Conventional compression ignition engines have high emission rates of Nitric oxides (NOx) and particulate matters (PM) than spark ignition engines despite being the most fuel efficient engines ever developed for transportation purposes thus the interest for research on it to make it more efficient and to meet the stringent legislation imposed by many nations’ government. Reduced N-Heptane (29 species, 52 chemical reactions) which is a representative of Diesel fuel was utilized in the model by importing it from the relevant files into the chemical reaction interface using the relevant governing equations and solved with COMSOL 5.0 which employs the finite difference method of solution. The model was used to study the effect of compression ratio and engine speed on the performance of the engine as it relates to species concentration, peak temperature and pressure, and the derived mechanical energy. The derived mechanical energy, peak temperature and pressure increased with increased compression ratio. The concentration of species n2, o2 and co2 also showed an increase with increase in compression ratio. The engine speed affects the period required to complete the combustion process, the time being shortened with increased engine speed. The derived mechanical energy also decreased with increased engine speed, the value being -1370.7J at a compression ratio of 18 and engine speed of 1500rpm and - 1353.6J at the same compression ratio but engine speed of 2000rpm.
- Page(s): 01-08
- Date of Publication: 05 September 2016
- Olumide A. TowojuDepartment of Mechanical Engineering, Adeleke University, Ede, Nigeria
- Ademola DareDepartment of Mechanical Engineering, University of Ibadan, Nigeria
References
[1]. John E. Dec. Advanced compression-ignition engines— understanding the in-cylinder processes. The Combustion Institute 32 (2009) 2727–2742 [2]. Alexandros G. Charalambides. Homogenous Charge Compression Ignition (HCCI) Engines.( https://dx.doi.org/10.5772/55807) [3]. John E. Dec. Advanced compression-ignition engines— understanding the in-cylinder processes. The Combustion Institute 32 (2009) 2727–2742 [4]. G. Rajendra Prasad, S. Chakradhar Goud, D. Maheswar. Alternative Fuels for HCCI Engine Techology and Recent Developments. (2012) International Journal of Advanced Research in Engineering and Applied Sciences ISSN: 2278- 6252 [5]. Joshi Anant, Poonia M.P., Jethoo A.S. Mathematical Modeling Of The Dual Fuel Engine Cycle. International Journal Of Engineering And Innovative Technology (Ijeit) Volume 2, Issue1, July 2012 [6]. M. Venkatesan, N. Shenbaga Vinayaga Moorthi, P. Arul Franco, A. Manivannan and R. Karthikeyan. Hydrous Methanol Fuelled HCCI Engine Using Ignition Improver CAI Method - ANN Approach. Mechanics and Mechanical Engineering Vol. 19, No. 1 (2015) 31–49 [7]. O. Laguitton, C. Crua, T. Cowell, M.R. Heikal, M.R. Gold. The Effect of Compression Ratio on Exhaust Emissions From a Pcci Diesel Engine. [8]. N.R. Banapurmath, B.M. Dodamani, S.V. Khandal, S.S. Hiremath, V.B. Math. Performance, Emission Characteristics of Dual Fuel (DF) & Homogeneous Charge Compression Ignition (HCCI) Engines Operated on Compressed Natural Gas (CNG) – Uppage Oil Methylester (UOME). Universal Journal of Renewable Energy 2 (2014), 32-44 [9]. Bhabani Prasanna Pattanaik, Chandrakanta Nayak, Basanta Kumar Nanda. Investigation on utilization of biogas & Karanja oil biodiesel in dual fuel mode in a single cylinder DI diesel engine. International Journal of Energy and Environment (IJEE), Volume 4, Issue 2, 2013, pp.279-290 [10]. M.Periyasamy, N.Vadivel. Experimental Investigation on LPGBiodiesel (Pongamia) Dual Fuelled Engine. International Journal on Applications in Mechanical and Production Engineering Volume 1: Issue 3: March 2015, pp 3-7.
Olumide A. Towoju, Ademola Dare "Modeling of Reduced N-Heptane Combustion in Compression Ignition Engine" International Journal of Latest Technology in Engineering, Management & Applied Science-IJLTEMAS vol.5 issue 8, pp.01-08 2016
Abstract—We develop a novel technique for resizable Hadoop cluster’s lower bounds, the template matching rectangular array of geometric spanner expressions. Specifically, fix an arbitrary hybrid kernel function f:{0,1}n ->{0,1} and let Af be the rectangular array of geometric spanner expressions whose columns are each an application of f to some subset of the variables x1, x2,... x4n. We prove that Af has bounded-capacity resizable Hadoop cluster’s complexity omega(d) , where d is the approximate degree of f . This finding remains valid in the MapReduce programming model, regardless of prior measurement. In particular, it gives a new and simple proof of lower bounds for robustness and other symmetric conjunctive predicates. We further characterize the discrepancy, approximate PageRank, and approximate trace distance norm of Af in terms of well-studied analytic properties of f , broadly generalizing several findings on small-bias resizable Hadoop cluster and agnostic inference. The method of this paper has also enabled important progress in multi-cloud resizable Hadoop cluster’s complexity..
