Key words: Cyclic Stress ratio, Standard Penetration Test, Cone Penetration Test, Plasticity index,
[2]. L.F., Haymes, M.E., Ishihara, K., Koester, J.P., Liao, S.S.C., Marcusson , W.F., Martin, G.R., Mitchell, J.K., Moriwaki, Y, Power, M.C., Robertson, P.K., Seed, R.B. and Stokoe, K.H. (2001) "Liquefaction Resistance of Soils: Summary Report from the 1996 NCEER and 1998 NCEER/NSF Workshops on Evaluation of Liquefaction Resistance of Soils, Journal of Geotechnical & Geoenvironmental Engineering, ASCE, Vol. 127, No. 10, pp 817- 833
[3]. Kishida, H.(1969) "Characteristics of Liquefied Sands during Mino-Owari, Tohnankai, and Fukui Earthquakes". Soils and Foundations, 9(1): 75-92.
[4]. Seed H.B. and Idriss, I.M. and I. Arango (1983). "Evaluation of Liquefaction Potential using Field PerformanceDdata." Journal of Geotechnical Engg, ASCE, 109(3); 458-482
[5]. Seed H.B., Tokimatsu, K., , L.F., and Chung, R. (1985), "Influence of SPT Procedures in Soil Liquefaction Resistance Evaluations" J. Geotechnical Engg., ASCE, 111(12), 861-878
[6]. Wang, W. (1979) "Some Findings in Soil Liquefaction" Report Water Conservancy and Hydro-electric Power Scientific Research Institute, Beijing, China, 1-17
[7]. Ishihara, K., and Koseki, J. (1989) "Cyclic Shear Strength of Fines-Containing Sands". Earthquake and Geotechnical. Engrg., Japanese Society of Soil Mechanics and Foundation Engineering, Tokyo, 101-106
Keywords – Casting, Kaduna river sand, use, desirable properties, profitability.
[2] Glownia, J. (1993). Metal Casting and Moulding Processes, In: Koshal, D. (Ed), Manufacturing Engineers Reference Book. Butherworh-Heineman Ltd. pp. 3/3 – 3/23.
[3] Jain, R.K. (2005). Production Technology, 16th Edition. Khanna Publishers Delhi. pp. 138 – 254.
[4] Lindberg, R.A. (1977). Processes and Materials of Manufacture, 2nd Edition. Allyn and Bacon, Inc. Boston, Massachusetts. pp. 271 – 292.
[5] Anonymouos (1963). Foundry Handbook. American Foundry Man‟s Society. pp. 2 – 98.
[6] Avalon, B; Baumeister, T. (1998). Standard Handbook for Mechanical Engineers. Mcgraw-Hill Company. pp. 13.1 – 13.6.
[7] Middleton, J.M. (1988). Foundry Practice, In: Sharpe, C. (Ed), Kempe‟s Engineers Year-Book. Morgan-Grampian Book publishing Co. Ltd. London. pp. DI/1 – DI/35.
[8] Dietert, H.W (1966). Sand Control. H.W. Dietert Co Michigan, U.S.A. pp. 1 – 40.
[9] Anonymous (1962. Moulding Materials and Methods‟. American Foundry Man‟s Society. pp. 1 – 30.
[10] Anonymous (1966). Method of Testing Prepared Foundry Sand. Third Report of the Joint Committee on Sand Testing, The Institute of British Foundry Man. pp.1 – 18.
Key words: Polymeric materials, Oxidation, Dielectric, SEM, XRD, IR, SbO2
[1]. Govindraj, N. V. Sastry and A. Venkataraman, Studies on -Fe2O3-High-Density Polyethylene Composites and their additives, J.
Appl. Poly. Sci., 92,2004, 1527-1533.
[2]. Syed Khasim, S.C.Raghvendra, M.Revansiddappa and MVN Ambika Prasad, Synthesis, Characterization and Electrical Properties
of Polyaniline/BaTiO3 Composites. Ferroelectrics, 325,2005, 111-116.
[3]. R. Sinha, Outline s of polymer technology, New Delhi:Prentice Hall of India private Limited, (2002).
