Archive

Abstract:

In late years, camera identification methods have drawn attention in the area of digital forensics. To detect the source camera through which the picture is caught, Photo-Response Non uniformity (PRNU) noise is utilized as a camera, impression, as it is a particular component that recognizes pictures taken from the comparable cameras. This paper introduces a camera identification technique which is based on Krawtchouk Moment invariant features. The Photo Response Non-Uniformity (PRNU) noise is a type of sensor finger impression, which permits to extraordinarily distinguish the camera that took an image. It is estimated from the denoised images using a denoised filter. Then estimate the Krawtchouk Moment invariants from the PRNU noise pattern. The Krawtchouk Moments are invariant to scaling, translation, rotation, and shear. These invariants are fed to Fuzzy Min-Max Neural Network with Compensatory Neuron (FMCN) and by performing ten-fold cross-validation technique, verification is made out. The experimental results show that the proposed technique achieves an average accuracy of 93.3% for first experiment and 98.3% for the second experiment.

Keywords:

Camera identification,photo response non-uniformity (PRNU),Krawtchouk moments,fuzzy min-max neural networkwith compensatory neuron (FMCN),

Refference:

I.Alessandro Piva, “Review Article an Overview on Image Forensics”, Hindawi Publishing Corporation ISRN Signal Processing, Volume 2013, Article ID 496701, http://dx.doi.org/10.1155/2013/496701 (2013).

II.A. Tuama, F. CombyandM. Chaumont, “Camera model identification based machine learning approach with high order statistics features”, 24th European Signal Processing Conference (EUSIPCO), Budapest, 2016, pp. 1183-1187.

III.Anass El affar, Khalid Ferdous, AbdeljabbarCherkaoui, Hakim El fadiliand Hassan Qjidaal, “Krawtchouk Moment Feature Extraction for Neural Arabic Handwritten Words Recognition”, IJCSNS International Journal of Computer Science and Network Security, Vol.9 No.1. 2009.

IV.Abhijeet V. Nandedkar, Prabir K. Biswas, “A Fuzzy Min-Max Neural Network Classifier with Compensatory Neuron Architecture”, IEEE Transactions On Neural Networks, Vol. 18. No. 1, 2007.

V.F. Meng, X. Kong and X. You, A new feature-based method for source camera identification, in Advances in Digital Forensics IV, I. Ray and S. Shenoi (Eds.), Springer, Boston, Massachusetts, pp. 207–218, 2008.

VI.F. Razzazi and A. Seyedabadi, “A robust feature for single image camera identification using local binary patterns,” 2014 IEEE International Symposium on SignalProcessing and Information Technology (ISSPIT), Noida, 2014, pp. 000462-000467.

VII.G. Xu, Y. Q. Shi, “Camera model identification using local binary patterns”, Proc. IEEE Int Conference on Multimedia and Expo (ICME), pp. 392-397, 2012.

VIII.I. Amerini, R. Caldelli, P. Crescenzi, A. Del Mastio, A. Marino, “Blind Image Clustering Based on the Normalized Cuts Criterion for Camera Identification”, Image Communication, ELSEVIER, pp. 1 -13, 2014.

IX.J. Lukas, J. FridrichandM. Goljan, “Digital camera identification from sensor pattern noise,” in IEEE Transactions on Information Forensics and Security, vol. 1, no. 2, pp. 205-214, June 2006.

X.K.R. Akshatha, A.K. Karunakar, H. Anitha, U. Raghavendra, Dinesh Shetty, “Digital camera identification using PRNU: A feature basedapproach”, Digital Investigation, Journal, Elsevier, 19 (2016).

XI.M. C. Stamm, M. Wu and K. J. R. Liu, “Information Forensics: An Overview of the First Decade”, in IEEE Access, vol. 1, pp. 167-200, 2013.

XII.M. Kharrazi, H.T. Sencar, N. Memon, “Blind source camera identification”, IEEE International Conference on Image Processing ICIP ’04., vol. 1, pp. 709-712, 2004.

XIII.M. KivancMihcak, I. Kozintsev and K. Ramchandran, “Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising,” 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), Phoenix, AZ, 1999, pp. 3253-3256 vol.6.

XIV.O. Celiktutan, B. Sankur, I. Avcibas, “Blind identification of source cell-phone model”, IEEE Transactions on Information Forensics and Security, vol. 3, no. 3, pp. 553-566, 2008.

XV.P. T. Yap, P. Raveendran and S. H. Ong, “Krawtchouk moments as a new set of discrete orthogonal moments for image reconstruction”, In Neural Networks, 2002. IJCNN’02. Proceedings of the 2002 International Joint Conference on (Vol. 1, pp. 908-912). IEEE, 2002.

XVI.P. T. Yap, R. