S. No

Volume-1 Issue-6, May 2013, ISSN: 2319-9598 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

B. K. Narendra

Paper Title:

Compressive Strength Development of Fly Ash Concrete for Different Cement Replacement Levels

Abstract: Compressive strength tests were conducted on design mixes M30, M40 and M50 for cement replacement levels (CRLs) of 20%, 35% and 50% for different curing periods of 3, 7, 28, 56 and 91 days. Three water binder (w/b) ratios for each CRL were considered for the study, so that the optimum w/b ratio could be chosen for further study, the optimum w/b ratio being that ratio, which gives a compressive strength equal to that of normal concrete for curing period of 28 days. Tabulated the test results of compressive strength of Fly ash concrete (FAC) and normal concrete (NC) for grades M30, M40 and M50 respectively.  The test results of compressive strength development of Fly ash concrete for different cement replacement levels with different curing periods and of normal concrete all of grades M30, M40 and M50 are discussed in this paper.

Keywords: Compressive strength, Cement replacement level, Fly ash concrete.

References:
1.        Ganesh Babu. K., Siva Nageshwara Rao., “Efficiency of Fly Ash Concrete with Age”, Cement and Concrete Research, Vol. 26, No. 3, 1996, pp.465-474.
2.        Mullick. A. K., “Use of Fly ash in Structural Concrete: Part I - Why?”, The Indian Concrete Journal, Vol. 79, 2005, pp.13-22.
3.        Mullick. A. K., “Use of Fly ash in Structural Concrete: Part II - How Much?”, The Indian Concrete Journal, Vol. 79, 2005, pp. 10-14.
4.        Nataraja. M. C., Ramalinga Reddy. B. M., Bhavanishankar. S., and Bharathraj Y. B., “Mix Design and Some Properties of Cement Replacement by Large Volumes of Fly ash”, Proceedings of the Second International Symposium as Concrete Technology for Sustainable Development, February - March 2005.
5.        Sivasundaram. V., “Evaluation of Indian Fly ashes for Use in HVFA Concrete-Part II: Behavior in Concrete”, The Indian Concrete Journal, 78 (11), 2004, pp.41- 50.
6.        Sivasundaram. V., Carette. G. G., and Malhotra. V. M., “Long-Term Strength Development of High-Volume Fly Ash Concrete”, Cement and Concrete Research, September 1990, pp.263-270.
7.        Tarun R. Naik., Shiw S. Singh., and Mohammad M. Hossain., “Permeability of Concrete containing Large Amounts of Fly ash”, Cement and Concrete Research, Vol. 24, No.05, April 1994,  pp.913-922.
8.        Torben C. Hansen., “Long-Term Strength of High Fly ash Concretes”, Journal of Cement and Concrete Composite, April 1990, pp. 193-196.

1-12

2.

Authors:

Ahmed Ali Saihood, Rakesh Kumar

Paper Title:

Enhanced Location Based Energy-Efficient Reliable Routing Protocol for Wireless Sensor Networks

Abstract: Designing energy efficient and reliable routing protocols for mobility centric applications of wireless sensor network (WSN) such as wildlife monitoring, battlefield surveillance and health monitoring is a great challenge since topology of the network changes frequently. Existing cluster-based mobile routing protocols such as LFCP-MWSN, LEACH-Mobile, LEACH-Mobile Enhanced and CBR-Mobile consider only the energy efficiency of the sensor nodes. However, reliability of routing protocols by incorporating fault tolerance scheme is significantly important to identify the failure of data link and sensor nodes and recover the transmission path. Most existing mobile routing protocols are not designed as fault tolerant. These protocols allocate extra timeslots using time division multiple access (TDMA) scheme to accommodate nodes that enter a cluster because of mobility and thus, increases end-to-end delay. Moreover, existing mobile routing protocols are not location aware and assume that sensor nodes know their coordinates. In this study the authors, enhanced the existing LFCP-MWSN to ELFCP-MWSN in which we reduce network energy consumptions and slightly less end-to-end data transmission delay than the existing LFCP-MWSN. ELFCP-MWSN also incorporates a simple range free approach to localise sensor nodes during cluster formation and every time a sensor moves into another cluster. Simulation results show that LFCP-MWSN protocol has about 25–30% less network energy consumptions and slightly less end-to-end data transmission delay than the existing LFCP-MWSN, in our study we try to decrease this percentage of energy consumption and more less end-to-end data transmission.

