IJRDET - Proceedings

1.

Physical and Numerical Model Studies on Cavitation Phenomenon-A Study on Nagarjuna Sagar Spillway

Authors: Rajasekhar. P, Santhosh, Y V G, Soma Sekhar S

Abstract— The Spillway of Nagarjuna Sagar dam across Krishna River was severely eroded during the floods of 2009 due to cavitation which was resulted from the negative pressures developed over the spillway. On further investigation of the problem, it was found that there was a large deviation of the existed profile of the spillway from the design profile, which actually led to the development of negative pressures in such a magnitude that could create the problem. In the present study, an attempt has been made to assess the cavitation damage due to negative pressures in terms of their magnitudes and locations on the spillway of Nagarjuna Sagar dam using the physical model studies on it. At the same time, to check the consistency and the reliability of the results obtained from the physical model studies, numerical models were applied to the problem thus ensuring the accuracy of the results. Further, the output from the numerical models improves the adequacy of the results to obtain the data for more number of points which may not be possible physically. Moreover, the numerical model can be applied to study the similar problems in future to any dam by merely changing the profile equations of the spillways. There is still a lot of scope for the future studies in showing the possible, applicable and most economical remedial measures to reduce the damage caused by cavitation.

Keywords— Cavitation, Model Studies, Vapour Pressure, Piezometer, Cumec, Curvilinear flow.

References-

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References-

[1]. Arndt,R. E. A, (April 1977), Discussion on ‗‗Cavitation from surface irregularities in high velocity‘‘, ASCE Journal of Hyd Div, 103(HY4).

[2]. Ball,J. W, (October 1959),‖Hydraulic Characteristics of Gate Slots‖, ASCE Journal of Hyd Div.

[3]. Bruce M. Savage and Michael C. Johnson, (August 2001), ―Flow Over Ogee Spillway: Physical and Numerical Model Case Study‖, Journal of Hydraulic Engineering, American Society of Civil Engineers, pp. 640-649.

[4]. Colgate, D., (V), ―Cavitation Damage in Hydraulic Structures,‖ International Conference on Wear of Materials, St. Louis, MO, pp. 433-438.

[5]. Corlin,B.; Larsen,P, (1979), Experience from some overflow and side spillway,13th ICOLD,Q 50,R37

[6]. Design and construction features of selected dams in India, publication no. 138, Central Board of Irrigation and Power, October 1979.

[7]. Ethembabaoglu,S., (1979),‖Some characteristics of static pressures in the vicinity of slots‖, 13th ICOLD, Q 50, R 29.

[8]. Falvey,H. T., (April1990), ―Cavitation in Chutes and Spillways‖, USBR Engineering Monograph No 42,.

[9]. Lowe III, J.; Bangash,H. D.;(1979), Chao,P. C.,‖Some experiences with high velocity flow at Tarbela dam project‖,13th ICOLD,Q 50,R 13.

1-10

2.

Rainfall-Runoff relationship in Central Kabul sub basin using NRCS-CN and Remote Sensing

Authors: Rajasekhar.P, Folad Ahmadzai,Lakshmi Sruthi.P

Abstract— Rainfall runoff is an important component contributing significantly to the hydrological cycle, design of hydrological structures and morphology of the drainage system. Estimation of the same is required in order to determine and forecast its effects. Estimation of Direct rainfall runoff is always efficient but is not possible for most of the location at desired time. Use of remote sensing and GIS technology can be used to overcome the problem of conventional method for estimating runoff caused due to rainfall. In this paper, modified National Resource conservation Service (NRCS) CN model is used for rainfall runoff estimation that considers parameter like slope, vegetation cover, area of watershed. Therefore, geographic information systems (GIS) are now being used in combination with the NRCS-CN method.NRCS-CN method is accepted and used worldwide because of 1). It’s simplicity, 2).Predictability, 3). It’s stability, 4). Because it relies only in one parameter and 5). It’s responsiveness. In most of cases, HSG and LULC maps were generated by graphical analyzing software such as ArcGIS, Imagin, Civil 3D. Central Kabul sub basin covers an area of near 419 km2 . For even more facilitating future analysis and studies Central Kabul sub basin was divided into five sections (Northern, Southern, Central, Eastern and Western) parts. Values of Curve Number for each of above-mentioned sections were calculated and by using NRCS-CN equations, the values for runoff for sub basin and its respective sections were calculated for most probable precipitation intervals. The Curve Number values are ranging from 54 to 71. Remote Sensing provided a powerful tool for estimating Curve Number values in Central Kabul sub basin.

Keywords— Runoff, ArcGIS, Curve Number, Hydrological soil group, Antecedent soil Moisture.

References-

[1]. Kirkby, M.J. 1985. Hillslope hydrology. In Hydrological forecasting, M.G. Anderson and T.B. Burt, eds. John Wiley and sons, New York, New York, 37-75

[2]. Harton, R.E 1933. The role of infiltration in the Hydrologic cycle. Transactions of the American geophysical union, 14: 446-460.

[3]. Mockus (1949) and Andrews (1954), National Engineering Handbook, Section 4, Department of agriculture, United States of America

[4]. Freeze, R. A. 1980. A stochastic- conceptual analysis of rainfall-runoff processes on a hill slope. Water resource research 16(2): 391-408.

[5]. Torge Tunnermieir and Dr. George Houben, 2003. Hydrology of the Kabul Basin, Part I, Geology, aquifer characteristics, climate, and hydrology.

[6]. Nayak, Verma and Hema, 2012 ―SCS curve number method in Narmada basin‖, INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES , Volume 3, No 1, 2012, p: 219-228

[7]. Jeffry and Koichiro, 2012 ―Analysis of CN, land use and land cover changes in the Jobaru river basin, Japan‖, ARPN Journal of Engineering and Applied Science, Vol. 7, No.7 pp.787-793, July 2012

[8]. Amy Hillier, 2011. Manual for working with ArcGIS 10.

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3.

Assessment of Confined Aquifer Parameters of North Kabul Sub-Basin of Kabul Basin