[4]. M.V.Murugendrappa and M V N Ambika Prasad. Chemical Synthesis Characterization and dc Conductivity of polypyrrole – γ –
Fe2O3 composites. J. Appl. Poly. Sci.103, 2007, 2797-2805.
[5]. M.V.Murugendrappa and M V N Ambika Prasad, Dielectric Spectroscopy of polypyrrole – γ – Fe2O3 Composites. Materials
Research Bull 41, 2006,1364-1371.
[6]. Shankarananda, Arunkumar Lagashetty and Sangshetty Kalyani, Chemical oxidation method for synthesis of Polyaniline-In2O3
Composites, Int. J Engg. Sci (In Press).
[7]. B. Mahesh, S. Basavaraj, S.D. Balaji, V. Shivakumar, A. Lagashetty A. Venkataraman, Preparation and characterization of
Polyaniline and Polyaniline-Silver Nanocomposites via interfacial Polymerisation.
[8]. Polymer Composites., 30, 2009, 1668-1677.
[9]. M.V.Murugendrappa and M V N Ambika, Chemical Synthesis Characterization and dc Conductivity of polypyrrole – γ – Fe2O3
composites. Prasad. J. Appl. Poly. Sci.. 103, 2007, 2797-2804.
[10]. H. S. Chin, K. Y. Cheong, K. A. Razak ,Review on oxides of antimony nanoparticles: synthesis, properties, and applications, J
Mater Sci. 45, 2010, 5993–6008
still a main concern. In this paper, we show that the address assignment scheme defined by ZigBee will perform
poorly in terms of address utilization. This paper makes contribution is that we show that the Automatic
Address Assignment. We thus propose Simple, Efficient. systematically formation of path connected cluster and
Automatic Address Assignment for Wireless Sensor Network.
Keywords: Automatic Address assignment, Wireless Sensor Network, ZigBee.
[1] F. Salvadori, M. de Campos, P. S. Sausen, R. F. de Camargo, C. Gehrke,C. Rech, M. A. Spohn, and A. C. Oliveira, "Monitoring in industrial systems using wireless sensor network with dynamic power management,"IEEE Trans. Instrum. Meas., vol. 58, no. 9, pp. 3104–3111, Sep. 2009.
[2] C.-T. Cheng, C. K. Tse, and F. C. M. Lau, "A clustering algorithm for wireless sensor networks based on social insect colonies," IEEE Sensors J., vol. 11, no. 3, pp. 711–721, Mar. 2011.
[4] C.-T. Cheng, C. K. Tse, and F. C. M. Lau, "A delay-aware data collection network structure for wireless sensor networks," IEEE Sensors J., vol. 11, no. 3, pp. 699–710, Mar. 2011.
[5] A. Chen, T. H. Lai, and D. Xuan, "Measuring and guaranteeing quality of barrier-coverage in wireless sensor networks," in Proc. ACM Int. Symp. Mobile Ad Hoc Netw. Comput., 2008, pp. 421–430.
[6] H.-S. Ahn and K. H. Ko, "Simple pedestrian localization algorithms based on distributed wireless sensor networks," IEEE Trans. Ind. Electron., vol. 56, no. 10, pp. 4296–4302, Oct. 2009.
[7] A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, and J. Anderson,"Wireless sensor networks for habitat monitoring," in Proc. ACM Int. Workshop Wireless Sensor Netw. Appl., 2002, pp. 88–97.
[8] Design and Construction of a Wildfire Instrumentation System Using Networked Sensors [Online]. Available: http://firebug.sourceforge.net/
[9] R. Casas, A. Marco, I. Plaza, Y. Garrido, and J. Falco, "ZigBee-based alarm system for pervasive healthcare in rural areas," IET Commun., vol. 2, no. 2, pp. 208–214, Feb. 2008.