Keywords: WSN, CH, BS, ELFCP-MWSN.

References:
1.        “21 ideas for the 21st century”, Business Week, Aug. 30 1999, pp. 78-167.
2.        S.K. Singh, M.P. Singh, and D.K. Singh, “A survey of Energy-Efficient Hierarchical Cluster-based Routing in Wireless Sensor Networks”, International Journal of Advanced Networking and Application (IJANA), Sept.–Oct. 2010, vol. 02, issue 02, pp. 570–580.
3.        S.K. Singh, M.P. Singh, and D.K. Singh, "Energy-efficient Homogeneous Clustering Algorithm for Wireless Sensor Network", International Journal of Wireless & Mobile Networks (IJWMN), Aug. 2010, vol. 2, no. 3, pp. 49-61.
4.        Jun Zheng and Abbas Jamalipour, “Wireless Sensor Networks: A Networking Perspective”, a book published by A John & Sons, Inc, and IEEEE, 2009.
5.        S. Misra et al. (eds.), Guide to Wireless Sensor Networks, Computer Communications and Networks, DOI: 10.1007/978-1-84882-218-4 4, Springer-Verlag London Limited 2009.
6.         Ivan Stojmenovic and Stephan Olariu. Data-centric protocols for wireless sensor networks. In Handbook of Sensor Networks, Chapter 13, pages 417–456. Wiley, 2005.
7.        Christopher Ho, Katia Obraczka, Gene Tsudik, and Kumar Viswanath, “Flooding for reliable multicast in multi-hop ad hoc networks”, In Proceedings of the 3rd International Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications (DIAL-M’99), 1999, pp. 64–71.
8.        Ming Liu, Jiannong Cao, Guihai Chen, and Xiaomin Wang, “An Energy-Aware Routing Protocol in Wireless Sensor Networks”, Sensors 2009, vol. 9, pp. 445-462.
9.        Luis Javier García Villalba, Ana Lucila Sandoval Orozco, Alicia Triviño Cabrera, and Cláudia Jacy Barenco Abbas, “Routing Protocol in Wireless Sensor Networks”, Sensors 2009, vol. 9, pp. 8399- 8421.
10.     E. Zanaj, M. Baldi, and F. Chiaraluce, “Efficiency of the Gossip Algorithm for Wireless Sensor Networks”, In Proceedings of the 15th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split–Dubrovnik, Croatia, September, 2007.
11.     Jamal Al-Karaki, and Ahmed E. Kamal, “Routing Techniques in Wireless Sensor Networks: A Survey“, IEEE Communications Magazine, vol 11, no. 6, Dec. 2004, pp. 6-28.
12.     I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A Survey on Sensor Network”, IEEE Communication Magazine, vol. 40, no. 8, Aug. 2002, pp. 102-114.
13.     Kemal Akkaya and Mohamed Younis, “A Survey on Routing Protocols for Wireless Sensor Networks”, Ad hoc Networks, vol. 3, no. 3, May 2005, pp. 325-349.
14.     N. Bulusu, J. Heidemann, and D. Estrin, “GPS-less Low Cost Outdoor Localization for Very Small Devices”, IEEE Personal Communication Magazine, vol. 7, no. 5, Oct. 2000, pp. 28-34.
15.     Y. X:u, J. Heidemann, and D. Estrin, "Geography-informed energy conservation for ad-hoc routing", Proceedings ACM/IEEE MobiCom'01, Rome, Italy, July 2001, pp. 70-84.
16.     M. Stemm and R. H. Katz, "Measuring and reducing energy consumption of network rfaces in handheld devices", IEICE Transaction on Communications, vol. E80-B, 8, Aug.1997, pp. 1125-1131.
17.     O. Kasten, "Energy Consumption", www.infethz.ch/-kasten/researchlbathtub/energyconsumption. html.
18.     P. Bahl and V. N. Padmanabhan, "Radar: A in-building rf-based user location and tracking system", Proceedings IEEE INFOCOM'OO, vol. 2, Tel-Aviv, Israel, Mar. 2000, pp. 775-784.
19.     L. Doherty, K. S. Pister, and L. E. Ghaoui, "Convex position estimation in wireless sensor networks",
20.     Y. Yu, R. Govindan, and D. Estrin, "Geographical and energy aware routing: A recursive data dissemination protocol for wireless sensor networks", Technical Report UCLA/CSD-TR-01-0023, UCLA Computer Science Department, May 2001.