Authors: Rajasekhar.P, Mansoor Manssori.A Shivakumar.G

Abstract— Assessment of confined aquifer parameters, namely, transmissivity T, storage coefficient S and hydrological boundaries, from pump-test data has been a continual field of research. Several conventional and computer based methods are available for analyzing pumptest data. A simple method by Sushil K. Singh has been presented for precise determination of aquifer parameters using early drawdown data. The method does not require curve matching, initial guess of the parameters, or special care to check for u< 0.01, and the computations involved can be performed on a calculator. This paper shows that these early drawdown data, especially in the neighborhood of u= 0.43, can yield accurate values of aquifer parameters. The present method converges with the Cooper-Jacob method when the late drawdown data are considered. The principle objective of this study is to estimate the confined aquifer parameters of the North Kabul sub-basin of Kabul city as accurate, reliable and economical as possible. Also to determine the parameters, hydrological boundaries and their respond to pumping with a method with site applicability and with short time of pump test, with a method applicable to time and resource constraints. The first study to estimate the aquifer parameters from pump test was analytically derived by Theis (1935), which is valid for groundwater radial unsteady flow based on the Darcy law. Later, a large number of graphical and analytical methods (Cooper and Jacob 1946, Chow 1952, Walton 1962, Papadopulos and Cooper 1967, Saleem 1970, Wikramaranta 1985, Yeh 1987b, Şen 1988) and numerical methods (Rai 1985, Yeh 1987a, Şen 1986, El-Khatib 1987, Bourdet et al 1989, Srivastava and Guzman-Guzman 1994, Singh 2001, Mesut Cimen 2008) based on the Theis equation are proposed by many scientists. Both graphical and analytical, and numerical methods have their own merits and demerits. Present method (Sushil K. Singh) for estimating confined aquifer parameters uses a few early drawdowns to yield accurate values. It enables estimation of aquifer parameters from short duration pump-test data or initial data recorded during an abandoned pump test, which might otherwise be considered inappropriate for reliable estimation of the aquifer parameters. Using the present method the confined aquifer parameter of Kabul Basin is estimated in 14 points. The T and S values estimated by this method have a very little difference between each other. In general based on average we can say that the transmissivity of confined aquifer of North Kabul sub-basin is 94.76m^/day and the storage coefficient is 0.00241. The reliability of these values is judged by calculation of the Standard Error of Estimate (SEE) considering variartions of observed and computed drawdown for early as well as late drawdown data. The minimum value of SEE = 6.1 * 10^-4 and the maximum value is 1.46 * 10^-3. These values shows the reliability of this method compared to other existing methods in the literature while this method used early drawdown data and again reproduced the early and late time drawdown data using estimated aquifer parameters(T&S) with such a SEE values. None of the above mentioned methods bearing such a SEE values on published drawdown data. As the confined aquifer of North Kabul sub-basin is like fossil there is no leakage between confined and unconfined is reported and also there is no recharge boundary is available [JICA]. The hydrological impervious boundary of the aquifer is not determined because of the location of the observation and pumped wells far from the boundary and no well is located near to the boundary.

Keywords—Confined Aquifer, Cooper Jacob Method, Transmissivity, Storage Coefficient, Standard Error of Estimate.

References-

[1]. Ajayi, Owolabi and T. O. Obilade., (1989). ―Numerical Estimation of Aquifer Parameters Using Two Observation wells.‖ Journal of Hydraulic Engineering, Vol. 115, ASCE, 982-988.

[2]. ―Availability of Water in the Kabul Basin, Afghanistan[2010]‖, USGS, p(1-4).

[3]. Bayless, Kristi and Avdhesh K. Tyagi1, (2005). ―Aquifer Parameters in Confined Porous Media.‖School of Civil and Environmental Engineering, Oklahoma State University.Published on ASCE.

[4]. Çimen, Mesut (2009). ―Effective Procedure for Determination of Aquifer Parameters from Late Time-Drawdown Data.‖ Journal of Hydrologic Engineering, Vol. 14, ASCE, 446-452.

[5]. Çimen, Mesut (2008). ―Confined Aquifer Parameters Evaluation by Slope-Matching Method.‖ Journal of Hydrologic Engineering, Vol. 13, ASCE, 141-145.

[6]. M. Amin Akbari, Mohammad Tahir, David W. Litke, and Michael P. Chornack ―Ground-Water Levels in the Kabul Basin, Afghanistan, 2004–2007‖, USGS, p(1-25)

[7]. Matanga, G. B. Member (1995), ―Characteristics in Evaluating Stream Fumctions in Groundwater Flow‖ Journal of Hydrologic Engineering, Vol. 1, ASCE, 49-53.

[8]. Mohammad Hassan Saffi (2011), ―Groundwater natural resources and quality concern in Kabul Basin, Afghanistan‖ DACAAR, 1-14.

[9]. Ojha,C. S. P., (2003). ―Aquifer Parameters Estimation Using Artesian Well Test Data.‖ Journal of Hydrologic Engineering, Vol. 9, ASCE, 64-67.

[10]. Papadopulos, Istavros S. An Cooper, Hiltonh J., (1967). ―Drawdown in a Well of Large Diameter.‖ Water Resources Division, U.S. Geological Survey Washington, D.C., Vol. 3, 241-244.

[11]. Raghunath, H. M., (2007). Groundwater. 3rd. edition., New Age Publishers, New Delhi

[12]. Robert E. Broshears, M. Amin Akbari, Michael P. Chornack, David K. Mueller, and Barbara C. Ruddy 2005, ―Inventory of Ground-Water Resources in the Kabul Basin, Afghanistan‖, USGS, p(9-25)

[13]. Sen, Zekai, (1996). ―Volumetric Leaky-Aquifer Theory and Type Straight Lines‖ Journal of Hydraulic Engineering, Vol. 122, ASCE, 272-280.

[14]. Singh, Sushil K., (2009). ―Simple Method for Quick Estimation of Leaky-Aquifer Parameters.‖ Journal of Irrigation and Drainage Engineering, Vol. 136, ASCE, 149-153.

[15]. Singh, Sushil K., (2003). ―Storage Coefficient and Transmissivity from Residual Drawdowns.‖ Journal of Hydraulic Engineering, Vol. 129, ASCE, 637-644.

[16]. Singh, Sushil K., (2002). ―Aquifer Boundaries and Parameter Identification Simplified.‖ Journal of Hydraulic Engineering, Vol. 128, ASCE, 774-780.

[17]. Singh, Sushil K., (2001). ―Identifying Impervious Boundary and Aquifer Parameters From Pump-Test Data.‖ Journal of Hydraulic Engineering, Vol. 127, ASCE, 280-285.

[18]. Singh, Sushil K., (2000). ―Simple Method for Confined Aquifer Parameter Estimation.‖ Journal of Irrigation and Drainage Engineering. Vol. 126 (6), ASCE, 336-338.

[19]. ―The Study on Groundwater Resources Potential in Kabul Basin.‖ Japan International Cooperation Agency (JICA), all reports(1-6).

[20]. Todd, David K., and Larry W. Mays (2005). Groundwater Hydrology. 3rd. ed,Wiley, New York.

[21]. Ward, Nicholas Dudley and Fox Colin, (2011). ―Identification of Aquifer Parameters from Pumping Test Data with Regard for Uncertainty.‖ Journal of Hydrologic Engineering, Vol. 17, ASCE, 769-781.

[22]. ―Water for Kabul Consultants 2011‖, ―Extension of the Kabul Water Supply System.‖, p(1-161).

[23]. Wikramaratna, R. S., (1985). ―A New Type Curve Method for the Analysis of Pumping Tests in Large-Diameter Wells.‖ Water Resources Research, Vol. 21, American Geophysical Union. 261-264.

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4.

Ground water Evaluation of Chinnar watershed (Koneri sub - watershed) Perambalur Dt With Arc GIS Platform

Authors: Dr.D.Srinivasan, T.Velmurugan

Abstract— Thirty groundwater samples have been collected from Chinnar watershed (Koneri sub-water shed), a purely hard rock terrain in south India for hydro chemical investigations to understand the chemical quality of groundwater for drinking and irrigation purposes .the quality of groundwater has been assessed by using SAR, RSC, Piper and USSL diagrams. Spatial analyst on extended module of ArcGIS 9.3 was used to find out the spatial behavior of the groundwater parameters.

Key Words – Chinnar water shed groundwater quality, sodium adsorption, residual sodium carbonate, and spatial distribution

References-

[1] Kelley, W. P.: Permissible composition and concentration of irrigation waters,Proc. ASCE, 1940, 66, 607.

[2] Handa, B. K.: Modified classification procedure for rating irrigation waters, Soil

[3] Wilcox. L. V.: The quality water for irrigation use. US Dept. Agric. Bull., 1948, 1962, 40. Sci., 1964, 98, 264- 269.