- Page(s): 09-27
- Date of Publication: 05 September 2016
- Ravi (Ravinder) Prakash GSenior Professor Research, BMS Institute of Technology & Management, Dodaballapur Road, Avalahalli Yelahanka, Bengaluru, India
References
[1]. Ravi Prakash G, Kiran M and Saikat Mukherjee. 2014. On Randomized Preference Limitation Protocol for Quantifiable Shuffle and Sort Behavioral Implications in MapReduce Programming Model. Parallel & Cloud Computing 3, Issue 1, 1-14. [2]. Greenlaw, R. and Kantabutra. 2008. On the parallel complexity of hierarchical clustering and CC-complete problems. Complexity 14, 18- 28. (doi:10.1002/cplx.20238) [3]. Ravi (Ravinder) Prakash G, Kiran M. 2014. On The Least Economical MapReduce Sets for Summarization Expressions. International Journal of Computer Applications 94, 13-20. (doi: 10.5120/16354-5732) [4]. Amazon Elastic MapReduce. https://aws.amazon.com/elasticmapreduce/ [5]. Ravi (Ravinder) Prakash G, Kiran M. "Problems on Inverted Index Summarization Expressions for Resizable Hadoop Cluster Channel and Cluster Complexity" International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), Volume V, Issue V, May 2016, Pages: 1-19, ISSN 2278 – 2540 [6]. N. Ailon, B. Chazelle, S. Comandur, D. Liu. 2007. Estimating the Distance to a Monotone Function. Random Structures and Algorithms 31, 371-383. (doi:10.1002/rsa.20167) [7]. A. Gavish, Abraham Lempel. 1996. Match-length functions for data compression. IEEE Transactions on Information Theory 42, 1375-1380. (doi:10.1109/18.532879) [8]. Ravi (Ravinder) Prakash G, Kiran M. "Is it Consistent with Counting that any Summarization Expressions with Resizable Hadoop Cluster Channel have a Cluster Complexity?" International Journal of Engineering Research and Management (IJERM), Volume-03, Issue-06, June 2016, Pages: 135-152, ISSN: 2349- 2058. [9]. Ping Wah Wong. 1997. Rate distortion efficiency of subband coding with crossband prediction. IEEE Transactions on Information Theory 43, 352-356. (doi:10.1109/18.567761) [10]. A. Lafourcade, Alexander Vardy. 1996. Optimal sectionalization of a trellis. IEEE Transactions on Information Theory 42, 689-703. (doi: 10.1109/18.490504) [11]. T.M. Cover. 1998. Comments on Broadcast Channels. IEEE Transactions on Information Theory 44, 2524-2530. (doi: 10.1109/18.720547) [12]. A. Lapidoth and P. Narayan. 1998. Reliable Communication Under Channel Uncertainty. IEEE Transactions on Information Theory 44, 2148-2177. (doi:10.1109/18.720535) [13]. Ralph Lorentzen, Raymond Nilsen. 1991. Application of linear programming to the optimal difference triangle set problem (Corresp.). IEEE Transactions on Information Theory 37, 1486-1488. (doi:10.1109/18.133274) [14]. Alfred J. Menezes, Tatsuaki Okamoto, Scott A. Vanstone. 1993. Reducing elliptic curve logarithms to logarithms in a finite field. IEEE Transactions on Information Theory 39, 1639-1646. (doi:10.1109/18.259647) [15]. Leo Breiman. 1993. Hinging hyperplanes for regression, classification, and function approximation. IEEE Transactions on Information Theory 39, 999-1013. (doi:10.1109/18.256506) [16]. S. R. Kulkarni, D. N.C. Tse. 1994. A paradigm for class identification problems. IEEE Transactions on Information Theory 40, 696-705. (doi:10.1109/18.335881) [17]. Donald Miner, Adam Shook, 2013, "MapReduce Design Patterns" O’Reilly Media, Inc.: 978-1-449-32717-0. [18]. Rudolf F. Ahlswede, Zhen Zhang. 1994. On multiuser write-efficient memories. IEEE Transactions on Information Theory 40, 674-686. (doi:10.1109/18.335880) [19]. B. Chazelle. 2000. The Discrepancy Method: Randomness and Complexity. Cambridge University Press. 978-0-521-77093-9. [20]. B. Chazelle, A. Lvov. 2001. A Trace Bound for the Hereditary Discrepancy. Discrete Computational. Geom. 26, 221-231. (doi:10.1007/s00454-001-0030-2) [21]. B. Chazelle, A. Lvov. 2001. The Discrepancy of Boxes in Higher Dimension. Discrete Computational. Geom. 25, 519-524. (doi:10.1007/s00454-001-0014-2) [22]. B. Chazelle, J. Matoušek, M. Sharir. 1995. An Elementary Approach to Lower Bounds in Geometric Discrepancy. Discrete Comput. Geom. 13, 363-381. (doi:10.1007/BF02574050) [23]. E. Arikan. 1994. An upper bound on the zero-error list-coding capacity. IEEE Transactions on Information Theory 40, 1237-1240. (doi:10.1109/18.335947) [24]. B. Chazelle, H. Edelsbrunner, L.J. Guibas, M. Sharir. 1991. A Singly Exponential Stratification Scheme for Real Semi-Algebraic Varieties and Its Applications. Theoretical Computer Science 84, 77-105. (doi:10.1016/0304-3975(91)90261-Y) [25]. Ravi (Ravinder) Prakash G, Kiran M. "How economical are Bounds on Inverted Index Summarization for Calculating Hadoop Channel?" International Journal of Applied Information Systems (IJAIS), Volume 11 – No. 1, June 2016, Pages: 19-35 ISSN ISSN : 2249-0868 [26]. B. Chazelle. 1999. Discrepancy Bounds for Geometric Set Systems with Square Incidence Matrices. Advances in Discrete and Computational Geometry, Contemporary Mathematics AMS 223, 103-107. [27]. B. Chazelle. 2004. The Discrepancy Method in Computational Geometry. Handbook of Discrete and Computational Geometry, CRC Press 44, 983-996. [28]. Fadika, Z.; Govindaraju, M. 2010. LEMO-MR: Low Overhead and Elastic MapReduce Implementation Optimized for Memory and CPUIntensive Applications. IEEE Second International Conference on Cloud Computing Technology and Science (CloudCom), 1-8. (doi:10.1109/CloudCom.2010.45) [29]. Fadika, Z.; Govindaraju, M. 2011. DELMA: Dynamically Elastic MapReduce Framework for CPU-Intensive Applications. 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 454-463. (doi: 10.1109/CCGrid.2011.71) [30]. Iordache, A.; Morin, C.; Parlavantzas, N.; Feller, E.; Riteau, P. 2013. Resilin: Elastic MapReduce over Multiple Clouds. 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 261-268. (doi:10.1109/CCGrid.2013.48) [31]. XiaoyongXu; Maolin Tang. 2013. A comparative study of the semielastic and fully-elastic mapreduce models. IEEE International Conference on Granular Computing (GrC), 380-385. (doi:10.1109/GrC.2013.6740440) [32]. Wei Xiang Goh; Kian-Lee Tan. 2014. Elastic MapReduce Execution. 14th IEEE/ACM, International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 216-225. (doi:10.1109/CCGrid.2014.14) [33]. B. Chazelle, W. Mulzer. 2011. Computing Hereditary Convex Structures. Discrete Comput. Geom. 45, 796-823. (doi:10.1007/s00454- 011-9346-8) [34]. B. Chazelle, H. Edelsbrunner, M. Grigni, L.J. Guibas, M. Sharir, E. Welzl. 1995. Improved Bounds on Weak ε-Nets for Convex Sets. Discrete Comput. Geom. 13, 1-15. (doi:10.1007/BF02574025) [35]. David P. Williamson, David B. Shmoys. 2011. The Design of Approximation Algorithms.Cambridge University Press, 978-0-521- 19527-0. [36]. Oded Goldreich. 2008. Computational Complexity: A Conceptual Perspective.Cambridge University Press, 978-0-521-88473-0. [37]. Sanjeev Arora, Boaz Barak. 2009. Computational Complexity: A Modern Approach.Cambridge University Press, 978-0-521-42426-4. [38]. Dimitri P. Bertsekas, Convex Optimization Algorithms, Athena Scientific, Hardcover Edition ISBN: 1-886529-28-0, 978-1-886529-28- 1, Publication: February, 2015, 576 pages. [39]. Ravi (Ravinder) Prakash G, Kiran M. "Does there exist lower bounds on numerical summarization for calculating aggregate resizable Hadoop channel and complexity?" International Journal of Advanced Information Science and Technology, April 2016, Pages: 26-44, ISSN: 2319:2682. [40]. Giri Narasimhan and Michiel Smid. 2007. Geometric Spanner Networks. Cambridge University Press, New York, NY, USA. [41]. Kevin P. Murphy. 2012. Machine Learning: A Probabilistic Perspective. The MIT Press. [42]. Koller and Nir Friedman. 2009. Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning. The MIT Press