21.     Awwad, S.A.B., Ng, C.K., Noordin, N.K., Rasid, M.F.A.: ‘Cluster based routing protocol for mobile nodes in wireless sensor network’. Int. Symp. on Collaborative Technologies and Systems, CTS’ 09, 2009, pp. 233–241.
22.     Durresia, A., Paruchuri, V., Barolli, L.: ‘Clustering protocol for sensor networks’. 20th Int. Conf. on Advanced Information Networking and Applications, AINA 2006, 2006, vol. 2, p. 5
23.     Martirosyan, A., Boukerche, A., Pazzi, R.W.N.: ‘A taxonomy of clusterbased routing protocols for wireless sensor networks’. Int. Symp. On Parallel Architectures, Algorithms, and Networks, I-SPAN 2008, 2008, pp. 247–253
24.     Kim, D.-S., Chung, Y.-J.: ‘Self-organization routing protocol supporting mobile nodes for wireless sensor network’. First Int. Multi-Symp. On Computer and Computational Sciences (IMSCCS’06), 2006
25.     Kumar, G.S., Vinu, M.V., Athithan, P.G., Jacob, K.P.: ‘Routing protocol enhancement for handling node mobility in wireless sensor networks’. TENCON 2008–2008 IEEE Region 10 Conf., 2008, pp. 1–6
26.     Bajaber, F., Awan, I.: ‘Dynamic/static clustering protocol for wireless sensor network’. Second UKSIM European Symp. on Computer Modeling and Simulation, EMS ’08, 2008, pp. 524–529
27.     Zheng, G.-p., Zhou, Y.: ‘An energy-aware cluster protocol for wireless sensor networks’. Second Int. Conf. on Innovative Computing, Information and Control, ICICIC ’07, 2007, pp. 473–473
28.     Huang, B., Hao, F., Zhu, H., Tanabe, Y., Baba, T.: ‘Low-energy static clustering scheme for wireless sensor network’. Int. Conf. on Wireless Communications, Networking and Mobile Computing, WiCOM 2006, 2006, pp. 1–4
29.     Duan, Z.-f., Guo, F., Deng, M.-x., Yu, M.: ‘Shortest path routing protocol for multi-layer mobile wireless sensor networks’. Int. Conf. on Networks Security, Wireless Communications and Trusted Computing, 2009, pp. 106–110
30.     Kweon, K., Ghirn, H., Hong, J., Yoon, H.: ‘Grid-based energy-efficient routing from multiple sources to multiple mobile sinks in wireless sensor networks’. Proce. Fourth Int. Conf. on Wireless Pervasive Computing, Melbourne, Australia, 11–13 February 2009, pp. 185–189
31.     Xing, G., Lu, C., Pless, R., Huang, Q.: ‘Impact of sensing coverage on greedy geographic routing algorithms’, IEEE Trans. Parallel Distrib. Syst., 2006, 17, (4), pp. 348–360
32.     Karim, L., Nasser, N., Salti, T.E.: ‘RELMA: a range free localization approach using mobile anchor node for wireless sensor networks’. IEEE Globecom 2010, Miami, Fl, 6–10 December 2010
33.     Tang, F., Guo, M., Li, M., Wang, Z., Cheng, Z.: ‘Scalable and secure routing for large-scale sensor networks,’. IEEE/IFIP Int. Conf. on Embedded and Ubiquitous Computing, EUC ’08, 2008, vol. 2, pp. 300–305
34.     Cho, J., Choe, J.: ‘A cluster-based routing protocol for supporting mobile sinks in sensor network’. Int. Conf. on Information Networking, ICOIN 2008, 2008, pp. 1–5
35.     Karim, L., Nasser, N.: ‘Energy efficient and fault tolerant clustering protocol for mobile sensor network’. IEEE Int. Communications Conf. (ICC’11), Kyoto, Japan, 5–9 June 2011
36.     Lambrou, T.P., Panayiotou, C.G.: ‘A survey on routing techniques supporting mobility in sensor networks’. Fifth Int. Conf. on Mobile Ad-Hoc and Sensor Network, MSN ’09, 2009, pp. 78–85
37.     Martirosyan, A., Boukerche, A.: ‘Performance evaluation of an energy-aware clustering protocol for wireless sensor networks’. Int. Conf. on Parallel Processing Workshops, ICPP-W ’08, 2008,pp. 67–72
38.     http://blogs.dolcera.com/blog/author/sateesh/

