iosr-JCE   National Electronic Conference On Communication And Networking

Abstract:This study is focused on equipping wireless devices (including sensors) with high-efficiency circuitry to harvest and convert ambient radio frequency (RF) power to direct current (dc). Our objective is to obtain energy effectively from electromagnetic radiations, radio waves. A Wi-Fi spot is only used to get connected to a network, but here we provide a means for collecting energy and using it to charge devices that are connected in that network. The Wi-Fi signals contain some amount of energy that is 100mW approx as it is very small it goes unnoticed. The signals are sensed and by using artificial intelligence this is employed using smart phones, if there is any case of dying charge, we provide a method to automatically retain the power from Wi-Fi signals amplify it and then transfer it into a consumable form and then utilizing it in low power consuming devices like smart phones to serve the purpose of emergency by charging these devices while accessing the Wi-Fi.This is an alternative optimized way to charge devices rather than to use electricity.

Keywords-amplification, artificial intelligence, minimizing electricity usage, socket/plug-free charging wireless networks Wi-Fi.

[1] Improved Design of Rectenna for Wireless Power Transmission S.P.K. Babu, A.V. Meenakshi, S. Banu, A. Vishwapriya and R. Yogamathi
[2] http://en.wikipedia.org/wiki/Amplifier
[3] http://www.techweekeurope.co.uk/news/scavenging-free-green-power-from-radio-waves-35622
[4] http://computer.howstuffworks.com/wireless-network1.htm

Abstract: A continuous and reliable supply of electricity is necessary for the functioning of today’s modern and advanced society. Since the early to mid 1980s, most of the effort in power systems analysis has turned away from the methodology of formal mathematical modeling which came from the areas of operations research, control theory and numerical analysis to the less rigorous and less tedious techniques of artificial intelligence (AI). Power systems keep on increasing on the basis of geographical regions, assets additions, and introduction of new technologies in generation, transmission and distribution of electricity. AI techniques have become popular for solving different problems in power systems like control, planning, scheduling, forecast, etc. These techniques can deal with difficult tasks faced by applications in modern large power systems with even more interconnections installed to meet increasing load demand. The application of these techniques has been successful in many areas of power system engineering.
Keywords - Artificial intelligence, Power system engineering

Books:
[1] Warwick K., Ekwue A. And Aggarwal R. (ed).Artificial intelligence techniques in power systems. The  Institution of Electrical Engineers, London, 1997

Journal Papers:
[2] International Journal of Engineering Intelligent Systems, The special issue on AI applications to power system protection, edited by M.M. Saha and B.Kasztenny, Vol.5, No.4, December 1997, pp.185-93.
[3] Dahhaghchi, I.,Christie, R.D, AI application areas in power systems, IEEE Expert,' Vol. 12, Issue 1 pages 58-66, Jan/Feb 1997
[4] Anis Ibrahim, W.R.; Morcos, M.M, Artificial intelligence and advanced mathematical tools for power quality applications: a survey, Power Delivery, IEEE Transactions, Vol. 17, Issue 2, Pages 668-673, April 2002.
Theses:
[5] Khedher M.Z., Fuzzy Logic in Power Engineering,, Regional Conference of CIGRE committees in Arab Countries, May 25-27 (1997), Doha, Qatar.
[6] Bachmann B., Novosel D., Hart D., Hu Y., Saha M.M., Application of artificial neural networks for series compensated line protection, Proc. of the Int. Conf. on Intelligent System Application to Power Systems, Orlando, January 28 - February 2, 1996, pp.68-73.
[7] Kirkpatrick S., Gelatt C. D., Vecchi M. P., 1983, "Optimization by simulated annealing". Science. New Series 220, pp.671–680. Lai, Loi Lei, 1998, Intelligent system applications in power engineering: evolutionary programming and neural networks, John Willey & Sons, UK.
Proceedings Papers:
[8] B. Kosko, Neural Networks and Fuzzy Systems, Prentice Hall, Englewood Cliffs, NJ, U.S.A., 1992.
[9] Alander J. T., 1996, An indexed bibliography of genetic algorithm in power engineering, Power Report Series 94-1.
[10] El-Hawary, Mohamed E., 1998, Electric power applications of fuzzy systems, John Wiley USA.

[11] Momoh James A., EL-Hawary Mohamed E., 2000, Electric systems, dynamics, and stability with artificial intelligence, Marcel Dekker, Inc. USA.