Ravi (Ravinder) Prakash G "What is a Geometric Spanner of Resizable Hadoop Channel for Homogeneous Lower Bounds?" International Journal of Latest Technology in Engineering, Management & Applied Science-IJLTEMAS vol.5 issue 8, pp.09-27 2016
There is manifold challenges of providing electricity to rural areas. The ever rising demand–supply gaps, the transmission and distribution losses, the high cost of electricity for the end user are a few of these. Use of renewable energy technologies for meeting basic energy needs of rural communities has been promoted by the Governments world over for many decades. In the recent past, India took a major step in its pursuit of sustainable development by revisiting and elevating Solar Energy sector. The off-grid solar energy has proved to be a winwin technology for the remote areas without grid connection for electricity. This paper attempts at reviewing and analyzing literature pertaining to decentralized rural electrification by solar energy. In this article, the present scenario of electricity and solar energy in India is discussed as well as the main applications of decentralized solar energy for rural electrification are highlighted. The literature on cost analysis including socioeconomic benefits of the population and diffused policies for promoting solar power is also reviewed in this article. The paper discusses the strategies and future prospects in its concluding remarks..
- Page(s): 28-33
- Date of Publication: 05 September 2016
- Boola ChoudharyDepartment of Humanities and Social Sciences Malaviya National Institute of Technology Jaipur, India.
- Dipti SharmaDepartment of Humanities and Social Sciences Malaviya National Institute of Technology Jaipur, India.
References
CITATIONS 1 IEA. ―The electricity access database‖. https://www.iea.org/weo/databaseelectricity/ electricity_access_database.htm 2 Chaurey, A., Kandpal, T.C. (2010). ―Assessment and evaluation of PV based decentralized Rural electrification: An overview‖, Renewable and Sustainable Energy Reviews (14), p.2266-2278 3 Choragudi, S. (2013). ―Off-grid solar lightening systems: A way align India’s sustainable and inclusive development goals‖, Renewable and Sustainable Energy Reviews (28), p.890-899 4 Purohit, I., Purohit, P. (2010). ―Techno-economic evaluation of concentrating solar power generation in India‖, Energy Policy (38), p.3015- 3029 5 Ramachandra, T.V., Jain, Rishab and Krishnadas, Gautham. (2011). ―Hotspots of solar potential India‖, Renewable and Sustainable Energy Reviews (15), p.3178-3186 6 Sharma, A. (2011). ―A comprehensive study of solar power in India and World‖,Renewable and Sustainable Energy Reviews (15), p.1767-1776 7 Timilsina, G.R., Kurdgelashvili, L., Narbel, P.A. (2012). ―Solar Energy: Markets, economics and policies‖, Renewable and Sustainable Energy Reviews (16), p.449-465 8 Sahoo, A., Shrimali, G. (2013). ―The effectiveness of domestic content criteria in India’s Solar Mission‖, Energy Policy (62), p.1470-1480 9 Purohit, I., Purohit, P., Shekhar, S. (2014). ―Evaluating the potential of concentrating solar power generation in Northwestern India‖, Energy Policy (62), p.157-175 10 Shrimali, G., Sahoo, A. (2014). ―Has India’s Solar Mission increased the deployment of domestically produced solar modules?‖ Energy Policy, https://dx.doi.org/10.1016/j.enpol.2014.02.023 11 Alafita, T., Pearce, J.M. (2014). ―Securitization of residential solar photovoltaic assets:Costs, risks and uncertainty‖, Energy Policy (67), p.488-498 Bibliography: [1]. BP Statistical Review of World Energy June 2010. British petroleum, /https:// www.bp.com/productlanding.do?categoryId=6929&contentId =7044622S; 2010 [accessed 15.03.11]. [2]. Central Electricity Authority (CEA), 2010. Monthly generation report, /http: // www.cea.nic.in/god/opm/Monthly_Generation_Report/18col_A_10_03/ actual-mar10.htmS; 2011 [accessed 15.03.11]. [3]. Indian renewable energy status report. NREL/TP 6A20-48948 _ October. /http: // www.nrel.gov/S; 2010. [4]. Ministry of Power Government of India. /https://www.powermin.nic.in/ JSP_SERVLETS/internal.sjspS; [accessed 15.09.12]. [5]. Approach Paper to Twelfth Five Year Plan, 2012–2017. Available from: 〈https:// planningcommission.nic.in/plans/planrel/12appdrft/appraoch_12plan.pdf〉 [accessed 06.02.13]. [6]. Barnaby F. Our common future. The ―Brundtland Commission‖ report. Ambio 1987; 16:217–8. [7]. Survey of renewable energy in India. TERI (Tata Energy Research Institute), New Delhi; 2001. [8]. Renewable energy in India. MNES report, Government of India; 2001. [9]. Muneer T, Asif M, Munawwar S. Sustainable production of solar electricity with particular reference to the Indian economy. Renewable and Sustainable Energy Reviews 2005; 9(5):444–73. October. [10].Garud, S, Purohit, I. ―Making solar thermal power generation in India a reality – Overview of technologies, opportunities, and challenges‖ The Energy and Resources Institute (TERI), Darbari Seth Block, IHC Complex, Lodhi Road, New Delhi 110003, India. [11]. The government of India. The Electricity Act, 2003. New Delhi: The Gazette of India; 2003 [Extraordinary, 2003]. [12]. Singh R, Sood YR. Current status and analysis of renewable promotional policies in Indian restructured power sector – a review. Renew Sustain Energy Rev 2011; 15:657–64. [13]. The government of India. National Electricity Policy 2005. Available: https://www.powermin.nic.in [online]. [14]. The government of India. Tariff policy; 2006 [online] https://www.powermin.nic.in. [15]. The government of India. National electricity policy and plan, Available: https://www.powermin.nic.in [online]. [16].Jawaharlal Nehru National Solar Mission. MNRE. Website of Ministry of New & Renewable Energy, Government of India, https://mnre.gov.in/; 2010. [17]. Matakiviti A. Energy adviser GOI national energy policy and rural electrification policy; 2006. [18].Ministry of New and Renewable Energy Source (MNRES). Policy support for grid interactive renewable power, Available: https://www.mnes.nic.in [online]. [19].Palit D. Solar energy programs for rural electrification: Experiences and lessons from South Asia. Energy for Sustainable Development2013; 17(3): 270-279. [20].PalitD, ChaureyA.Off-gridruralelectrificationexperiencesfromSouthAsia: statusand best practices. Energy Sustain Dev 2011; 15:266–76. [21].Palit D, Sarangi GK. A comparative analysis of the solar energy programs for rural electrification: experiencesandlessonsfromSouthAsia.Proceedingsofthirdinter-national conference on addressing climate change for sustainable development through up scaling renewable energy technologies; 2011. [22].Palit D, Shukla A. Performance and impact of solar thermal and photovoltaic devices disseminated in northeastern region ofIndia.Proceedings ofthe2nd International Conference on Renewable Energy Technology for Rural Development (RETRUD-03), CenterforEnergyStudies.Kathmandu, Nepal: TribhuvanUniversity, NepalSolarEnergy Society; 2003. [23]. Palit D, Singh J. Lighting a billion lives — empowering the rural poor; boiling point; issue 59; 2011.