[10] Z. Sun, P. Wang, M. C. Vuran, M. A. Al-Rodhaan, A. M. Al-Dhelaan, and I. F. Akyildiz, "MISE-PIPE: Magnetic induction-based wireless sensor networks for underground pipeline monitoring," Ad Hoc Netw., vol. 9, no. 3, pp. 218–227, 2011.
the sufficient and necessary conditions and algorithms of Kronecker product(KPD), KPGD, and KPID are
discussed; At last, some useful properties of the rank of the sum of Kronecker product gemel decompositions are
obtained.
Keywords – product decomposition, gemel decomposition, isomer decomposition, rank, dimensionality reduction. MSC: 65L09,65Y04,15A69.
[1] C.F. Van Loan, The ubiquitous Kronecker product, Journal of Computational and Applied Mathematics, 123(2000) 85-100.
[2] A.Wu, G.Duan, Y.Xue, Kronecker maps and Sylvester-polynomial matrix equations, IEEE Trans, Automat. Control 52(5)(2007)905-910.
[3] Hu,J, Fuxiang Liu, and S. Ejaz Ahmed, Estimation to parameters in the growth curve model without assumption of normality via analogy and least squares, submitted to J. Multivariate Anal.
[4] A. Barrlund, Efficient solution of constrained least squares problems with Kronecker product structure, SIAM J. Matrix Anal. Appl.19(1998) 154-160.
[5] B.J. Lei E.A. Hendriks, Multi-Step Simultaneous Multiple Camera Calibration, ASCI, pp. 06, 2003.
[6] D.W. Fausett, C.T. Fulton, Large least squares problems involving Kronecker products, SIAM J. Matrix Anal. 15(1994) 219-227
[7] J. Granata, M. Conner, R. Tolimieri, Recursive fast algorithms and the role of the tensor product, IEEE Trans. Signal Process. 40(1992) 2921-2930.
[8] P.A. Rgealia, S. Mitra, Kronecker products, unitary matrices, and signal processing applications, SIAM Rev. 31(1989) 213-224.
[9] N.P. Pitsianis, The Kronecker product in approximation, fast transform generation, Ph.D. Thesis, Department of Computer Science, Cornell University,1997.
[10] Amy N.Langville, William J.Stewart, The Kronecker product and stochastic automata networks, Journal of Computational and Applied Mathematics,167 (2004) 429C447
The discovery of association rules has been known to be useful in selective marketing, decision analysis, and business management. An important application area of mining association rules is the market basket analysis, which studies the buying behaviors of customers by searching for sets of items that are frequently purchased together. With the increasing use of the record-based databases whose data is being continuously added, recent important applications have called for the need of incremental mining. In dynamic transaction databases, new transactions are appended and obsolete transactions are discarded as time advances. Several research works have developed feasible algorithms for deriving precise association rules efficiently and effectively in such dynamic databases.
Key Words: Association Rule Mining, Data Mining, Incremental Mining , Frequent Itemset, Minimum Support, Minimum Confidence.
[1] R. Agrawal, T. Imielinski, and A. Swami. Mining Association Rules between Sets of Items in Large Databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pages 207—216, May 1993.
[2] R. Agrawal and R. Srikant. Mining Sequential Patterns. Proceedings of the 11th International Conference on Data Engineering, pages 3—14, March 1995.
[3] J. M. Ale and G. Rossi. An Approach to Discovering Temporal Association Rules. Proceedings of the 2000 ACM Symposium on Applied Computing, pages 294—300, March 2000.
[4] X. Chen and I. Petr. Discovering Temporal Association Rules: Algorithms, Language and System. Proceedings of the 16th International Conference on Data Engineering, 2000.
[5] M.-S. Chen, J. Han, and P. S. Yu. Data Mining: An Overview from Database Perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6):866—883, December 1996.
[6] M.-S. Chen, J.-S. Park, and P. S. Yu. Efficient Data Mining for Path Traversal Patterns. IEEE Transactions on Knowledge and Data Engineering, 10(2):209—221, April 1998.
[7] J. Han, G. Dong, and Y. Yin. Efficient Mining of Partial Periodic Patterns in Time Series Database. Proceeding of the 15th International Conference on Data Engineering, pages 106—115, March 1999.
