud Environment

Abstract: Cloud computing became now first choice and priority for every person who access the internet, one of the advantageous features of cloud computing is its scalability and flexibility. Auto scaling offers the facility to the individuals to scale up and scale down the resources as per their requirements, using only the needed resouce and paying for what they have used i.e "pay-as-you-use". As everything take place in automatic manner, so human involvement errors are less and reduce the manpower and costs. so to make use of elasticity user must use auto scaling technique that balances the incoming workload, and reduce the total cost and maintain the Service Level Agreement (SLA).In this work main ideas revolve around the problems in scalable cloud computing systems. In modern days, management of resources is in boom and most talked topic in cloud environment. we present some of the existing load balancing policies and about Autoscaling categories.

Keywords: cloud computing, scaling, auto scaling, load balancing.

References:
1.    Fang Liu, Jin Tong, Jian Mao, Robert Bohn, John Messina, Lee Badger and Dawn Leaf,"NIST Cloud Computing Reference Architecture", NIST Special Publication 500-292, September 2011.
2.    M.Kriushanth, L. Arockiam and G. JustyMirobi,"Auto Scaling in Cloud Computing: An Overview", International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 7, July 2013, ISSN (Print): 2319-5940,ISSN (Online) : 2278-1021.
3.    Tania Lorido-Botran, Jose Miguel-Alonso , Jose A. Lozano, "A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments", ARTICLE in JOURNAL OF GRID COMPUTING DECEMBER 2014, Impact Factor: 1.51 • DOI: 10.1007/s10723-014-9314-7.
4.    ChenhaoQu, Rodrigo N. Calheiros, and RajkumarBuyya,"A Reliable and Cost-Ecient Auto-Scaling System for Web Applications Using Heterogeneous Spot Instances", Cloud Computing and Distributed Systems (CLOUDS) Laboratory, Department of Computing and Information Systems, The University of Melbourne, Australia, September 17, 2015.
5.    Gunpriya Makkar, Pankaj Deep Kaur,"A Review of Load Balancing in Cloud Computing", Guru Nanak Dev University, Jalandhar, India, Volume 5, Issue 4, 2015 ISSN: 2277 128X.
6.    Priyanka P. Kukade and Geetanjali Kale “Survey of Load Balancing and Scaling approaches in cloud” vol.4 Feb 2015.
7.    Ashalatha R Evaluation of Auto Scaling and Load Balancing Features in Cloud” vol.117 may 2015.
8.    Dr. D .Ravindran, Ab Rashid Dar 鼎loud Based Resource Management with Autoscaling・ vol.2 .

28-30

2.

Authors:

Cini K.

Paper Title:

Value Based Reliability Evaluation of Primary Power Distribution System

Abstract: Distribution system reliability is concerned with the availability and quality of power supply at each customer’s service entrance. Analysis of customer failure statistics shows that failure in distribution system contribute as much as 90% towards the unavailability of supply to a load as compared with each part of electric systems. These statistics reinforces the need for reliability evaluation of distribution systems. In recent years with the advent of smart grids the significance of distribution system has enhanced because of the importance of co generation and distributed generation. The different causes and duration of failures are analysed season wise. The failure rate of the different feeders of the system under study was calculated and the reliable feeders were identified. Suggestions are given to improve the reliability of the feeders. This type of analysis will help the operation and maintenance engineers to maintain the quality service to the customers and schedule the maintenance services.  

Keywords: Distribution Systems, Reliability Indices, Failure Rate, Availability.