Abstract:”Casattenta” (Aware home, in Italian) is the demonstrator of a research project on ”Ambient Intelligence”, “Sensor Fusion” and “Wireless Sensor Networks”. The result is a system composed of fixed and wearable sensor nodes, providing elderly people living alone in their house (but also persons in other situations and environments) with adequate and non intrusive monitoring in order to improve the quality of their life. The system consists of fixed smart sensors distributed in the environment and wearable ones monitoring inhabitants health and activity. The interaction between fixed and mobile nodes, based on the ZigBee wireless protocol, allows indoor tracking and identification of dangerous events. CR Categories: C.2.4 [Computer-Communication Networks]: Distributed Systems—Wireless Sensor Networks; J.m [Computer Applications]: Miscellaneous

Keywords-Ambient Intelligence, Smart Home, Wireless Sensor Networks

[1] European health for all database (hfa-db). http:// www.euro.who.int/hfadb, Updated November 2007.
[2] Chen Xijun; Meng, M.Q.H.; Ren Hongliang, "Design of Sensor Node Platform for Wireless Biomedical Sensor Networks," Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the , vol., no., pp.4662-4665, 17-18 Jan. 2006
[3] Schloesser, Mario; Schnitzer, Andreas; Ying, Hong; Silex, Carmen; Schiek, Michael, "iSANLA: Intelligent Sensor and Actuator Network for Life science Applications," Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE , vol., no., pp.2369-2372, 20-25 Aug. 2008
[4] Chandrakasan, A. P., N. Verma, D. C. Daly, "Ultralow-Power Electronics for Biomedical Applications," Annual Review of Biomedical Engineering, August 2008
[5] http://www.moteiv.com/
[6] http://www.tinyos.net/
[7] http://www.ti.com/
[8] Adaptive House, University of Colorado http:// www.cs.colorado.edu/~mozer/nnh/
[9] Georgia Tech Aware Home: http://www.cc.gatech.edu/ fce/ahri/
[10] MIT House_n: http://architecture.mit.edu/house_n/

Abstract: In this paper we have stated about solving the problem of electricity insufficiency everywhere. It’s an innovative idea for a new type of power plant. It’s almost impossible to produce electricity from lighting, so we have decided to take current from clouds. Clouds have plenty of charges stored in it, here we give some ideas to make that charges useful. So here I have made use of Pelletron generator (similar toVan de Graaff generators) to do this job. It uses the principle of electrostatic induction. In this I make a giant pelletron generator in upside-down direction, instead of giving charges to it, it take charges stored in cloud and increases the charges at the other end. So we can get high voltages of about 30 MV from a single pelletron generator. This pelletron generator are to be placed in mountains it may be seem to be tedious but it is an idea of new power plant, so we have to make such big this construction on mountain so that we could have a constant production of electricity in all seasons.

Index Terms—Charge,Cloud Energy, electricity,new power plant, lighting, Pelletron generator.

[1] Article "Van de Graaff's Generator", in "Electrical Engineering Handbook", (ed)., CRC Press, Boca Raton, Florida USA, 1993 ISBN 0-8493-0185-8
[2] J. Takacs, Energy Stabilization of Electrostatic Accelerators, John Wiley and Sons, Chichester, 1996
[3] Junhong Chen, "Direct-Current Corona Enhanced Chemical Reactions", Ph.D. Thesis, University of Minnesota, USA. August 2002.
[4] F.W. Peek (1929). Dielectric Phenomena in High Voltage Engineering. McGraw-Hill. ISBN 0-9726596-6-8.
[5] Cope, Thomas A. Darlington. Physics. Library of Alexandria. ISBN 1465543724.

[6] Hadley, Harry Edwin (1899). Magnetism & Electricity for Beginners. Macmillan & Company. p. 182.

Abstract:In the flight tracer project, the feasibility of transmitting data to ground station in real time is investigated.  Instead of waiting for a prolonged time for locating the black box, the valuable information can be recovered instantly via stations. .With any airplane crash, there are many unanswered questions as to what brought the plane down.  The answers are hid inside the black box and the teams are dispatched at a considerable cost to find it.    The flight tracer project includes the distribution of area into segments and the subsequent communication between the plane and the ground station of the particular segment.  A set of algorithms is developed to packetize the flight data, transmit it to the ground station and integrate the transmitted data to recover the flight information.  We hope this work will contribute towards saving human lives and improving the safety and reliability of aircrafts.