[8] R. T. Ng and J. Han. Efficient and Effective Clustering Methods for Spatial Data Mining. Proceedings of the 20th International Conference on Very Large Data Bases, pages 144—155, September 1994.
[9] K. Wang, S. Q. Zhou, and S. C. Liew. Building Hierarchical Classifiers Using Class Proximity. Proceedings of the 25th International Conference on Very Large Data Bases, pages 363—374, 1999.
[10] C. Yang, U. Fayyad, and P. Bradley. Efficient Discovery of Error-Tolerant Frequent Itemsets in High Dimensions. Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001.
Data mining application in agriculture is relatively a new approach for predicting agricultural crop productivity. This paper provides an expert system about agriculture which helps the farmer to cultivate the crops for high yield and giving awareness about the organic farming. This expert system contains three sections namely training, best combination for cultivation and awareness of organic farming. The training section gives basic needs of agriculture. The second section is about predicting the best combination for high yield in the crop cultivation. The third section gives awareness to farmers about organic farming. This system helps a new farmer to query his clarifications related to agriculture for better yield before cultivation.
Key Words: Expert system – Data Mining – Classification – Decision Tree – Training – Testing.
[1] PhilomineRoseline,"Design and Development of Fuzzy Expert System for Integrated Disease Management in Finger Millets" International Journal of Computer Applications (0975 – 8887) Volume 56– No.1, October 2012
[2] Xinxing Li, Lingxian Zhang," The corn disease remote diagnostic system in China" Journal of Food, Agriculture & Environment Vol.10 (1): 617-620. 2012.
[3]. Prof. Chandrakanth. Biradar," An Statistical Based Agriculture Data Analysis" International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 9, September 2012).
[4] S.S.Patil, B.V.Dhandra, "Web based Expert System for Diagnosis of Micro Nutrients' Deficiencies in Crops" proceedings of the World Congress on Engineering and Computer Science 2009 Vol I WCECS 2009, October 20-22, 2009, San Francisco, USA
[5]. S.Veenadhari, Dr.BharatMishra,"Soybean Productivity Modelling using Decision Tree Algorithms" International Journal of Computer Applications (0975 – 8887) Volume 27– No.7, August 2011
[6]. Kym Anderson and Anna Strutt Agriculture and Food Security in Asia by 2030
[7]. Puja Shrivastava," Implementation of an Expert System as Spiritual Guru for Personality Development" International Journal of Computer Theory and Engineering, Vol.3, No.1, February, 2011,1793-8201
This paper deals with the design and implementation of an Integrated Fuzzy Logic Controller (IFLC) for temperature control in greenhouse. The proposed controller employs an architecture that is an integration of Fuzzy logic and PID controller. The controller can handle two inputs, two outputs and 27 fuzzy rules. The PWM outputs were generated to control the temperature according to the set point value.
Key Words: Digital PID, FLC, Fuzzy Logic, IFLC, Integrated Fuzzy Logic
[1] A. Sriraman and R. V. Mayorga, A Fuzzy Inference System Approach for Greenhouse Climate Control, Environmental Informatics Achieves, Vol. 2(2004), pp699-710.
[2] ZHOU Xiaobo, WANG Chengduan and LAN Hong, The Research and PLC Application of Fuzzy Control in Greenhouse Environment, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, pp340-344.
[3] R. Caponetto, L. Fortuna, G. Nunnari and L. Occhipinti, A Fuzzy Approach to Greenhouse Climate Control, Proceedings of the American Control Conference Philadelphia, Pennsylvania, June 1998, pp1866-1870.
[4] John Yen, Reza Langari, Fuzzy Logic Intelligence, Control and Information (Pearson Education, 2003).
[5] Ming-Yuan Shieh and Tznu-Hseng S. Li, Integrated Fuzzy Logic Controller Design, IEEE 1993, pp279-284.
[6] S. S. Patil and P. Bhaskar, Design and Real Time Implementation of Integrated Fuzzy Logic Controller for a High Speed PMDC Motor, International Journal of Electronic Engineering Research, Vol.1 No.1 (2009), pp13-25.