Boola Choudhary and Dipti Sharma "Decentralized Solar Power for Rural Electrification in India: A Review" International Journal of Latest Technology in Engineering, Management & Applied Science-IJLTEMAS vol.5 issue 8, pp.28-33 2016
The NiFe2O4 nanoparticle was found to be an excellent solid base catalyst for the one-pot synthesis of 1,4- DHP via Hantschz reaction of various aromatic aldehyde, ammonium carbonate and a β–dicarbonyl compound at 600C. This process offers key advantages including short reaction time, high yield, an environmentally friendly synthesis and simple work up process. Nano catalyst easily recovered and recovered catalyst was reused for number of times for compounds synthesis. The yield of product found to be excellent without loss of catalyst activity.
- Page(s): 34-39
- Date of Publication: 05 September 2016
- Vijay V. DabholkarOrganic Research Laboratory, Department of Chemistry, K. C. College, Churchgate , Mumbai -400 020, India
- Swapnil K. KuradeOrganic Research Laboratory, Department of Chemistry, K. C. College, Churchgate , Mumbai -400 020, India
- Keshav S. BadheOrganic Research Laboratory, Department of Chemistry, K. C. College, Churchgate , Mumbai -400 020, India
[1]. Shan,R., Velazquez,C. and Knaus,E.E.(2004) J. Med. Chem.,47, 254-261. [2]. Vo,D., Matowe,W.C., Ramesh, M., Iqbal, N., Wolowyk,M.W., Howlett,S.E. and Knaus,E.E.(1995) J. Med. Chem., 38, 2851 [3]. Ogawa, A.K., Willoughby,C.A., Bergeron,R., Ellsworth,K.P., Geissler,W.M., Myer,R.W.,Yao,J., Harris,G. and Chapman,K.T.(2003) Bioorg. Med. Chem. Lett.,13, 3405-3408. [4]. Klusa,V.(1995) Cerebrocrast, neuroprotectant cognition enhancer. Drugs Future, 20,135-138. [5]. Bretzel,R.G.,Bollen,C.C., Maeser E. and Federlin,K.F.(1993) Am. J. Kidney Dis.,21,53-64. [6]. Yang,J.W., Fonseca,M.T.H. and List,B.A.(2004) Angew. Chem. Int. Ed. Engl.,43, 6660-6662; [7]. Martin,N.J.A. and List,B.(2006) J. Am. Chem. Soc.,128,13368- 13369. [8]. Rueping,M., Antonchick,A.P. and Theissmann ,T.A. (2006) Angew. Chem. Int. Ed. Engl.,45, 3683-3686. [9]. Hoffmann, S., Nicoletti,M. and List,B.(2006) J. Am. Chem. Soc.,128,13074-13075. [10]. Jin,T.S.,Wang,A.Q.,Cheng,Z.L., Zhang,J.S. and Li,T.S.(2005) Synth.Commun.,35,137. [11]. Ren,Z.,Cao,W.,Tong,W. and Jin,Z.(2005) Synth.Commun.,35,2509. [12]. B.Love,B. and Snader, K.M.(1965) J. Org. Chem.,30,1914-1916. [13]. Guo,S. and Yuan,Y.(2010)Chin. J. Chem.,28, 811-817; [14]. Ruiz,E., Rodríguez,H., Coro,J., Niebla,V., Rodríguez,A., Martínez-Alvarez,R., Novoa de Armas,H., Suárez,M. and Martín, N.(2012) Ultrason. Sonochem.,19, 221-226. [15]. Yadav,J.S., Reddy,B.V.S., Basak,A.K. and Narsaiah, A.V. (2003)Green Chem.,5, 60-63. [16]. Wang,L.M., Sheng,J., Zhang,L., Han,J.W., Fan,Z., Tian,H. and Qian,C.T.(2005) Tetrahedron,61, 1539-1543. [17]. Ko,S., Sastry, M.N.V., Lin,C. and Yao , C.-F.(2005) Tetrahedron Lett.,46, 5771-5774. [18]. Anniappan,M., Muralidharan,D. and Perumal,P.T.(2002) Synth. Commun.,32, 659-663; [19]. Khadikar,B.M.,Gaikar,V.G. and Chitnavis,A.A.(1995) Tetrahedron Lett.,36, 8083-8086; [20]. Öhberg, L and Westman, J.(2001) Synlett,1296-1298; [21]. Agarwal,A.and Chauhan,P.M.S.(2005) Tetrahedron Lett.,46,1345- 1348; [22]. Kuraitheerthakumaran,A.,Pazhamalai,S. and Gopalakrishnan, M.(2011)Chin. Chem. Lett.,22, 1199-1202. [23]. Chari,M.A. and Syamasundar,K.(2005)Catal. Commun.,6, 624- 626. [24]. Sabitha,G., Reddy,G.S.K.K., Reddy,Ch.S. andYadav,J.S.(2003) Tetrahedron Lett.,44, 4129-4131. [25]. Sharma,G.V.M., Reddy,K.L., Lakshmi,P.S. and Krishna,P.R.(2006) Synthesis,1, 55-58. [26]. Xu,Q., Wei,Y., Liu,Y., Ji, X., Yang,L. and Gu,M.(2009) Solid State Sci .,11(2), 472-478. [27]. M.B.Tian, Magnetic Material. Beijing,Tsinghua (2001)University Press. [28]. Sloczynski,J., Janas,J., Machej,T., Rynkowski,J. and Stoch,J.(2000) Appl Catal B., 24(1),45-60. [29]. Ajayan,P.M. and Redlich, P.(1997) J. Micro.,185(2) ,275-282. [30]. Wang,X., Yang,G., Zhang,Z.,Yan,L. and Meng,J.(2007) Dyes Pigm,74(2),269-272. [31]. Joshi,S., Kumar,M., Chhoker,S., Srivastava,G., Jewariya ,M. and Singh,V.N.(2014) Journal of Molecular Structure,1076, 55–62. [32]. Nasr-Esfahani,M., Montazerozohori,M. and Raeatikia,R.(2014)Maejo Int.J.Sci.Technol., 8(01) ,32-40. [33]. Moshtaghi Zonouz, A.and Moghani,D.(2011) Synth. Commun. , 41,2152. [34]. Marco-Contelles,J., León,R., de los Ríos,C., Guglietta,A., Terencio,J., López,M.G., García,A.G. and Villarroya M.(2006) J. Med.Chem.,49, 7607..