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3.

Authors:

Pooja R. Bande, S. D. Bansod         

Paper Title:

Decimation Filter Design for Multistandard Wireless Communication

Abstract: In this work, we propose a decimator filter able to deal with GSM, WCDMA, WLAN 802.11(a/b/g) and WiMAX By taking into account the operating frequency, bandwidth and intermodulation effects, an appropriate input sampling frequency is selected. Meanwhile, with the parameters of blocking and interference profile, Passband limits and Passband ripple, for all this radio communication standard, we are designing decimation filter design tool in Matlab using “GUIDE “environment  for visual analysis. so, user can select required wireless standard among this standard and obtain corresponding multistage decimation filter design and analysis. Through the analysis of the frequency responses we can see how the filter selects signal channels for multimode communication. The decimation filter design toolbox helps the user or design engineer to perform a quick design and analysis of decimation filter for multiple standards without doing extensive calculation.

Keywords: Decimation filter, MATLAB toolbox, Multistandard Receiver, Sigma-delta A/D converter.

References:
1.        ”Comb-Based Decimator for SDR Applications” Gordana Jovanovic Dolecek Institute INAOE, Department of Electronics, Puebla, Mexico.
2.        “CIC Filter Introduction” Matthew Donadio, IEEE Int. conf. 2000.
3.        “Sample Rate Conversion Technology in Software Defined Radio”, T. Wang   and Cheng Li, IEEE   Int .conf. May 2006.
4.        “Multistandard Digital Channel Selection Using Decimation   Filtering for ADC Modulator” Chiheb REBAI, Salma BOURBIA, Nejmeddine JOUIDA IEEE Int. conf 2008.
5.        “Richard schreier and William Martin Snelgrove “Decimation for Band pass sigmadeltaanalogtodigitalconversion”IEEE2002.

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4.