[7] S. S. Patil, P. Bhaskar and L. Shrimanth Sudheer, Design and Implementation of An Integrated Fuzzy Logic Controller for a Multi-Input Multi-Output System, Defence Science Journal, Vol. 61, No. 3 May 2011, pp219-227.
[8] Timothy J. Ross, Fuzzy Logic with Engineering Applications (Wiley-India, 2005).
[9] Scott S. Lancaster and Mark J. Wierman, Empirical Study on Defuzzification, IEEE (2003), pp121-126.
The recently noticed dipolar nature of convection on either side of Indian Ocean called EQUINOO is subjected to more observations and analysis to understand its effect on ISMR between the period from 1950-2005. The positive phase of EQUINOO with increased cloud on the western side of Indian Ocean accompanied with dominating easterly wind anomaly found to affect ISMR strength positively and the reverse conditions leads to negative results. The floods and droughts go well with EQUINOO incidents but normal monsoon do not display equal uniformity with EQUINOO affect. It is not binding that the positive neither negative phase of this phenomena should show expected results when it co-occur with similar big oceanic or atmospheric oscillations. The ambiguity about the origin of EQUINOO if removed throwing more light into IOD EQUINOO connection and proper parameterization of the issue may perhaps wide open the arena for inclusion of EQUINOO for predictive purposes, model studies and disaster management etc. The work analyze the scenario of ISMR, EQUINOO and ENSO from 1950-2005 and try to understand how it complement and contradict each other and the conditions leading to it. After 1995 the El Nino effect on ISMR has gradually decreased and the paper studies in detail the effect of ENSO on ISMR for long 55 years. The SST, zonal wind and OLR anomalies of individual EQUINOO years are cross checked and their composites are also drawn. The paper also checks the validity of the index used for measuring EQUINOO strength. It emphasizes the necessity of developing an OLR index for better assessment of this oscillation. Zonal wind observation on EQUINOO region during the pre-monsoon month can leave good clue about the performance of forthcoming monsoon season claims the work.
Key Words: monsoon, ENSO, index, SST, Equatorial Indian Ocean Oscillation.
[1]. Nagio Hirota, Yukari N.T, Masahiro Watanabe, Masahide Kimoto, 2011: Precipitation Reproducibility over Tropical Oceans and Its Relationship to the Double ITCZ Problem in CMIP3 and MIROC5 Climate Models. J. Climate, 24, 4859–4873.
[2]. Jiao and Colin Jones 2008 Comparison studies of cloud and convection –related processes simulated by Canadian Regional Climate Model over the Pacific Ocean Monthly Weather Review 136 pp 4168-4187
[3]. Lin H, G. Brunet and J. Derome, 2009: An observed connection between the North Atlantic Oscillation and the Madden Julian oscillation
[4]. J Climate, 22, 364–380.
[5]. Lin. H., and G. Brunet, 2011: Impact of the North Atlantic Oscillation on the forecast skill of the Madden-Julian oscillation. Geophysical Res. Letter., 38, L02802
[6]. Wheeler, M. and Hendon, H. H. (2004), An All-Season Real-time Multivariate MJO Index: Development of the index for monitoring and prediction in Australia. Monthly Weather Review, 132, 1917-1932
[7]. Lau, Ngar-Cheung, Mary Jo Nath, 2000: Impact of ENSO on the Variability of the Asian–Australian Monsoons as Simulated in GCM Experiments. J. Climate, 13, 4287–4309.
[8]. Gadgil, S., P. N. Vinayachandran, and P. A. Francis (2003), Droughts of Indian summer monsoon: Role of clouds over the Indian Ocean, Current Science., 85, 1713– 1719.
[9]. Sulochana, G., Vinaychandran, P. N., Francis, P. A. and Gadgil, S., 2004 Extremes of Indian summer monsoon rainfall, ENSO and equatorial Indian Ocean Oscillation. Geophysical Research. Letter, 31, [10]. Xie, Pinging, and Phillip A. Arkin, 1998: Global Monthly Precipitation Estimates from Satellite-Observed Outgoing Long wave Radiation. J. Climate, 11, 137–164.