Vijay V. Dabholkar, Swapnil K. Kurade, Keshav S. Badhe "Nickel Ferrite Heterogeneous Base Catalyst for Synthesis of Dihydropyridines" International Journal of Latest Technology in Engineering, Management & Applied Science-IJLTEMAS vol.5 issue 8, pp.34-39 2016
Water demand always exceeds rainfall, but the water deficit is quite low in the northeast monsoon period. Hence, due to severe water deficit, drought recurs during the southwest monsoon and also in summer months in Tamil Nadu. In recent decades, the exploitation of groundwater has increased greatly particularly for agricultural purpose, because large parts of the country have little access to rainfall due to frequent failures of monsoon. The aim of the study is to asses the status of rainfall and groundwater for agriculture with the following objectives. to analyze the mean rainfall and groundwater level to associate rainfall and water level during wet, dry and normal years. To assess the rainfall and groundwater for different cropping period. The results were effectively portrayed using simple graphs and thematic maps with the aid of open source GIS software. Tiruchchirappalli district, same name is located in the central part of Tamil Nadu between 78°09’00” - 79°03’00” east longitudes and 10°17’00” - 11°25’00” north latitudes. Where ever the excess rainfall occurrences have observed the groundwater level either goes high or low may caused due to the trend of groundwater utilization over the particular area.
- Page(s): 40-52
- Date of Publication: 05 September 2016
- Alaguraja PalanichamyPost Doctoral Fellow, UGC Dr.S Radhakrishnan, Department of Geography, Madurai Kamaraj University, Madurai, India
References
[1]. Alaguraja .P and Manivel .M, Nagarathinam S .R and Yuvaraj .D - Rainfall Distribution Study in Coimbatore District Tamil Nadu Using GIS. (2010) “I.K International Publication. New Delhi- India, pp- 92-115. [2]. Balachandar.D, Alaguraja.P, Sundrajan.P, Ruthravelmurthy. K, Kumaraswamy. K, (2010) Application of Remote Sensing and GIS for Artificial Recharge Zone in Sivaganga District, International Journal of Geomatics and Geosciences Volume 1, No 1, pp-84-97 [3]. Banukumar. K, Rajamanickam. G.V and Aruchamy. S, (2005) Study of Drought Prone Areas in Pudukkottai Taluk, Tamil Nadu –A Hydrogomorphological Approach, Indian Journal of Geomorphology, Volume 10 1 and 2 pp.23-36. [4]. Banukumar. K, S.Aruchamy, (2007) Climatic Types of Tamil Nadu, India, Journal of Spatial Science, Volume I 1 and 2, pp.1-8 [5]. Begchi .K. and jean, M.M. (1974) The Crop combination and spatial pattern of land utilization in lower silabaty basin, Geographical Review of India, 36, 4, pp.323-22. [6]. Berndt .R.D and White B.J, (1976) A simulation based evaluation of three crop systems on clay soil in a summer rainfall environment, Agricultural Meteorology, 16, pp.211-229. [7]. Forkuor. G, Pavelic.P, Asare.E and Obuobie.E, (2013) Modelling potential areas of groundwater development for agriculture in northern Ghana using GIS/RS,Hydrological Sciences Journal pp- 437-451 [8]. Genesis Tambang Yengoh, Frederick Ato Armah, Edward Ebo Onumah and Justice O. Odoi, (2010) Trends in Agriculturally- Relevant Rainfall Characteristics for Small-Scale Agriculture in Northern Ghana Journal of Agricultural Science Vol. 2, No. 3; September 2010 Geophysics, 27 pp.23-28. [9]. George. C.J and Ramasashi K.S. (1975) Agricultural drought of 1972 kharif season, Indian Journal of Meteorology, Hydrology and Geophysics, 26 (1), pp. 89-96 [10]. George. C.J, (1972) An index of agricultural drought, Symposium on drought in the Asiatic Monsoon Area, Indian Meteorological Department, pune, pp.18. [11]. Guhathakurta.P and M. Rajeevan (2006), Trends in the rainfall pattern over India, National Climate Centre, Research Report No: 2/2006, Meteorological Department pune. India, pp.1-23 [12]. Krishnan .A, (1972) A Climatic approach to cropping pattern adoptability in western Rajasthan, Proceeding of the Symposium on Cropping Patterns in India, ICAR, New Delhi, ICAR, pp. 165- 171. [13]. Ramasundaram.M, Banukumar.K, Alaguraja.P, Yuvaraj.D and Nagarathinam.S.R, (2012) A study on crop combination regions in Tamil Nadu, India using MapInfo and GIS International. Journal of Advances in Remote Sensing and GIS, Vol. 1, No. 1, 2012, pp- 1-8 [14]. Richard M. et. al., (2005) Rainfall reliability, a neglected factor in explaining convergence and divergence of plant traits in fire-prone Mediterranean-climate ecosystems. Global Ecology and Biogeography 14, pp.509-519 [15]. Roy.B.K, (1967) crop combinations and changes in crops in Gengrahaghara Doab East, National Geographical Journal of India, 13(4), 194- 207. [16]. Weaver .J.C, (1954) Changing patterns of cropland use in the Middle West, Economic Geography 30(1) pp.1-47. [17]. Weaver. J.C, (1954) Crop combination regions for 1919 and 1929 in the Middle West, Geographical review, 44(4), pp.560 – 572. [18]. Yuvaraj.D, Alaguraja. P, Manivel.M, Sekar.M, and Muthuveerran.P, (2010 ) Drinking Water Supply in Coimbatore City Corporation, Tamil Nadu, India, Using Remote Sensing and GIS Tools, International Journal of Environmental and Sci. Volume 1, No 1, pp- 71-76.