Authors:

Deepak S. Gaikwad, Sampada Pimpale

Paper Title:

Routing Alternatives for Network Lifetime Maximization of WSNs Using Heuristic and Fuzzy Logic Approach

Abstract: Recently many network lifetime maximization approaches and techniques have gained importance and are proposed for enhancing the lifetime of the wireless sensor networks. The distributed and dynamic nature of the WSNs, demand for special requirements in routing protocols in order to minimize the energy consumption and enhance the network lifetime. A large number of routing strategies based on Fuzzy logic approach are proposed in the past for energy aware routing in WSNs. Also heuristic method such as A star routing scheme which is based on informed search method is used to increase network life. A star algorithm finds an optimal shortest path from a source node to target node taking a minimum number of hops and also avoids network partitioning. In this paper the effectiveness of two methods in terms of maximization of network lifetime and balancing the energy consumption has been compared. The simulation results show that the Fuzzy logic approach gives more good results than the A star algorithm in different topographical situations.

Keywords: Wireless sensor Network, Network lifetime maximization, A star algorithm, Fuzzy logic.

References:
1.        F. L. Lewis, "Wireless Sensor Networks", in Smart Environments: Technologies, Protocols, Applications, ed. D. J. Cook and S. K. Das, Wiley, New York, 2004.
2.        Imad S. AlShawi, Lianshan Yan, Senior Member, IEEE, Wei Pan, Member, IEEE, and Bin Luo, Member, IEEE, Lifetime Enhancement in Wireless Sensor Networks Using Fuzzy Approach and A-Star Algorithm, IEEE Sensors Journal, vol. 12, no. 10, Oct 2012
3.        H. Zhang and H. Shen, “Balancing energy consumption to maximize network lifetime in data-gathering sensor networks,” IEEE Trans. Parallel Distrib. Syst., vol. 20, no. 10, pp. 1526–1539, Oct. 2009.
4.        J. N. Al-Karaki and A. E. Kamal, “Routing techniques in wireless sensor networks: A survey,” IEEE Wireless Commun., vol. 11, no. 6, pp. 6–28, Dec. 2004.
5.        H. R. Karkvandi, E. Pecht, and O. Yadid, “Effective lifetime-aware routing in wireless sensor networks,” IEEE Sensors J., vol. 11, no. 12, pp. 3359–3367, Dec. 2011.
6.        K. Akkaya and M. Younis, “A survey of routing protocols in wireless sensor networks,” Ad Hoc Netw., vol. 3, no. 3, pp. 325–349, May 2005.
7.        F. Ren, J. Zhang, T. He, C. Lin, and S. K. Das, “EBRP: Energy-balanced routing protocol for data gathering in wireless sensor networks,” IEEE Trans. Parallel Distrib. Syst., vol. 22, no. 12, pp. 2108–2125, Dec. 2011.
8.        C. Hua and T. P. Yum, “Optimal routing and data aggregation for maximizing lifetime of wireless sensor networks,” IEEE ACM Trans. Netw., vol. 16, n
9.        K. M. Rana and M. A. Zaveri, “ASEER: A routing method to extend life of two-tiered wireless sensor network,” Int. J. Adv. Smart Sensor Netw. Syst., vol. 11, no. 2, pp. 1–16, Oct. 2011.
10.     J. M. Mendel, “Fuzzy Logic Systems for Engineering: A Proceedings of the IEEE vol. 83 no. 3 Tutorial”, March1995, pp. 345-377.
11.     Y. M. Lu and V. W. S. Wong, “An energy-efficient multipath routing protocol for wireless sensor networks,” in Proc. IEEE 64th Vehicular Technol. Conf., Sep. 2006, pp. 1–5.
12.     O. Zytoune, M. El-Aroussi, and D. Aboutajdine, “A uniform balancing energy routing protocol for wireless sensor networks,” Wireless Personal Commun., vol. 55, no. 2, pp. 147–161, Oct. 2010.
13.     M. A. Azim and A. Jamalipour, “Performance evaluation of optimized forwarding strategy for flat sensor networks,” in Proc. IEEE Global

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5.