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[5] Hem, J. D.: Study and interpretation of the chemical characteristics of natural water. USGS Water Supply Paper, 1985, 2254, pp 117-120.

[6] Zaporozee, A.: Graphical interpretation of water quality data, groundwater,1972, 10, 32-43.

[7] Zaporozee, A.: Graphical interpretation of water quality data, groundwater,1972, 10, 32-43.

[8] Stiff Jr., H. A.: The interpretation of chemical water analysis by means of patterns, J. Petrol. Technol., 1940, 3, 15-16.

[9] Subramanian. Hydro geological studies of the coastal aquifers of Tiruchendur, Tamil nadu. PhD thesis, Manonmanian sundaranar University, Thiruneveli, 1994.

[10] Todd, D. K.: Groundwater Hydrology, Wiley, New York, 1980, 2nd edn. P-315.

[11] Director of Census Operations. District Census Handling of Karnataka, Census of India, 1991.

[12] Standard method for examination of water and wastewater, American PublicHealth Association, NW, DC 20036, 1994.

[13] Piper, A. M.: A graphic procedure I the geochemical interpretation of water analysis, USGS Groundwater Note no, 1953, 12.

[14] Back, W.; Hanshaw, B. B.: Advances in hydroscience. In chemical Geohydrology, Academic Press, New York, 1965, Vol. 11, p

[15] Schroeder. H. A.: Relations between hardness of water and death rates from certain chronic and degenerative diseases in the United States, J. Chron disease,1960, 12:586-591

[16] Sawyer G. N.; McCarthy D. L.: Chemistry of sanitary Engineers, 2nd ed, McGraw Hill, New York, 1967, p-518.

[17] Wicox, L. V.: Classification and use of irrigation waters, US Department of Agriculture, Washington Dc, 1995, p-19

[18] Eaton, E. M.: Significance of carbonate in irrigation water. Soil Sci, 1950, 69 12-133.

[19] Kumaresan, M.; Riyazuddin: Major ion chemistry of environmental samples around sub- urban of Chennai city, Curr, Sci,, 2006, Vol. 91. No 12

[20] Standard methods of analysis of water and waste water(APHA) (1998)., American Public Health Association, Washington D.C.

[21] Standard methods of analysis of water and waste water(APHA) (1998)., American Public Health Association, Washington D.C.

[22] Gadhave A.G., Thorat D.G. and Uphade B.K.( 2008), Rasayan J. Chem., 1(4),853

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[30] APHA, AWWA standards methods for the examinations of water and waste water, American public health association, 1998, Washington D.C

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5.

Review Paper on Effect of Cylinder Block Fin Geometry on Heat Transfer Rate of Air-Cooled 4S SI Engine

Authors: Arvind S.Sorathiya, Ashishkumar N. Parmar, Pravin P. Rathod

Abstract— Indian two-wheeler market is the world's second biggest market. Among the 3 segments (motorcycles, scooters and mopeds) of the Indian two wheeler market, major growth trends have been seen in the motorcycle segment over the last four to five years due to its resistance and balance even on bad road conditions. In Indian motorcycles, Air-cooling is used due to reduced weight and simple in construction of engine cylinder block. As the air-cooled engine builds heat, the cooling fins allow the wind and air to move the heat away from the engine. Low rate of heat transfer through cooling fins is the main problem in this type of cooling. The main of aim of this work is to study various researches done in past to improve heat transfer rate of cooling fins by changing cylinder block fin geometry, climate condition and material.

Keywords—Air Cooling, ANSYS, CFD, Cylinder block, Engine Performance, Fins, Heat Transfer, Internal Combustion Engine.

References-

[1] Biermann, A. E. and B. Pinkel (1934). Heat Transfer from finned metal cylinders in an air stream, NACA Report No.488

[2] J.C.Sanders, et al. (1942). Cooling test of an air-cooled engine cylinder with copper fins on the barrel, NACA Report E-103

[3] Denpong Soodphakdee, et al. (2001). "A Comparison of Fin Geometries for Heatsinks in Laminar Forced Convection Part 1 - Round, Elliptical, and Plate Fins in Staggered and In-Line Configurations." The International Journal of Microcircuits and Electronic Packaging 24(1).

[4] Fernando Illan and M. Alarcon (2002). "Optimization of Annular Cylindrical and Spherical Fins in an Internal Combustion Engine under Realistic Conditions." Journal of Thermal Science and Engineering Applications 2.

[5] A. Bassam and K. A. Hijleh (2003). "Enhanced Forced Convection Heat Transfer from a Cylinder Using Permeable Fins." ASME Journal of Heat Transfer 125.

[6] Yoshida Masao, et al. (2005). "Air-Cooling Effects of Fins on a Motorcycle Engine." Nippon Kikai Gakkai Ronbunshu B Hen (Transactions of the Japan Society of Mechanical Engineers Part B) (Japan) 17(9): 2324-2330.

[7] C. Han-Taw and C. Jui-Che (2006). "Investigation of naturalconvection heat transfer coefficient on a vertical square fin of finned-tube heat exchangers." International Journal of Heat and Mass Transfer 49(17-18): 3034-3044.

[8] A. Mohammadi, et al. (2008). "Analysis of local convective heat transfer in a spark ignition engine." International Communications in Heat and Mass Transfer 35.

[9] D.G.Kumbhar, et al. (2009). Finite Element Analysis and Experimental Study of Convective Heat Transfer Augmentation from Horizontal Rectangular Fin by Triangular Perforations. Proc. of the International Conference on Advances in Mechanical Engineering.

[10] N.Nagarani and K. Mayilsamy (2010). "EXPERIMENTAL HEAT TRANSFER ANALYSIS ON ANNULAR CIRCULAR AND ELLIPTICAL FINS." International Journal of Engineering Science and Technology 2(7): 2839-2845.

[11] A. T. Pise and U. V. Awasarmol (2010). "Investigation of Enhancement of Natural Convection Heat Transfer from Engine Cylinder with Permeable Fins." International Journal of Mechanical Engineering & Technology (IJMET) 1(1): 238-247.

[12] P. Agarwal, et al. (2011). Heat Transfer Simulation by CFD from Fins of an Air Cooled Motorcycle Engine under Varying Climatic Conditions. Proceedings of the World Congress on Engineering.

[13] S. Barhatte, et al. (2011). "Experimental and Computational Analysis and Optimization for Heat Transfer through Fins with Different Types of Notch." Journal of Engineering Research and Studies E-ISSN 976: 7916.

[14] M. Matkar and P. M. Ravanan (2011). Thermal Analysis of Copper Fin by FEA. International Conference on Operations and Quantitative Management. Nasik, India.

[15] G.Raju, et al. (2012). "Optimal Design of an I.C. Engine Cylinder Fin Arrays Using a Binary Coded Genetic Algorithms." International Journal of Modern Engineering Research (IJMER) 2(6): 4516-4520.

[16] U. Magarajan, et al. (2012). "Numerical Study on Heat Transfer of Internal Combustion Engine Cooling by Extended Fins Using CFD." International Science Congress Association 1(6): 32-37.

[17] A. Mishra, et al. (2012). "Heat Transfer Augmentation of Air Cooled Internal Combustion Engine Using Fins through Numerical Techniques." Research Journal of Engineering Sciences ISSN 2278: 9472.

[18] J.A. Paul, et al. (2012). "Experimental and Parametric Study of Extended Fins in the Optimization of Internal Combustion Engine Cooling Using CFD." International Journal of Applied Research in Mechanical Engineering (IJARME) 2(1).