References:
1.       Biyun Chen; Qianyi Chen “The whole-process reliability evaluation  of  power  system including generation, transmission, transformation and distribution” IEEE 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), pp 482-487
2.       H. 2. Andrews, Laura, Samuel” Novel Power System Reliability Indices calculation method” 23rd International Conference on Electricity Distribution, Lyon  15-18, June .
3.       Roy Billinton and Peng Wang “ Distribution System Reliability Cost/worth analysis Using Analytical and sequential Simulation Techniques” IEEE transactions on power systems, Vol.13, No.4, November 1998,pp1245-1250.
4.       R. Billinton and J. E. Billinton, “Distribution System Reliability Indices”, IEEE Trans. on Power Delivery, Vol. 4, No. 1, Jan. 1989, pp. 561-568.
5.       Vito Longo ,Walter R. Puntel, “Evaluation of Distribution System Enhancements Using Value-Based Reliability Planning” Procedures IEEE Transactions on Power
systems, vol. 15, no. 3, august 2000.
6.       Billinton, R., and Allan, R. N., "Reliability Evaluation of Power Systems",Pitman Books, New York and London, 1984.
7.       Billinton, R., "Evaluation of Reliability Worth in an Electric Power system". Reliability Engineering and System Safety, Vol. 46, No. 1, 1994.
8.       Carlos Eduardo Paida Tenemaza “State of Art, Reliability In Electrical Distribution Systems Based On Markov Stochastic Model”  IEEE Latin America Transactions, Volume: 14, Issue: 2, pp 799-804.
9.       Farajollah Soudi and Kevin Tomsovic  “Optimal Trade-Offs in Distribution Protection Design” IEEE  transactions on power delivery, vol. 16, no. 2, April 2001.
10.    Amir Safdarian; Mohammad Farajollahi; Mahmud Fotuhi-Firuzabad “ Impacts of Remote Control Switch Malfunction on Distribution System Reliability” IEEE Transactions on Power Systems, Volume: 32, Issue: 2, 2017, pp 1572-1573.
11.    Siripha Junlakarn; Marija Ilić , “Distribution System Reliability Options and Utility Liability”  IEEE Transactions on Smart Grid , Volume: 5, Issue: 5, 2014, pp 2227-2234.

6-10

3.

Authors:

S. L. Deshpande, D S Chaudhari

Paper Title:

Wireless Nodes Assisted Micro-Irrigation System: an IoT Approach

Abstract: Irrigation systems deployed with Wireless Sensor Network (WSN) while transforming them to Micro-Irrigation systems are emerging as fruitful solution to ongoing ground water crisis. Field parameters like soil moisture, temperature and humidity can be monitored taking help of sensor array and can be fed back to decision making control system. Organized parametric results can help the optimized use of the water. By using wireless communication and environmental energy harvesting techniques, sensor network can be made totally wireless. Internet of Things (IoT) is another emerging technology that goals to extend the application of internet from complex computational machines (computer) to the stand alone devices such as consumer electronics. Integrating IoT to WSN not only can provide remote access but also allow two distinct information systems to frequently collaborate and provide common services. Also the user can be provided with flexible interface like mobile application. The miniaturization in technology and even more reliable communication are the strongest suits of such sensor network. This paper reviews for various technologies to fulfil requirement of such application and the shows some system characteristics.

Keywords:  WSN, IoT, Irrigation, Moisture, Humidity, Energy Harvesting, etc.

References:
1.       Basic Botany, Physiology, and Environmental Effects on Plant Growth, AZ master gardner manual, The University of Arizona, AZ, 1998.
2.       M. Morris. (2006). Soil Moisture Monitoring: Low-Cost Tools and Methods [Online]. Available FTP: attra.ncat.org Directory: attra-pub/PDF File: soil moisture.pdf
3.       Y. Kim, R. Evans and W. Iversen, "Remote Sensing and Control of an Irrigation System Using a Distributed Wireless Sensor Network," in IEEE Transactions on Instrumentation and Measurement, vol. 57, pp. 1379ꟷ1387, July 2008.
4.       W. Wang and S. Cao, "Application Research on Remote Intelligent Monitoring System of Greenhouse Based on ZIGBEE WSN," 2nd International Congress on Image and Signal Processing, Tianjin, pp. 1-5, 2009.
5.       Yu, Y. Cui, L. Zhang and S. Yang, "ZigBee Wireless Sensor Network in Environmental Monitoring Applications," 5th International Conference on Wireless Communications, Networking and Mobile Computing, Beijing, pp. 1ꟷ5, 2009.
6.       Z. Rasin, H. Hamzah and M. Aras, "Application and evaluation of high power Zigbee based wireless sensor network in water irrigation control monitoring system," IEEE Symposium on Industrial Electronics & Applications, Kuala Lumpur, pp. 548ꟷ551, 2009.
7.       M. Zorzi, A. Gluhak, S. Lange and A. Bassi, "From today's INTRAnet of things to a future INTERnet of things: a wireless- and mobility-related view," in IEEE Wireless Communications, vol. 17, no. 6, pp. 44-51, December 2010.
8.       G. Kortuem, F. Kawsar, V. Sundramoorthy and D. Fitton, "Smart objects as building blocks for the Internet of things," in IEEE Internet Computing, vol. 14, no. 1, pp. 44-51, Jan.-Feb. 2010.
9.       K. Langendoen, A. Baggio and O. Visser, "Murphy loves potatoes: experiences from a pilot sensor network deployment in precision agriculture," Proceedings 20th IEEE International Parallel & Distributed Processing Symposium, Rhodes Island, pp. 1ꟷ8, 2006.
10.    L. Li, H. Xiaoguang, C. Ke and H. Ketai, "The applications of WiFi-based Wireless Sensor Network in Internet of Things and Smart Grid," 6th IEEE Conference on Industrial Electronics and Applications, Beijing, pp. 789-793, 2011
11.    M. Lee, J. Hwang and H. Yoe, "Agricultural Production System Based on IoT," IEEE 16th International Conference on Computational Science and Engineering, Sydney, NSW, pp. 833-837, 2013.