Authors:

Deepak Kumar Gupta, Bhupendra Kumar

Paper Title:

Study of Power Generation of Doubly Fed Induction Generator in Wind Energy Conversion System

Abstract: In recent days the wind power is rapidly growing renewable energy source The combustion of conventional fossil fuel across the globe has caused increased level of environmental pollution. Several international conventions and forums have been set up to address and resolve the issue of climate change. Renewable energy like solar, wind, and tidal currents of oceans is sustainable, inexhaustible and environmentally friendly clean energy. In this paper firstly we present the literature survey DFIG application in wind energy conversion system. The main problem of grid is being discussed and analytical approach is given for the issues. Future we study the different topology of wing turbines advantage, application. This paper we study the measure grid problem and grid codes operation and grid connection of wind farms. 

Keywords: Fixed speed turbine, variable speed turbine, grid problem, DFIG.

References:
1.        “Global wind Report: Annual market update,” Global Wind Energy Council, pp.18-19, 2010.
2.        A. D. Hansen, L. H. Hansen, “Market penetration of wind turbine concepts over the years,” European Wind Energy, EWEA, vol. 10, pp. 81-97, 2007.
3.        T. Ackermann, “Wind power in power systems,” John Wiley and sons, England, 2005.
4.        R. Pena, J. C. Clare, G. M. Asher, “Doubly fed induction generator using back-to-back PWM converters and its application to variable-speed wind-energy generation,” IEE Proc. Elect. Power Appl., vol. 143, no. 3, pp. 231-241, 1996.
5.        S. Soter, R. Wegener, “Development of induction machines in wind power technology,” Proc. IEEE Int. Electric Mach. Drives Conf., vol. 2, pp. 1490-1495, 2007.
6.        W. Leonard, “Control of Electrical Drives,” Springer, New York, 2001.
7.        P. W. Carlin et all ,” The History and State of the Art of Variable-Speed wind Turbine Technology” National Renewable Energy Laboratory/National Wind Technology Center, 1617 Cole Boulevard, Golden, CO 80401, USA
8.        Rishabh Dev Shukla et all ,” Dynamic Performance Of  DFIG Based WECS Under Different Voltage Sag’’ International Journal of Chem Tech Research CODEN( USA): IJCRGG ISSN : 0974-4290 Vol.5, No.2, pp 980-992, April-June 2013.
9.        M. G. Simoes, B. K. Bose, R .J. Spiegel, “Fuzzy logic based intelligent control of a variable speed cage machine wind generation system,” IEEE Trans. Power Electron., vol. 12, no. 1, pp. 87-95, 1997.
10.     N. Mohan, T. M. Undeland, W. P. Robbins, “Power Electronics: Converters, Applications and Design,” Clarendon Press, Oxford, UK, 1989.
11.     S. R. Jones, R. Jones, “Control strategy for sinusoidal supply side convertors,” IEE Colloq. Developments in real time control for induction motor drives, vol. 24, 1993.
12.     A. Nicastri, A. Nagliero, “Comparison and evaluation of the PLL techniques for the design of the grid connected inverter systems,” Proc. IEEE Int. Symp. Ind. Electron. pp. 3865-3870, 2010.
13.     T. Sun, Z. Chen, F. Blabejerg, “Flicker study on variable speed wind turbines with doubly fed Induction Generators,” IEEE Trans. Energy Convers., vol. 20, no. 4, pp.896-905, 2005.
14.     M. G. Simoes, B. K. Bose, R .J. Spiegel, “Fuzzy logic based intelligent control of a variable speed cage machine wind generation system,” IEEE Trans. Power Electron., vol. 12, no. 1, pp. 87-95, 1997.
15.     F. A. Bhuiyan, A. Yazdani, “Multimode control of a DFIG based wind power unit for remote applications,” IEEE Trans. Power Del., vol. 24, no. 4, pp. 2079-2089, 2009.
16.     O. A. Lara, N. Jenkins, J. Ekanayake, P.Cartwright, M. Hughes, “Wind energy generation: Modeling and Control”, John Wiley and Sons, UK, 2009.
17.     S. N. Bhadra, D. Kastha, S. Banerjee, “Wind Electrical Systems,” Oxford University Press, New Delhi, 2009.
18.     B. K. Bose, “Modern Power Electronics and AC Drives,” Prentice-Hall, Inc., New Delhi, 2002.
19.     Ch. Eping, J. Stenzel, M. Poller, and H. Muller, “Impact of Large Scale Wind Power on Power System Stability”. DIgSILENT GmbH, Germany, Apr. 2005. [Online]. Available: http//www.digsilent.de/Consulting/Publications/PaperGlasgow_DIgSILENT.pdf.
20.     Ahmed G. Abo-Khalil et all “Synchronization of DFIG output voltage to utility grid in wind power system” renewable energy 44(2012) 193-198.