Alaguraja Palanichamy "Assessment of Rainfall and Groundwater for Agriculture of Tiruchirappalli District, Tamil Nadu, using Geospatial Technology" International Journal of Latest Technology in Engineering, Management & Applied Science-IJLTEMAS vol.5 issue 8, pp.40-52 2016
Cash is very important component in business. The main purpose of this study is to examine the incremental information content of operating cash flows in predicting the cash flow position of Tata Steel Limited. The present study covers 15 financial years from 2000-01 to 2014-15 to make analysis of cash flows is deemed quiet sufficient. Detailed cash flow analysis was made in Tata Steel (Standalone) company by adopting case study method. Ratio analysis, Simple percentage and cash management efficiency model were used for analysis. The overall cash flows of the company are good. If the company increases the operating cash flows to meet the total debt capital, it will lead to increase the better performance in cash flows.
- Page(s): 53-57
- Date of Publication: 05 September 2016
- R. Sathish KumarDepartment of Commerce MIET Arts & Science College, Gundur, Tamil Nadu, India
[1] Annual Reports, Tata Steel Limited, Mumbai, 2000-01 to 2014-15. https://tatasteel.com/ [2] Khan M. Y. and P. K. Jain, Basic Financial Management: Financial Statement Analysis. New Delhi: Tata McGraw-Hill Publishing Company Limited, 2006. [3] Maheshwari, S. N. Financial Management: Ratio Analysis. New Delhi: S. Chand and & Sons Educational Publishers, 2011. [4] Sharmila, D. Management of Short-term Funds - A Study with Special Reference to JSW Steel Limited, Mumbai, (M.Phil., thesis, Annamalai University 2013). [5] Rajagopalan, N. V. R. “Financial Performance of Indian Cement Limited.” M.Phil. thesis, Annamalai University, 2005. [6] Rajagopalan, N.V.R. Equivalent Cash Points Model: A New Dimension to Cash management Efficiency, Journal of Business & Finance, Vol. 2, No. 1, January-June, 2009.
R. Sathish Kumar, "Study on Measurement and Management of Cash Flow Efficiency of Tata Steel Limited (Standalone Company)" International Journal of Latest Technology in Engineering,Management & Applied Science-IJLTEMAS vol.5 issue 8, pp.53-57 2016
Wireless Sensor Networks (WSNs) consists of densely distributed self-organizing wireless nodes with a tiny amount of CPU memory, low processing power and a very low battery capacity. These wireless nodes sense the environmental situations and generate different types of data packets, such as real-time and non-real-time data packets. Scheduling these different types of data packets in the network is a challenging task. Many sensor applications rely on information being delivered in a timely manner, so it is important to reduce the total end-to-end delay. Many of the existing packet-scheduling algorithms in WSNs use, First Come First Serve (FCFS), Earliest Deadline First (EDF), Shortest Job First (SJF), Preemptive priority, non-preemptive priority. FCFS, EDF and SJF algorithms don’t provide any priority to real-time data packets this leads to starvation of realtime data packets when non-real-time packets arrive with a higher Burst time. In priority based algorithms, non-real-time data packets starve because of continuous arrival of higher priority real-time data packets. Some scheduling algorithms are based on number of queues in the sensor node. Existing algorithms incur a high processing overhead and large end-toend delay. These algorithms are not dynamic in nature to adapt the changing requirements of the Wireless Sensor Networks. A Dynamic Multilevel Priority (DMP) Packet Scheduling scheme is proposed to overcome the starvation problem, to reduce processing overhead and end-to-end delay. This scheduling algorithm divides the ready queue into three individual priority queues. Real-time data packets are allotted the highest priority and are placed in the priority 1 queue, non-real-time remote data packets are allocated to priority 2 queue and local non-real-time data to the priority queue 3. This algorithm uses a zone-based technology and visualizes the whole network as a hierarchical structure. The sensor nodes that are adjacent to the Base Station are considered to be present at level 0, nodes which are at one hop distance are said to be at level 1. Each level in the hierarchy is allocated with a time slot of varying time quantum using a TDMA scheme.
- Page(s): 58-63
- Date of Publication: 05 September 2016
- Shantveer Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India
- Dr. Jagadish S Kallimani Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India
References
[1]. G. Anastasi, M. Conti, and M. Di Francesco, “Extending the lifetime of wireless sensor networks through adaptive sleep,” IEEE Trans. Industrial Informatics, vol. 5, no. 3, pp. 351–365, 2009. P. Guo, T. Jiang, Q. Zhang, and K. Zhang, “Sleep scheduling for critical event monitoring in wireless sensor networks,” IEEE Trans. Parallel Distrib. Syst., vol. 23, no. 2, pp. 345–352, Feb. 2012. [2]. Anjaly Paul , Robin Cyriac,” A Review of Packet Scheduling Schemes in Wireless Sensor Networks” in, International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 3, March 2014. [3]. W. Stallings, Operating Systems, 2nd edition. Prentice Hall, 1995. [4]. Y. Zhao, Q. Wang, W. Wang, D. Jiang, and Y. Liu, “Research on the priority-based soft real-time task scheduling in TinyOS,” in Proc. 2009 International Conf. Inf. Technol. Comput. Sci., vol. 1, pp. 562–565. [5]. Y. Wang, D. Wang, W. Fu, and D. P. Agrawal, “Hops-based sleep scheduling algorithm for enhancing lifetime of wireless sensor networks,” in Proc. 2006 IEEE International Conf. Mobile Adhoc Sensor Syst., pp. 709–714. [6]. Varsha Jain, Shweta Agarwal, Shweta Agarwal,” Dynamic Multilevel Priority Packet Scheduling Design for WSN, 2014 IEEE [7]. Nidal Nasser, Lutful Karim & Tarik Talib, “Dynamic Multilevel Priority Packet Scheduling Scheme for wirelesssensor network”, IEEE Trans on wireless communication, vol 12, NO. 4, April 2013 [8]. P. A. Levis, “TinyOS: an open operating system for wireless sensor networks (invited seminar),” in Proc. 2006 International Conf. Mobile Data Manag., p. 63. [9]. C. Lu, B. M. Blum, T. F. Abdelzaher, J. A. Stankovic, and T. He, “RAP: a real-time communication architecture for large-scale wireless sensor networks,” in Proc. 2002 IEEE Real-Time Embedded Technol. Appl. Symp., pp. 55–66. [10]. Hoon Kim , Sung-Gi Min, "Priority-based QoS MAC Protocol for Wireless Sensor Networks," IEEE Parallel & Distributed Processing ( IPDPS )2009. [11]. S. Chachra and M. Marefat, “Distributed algorithms for sleep scheduling in wireless sensor networks,” in Proc. 2006 IEEE International Conf. Robot. Autom., pp. 3101–3107. [12]. L. Karim, N. Nasser, and T. El Salti, “Efficient zone-based routing protocol of sensor network in agriculture monitoring systems,” in Proc. 2011 International Conf. Commun. Inf. Technol., pp. 167– 170..