[19] G. Babu and M. Lavakumar (2013). "Heat Transfer Analysis and Optimization of Engine Cylinder Fins of Varying Geometry and Material." IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) 7(4): 24-29.

[20] S.S.Chandrakant, et al. (2013). "Numerical and Experimental Analysis of Heat Transfer through Various Types of Fin Profiles by Forced Convection." International Journal of Engineering Research & Technology (IJERT) 2(7).

[21] A.R. Kumar, et al. (2013). "Heat Transfer Analysis in the Cylinder Head of a Four-Stroke Si Engine." International Journal of Engineering Research & Technology (IJERT) 2(5).

[22] R.P. Patil and P. H. M. Dange (2013). "Experimental and Computational Fluid Dynamics Heat Transfer Analysis on Elliptical Fin by Forced Convection." International Journal of Engineering Research & Technology (IJERT) 2(8).

[23] N.P.R. Rao and T. V. Vardhan (2013). "Thermal Analysis of Engine Cylinder Fins By Varying Its Geometry and Material." International Journal of Engineering 2(8).

[24] [24.] S. Wange and R. Metkar (2013). "Computational Analysis of Inverted Notched Fin Arrays Dissipating Heat by Natural Convection." International Journal of Engineering and Innovative Technology (IJEIT) 2(11).

[25] V. GANESAN, (2008). I C Engines, McGraw-Hill Education (India) PVT Limited.

[26.] R. K. Rajput, (2007). Heat & Mass Transfer (M.E.), S. Chand Limited.

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6.

Delay Adjusted Low Power Pattern Generator for BIST

Authors: B.Syed Ibrahim, M.Rajendiran, D.Sankareshwaran

Abstract— Most of the pattern generators used for BIST are sequential circuits consisting of flip-flops. LFSR is one of the most widely used pattern generators comprising of a series of flip-flops with additional xor taps. The use of flip-flops in circuits increases circuit complexity, propagation delay, power consumption and requirement of additional external signals such as reset and clock. Here in this paper we propose a new combinational architecture for pattern generator which constitutes only few logic gates and not flip-flops. Thus this method aids in providing an efficient and effective way in greatly reducing power consumption, delay and complexity.

Keywords-power consumption;

References-

[1] Y. Zorian, "A distributed BIST control scheme for complex VLSI devices," Proc. 11th IEEE VLSI Test Symp., Atlantic City, New Jersy, pp.4-9, Apr. 1993.

[2] T. V. Ramabadran and S. S. Gaitonde, ―A tutorial on CRC computations,‖ IEEE Micro., Aug. 1988.

[3] R. E. Blahut, Theory and Practice of Error Control Codes. Reading, MA: Addison-Wesley,1984.

[4] W. W. Peterson and D. T. Brown, ―Cyclic codesfor error detection,‖ Proc. IRE, vol. 49, pp. 228–235, Jan. 1961.

[5] N. Oh, R. Kapur, and T. W. Williams, ―Fast speed computation for reseeding shift register in test pattern compression,‖ IEEE ICCAD, pp. 76–81, 2002.

[6] Manohar Ayinala and Keshab K. Parhi, ―High-Speed Parallel Architectures for Linear Feedback Shift Registers,‖ IEEE Transactions On Signal Processing, vol. 59,pp. 4459-4469, Sep. 2011.

[7] Tomoaki Sato Rena Sakuma, Daisuke Miyamori, and Masa-aki Fukase, ―Waved-PRNG for a Wave-Pipelining Test Circuit,‖ 12th NASA Symposium on VLSI Design, Oct. 2005.

[8] Pong P. Chu and Robert E. Jones, ―Design Techniques of FPGA Based Random Number Generator,‖ Nasa Grant NAG3-2040.

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7.

Analysis of Automatic Crack Detection in Metal

Authors: P. S. S. Akilashri, Dr. E. Kirubakaran

Abstract- The streak image caused by metal implants degrades the image quality and limits the applications of metal, that results in loss of image quality. The proposed method uses LDA with fractional wavelet transformation to extract the texture features. Diffusion method used in this work is efficient for detecting the pitting and laminating defects. By using Gradient Magnitude and structure coherance the exact level of defect is found.

Keywords-- LDA, Lamination, Automatic metal crack detection.

References-

[1] V. Märgner, H. El-Abed, P. Meinlschmidt, F. Schlüter “Quality Control of Invisible Defects in the Laminating Process using a New Thermo graphic Online System” IEEE 2007.

[2] Tae-Kyun Kima, Hyunwoo Kima, Wonjun Hwanga, Josef Kittlerb “Component-based LDA Face Description for Image Retrieval and MPEG-7 Standardization” Feb 2005.

[3] Sindhu Ghanta, Tanja Karp and Sangwook Lee, “Wavelet Domain Detection of Rust in Steel Bridge Images”, IEEE 2011.

[4] Mariana P. Bento, Fatima N. S. De Medeiros, Ialis C. De Paula Jr. and Geraldo L.B. Ramalho “Image Processing Techniques applied for Corrosion Damage Analysis” 20th Sep 1995.

[5] Vikram Garg Department of Electrical Engineering IIT Delhi “Algorithms for Image Processing and Image Analysis” 19 th Dec 2008.

[6] Zhonghua Shao Lihong Liang “Automatic Segmentation of Cracks in X-ray Image Based on OTSU and Fuzzy Sets” 2010 3rd International Congress on Image and Signal Processing (CISP2010)

[7] Ioannis Valavanis, Dimitrios Kosmopoulos” Multiclass Defect Detection and Classification In Weld Radiographic Images Using Geometric And Texture Features” 2010 Elsevier Ltd.

[8] E. Padillaa, N. Chawlaa,, L.F. Silvab, V.R. dos Santosb, S. Paciornikb” Image analysis of cracks in the weld metal of a wet welded steel joint by three dimensional (3D) X-ray micro tomography”25 June 2013

[9] Ge Liling, Zhang Yingjie” Hierarchical Segmentation Approach to Detection of Defects on Welding Radiographic Images” 2009 IEEE.

[10] E.N. Codaro, R.Z. Nakazato, A.L. Horovistiz, L.M.F. Ribeiro, R.B.Ribeiro, L.R.O. Hein “An image analysis study of pit formation on Ti-/6Al-/4V” 28 March 2002

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8.

CBIR By Integration of Color and Texture Features

Authors:Vaishali Khandave, Nitin Mishra

Abstract— Content Based Image Retrieval (CBIR) is the retrieval of images based on visual features such as color, texture and shape. Image retrieval using single feature cannot provide good solution for accuracy and efficiency. High level feature describes the concept of human brain will reduce the query efficiency and low level features such as color, texture and shape will reduce query accuracy, so it is better way to use multi features for image retrieval. The most important visual features are Color and texture. In this paper technique used for retrieving the images based on their content namely color, texture and combination of both color and texture. The technique verifies the superiority of image retrieval using multifeature than the single feature.

Keywords— Combined feature, Content Based Image retrieval (CBIR), Co-occurrence matrix, Euclidian distance

References-

[1] Swapnalini Pattanaik, Prof.D.G.Bhalke ,”Beginners to Content Based Image Retrieval”International Journal of Scientific Research Engineering &Technology (IJSRET) Volume 1 Issue2 pp 040-044 May 2012.