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6.

Authors:

Akashdeep Gupta, Anjali Gautam, Chirag Jain, Himanshu Prasad, Neeta Verma

Paper Title:

Time Series Analysis of Forecasting Indian Rainfall

Abstract: This paper presents a study of neural network model for prediction of Indian rainfall. The purpose of this paper is to evaluate the applicability of ANN. In this paper the performance of different networks have been evaluated and tested.The multilayered artificial neural network with learning by back-propagation algorithm is used .The paper implements weather prediction by building training and testing data sets and finding the number of hidden neurons in these layers for the best performance. The proposed model has been able to predict values with suitable results. The prediction is made on the bases of previous data. The criteria for prediction in the model are correlation, RMSE, standard deviation .Prediction of Rainfall is necessary for Agricultural & Metrological Department. In India, most of our Economy is dependent on agriculture. A big percentage of GDP is contributed by agriculture. In India, agriculture provides around 70% of employment either directly or indirectly. This is major reason for analysis of prediction of rainfall.

Keywords: Artificial Neural Network, Root Mean Square Error, Standard Deviation, and Backpropagation.

References:
1.        Tripathi ,K.C. ,Das ,I.M.L , and Sahai ,A.K. “Predictability of sea surface temperature anamolies in the Indian ocean using Artifical neural network” ,Indian Journal of Marine Sciences ,vol.35(3), September 2006 ,pp. 210-220.
2.        Tripathi ,K.C. ,Das ,I.M.L , and Panday ,A.C. , ”Southern Indian Ocean indices as early predictors of Indian summer monsoon ” ,Indian Journal of Marine Science vol. 37(1) ,March 2008 ,pp. 70-76
3.        Hsieh W. William ,Tang Benyang “Applying neural network models to prediction and data analysis in meterology and oceanography “Bulletin of the American Meteorological Society.
4.        Paras, Sanjay Mathur, Avinash Kumar, and Mahesh Chandra, “A feature based on weather prediction using ANN”World Academy of Science, Engineering and Technology 34 2007
5.        (ftp://www.tropmet.res.in/pub/data/rain/iitm-regionrf.txt).
6.        Enireddy Vamsidhar et. al.,”Prediction of rainfall Using Backpropagation   Neural Network Model”, International Journal on Computer Science and Engineering Vol. 02, No. 04, 2010, 1119-1121
7.        Dr S. Santosh Baboo and I. Khadar Shareef, “An efficient Weather  Forecasting Model using Artificial Neural Network”, International Journal of Environmental Science and Development, Vol. 1, No. 4,October 2010.
8.        Ben Krose and Patrick van der Smagt , “An introduction to neuralnetworks”, Eighth edition, November 1996.

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7.

Authors:

Syed Asif Ali, Monir Ahmed

Paper Title:

ICT’s Impact on HRM: Special Peoples Endeavors

Abstract: This research focuses on Information and communication technology (ICT) impact of human resource management for endeavoring of special people. Special people are the part of every country all over the world. They need special requirements to spend their life successfully. In most of the aspects of life they are fully or partially depend on their family or other social persons.  Their family even supposes them as a social burden. There is no doubt to say that they are also financially disabled. They have no resource to fulfill their social needs and are dependent on others. Especially in developing or under developing countries their condition is very worst. The unemployment rate of special persons is almost double to normal person’s employment. This research work represents how Information and communication technology (ICT) is used to help out special people to accommodate them in workplace to overcome their financial needs so that they spend their life successfully.