Shantveer and Dr. Jagadish S Kallimani "A Study on Dynamic Multilevel Priority Packet Scheduling Scheme for WSNs" International Journal of Latest Technology in Engineering,Management & Applied Science-IJLTEMAS vol.5 issue 8, pp.58-63 2016
Plastic composite material has been in to frontier of research as one of the new competitive materials in engineering. Especially, Particle reinforced plastic is a relatively new class of composite material manufactured from particles, nanoparticles and resins, and has proven efficient and economical for the development and repair of new and deteriorating structures. In this project, we report the mechanical properties of epoxy composites strengthened with Multi walled carbon nanotubes. Different composition (0.1%, 0.5%, 1%, 1.5%) of Multi Walled Carbon Nanotubes (MWCNT) are mixed with epoxy resin and castings are prepared by moulding technique. The different mechanical properties such as Tensile strength, Flexural strength and Hardness are evaluated at room temperature. One set of samples are immersed in salt water for moisture absorption and strength degradation studies. The mechanical properties are determined and are compared with that of dry specimens. The Results show that the Mechanical Properties increases with the increase in Percentage of MWCNT for the prepared Composites.
- Page(s): 64-71
- Date of Publication: 05 September 2016
- Udit DineshStudents of Department of Mechanical Engineering, PESIT South Campus, India
- Suriyaprabu VijayaprabuStudents of Department of Mechanical Engineering, PESIT South Campus, India
- Azharuddin KaziFaculty of Department of Mechanical Engineering, PESIT South Campus, India
References
[1]. Khalid R. Al-Rawi, Adawiya j. Hedar ,OlfatA.Mahmood, “Effect Different MultiWalled Carbon Nanotubes MWCNTs Type on Mechanical Properties of Epoxy Resin Nanocomposites”, International Journal of Application or Innovation in Engineering & Management (IJAIEM). [2]. Marcio Rodrigo Loos, Luiz Antonio Ferreira Coelhoa, Sérgio Henrique Pezzin, Sandro Campos Amico, “Effect of Carbon Nanotubes Addition on the Mechanical and ThermalProperties of Epoxy Matrices”, Materials Research, Vol. 11, No. 3, 347-352, 2008. [3]. EwelinaCiecierska, Anna Boczkowska, Krzysztof Jan Kurzydlowski, Iosif Daniel Rosca, Suong Van Hoa, “The effect of carbon nanotubes on epoxy matrixnanocomposites.” [4]. Shiuh-ChuanHer ,Chun-Yu Lai, “Dynamic Behavior of Nanocomposites Reinforced with Multi-Walled Carbon Nanotubes (MWCNTs)”, Materials 2013. [5]. SmrutisikhaBal, “Dispersion and reinforcing mechanism of carbon nanotubes in epoxy nanocomposites”, Bull. Mater. Sci., Vol. 33, No. 1, February 2010, pp. 27–31. [6]. Caio Enrico Pizzutto, Jaqueline Suave, Jonas Bertholdi, Sérgio Henrique Pezzin, Luiz Antonio Ferreira Coelho, Sandro Campos Amico, “Study of Epoxy/CNT Nanocomposites Prepared Via Dispersion in the Hardener”, Materials Research 2011. [7]. JiHoon Lee, KyongYop Rhee, JoongHee Lee, “Effects of moisture absorption and surface modification using 3- aminopropyltriethoxysilane on the tensile and fracture characteristics of MWCNT/epoxy nanocomposites”, Applied Surface Science, Elsevier. [8]. Carolina Fernández, Paulo Flores, Henri Michel Montrieux and Jacqueline LecomteBeckers,“Influence Of The Addittion Of Functionalized Mwcnt On Mechanical Properties On Epoxy/Carbon Fiber And Epoxy/Carbon-Aramid Fiber Composites” Brazilian Conference On Composite Materials BCCM1 Natal-RN, July16-19, 2012. [9]. P.S. Shivakumar Gouda, RaghavendraKulkarni, S.N. Kurbet, DayanandaJawali, “Effects of multi walled carbon nanotubes and graphene on the mechanical properties of hybrid polymer composites”, Advanced Materials Letters 2013. [10]. SubhranshuSekharSamal, “Role of Temperature and Carbon Nanotube Reinforcement on Epoxy based Nanocomposites”, Journal of Minerals & Materials Characterization & Engineering, Vol. 8, No.1, pp 25-36. [11]. Mahesh V. M., B. K. Muralidhara, Raji George, “Studies Of Influence on Multiwalled Carbon Nanotubes (MWCNT’s) Reinforced Epoxy Based Composites”, International Journal Of Modern Engineering Research (IJMER). [12]. Jeena Jose Karippal, H. N. Narasimha Murthy, K. S. Rai, M. Krishna, and M. Sreejith,“The Processing and Characterization of MWCNT/Epoxy and CB/Epoxy Nanocomposites Using Twin Screw Extrusion”, Polymer-Plastics Technology and Engineering. [13]. K.SudhaMadhuri, Dr.H.RaghavendraRao, “An investigation of mechanical and thermal properties of reinforced sisal-glass fibers epoxy hybrid composites”, International Journal of Engineering Research, Volume No.3 Issue No: Special 1, pp: 112-115. [14]. SmrutisikhaBal, “Experimental study of mechanical and electrical properties of carbon nanofiber/ epoxy composites”, Journal of Material and Design 2009.