[2] Ruziana Mohamad Rasli, T Zalizam T Muda, Yuhanis Yusof, Juhaida Abu Bakar, “Comparative Analysis of Content Based Image Retrieval Technique using ColorHistogram. A Case Study of GLCM and K-Means Clustering”,Third International Conference on Intelligent Systems Modelling and Simulation,2012

[3] Fan-Hui Kong, “Image Retrieval Using Both Color And Texture Features”, Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, Baoding, 12-15 July 2009.

[4] Pushpa B. Patil, Manesh B. Kokare,” Relevance Feedback in Content Based Image Retrieval: A Review”, Journal of Applied Computer Science & Mathematics, no. 10 (5) /2011, Suceava

[5] Rafael C.Gonzalez, Richard E.Woods “Digital Image Processing”,Third Edition

[6] P.W. Huang, S.K. Dai, “Image retrieval by texture similarity”, Pattern Recognition 36 (3) (2003) 665–679.

[7] N. Jhanwar, S. Chaudhuri, G. Seetharaman, B. Zavidovique,” Content based image retrieval using motif co-occurrence matrix”, Image and Vision Computing 22 (2004) 1211–1220

[8] M.Babu Rao, Dr. B.Prabhakara Rao, Dr. A.Govardhan, “CTDCIRS: Content based Image Retrieval System based on Dominant Color and Texture Features”, International Journal of Computer Applications (0975 – 8887) Volume 18– No.6, March 2011

[9] Aman Chadha, “Comparative Study and Optimization of FeatureExtraction Techniques for Content based Image Retrieval”,Department of Electrical and Computer Engineering, University of Wisconsin-Madison, International Journal of Computer Applications (0975 – 8887) Volume 52– No.20, August 2012

[10] Rishav Chakravarti, Xiannong Meng, “A Study of Color Histogram Based Image Retrieval”,Sixth International Conference on Information Technology,2009

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9.

Discussion Summarization

Authors: N. Lalithamani, K. Alagammai, Kolluru Kamala Sowmya, L. Radhika, Raga Supriya Darisi, S. Shanmugapriya

Abstract — Discussion summarization is the process of condensing a text document which is a collection of discussion threads, using CBS (Cluster Based Summarization) approach in order to create a relevant summary which enlists most of the important points of the original thematic discussion, thereby providing the users, both concise and comprehensive piece of information. This outlines all the opinions which are described from multiple perspectives in a single document. This summary is completely unbiased as they present information extracted from multiple sources based on a designed algorithm, without any editorial touch or subjective human intervention. Extractive methods used here, follow the technique of selecting a subset of existing words, phrases, or sentences in the original text to form the summary. An iterative ranking algorithm is followed for clustering. The NLP (Natural Language Processing) is used to process human language data. Precisely, it is applied while working with corpora, categorizing text, analyzing linguistic structure. Thus, the quick summary is aimed at being salient, relevant and non-redundant. The proposed model is validated by testing its ability to generate optimal summary of discussions in Yahoo Answers. Results show that the proposed model is able to generate much relevant summary when compared to present summarization techniques.

Keywords — Bi-Type graph model, clustering, discussion summarization, ranking, score calculation, TCC approach

References-

[1] Xiaoyan Cai and Wenjie Li, ―Ranking Through Clustering: An integrated approach to multi-document summarization‖, IEEE Transactions on Audio, Speech and Language Processing, Vol. 21, No. 7, July 2013.

[2] S. Fisher and B. Roark, ―Query-focused summarization by supervised sentence ranking and skewed word distributions,‖ in Proceedings Document Understanding Conference, 2006

[3] Xiaoyan Cai and Wenjie Li,‖Mutually reinforced manifold-ranking based relevance propagation model for query-focused multidocument summarization‖, IEEE Transactions on Knowledge And Data Engineering, Vol. 25, No. 8, August 2013.

[4] Hien Nguyen, Eugene Santos, and Jacob Russell, ―Evaluation of the impact of user-cognitive styles on the assessment of text summarization‖ IEEE Transactions on Systems, Man and Cybernetics, Vol. 41, No. 6, November 2011.

[5] Chien Chin Chen and Meng Chang Chen, ―A content anatomy approach to temporal topic summarization‖ IEEE Transactions on Knowledge And Data Engineering, Vol.24, No. 1, January 2012.

[6] Elias Iosif and Alexandros Potamianos, ―Unsupervised semantic similarity computation between terms using web documents‖ IEEE Transactions on Knowledge And Data Engineering, Vol. 22, No. 11, November 2010.

[7] Davide Falessi, Giovanni Cantone, and Gerardo Canfora, ―Empirical principles and an industrial case study in retrieving equivalent requirements via natural language processing Techniques‖ IEEE Transactions on Software Engineering, Vol. 39, No. 1, November 2013.

[8] Manning, C.D., Raghavan, P. and Schütze, H. (2009) An Introduction to Information Retrieval. Cambridge, England: Cambridge University Press. International Journal of Recent Development in Engineering and Technology Website: www.ijrdet.com (ISSN 2347 - 6435 (Online) Volume 2, Issue 1, January 2014) 62

[9] X. Wan, ―Towards a Unified Approach to Simultaneous SingleDocument and Multi-Document Summarizations,‖ Proceedings 23rd International Conference Computational Linguistics, pp. 1137-1145, http://portal.acm.org/ citation.cfm?id=1873781.1873909, 2010.

[10] X. Wan, ―An Exploration of Document Impact on Graph-Based Multi-Document Summarization,‖ Proceedings Conference Empirical Methods in Natural Language Processing, pp. 755-762, http://portal.acm.org/ citation.cfm?id=1613715.1613811, 2008.

[11] Celikyilmaz and D. Hakkani-Tur, ―Discovery of topically coherent sentences for extractive summarization,‖ in Procedings 49th Association for Computational Linguistics Conference 2011, 2011, pp. 491–499.

[12] H. Lin and J. Bilmes, ―A class of sub modular functions for document summarization,‖ in Proceedings 49th Association for Computational Linguistics Conference, 2011.

[13] Mani and M. T. Maybury, Advances in Automatic Text Summarization. Cambridge, MA: MIT Press, 1999.

[14] Automatic Summarization online: http://www.en.wikipedia.org/wiki/Automatic_summarization. [15] L. Antiqueris, O. N. Oliveira, L. F. Costa and M. G. Nunes, ―A complex network approach to text summarization,‖ Information Sciences, vol. 175, no.5, pp. 297–327, February 2009.

[16] Shasha Xie, Yang Liu, ‖ Using Corpus And Knowledge-Based Similarity Measure In Maximum Marginal Relevance For Meeting Summarization, ‖ The University of Texas at Dallas, Richardson, TX, USA.

[17] Jaime Carbonell and Jade Goldstein, ―The use of MMR, diversitybased reranking for reordering documents and producing summaries,‖ in Proceedings of Special Interest Group in Information Retrieval, 1998.

[18] A. Nenkova, ―Automatic Text Summarization of Newswire: Lessons Learned from the Document Understanding Conference,‖ Proceedings 20th National Conference on Artificial Intelligence (AAAI), pp. 1436-1441, 2005.

[19] J. G. Corbonell and J. Goldstein, ―The use of MMR, diversity-based reranking for reordering documents and producing summaries,‖ in Proceedings 21st Special Interest Group in Information Retrieval Conerenc., 1998, pp. 335–336

[20] V. Qazvinian and D. R. Radev, ―Scientific paper summarization using citation summary networks,‖ in Proceedings 17th Computational LinguisticsConference, 2008, pp. 689–696.

