@Research Paper
<#LINE#>Study on Discovering Seepage in Buried Water Distribution Conduits via Microwave Technology <#LINE#>Dinesh Kshatri @ Baniya ,Nanda Bikram @Adhikari  ,Mandira@Adhikari<#LINE#>1-6<#LINE#>1.ISCA-RJEngS-2014-65.pdf<#LINE#>2 Department of Electronics and Computer Engineering, Institute of Engineering, Pulchowk Campus, Tribhuvan University, NEPAL @ Bhaktapur Multiple Campus, Tribhuvan University, NEPAL <#LINE#>1/10/2014<#LINE#>9/10/2014<#LINE#>Water loss from corroded, wrecked, or leaky underground pipelines is a global crisis. Water supply channels crisscrossing beneath the ground are the main transporters of drinking water. The populations of developing and underdeveloped nations are the major sufferers of water borne diseases due to pollution creeping into dilapidated water delivery pipes. The sub terrain location of water conduits increases the difficulty of gauging their operating conditions and immediately administering repair work.  The intent of this study is to be able to map out leakage points in buried waterways without having to unearth the entire structure. In order to accomplish the above mentioned goal, this study relies on microwave sensing techniques with transceivers operating at frequencies of 1.6 GHz ad 10 GHz. The unique and differing transmittance profile of microwaves helped differentiate moist and dry soil. Transmittance measurements in the lab using plastic boxes filled with dry and wet mud provided a clear distinction between the two kinds of mud. The idea was further tested in a mock-up pipeline system, where an arrangement of galvanized iron (G.I.) pipes with holes, to simulate water leakage, was used to wet a certain area of the ground. Transmittance profiles for both 1.6 GHz and 10 GHz frequencies in wet and dry ground areas were obtained and analyzed. Outcomes from both the laboratory setup and the pipeline network show that moist mud can be differentiated from dry mud using microwave transmittance methodology. These results indicate the possibility of a convenient and practical technique of locating pipeline leaks occurring beneath the surface.<#LINE#> @ @ Pandey V.P., Chapagain S.K. and Kazama F., Evaluation of groundwater environment of Kathmandu Valley, Environmental Earth Sciences, 60, 1329-1342 (2010) @No $ @ @ Segelstein D., The Complex Refractive Index of Water, M.S. Thesis at University of Missouri--Kansas City, (1981) @No $ @ @ Katze U., Complex permittivity of water as a function of frequency and temperature, Journal of Chemical Engineering Data, 34, 371-374 (1984) @No $ @ @ Somaraju R. and Trumpf J., Frequency, Temperature and Salinity Variation of the Permittivity of Seawater, IEEE Transactions on Antennas and Propagation, 54, 3441–3448 (2006) @No $ @ @ Gaiduk V., Dielectric Relaxation and Dynamics of Polar Molecules, World Scientific Publishing, (1999) @No 
@Research Article
<#LINE#>Accuracy Analysis of 5-Bit Data and Decision Fusion Strategies in Cognitive Radio Networks<#LINE#>Megha@Motta<#LINE#>7-15<#LINE#>2.ISCA-RJEngS-2014-57.pdf<#LINE#> Department of Electronics and Communication, Acropolis Institute of Technology And Research, Indore, INDIA <#LINE#>5/9/2014<#LINE#>14/10/2014<#LINE#>Cognitive radio allows unlicensed users to access licensed frequency bands through dynamic spectrum access so as to reduce spectrum scarcity. This requires intelligent spectrum sensing techniques like co-operative sensing which makes use of information from number of users. This paper investigates the use of energy detector and its simulation in MATLAB for licensed user detection. Simulation results show that implementing cooperative spectrum sensing help in better performance in terms of detection. Quantized combination scheme provides a better compromise between detection performance and complexity. This paper is further extended to the 5 bit data and decision fusion. Simulation results shows the accuracy of 5-bit decision at variable SNR values. <#LINE#> @ @ Rakesh Rajbanshi, OFDM-Based Cognitive Radio for DSA Networks, Technical Report ITTC-FY2008-TR 31620-05; September (2007) @No $ @ @ Haykin S., Cognitive radio Brain-empowered wireless communications, IEEE J. Selected. Areas of Communications, 23(2), 201-220, (2006) @No $ @ @ S.M. Kay, Optimal stopping rules, 2. Prentice Hall, (1998) @No $ @ @ Urkowitz H., Energy detection of unknown deterministic signals, Proc. IEEE, 55(4), 523 531 (1967) @No $ @ @ Zeng Y., Liang Y.C., Hoang A.T. and Zhang R., A review on spectrum sensing for cognitive radio: Challenges and solutions, EURASIP Journal on Advances in Signal Processing, 115, (2010) @No $ @ @ Peh E. and Liang Y.C., Optimization for cooperative sensing in cognitive radio networks, in Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC 07), 2732, Hong Kong, March (2007) @No $ @ @ Ashish P. and Linnartz J.P., Performance analysis of primary user detection in a multiple antenna cognitive radio, IEEE International Conference on Communications7, 6482-6486 (2007) @No $ @ @ J. Ma and Y. Li., Soft combination and detection for cooperative spectrum sensing in cognitive radio networks, in Proc., IEEE Global Telecomm. Conf.,(2007) @No 
@Review Paper
<#LINE#>Tar removal from Producer Gas: A Review<#LINE#>R.N.@Singh ,S.P.@Singh,J.B.@Balwanshi<#LINE#>16-22<#LINE#>3.ISCA-RJEngS-2014-68.pdf<#LINE#>  Devi Ahilaya Vishwavidhlya, Takshila Campus, Indore, INDIA<#LINE#>14/10/2014<#LINE#>21/10/2014 <#LINE#>Gasification is the most appropriate technology for conversion of solid fuel (biomass) into a gaseous fuel, known as producer gas. Producer gas is a mixture of gases which consists of hydrogen, carbon monoxide, methane, carbon dioxide, water vapor, nitrogen, tar and suspended particulate matter. For motive applications such as internal combustion engines, the tar present in producer gas may create problem, if the tar content in the producer gas is above 50-100 mg/Nm. A tar-free gaseous fuel can be obtained in a suitably designed producer gas conditioning unit whose sole purpose is to provide clean producer gas. Gas cleaning and conditioning systems to control tar levels are being continuously modified for better efficiency and cost effectiveness. Major techniques used in tar cleaning are thermal cracking, catalytic cracking and physical removal of tar. Many a times, combination of these techniques are used for better cleaning of producer gas. The following paper critically reviews the different techniques used for collection, identification and quantification of tars in producer gas obtained from biomass. <#LINE#> @ @ Morris M., Catalytic cracking of tar in product gas from wood gasification, TPS; Termiske Processor AB, (2004) @No $ @ @ Neeft J.P.A., Knoef H.A.M., Onaji P., Behavior of tar in biomass gasification systems. Tar related problems and their solutions, November Report No.9919. 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