Udit Dinesh, Suriyaprabu Vijayaprabu, Azharuddin Kazi "Properties of Epoxy Composites Reinforced with Multi-walled Carbon Nanotubes" International Journal of Latest Technology in Engineering, Management & Applied Science-IJLTEMAS vol.5 issue 8, pp.64-71 2016
Life, both animal and plant, is impossible without water. Water scarcity is a vital scenario to be considered in the current world. In India with the increasing population and growth of industries the water availability is becoming inadequate to meet up the requirements of people. So predicting the water level is significant to know about water scarce in the impending years. Here we use Big Data Analytics in the study of developing forecasting models for predicting underground water levels. In this prediction first the data of present underground water level is collected as an ingest data operation, which is next moved into the big data storage. Then dynamic linear modelling algorithm is used to observe the pattern of historical data and predict the future underground water levels by applying data-driven analytics and data mining concepts.
- Page(s): 72-74
- Date of Publication: 05 September 2016
- Raguvaran.SAssistant Professor, Department of Computer Science and Engineering KPRIET, Coimbatore, India
- Joyce Beryl Princess.PDepartment of Computer Science and Engineering KPRIET Coimbatore, India
References
[1]. Prashant Shrivastava, S. Pandiaraj and Dr. J. Jagadeesan “Big Data Analytics In Forecasting Lakes Levels”, CSE Department, SRM University, Chennai, India, IJAIEM, March 2014. [2]. Leixiao Li,Zhiqiang Ma, Limin Liu and Yuhong Fan College of Information Engineering, Inner Mongolia University of Technology Huhhot, China.”Hadoop-based ARIMA Algorithm and its Application in Weather Forecast”. International Journal of Database Theory and Application Vol.6, No.5 (2013). [3]. Giovanni Petris, Sonia Petrone, Patrizia Campagnoli, “Dynamic Linear Models with R”, 2014. [4]. David Makowski, Lucie Michel, AgroParisTech, “Use of dynamic linear model for predicting crop yield trends in foresight studies on food Security”, France, 2013. [5]. https://www.worldometers.info/world-population/india-population/ [6]. https://www.india-wris.nrsc.gov.in/ [7]. https://www.math.unm.edu/~ghuerta/tseries/dlmch2.pdf [8]. Cyrille Szymanski , “Package modeler”, February 19, 2015. [9]. Roberto Rivera, "A Dynamic Linear Model to Forecast Hotel Registrations in Puerto Rico Using Google Trends Data”,December 2015.
Raguvaran.S, Joyce Beryl Princess.P "Leveraging Hadoop Framework for Predicting Underground Water Levels" International Journal of Latest Technology in Engineering, Management & Applied Science-IJLTEMAS vol.5 issue 8, pp.72-74 2016
Nowadays Universal Serial Bus (USB) has emerged as a very popular, inexpensive and easy-to-use short range (5m) communication media for data transfer with a PC (host), where the communication device acts as a client. The USB was originally designed to connect PC with its peripherals (keyboard, mouse etc.). However, it has proven useful for many other applications, including measurement and automation. An approach to develop a USB based data acquisition system is presented; where a PIC microcontroller (Microchip’s 18F2550) based standalone embedded system is used as a front-end data collection and also field control device. The developed USB based device continuously scans its input analog and digital channels sequentially and updates the PC with these values, which are updated in a Visual BASIC based application GUI (Graphical User Interface). Here USB device has been programmed as Human Interface Device (HID) class, which provides an additional advantage of not requiring any device specific driver. The firmware used in the microcontroller is developed in PicBASICPro platform. A second Generation JDM programmer has been developed, which is used along with WINPIC800 to program the microcontroller. This device can accept an analog input voltage between 0 – 5Vdc from any real life sensor(s) and generate digital output (8 bits) as control action.
- Page(s): 75-81
- Date of Publication: 05 September 2016
- Don BiswasAssistant Professor, HNB Garhwal University, Srinagar Garhwal, Uttarakhand, India
- Gambheer Singh KathaitAssistant Professor, HNB Garhwal University, Srinagar Garhwal, Uttarakhand, India
- Vishal RohillaAssistant Professor, HNB Garhwal University, Srinagar Garhwal, Uttarakhand, India
References
[1]. Microcontroller data sheet: By Microchip Technology. [2]. Universal Serial Bus specification: USB complete by Jan Axelson. [3]. Visual Basic 6.0: Learn Visual Basic 6.0 by Lou Tylee. [4]. Human Interface Device (HID) class: USB complete by Jan Axelson. [5]. 2nd generation JDM PIC programmer and WinPic800 V3.6 foxdelta.com, Prof. A.Rakshit Jadavpur University [6]. USB Implementers Forum: USB 2.0 Specification: www.usb.org/developers/docs [7]. Microsoft's HID documentation: www.usb.org/developers/hidpage/microhid/ [8]. USB-IF HID Tools: www.usb.org/developers/hidpage/ [9]. USB Implementers Forum: Approved Class Specification Documents: www.usb.org/developers/devclass_docs [10]. Microchip Website: www.microchip.com [11]. Universal Serial Bus specification: www.usb.org [12]. PCB making Software – EPCPROX [13]. Mplab IDE: www.microchip.com [14]. Visual Basic: www.microsoft.com/vbasic/ https://www.microsoft.com/vbasic/download https://www.microsoft.com/vbasic/techmat [15]. “USB Based ECG Acquisition System” By J. Mihel, R. Magjarevic University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia [16]. “Temperature-Meter via USB Based on PIC 18F2550 for Solar Energy Concentrator System” By González Manzanilla, Arízaga Silva, Moreno Barrera [17]. “USB Connectivity for Microcontrollers” Instrumentation Viewpoint, Sarti News Bulletin [18]. “A Novel Design of an Industrial Data Acquisition System” By Ziad Salem (Aleppo University), Ismail Al Kamal (American University of Beirut), Alaa Al Bashar (Aleppo University)
Don Biswas, Gambheer Singh Kathait, Vishal Rohilla "To Design and Measure Physical Signal from a USBBased Data Acquisition System" International Journal of Latest Technology in Engineering, Management & Applied Science-IJLTEMAS vol.5 issue 8, pp.75-81 2016