[21] D.R. Radev and K.R. McKeown, ―Generating Natural Language Summaries from Multiple Online Sources,‖ Computational Linguistics, Vol. 24, No. 3, 1998, pp. 469-500.

[22] Summarizing Text for Intelligent Communication Symposium (Dagstuhl, Germany, 1993); http://www.ik.fhhannover.de/ik/projekte/Dagstuhl/Abstract

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10.

Spatio-Temporal Analysis of Urban Sprawl in Greater Hyderabad Region and Its Impacts on Rural Urban Fringe Areas Using Geoinformatics Technology.

Authors: T. Phanindra Kumar, Dr. P. Kesav rao, Dr. V Madhava Rao, Yuva Kishore

Abstract- Urbanization is an index of transformation from traditional rural economies to modern industrial one. It is a progressive concentration of population in an urban unit. It takes place either in radial direction around a wellestablished city or linearly along the highways. This dispersed development along highways or surrounding the city and in rural countryside is generally referred as sprawl. Sprawl is a term that is often used to describe perceived inefficiencies of development, including disproportionate growth of urban areas and excessive leapfrog development. Sprawl is a cumulative result of many individual decisions and it requires not only an understanding of the factors that motivate an individual landowner to convert land, but also an understanding of how these factors and individual landuse decisions aggregate over space. Some of the causes of the sprawl include - population growth, economy and proximity to resources and basic amenities. The measurement and monitoring of land-use change are crucial to government officials and planners who urgently need updated information and proper planning tools. The entropy method is most popularly used method for the measurement and monitoring of urban sprawl by the integration of remote sensing and GIS. The advantages of the entropy method are its simplicity and easy integration with GIS. GIS and remote sensing data along with collateral data help in analyzing the growth, pattern and extent of sprawl. With the spatial and temporal analyses it is possible to identify the pattern of sprawl and subsequently predict the nature of future sprawl. This project brings out the extent of sprawl taking place over a period of six years using GIS and Remote Sensing. An attempt was made to study the implications of urban sprawl on land-use & land-cover pattern of a typical regions located in Greater Hyderabad city & its surround rural-urban fringe areas in the state of Andhra Pradesh.

KeyWords- Land Use Planning, classification, NDVI, Change Detection, Shanons Entropy, Compactness Index etc.

References-

[1] Aggrey Daniel Maina Thuo(2010): Community and social responses to land use transformations in the Nairobi rural-urban fringe, Kenya, Field actions science reports(online), special issue(2010, online since 19 March2010,connectinon 12 October 2010.

[2] Alberti M (2005) the effects of urban patterns on ecosystem function. International Regional Science Review. 28, 168– 192.

[3] Barnsley, M.J., and. Barr, S.L. (1997). Distinguishing urban land use categories in fire spatial resolution land cover data using a graph-based, structural pattern recognition system. Computers environment and urban systems 21(3/4): 209-225.

[4] Barr. S., and Barnsley, M. (2000). Reducing structural clutter in land use mapping. Computers and geosciences 26:433-439.

[5] Bosselman, Fred P (1968): Alternatives to Urban Sprawl: Legal Guidelines for Governmental Action, the National Commission on Urban Problems, Research Report No 15, Washington, D.C 1968

[6] Burrough, Peter A, and Rachel A. McDonnell. 1998. Principles of Geographic Information Systems. New York: Oxford University Press.

[7] Batty M., Xie Y., Sun Z. The dynamics of urban sprawl. Working Paper Series, Paper 15, Centre for Advanced Spatial Analysis, University College, London (1999).

[8] Cervero, R. and Kockelman, K. (2002) Travel demand and the 3Ds: density, diversity and design, Transportation research part D, 2(3), 199-219.

[9] Cheng, J., Masser, I. Urban growth pattern modeling: a case study of Wuhan City, PR China. Landscape Urban Plan. (2003). 62, 199–217.

[10] Civco, D.L., Hurd, J.D., Wilson, E.H., Arnold, C.L., Prisloe, M, Eng. Remote Sensing, (2002).68 (10), 1083–1090.

[11] CST (Centre for sustainable transportation), (2002) Sustainable transportation performance indicators (STPI) Project report on phase 3 http://www.cstctd.org/CSTTThomepage.htm

[12] Elena G.I., and Nancy E.B (2004). Land use externalities, open space preservation, and urban sprawl. Regional science and urban economics 34: 705-725.

[13] Epstein, J., Payne, K., Kramer, E., 2002. Techniques for mapping suburban sprawl. Photogrammetry. Eng. Remote sensing. 63(9), 913–918.

[14] Fan, F., Wang, Y., Qiu, M. and Wang, Z. (2009) Evaluating the temporal and spatial urban expansion patterns of Guangzhou from 1979 to 2003 by remote sensing and GIS methods, International Journal of Geographical Science 23, 1371 – 1388.

[15] Fung, T. and LeDrew, E. Application of principal components Analysis change Detection, photogram metric Engineering and Remote sensing of Environment (1987).

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11.

Key Frame Extraction Using Features Aggregation

Authors: B. F. Momin, S. B. Pawar

Abstract— In Video Surveillance System, the surveillance of video in its different application such as performing real time online event detection, crime prevention, scene analysis and offline analysis and retrieval of interested events requires very huge computation and memory too. Key frame Extraction (KFE) is selection of frames which represents the object moves and changes in subsequent frames in the video. KFE may be used as pre-processing in surveillance application. Changes in frames are decided on the basis of frame’s features. To describe frame in better way, more than one feature are used. Visual Feature Differences are found which are calculated by considering local threshold and Frame Difference is by considering global threshold. Three features are calculated for finding Frame Difference. Three feature differences are then aggregated using aggregation functions. Different Weights for different features are set according to the contribution of corresponding feature in finding frame difference to get better results.

Keywords— Aggregation Function, Feature Extraction, Key frame, Thresholds, Video Summarization.

References-

[1] Q. Ji, Z. fong, Z. Xie and Z. Lu, ―Video Abstraction Based on Visual Attention Model and Online Clustering‖, Elsevier B.V. on Signal Processing: Image Communication, 28(2013), pp.241-253.

[2] N. Ejaz, T. Tariq and S. Baik, ―Adaptive Key Frame Extraction using an Aggregation Mechanism‖, Elsevier Inc J. Vis commun Image R. 23(2012), pp. 1031-1040.

[3] H. Shih, ―A Novel Attention- Based Key-Frame Determination Method‖, IEEE Trans. on Broadcasting, May 2013.

[4] J. Besocs, G. Cisneros, J. Martinez and J. Menendez, ―A Unified Model on Techniques on Video Shot Transition Detection‖, IEEE Trans. on Multimedia, Vol. 7 no. 2, pp. 293-307, 2005.

[5] J. Yu, M. Srinath, ―An Efficient Method for Scene Cut Detection‖, Elsevier Pattern Recognition Letters, 22(13), 2001.

[6] P. Kathiriya, D. Pipalia, G. Vasani, A. Thesiya and D. Varanva, ―Χ2 (Chi-Square) Based Shot Boundary Detection and Key Frame Extraction for Video‖, International Journal of Engineering and Science, ISSN: 2278-4721, Vol. 2, pp 17-21, Jan 2013.

[7] K. Sze, K. Lam and G. Qiu, ―A New Key Frame Representation for Video Segment Retrieval‖, IEEE Trans. on Circuits and Systems for Video technology, Vol. 15, no. 9, sept. 2005.