11-14

4.

Authors:

Sajith A.G, Hariharan S

Paper Title:

A Region based Active Contour Approach for Liver CT Image Analysis Driven by Local likelihood Image Fitting Energy

Abstract: Computer tomography images are widely used in the diagnosis of liver tumor analysis because of its faster acquisition and compatibility with most life support devices. Accurate image segmentation is very sensitive in the field of medical image analysis. Active contours plays an important role in the area of medical image analysis. It constitute a powerful energy minimization criteria for image segmentation. This paper presents a region based active contour model for liver CT image segmentation based on variational level set formulation driven by local likelihood image fitting energy. The neigh bouring intensities of image pixels are described in terms of Gaussian distribution. The mean and variances of intensities in the energy functional can be estimated during the energy minimization process. The updation of mean and variance guide the contour evolving toward tumor boundaries. Also this model has been compared with different active active contour models. Our results shows that the presented model achieves superior performance in CT liver image segmentation. 

Keywords: Active Contours, Chan-Vese model, Level sets

References:
1.       Kass, M., Witkin, A., and Terzopoulos, D.: ‘Snakes: Active contour models’, International journal of computer vision, 1988, 1, (4), pp. 321-331
2.       Osher, S., and Sethian, J.A.: ‘Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations’, Journal of computational
physics, 1988, 79, (1), pp. 12-49
3.       Caselles, V., Kimmel, R., and Sapiro, G.: ‘Geodesic active contours’, International journal of computer vision, 1997, 22, (1), pp. 61-79
4.       Kimmel, R., Amir, A., and Bruckstein, A.M.: ‘Finding shortest paths on surfaces using level sets propagation’, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17, (6), pp. 635-640
5.       Li, C., Xu, C., Gui, C., and Fox, M.D.: ‘Distance regularized level set evolution and its application to image segmentation’, IEEE transactions on image processing, 2010, 19, (12), pp. 3243-3254
6.       Malladi, R., Sethian, J.A., and Vemuri, B.C.: ‘Shape modeling with front propagation: A level set approach’, IEEE transactions on pattern analysis and machine intelligence, 1995, 17, (2), pp. 158-175
7.       Vasilevskiy, A., and Siddiqi, K.: ‘Flux maximizing geometric flows’, IEEE transactions on pattern analysis and machine intelligence, 2002, 24, (12), pp. 1565-1578
8.       Xu, C., and Prince, J.L.: ‘Snakes, shapes, and gradient vector flow’, IEEE Transactions on image processing, 1998, 7, (3), pp. 359-369
9.       Chan, T.F., and Vese, L.A.: ‘Active contours without edges’, IEEE Transactions on image processing, 2001, 10, (2), pp. 266-277
10.    Cremers, D., Rousson, M., and Deriche, R.: ‘A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape’, International journal of computer vision, 2007, 72, (2), pp. 195-215
11.    He, L., Peng, Z., Everding, B., Wang, X., Han, C.Y., Weiss, K.L., and Wee, W.G.: ‘A comparative study of deformable contour methods on medical image segmentation’, Image and Vision Computing, 2008, 26, (2), pp. 141-163
12.    Li, C., Huang, R., Ding, Z., Gatenby, J.C., Metaxas, D.N., and Gore, J.C.: ‘A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI’, IEEE Transactions on Image Processing, 2011, 20, (7), pp. 2007-2016