Keywords: black box, distributed servers, flight data

[1].  Review of  US Civil Aviation Accidents,2007-2009(ARA-II-0I)
http://www.ntsb.gov/doclib/repors/201I1ARAIIO1.pdf
[2].  K.Kavi “Beyond the black box”, IEEE Spectrum, August 2010,46-51.
[3].  K.Kavi and M.Aborizka.”Glass-Box: An intelligent flight data recorder and real time monitoring systems”,Proc. Of the 39th AIAA Aerospace Sciences Meeting, Jan 8-11-2001.
[4].  J.Adler,: “The black Box”, Wired Magazine,Issue 19.07.2011.Pages 142-147,166,170.
[5].WGL:Wireless Ground Link),
http://www.teledynecontrols.com/Productsolution1wirlessgroundlink1groundlink.asp(Last accessed June 2012)

[6].”Malaysia Airlines MH370: Why airlines don’t live-stream black box data,”Technology and Science(CBC News). 2005-08-04.  Retrived 2014-03-31.

Abstract: Safety-critical systems are embedded systems that could cause injury or loss of human life if they fail or encounter errors. Flight-control systems, automotive drive-by-wire, nuclear reactor management, or operating room heart/lung bypass machines naturally come to mind. Small system defects or situations can cascade into life-threatening failures very quickly.A low-level failure in some small part of a system can be viewed as a fault at another level, which can lead to errors at that level that can trigger failures that can turns out to as fault at a higher level. If these faults are allowed to "avalanche" to system-level failures, they can lead to hazards that have the potential to threaten injury or loss of life. For safety-critical systems, a thorough hazard analysis and risk analysis must also be done. Only then can architectural design get started. In this paper, the selection of safety-critical system architecture is discussed by reviewing a rigorous hazard analysis followed by risk analysis, in addition to conventional system requirements definition. System design may include combinations of redundant sensor configurations, shutdown systems, actuation monitoring, multiple channel architectures, and/or monitor-actuator structuring. Their true value is in protecting and saving human lives. Also a brief study of Fukushima incident in Japan and safety features in Koodankulam   Nuclear Power Plant based on safety critical system is dealt in this paper.
Keywords: NUCLEAR FISSION,NUCLEAR FUSION,NUCLEAR REACTOR MANAGMENT

[1] Storey, N. Safety-Critical Computer Systems. Harlow, UK: Addison-Wesley, 1996.
[2]  Douglas, B. P. Real-Time Design Patterns. Boston, MA: Addison-Wesley, 2003.
[3]  Dunn, W. R. Practical Design of Safety-Critical Computer Systems. Solvang, CA: Reliability Press, 2002.

[4]  Fukushima Nuclear Accident – a simple and accurate explanation @ BraveNewClimate.com

Abstract: Recent gesture controlled robots operates with humans next to them. Whereas, our project deals with interface of robots through gesture controlled technique but far away from the user. This can be achieved through image processing technique.The command signals are generated from these gestures using image processing. These signals are then passed to the robot to navigate it in the specified direction.

Keywords: far away from user, gesture controlled technique, image processing technique,navigate in specific direction, operates with humans.

[1]. MARK BECKER, EFTHIMIA KEFALEA, ERIC MA¨EL, CHRISTOPH VON DER MALSBURG∗∗, MIKE PAGEL, JOCHEN TRIESCH, JAN C. VORBR¨ UGGEN, ROLF P. W¨ URTZ, AND STEFAN ZADEL , Grip See: A Gesture-controlled Robot for Object Perception and Manipulation.

[2] L293D Motor Driver http://luckylarry.co.uk/arduino-projects/control-a-dc-motor-with-arduino-and-l293d-chip/

[3] DC Motors www.globalspec.com/learnmore/motion_controls /motors/dc_motors

[4]. Halide (http://halide-lang.org/) http://www.microchip.com/pagehandler/en-us/family/8bit/architecture/pic18.html)
[6]. Embedded Wi-Fi (http://www.microchip.com/pagehandler/en-us/technology/wifi/products/home.html)

Abstract: In this paper, we propose a color-based segmentation method that uses the K-means clustering technique to track tumor objects in magnetic resonance (MR) brain images. The key concept in this color-based segmentation algorithm with K-means is to convert a given gray-level MR image into a color space image and then separate the position of tumor objects from other items of an MR image by using K-means clustering and histogram-clustering. Experiments demonstrate that the method can successfully achieve segmentation for MR brain images to help pathologists distinguish exactly lesion size and region. In our proposed method, to find the stages of tumor whether it is in Initial, Intermediate or Final stage we used the technique Artificial Neural Network.  In order to classify the stages using ANN, we have to implement two algorithms namely Back propagation and Feed forward methods. Thus the final result is obtained as the stages of brain tumor.