[8] R. Mishra and S. Singhai, ―A Review on Different Methods of Video Shot Boundary Detection‖, International Journal of Electrical and Electronics Engineering (IJEEE), Vol.1, pp. 46-57, Aug 2012.

[9] X. Zeng, W. Hu, W. Li, X. Zhang and B. Xu, ―Key-Frame Extraction using Dominant-Set Clustering‖, pp. 1285-1288.

[10] X. Zeng, W. Hu, W. Li, X. Zhang and B. Xu, ―Key-Frame Extraction using Dominant-Set Clustering‖, pp. 1285-1288.

[11] Y.-F. Ma, X.-S. Hua, L. Lu, and H.-J. Zhang, ―A generic framework of user attention model and its application in video summarization,‖ IEEE Trans. Multimedia, vol. 7, no. 5, pp. 907–919, Oct. 2005.

[12] B.T. Truong, S. Venkatesh, ―Video Abstraction: A Symentic Review and Classification‖, ACM Trans. Multimedia Comput.Commun. Appl. 3, 1, Article (Feb 2007).

[13] D. DeMenthon, V. Kobla, D. Doermann, ―Video summarization by curve simplification‖, in: Proc. ACM International Conference on Multimedia, NewYork, USA, 1998, pp. 211–218.

[14] S.E.D. Avila, A.B.P. Lopes, L.J. Antonio, A.d.A. Araújo, ―VSUMM: a mechanism designed to produce static video summaries and a novel evaluation method‖, Pattern Recognition Letters 32 (1) (2011) 56–68.

[15] J. Flusser, T. Suk, B. Zitove, Moments and Moment Invariants in Pattern Recognition, Wiley & Sons Ltd, 2009.

[16] J. Flusser, T. Suk, B. Zitove, Moments and Moment Invariants in Pattern Recognition, Wiley & Sons Ltd, 2009.

[17] Z. Li, G.M. Schuster, A.K. Katsaggelos, Minmax optimal video summarization, IEEE Transactions on Circuits and Systems for Video Technology 15 (10) (2005) 1245–1256.

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12.

Cuff less Continuous Non-Invasive Blood Pressure Measurement Using Pulse Transit Time Measurement

Authors: Surendhra Goli, Jayanthi T

Abstract— High Blood Pressure or Hypertension is the most common cause of heart disease and coronary artery disease. Hypertension is also a major risk factor for stroke, aneurysms of the arteries, peripheral arterial disease and is a cause of chronic kidney disease. And it is estimated that the number of patients in India with high blood pressure is likely to rise from about 140 million in 2008 to nearly 215 million by 2030. And it’s not just an old age problem anymore. Hypertension is rarely accompanied by any symptom, and its identification is usually through screening of continuous monitoring of blood pressure. Blood pressure measurement is performed either invasively by an intra arterial catheter or noninvasively by cuff sphygmomanometery. The invasive method is continuous and accurate but has increased risk, the cuff is safe but less reliable and infrequent. A reliable continuous non-invasive blood pressure measurement is highly desirable. While the possibility of using Pulse Transit Time (PTT) and Pulse Wave Velocity (PWV) were shown to have co-relation with arterial blood pressure (BP) and have been reported to be suitable for indirect BP measurement. Arterial blood pressure (BP) was estimated from Electrocardiography (ECG) and PPG waveform. PTT is a time interval between an R-wave of electrocardiography (ECG) and a photoplethysmography (PPG) signal. This method does not require an air cuff and only a minimal inconvenience of attaching electrodes and LED/photo detector sensors on a subject.PTT computed between the ECG R-wave and the maximum first derivative PPG was strongly correlated with systolic blood pressure (SBP) (R=0.734) compared with other PTT values, and the diastolic time proved to be appropriate for estimation diastolic blood pressure (DBP) (R = 0.731). Our proposed method can be used for continuous BP monitoring for the purpose of personal healthcare.

Keywords— Electrocardiography (ECG), Hypertension, Photoplethysmography (PPG), Pulse Transit Time(PTT), Pulse Wave Velocity(PWV).

References-

[1] Soo-young Ye, Gi-Ryon Kim, Dong-Keun Jung, Seong-wan Baik, and Gye-rok Jeon ―Estimation of Systolic and Diastolic Pressure using the Pulse Transit Time‖,World Academy of Science, Engineering and Technology 43 2010.

[2] MW Claridge, GR Bate, JA Dineley, PR Hoskins, T Marshall, DJ Adam, AW Bradbury and AB Wilmink, ‖A reproducibility Study of a TDI based method to calculate indices of arterial stiffness‖, Ultrasound in Medicine and Biology: 34(2): 2008, pp 215-220.

[3] Robin P Smith, Jerome Argod, Jean-Louis Pepin, Patrick A Levy, ‖Pulse transit time: an appraisal of potential clinical applications‖, Thorax, 1999, 54, pp 452-458.

[4] J McLaughlin, M McNeill, B Braun and P D McCormack, ‖Piezoelectric sensor determination of arterial pulse wave velocity‖, Physiological Measurement, 24, 2003, pp 693-702.

[5] Soo-young Ye, Do-un Jeong, ‖Relation between heart rate variability and pulse transit time according to anesthetic concentration‖, pp 566- 569.

[6] A. Hennig, A. Patzak, ― Continuous blood pressure measurement using pulse transit time‖, Somnologie 2013 · 17:104–110 DOI 10.1007/s11818-013-0617-x Springer-Verlag Berlin Heidelberg 2013.

[7] Parry Fung, Guy Dumont, Craig Ries, Chris Mott1, Mark Ansermino, ―Continuous Noninvasive Blood Pressure Measurement by Pulse Transit Time‖, 26th Annual International Conference of the IEEE EMBS San Francisco, CA, USA • September 1-5, 2004.

[8] Xiaochuan He, Rafik A. Goubran, Xiaoping P. Liu, ―Evaluation of the Correlation between Blood Pressure and Pulse Transit Time‖, 978-1-4673-5197-3/13/$31.00 2013 IEEE.

[9] Sujay Deb, Chinmayee Nanda1, D. Goswami, J. Mukhopadhyay and S. Chakrabarti1, ― Cuff-less Estimation of Blood Pressure using Pulse Transit Time and Pre-ejection Period‖, 2007 International Conference on Convergence Information Technology, 0-7695-3038- 9/07 IEEE,DOI 10.1109/ICCIT.2007.206.

[10] X. Teng and Y. Zhang, ―Continuous and noninvasive estimation of arterial blood pressure using photoplethysmographic approach,‖ in Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2003.

[11] Razvan A. Ciobotariu, Cristian Fosalau, Cristian Rotariu, ―Pulse Wave Velocity Measuring System using Virtual Instrumentation on Mobile Devices‖.

[12] http://www.japi.org/february_2013_special_issue_hypertension_gui delines/05_definition_and_classification.pdf

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13.

Texutural Characteristics and Depositional Environment of Olistostromal Sandstone of Ukhrul, Manipur

Author: Thokchom Devala Devi

Abstract-- Grain size analysis of olistostromal sandstone samples associated with Nagaland-Manipur Ophiolite Belt in and around Ukhrul Town which lies between 24º25΄- 25º10΄N latitude and 94º10΄-94º30΄E longitudes has been carried out to find out textural characteristics and depositional environment. These sandstones are highly variable in size and shape; texturally and mineralogical immature. Most of these sandstones have unimodal distribution. Median values vary between 0.3ф-3.75ф and mean size varies from 0.86ф-3.81ф. They are positively, negatively as well as nearly symmetrically skewed. Bivariate and multivariate analysis indicates diversity in the depositional environment. However marine and turbidity is the most dominant environment.