13.    Li, C., Kao, C.-Y., Gore, J.C., and Ding, Z.: ‘Minimization of region-scalable fitting energy for image segmentation’, IEEE transactions on image processing, 2008, 17, (10), pp. 1940-1949
14.    Paragios, N., and Deriche, R.: ‘Geodesic active regions and level set methods for supervised texture segmentation’, International Journal of Computer Vision, 2002, 46, (3), pp. 223-247
15.    Ronfard, R.: ‘Region-based strategies for active contour models’, International journal of computer vision, 1994, 13, (2), pp. 229-251
16.    Samson, C., Blanc-Féraud, L., Aubert, G., and Zerubia, J.: ‘A variational model for image classification and restoration’, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22, (5), pp. 460-472
17.    Tsai, A., Yezzi, A., and Willsky, A.S.: ‘Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification’, IEEE transactions on Image Processing, 2001, 10, (8), pp. 1169-1186
18.    Vese, L.A., and Chan, T.F.: ‘A multiphase level set framework for image segmentation using the Mumford and Shah model’, International journal of computer vision, 2002, 50, (3), pp. 271-293
19.    Li, C., Kao, C.-Y., Gore, J.C., and Ding, Z.: ‘Implicit active contours driven by local binary fitting energy’, in Editor (Ed.)^(Eds.): ‘Book Implicit active contours
driven by local binary fitting energy’ (IEEE, 2007, edn.), pp. 1-7
20.    Wang, L., He, L., Mishra, A., and Li, C.: ‘Active contours driven by local Gaussian distribution fitting energy’, Signal Processing, 2009, 89, (12), pp. 2435-2447
21.    Zhang, K., Song, H., and Zhang, L.: ‘Active contours driven by local image fitting energy’, Pattern recognition, 2010, 43, (4), pp. 1199-1206
22.    Mumford, D., and Shah, J.: ‘Optimal approximations by piecewise smooth functions and associated variational problems’, Communications on pure and applied mathematics, 1989, 42, (5), pp. 577-685

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5.

Authors:

Ogundare A.B, Ihiovi M.M

Paper Title:

Design of a 3 Phase Automatic Change-Over Switch using a PIC Microcontroller (PIC16F877A)

Abstract: Change over process involves switching electrical load from one power source to another, when the load is powered by two alternative sources (main utility and stand by generator). The process can be complex if it involves starting and stopping of source like generator and monitoring of mains. This paper presents a method to ease this rigorous process. A 3 phase automatic change over which uses generator control mechanism is designed to select between two available sources of power in this case, generator and utility with preference to the utility. The system monitors the utility mains supply and checks for complete failure as well as phase failure upon which it automatically start the generator, run it on idle for a minute, then switch the load to it. The system keeps monitoring the utility source for power restoration, it also monitor the generator output for failure upon any of which it switches back the load to utility supply and automatically switch off the generator. Once power is restored, the system delays for two minute before transferring the load to the utility supply. Success was recorded as the above processes were automated. This was achieved with the combination of discrete electrical and electronics components

Keywords:  Electrical Load, Utility, Generator, Electrical and Electronics Components.