Keywords- K-means clustering, Histogram,  magnetic resonance, artificial neural network

[1]. Gonzalez, R. C.; Woods, R. E., Digital Image Processing, 2nd ed., Prentice-Hall, Englewood Cliffs, NJ, 2002.
[2]. Anderson, J. A., 1995, Introduction to Neural Networks (Cambridge, MA: MIT Press).
[3]. Chen, C. W, Luo, J., Parker, K. J., "Image Segmentation via Adaptive K-mean Clustering and Knowledge-based Morphological Operations with Biomedical Applications," IEEE Transactions on Image Processing, Vol. 7, No, 12, Dec. 1998, pp. 1673-1683.
[4]. Ng, H. P., Ong, H. H., Foong, K. W. C., Goh, P. S.,Nowinski, W. L., "Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm," IEEE Southwest Symposium on Image Analysis and Interpretation.
a. www.google.com
b. www.wikipedia.com

Paper Type	:	Research Paper
Title	:	Maximizing the Performance and Reliability of Wireless Network by Preventing Jamming Attacks
Country	:	India
Authors	:	B. Mahalakshmi, S.V. Shri Bharathi, W. Lydia Shammi

Abstract: Wireless networks are computer networks that are not connected by cables of any kind. It enables the wireless connectivity to the Internet via radiowaves. So it is more sensitive to the Denial-of-Service attacks. This paper proposes a Strong Hiding Commitment schemes, Cryptographic Puzzles Hiding Schemes, and wormholes for sending the message in wireless network safely even if the attacker is present, and also alerts the other nodes about the presence of jammer. Thus, it improves the performance and reliability of wireless sensor networks.

Keywords -Cryptographic puzzles, Denial-of- Service attack, Strong hiding schemes, Wormholes

[1] T.X. Brown, J.E. James, and A. Sethi, “Jamming and Sensing of Encrypted Wireless Ad Hoc Networks,” Proc. ACM Int’l Symp. Mobile Ad Hoc Networking and Computing (MobiHoc), pp. 120-130,2006.
[2] M. Cagalj, S. Capkun, and J.-P.Hubaux, “Wormhole-Based Anti- Jamming Techniques in Sensor Networks,” IEEE Trans. Mobile Computing, vol. 6, no. 1, pp. 100-114, Jan.2007.
[3] A. Chan, X. Liu, G. Noubir, and B. Thapa, “Control Channel Jamming: Resilience a n d I d e n t i f i c a t i o n of Traitors,” Proc. IEEE Int’l Symp. Information Theory (ISIT), 2007.
[4] W. Xu, W. Trappe and Y. Zhang, “Anti-Jamming Timing Channels for Wireless Networks,” Proc. ACM Co n f .Wireless Network Security (WiSec), pp. 203-213, 2008.
[5] R. Rivest, “All-or-Nothing Encryption and the Package Trans- form,” P roc . Int’l Workshop Fast Software Encryption, pp. 210-218,1997.
[6] R. Rivest, A. Shamir, a n d D . Wagner, “Time Lock Pu z z l e s and Timed-Release Crypto,” technical report, Massachusetts Inst. of Technology, 1996.
[7] P. Tague, M. Li, and R. Poovendran, “Mitigation of Control Channel Jamming under Node Capture Attacks,” IEEE Trans.Mobile Computing, vol. 8, no. 9, pp. 1221-1234, Sept. 2009
[8] A. Juels and J. Brainard, “Client Puzzles: A Cryptographic Countermeasure against Connection Depletion Attacks,” Proc. Network and Distributed System Security Symp. (NDSS), pp. 151-165, 1999
[9]A.D.Wood and J.A. Stankovic, “Denial of service in sensor networks,” Computer, vol. 35, no. 10, pp. 54-62, oct. 2002.
[10] J. McCune, E.Shi, A.Perrig, and M.K.Reiter, “Detection of Denial-of-message attacks on sensor networks broadcasts”, Proc. IEEE symp. Security and Privacy, May 2005.