Keyword-- Grain size analysis, depositional environment, Nagaland-Manipur Ophiolite Belt, olistostromal sandstone

References-

[1] Acharyya,S.K. (1991): “Late Mesozoic Early Basin Evolution along the Indo-Burmese Range and Andaman Island Arc,” in Sedimentary Basins of India, Tectonics context. (Eds) Tandon, S.K. Charu C. Pant and Casshyap, S.M. Gyanodaya Prakashan, Nainital, p 104-113.

[2] Ashok K. S and Rupesh S. M. (2009a): Grain-size analysis and depositional environment of Lameta sediments exposed at Salbardi and Belkher, Amravati district, Maharastra and Betul district, Madhya Pradesh. Jour. Ind Ass.Sedimentologists, v. 28, No.1 (2009), p 73-83.

[3] Ashok K. S and Rupesh S. M. (2009b): Grain-size analysis and depositional pattern of Upper Gondwana Sediments (Early Cretaceous) of Salbari Area, Districts Amravati, Maharastra and Betul, Madhya Pradesh. Jour. Geol. Soc. Ind. V. 73, March 2009, p 393-406.

[4] Ashok K. S and Neloy Khare (2009): Granulometric Analysis of Glacial Sediments, Schirmacher Oasis, East Antarctica. Jour. Geol. Soc. Ind. V. 73, May 2009, p 609-620.

[5] Bagnold, R.A. (1946): Motion of waves in shallow water, interaction between waves and sand bottoms; Royal Soc. London, Pro. Ser. A vol. 187, p 1-18.

[6] Bagnold, R.A. (1956): The flow of cohesionless grains in fluids; Rol. Soc. London Phil. Trans. Ser. A vol. 249, p 235-297.

[7] Bhat, M.S., Chavadi,V.C. and Hegde, V.S. (2002): Morphological and textural characteristics of Kudle beach, Karnataka, central west coast of India. Lour.Geol.Soc.India, 59, 125-131.

[8] Brunnschweiler, R.O. (1966): On the geology of Indo-Burma Ranges. Jour.Geol. Soc. Australia. Vol.13, pt.1 p. 137-194

[9] Buller, A.T. and Mc Manus, J. (1972): Simple matric sedimentary statistics used to recognise different environment; sedimentology, vol. 18, p 1-21.

[10] Burhanuddin, M. (2007): Textural analysis in interpreting the depositional environmental of Lower Gondwana Sandstone in central part of Godavari basin, Andhra Pradesh. Jour. Geol. Soc. India. V. 69, p 1335-1341.

[11] Chappell, J (1967): Recognising fossil strand lines from grain-size analysis, JSP, vol. 37, p 157-165. [12] Daura and Debadhikari (1968): In Director General’s general report, 1963-64, Geol. Sur. Ind. Rec. 98 (1), 7.

[13] Dayal,B. and Duara, B.K. (1963): Report on the systematic geological mapping and mineral survey in parts of Manipur state. G.S.I. Prog. Rep. for F.S. 1962-63.

[14] Doeglas, D. J. (1946): Interpretation of the results of mechanical analysis. JSP, V-16, p. 13-40.

[15] Evans, P. (1932): Explanatory notes to accompany a table showing the Tertiary succession in Assam. Trans. Mining and Geol. Inst. vol 27, p. 155-260.

[16] Folk, R.L. (1980): Petrology of Sedimentary Rocks. Hemphill Austin, Texas, 159p.

[17] Folk, R.L. and Ward, W.C. (1957): “Brazos River Bar: A study in the significance of grain size parameters,” Jour. Sed. Pet., vol. 27, p. 3-27.

[18] Friedman, G.M. (1961): Distinction between Dune, Beach and River sands from their textural characteristics. Jour. Sed. Pet, V31, p 514-529.

[19] Friedman, G.M. (1967): “Dynamic processes and statistical parameters compared for size frequency distribution of Beach and River sands,” Jour. Sed. Pet., vol. 37, p. 327-354.

[20] Hails, J.R. and Hoyt, J. H. (1969): The significance and limitation of statistical parameter for distinguishing ancient and modern sedimentary environment of the Lower Georgia coastal plain, JSP vol. 39 p 559-580.

[21] Hanamgond, P.T. and Chavadi, V. C. (1998): Sedimentological study of Kwada and Belekeri bay beaches, Uttara Kannada, west coast, India. Jour. Geol. Soc. India, 51, 193-200.

[22] Harris, S.A. (1958a): Differentiation of various Egyptian Aeolian micro-environment by mechanical composition, Jour. Sed. Pet, vol. 28, p 164-174.

[23] Hjulstorm, F. (1939): Transportation of detritus by moving water. In: Recent marine sediments edited by P.D. Trask: Am. Assoc. Petroleum Geologists, Tulsa, Oklahoma, 5-31.

[24] Inman, D.L. (1949): Sorting of sediments in the light of fluid mechanics: Jour. Sed. Pet., vol. 19, p. 51-70. [25] Inman, D.L. (1952): Measures for describing the size distribution of sediments, Jour. Sed. Pet., vol. 22, p. 125-145.

[26] Joshep, S., Thrivikramaji, K.P. and Anirudhan, S. (1997): Textural parameters, discriminant analysis and depositional environments of the Teri sands, southern Tamil Nadu. Jour. Geol. Soc. India, 50, 323-329.

[27] Khin, A. and Win, K. (1969): Geology and Hydrocarbon prospects of Burma Tertiary Geosyncline. Union Burma Gr Sc Tech 2 53-81.

[28] Klovan, J.E. (1966): The use of factor analysis in determining depositional environment from grain size distribution. Jour. Sed. Pet vol. 36, p 115-125.

[29] Koldijk, W.S. (1968): On environmental sensitive grain-size parameters, Sedimentology, vol. 10, p 57-69.

[30] Krinsley, D. and Funnell, B (1965): Environmental history of sand grain from the lower and middle Pleistocene of Novfolk, England. Quart. Jour. Geol. Soc. London, vol.121, p435-461.

[31] Krumbein, W. C. and Pettijohn, F. J (1938): Manual of sedimentary petrography, Appleton-Century-Gofts, New York.

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14.

Organizational Judgmental Decision Jury of Executive Opinion Method Model

Authors: Nasser Fegh-hi Farahmand

Abstract--The organization’s immediate external environment posses a second set of challenging factors. In order to performance increase of industrial companies because of competition conditions in nowadays world with more various threats, perform of necessary actions are required. Meanwhile, in accordance with mentioned opinions, this research is found that if the total average values of each person were very high, create judgmental decision jury of executive opinion method will be suitable for him, and if such values be very low, the execution place will be proposed. In other wise, if the total average values of person be medium, he or she will put in balancer or supporting judgmental decisions jury of executive opinion method place. All of the organizations, before choosing of alternatives for improve of company performance, proposed for test and evaluation of the model of this research, and if they couldn’t receive of suitable results from perform of it, in that case will be free for choosing and selecting another alternative. For these reasons, after determination of judgmental decision jury of executive opinion method places for manufacturing organizations, the find of alternatives for perform of it is very important.

Keywords-- Organizational performance, judgmental decision jury of executive opinion method, organization, judgmental decision jury of executive opinion method position

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