References:
1.       Ahmed M.S., Mohammed A.S. and Agusiobo O.B. (2006) ‘’Development of a Single Phase Automatic Change-Over Switch’’. AU J.T. 10(1): 68-74. Federal University of Technology Minna, Nigeria. (Jul. 2006)
2.       Amos, S.W. and James, M. (1981). Principles of transistor circuit: Introduction to the design of amplifiers, receivers and digital circuits. 6th ed., Hartnolls ltd., bodmin.UK.
3.       Atser A. Roy et-al, (2014). Design and Implementation of a 3-Phase Automatic Power Change-over Switch. e-ISSN : 2320-0847 p-ISSN : 2320-0936 Volume-3, Issue-9, pp-07-14
4.       Ezema L.S., Peter B.U., Harris O.O. (2012). Design of automatic change over switch with Generator control mechanism. SAVAP international.
5.       L.S. Ezema et-al, (2012). Design of Automatic Change Over Switch with Generator Control Mechanism. ISSN-L: 2223-9944. Vol.3, No.3, November 2012.
6.       Faissler, W.L. (1991). An introduction to modern Electronics, Willey, New York, NY, USA.
7.       Horowitz, P. and Winfield, H. (2002). The Art of Electronics, 2nd ed. Cambridge Univ. Press, Cambridge, UK
8.       Owen, B. (1995). Beginner’s Guide to Electronics 4th Ed. A Newness Technical Book, McGraw-Hill Companies Inc. New York, N.Y, USA.
9.       Oduobuk, E. J. et-al (2014). Design and Implementation of Automatic Three Phase Changer over Using LM324 Quad Integrated Circuit. International Journal of
Engineering and Technology Research Vol. 2, No. 4, April 2014, pp. 1 - 15, ISSN: 2327 – 0349.
10.    Rocks G. and Mazur G., (1993). Electrical motor controls. American Technical Publication, New-York, N.Y, USA.
11.    Ragnar, H. (1958). Electric Contacts Handbook. 3rd Edition, Springer-Verlag, Berlin/ Göttingen /Heidelberg. pp. 331-342.
12.    Theraja, B.L.; and Theraja, A.K. 2002. Electrical Technology, 21st ed. Ranjendra Ravida, New Delhi, India.

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6.

Authors:

Pooja C.S, K. R. Prassana Kumar 

Paper Title:

Survey on Load Balancing and Auto Scaling techniques for cloud Environment

Abstract: Cloud computing became now first choice and priority for every person who access the internet, one of the advantageous features of cloud computing is its scalability and flexibility. Auto scaling offers the facility to the individuals to scale up and scale down the resources as per their requirements, using only the needed resouce and paying for what they have used i.e "pay-as-you-use". As everything take place in automatic manner, so human involvement errors are less and reduce the manpower and costs. so to make use of elasticity user must use auto scaling technique that balances the incoming workload, and reduce the total cost and maintain the Service Level Agreement (SLA).In this work main ideas revolve around the problems in scalable cloud computing systems. In modern days, management of resources is in boom and most talked topic in cloud environment. we present some of the existing load balancing policies and about Autoscaling categories.

Keywords: cloud computing, scaling, auto scaling, load balancing.

References:
1.    Fang Liu, Jin Tong, Jian Mao, Robert Bohn, John Messina, Lee Badger and Dawn Leaf,"NIST Cloud Computing Reference Architecture", NIST Special Publication 500-292, September 2011.
2.    M.Kriushanth, L. Arockiam and G. JustyMirobi,"Auto Scaling in Cloud Computing: An Overview", International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 7, July 2013, ISSN (Print): 2319-5940,ISSN (Online) : 2278-1021.
3.    Tania Lorido-Botran, Jose Miguel-Alonso , Jose A. Lozano, "A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments", ARTICLE in JOURNAL OF GRID COMPUTING DECEMBER 2014, Impact Factor: 1.51 • DOI: 10.1007/s10723-014-9314-7.
4.    ChenhaoQu, Rodrigo N. Calheiros, and RajkumarBuyya,"A Reliable and Cost-Ecient Auto-Scaling System for Web Applications Using Heterogeneous Spot Instances", Cloud Computing and Distributed Systems (CLOUDS) Laboratory, Department of Computing and Information Systems, The University of Melbourne, Australia, September 17, 2015.
5.    Gunpriya Makkar, Pankaj Deep Kaur,"A Review of Load Balancing in Cloud Computing", Guru Nanak Dev University, Jalandhar, India, Volume 5, Issue 4, 2015 ISSN: 2277 128X.
6.    Priyanka P. Kukade and Geetanjali Kale “Survey of Load Balancing and Scaling approaches in cloud” vol.4 Feb 2015.
7.    Ashalatha R Evaluation of Auto Scaling and Load Balancing Features in Cloud” vol.117 may 2015.
8.    Dr. D .Ravindran, Ab Rashid Dar 鼎loud Based Resource Management with Autoscaling・ vol.2 .

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