Keywords: Special People, Human Resource Management, Information and communication technology (ICT).

References:
1.        M.Mobin Uddin, 2008, “A Country Report Islamic Republic of Pakistan”.
2.        Disability framework in Pakistan, 2005, Compiled by STEP (Special Talent Exchange Program) with the support of Sightsavers International Pakistan.
3.        M.Mobin Uddin, 2008, “A Country Report Islamic Republic of Pakistan”.
4.        J. KURSHA, 1959, ‘Recruiting the Technically Creative A Dual Responsibility’, IRE Ransactions on Engineering Management.
5.        Judith Waterfield  and  Bob West , 2005, Meeting the specific requirements of Blind and Partially Sighted Students studying in Higher Education in the UK A guide to support academic staff to help meet the anticipatory and positive duties under the DDA.
6.        Bryan Ayres, 2006, ‘Technology Skills Build Future Success’, a Collaboration between the Arkansas Department of Education, Special Education and Easter Seals of Arkansas.
7.        Marty Rosenheck, 2010, ‘Navigating the Interactive Workplace’, www.clomedia.com
8.        Syed Asif Ali, 2013,’Sign Language Information Detection through Mobile or Telephonic Communication. International Journal of Computer Applications’ 64(19):26-28, February 2013. Published by Foundation of Computer Science, New York, USA.
9.        S.Asha, C.Chellappan, 2011, ‘Voice Activated E-Learning System for the Visually Impaired’, International Journal of Computer Applications (0975 – 8887), Volume 14.

46-49

8.

Authors:

Syed Asif Ali

Paper Title:

Detection of Urdu Sign Language using Harr Algorithms

Abstract: This research focuses on detecting process of Urdu sign language. Sign language is a language used in culture of deaf people. There are two approaches used in the development of this work, the first one is text to sign conversion and the other is sign to text conversion. In the first strategy, ‘text to sign’ involves a text input from a text box and the output will be displayed in image form on the screen. In the second strategy, ‘sign to text’ involves a sign as an input through webcam and its output will be in text form which will actually stand for the actual sign. The purpose of developing this project is to reduce the communication gap between the normal and physically challenged persons such as deaf people as they are also part our community. 

Keywords: Sign Language, Urdu Sign Language, Sign Detection, Haar Algorithms.

References:
1.        ASL II American Sign Language Thesaurus by Don Cabbage Ph.D. Published by International Communication Publishers PO Box 371007, E1 Paso, TX79937 (915)855-7575 doncabbage@juno.com
2.        Martin L.A. Sternberg, „American Sign Language Dictionary, Third Edition‟, Third Edition.
3.        Dr. Nasir Sulman,Sadaf Zuberi,2000, Pakistan Sign Language – A Synopsis, Sustainable Development Networking Programme, Pakistan Ô IUCN- The World Conservation Union.
4.        Syed Asif Ali, 2013,’Sign Language Information Detection through Mobile or Telephonic Communication. International Journal of Computer Applications’ 64(19):26-28, February 2013. Published by Foundation of Computer Science, New York, USA.
5.        JongSeok Lim, WookHyun Kim,2012,’Detection of Multiple Humans Using Motion Information and Adaboost Algorithm based on Harr-like Features’, International Journal of Hybrid Information Technology, Vol. 5, No. 2, 243-248 pp.
6.        Son Lam Phung, A. Bouzerdoum,2007, ‘Detecting People in Images: An Edge Density Approach’, IEEE International Conference on Acoustics, Speech and Signal Processing, 2007 (ICASSP 2007), Honolulu, Hawaii, USA, 15-20 April, 2007, 1, I-1229-I-1232.

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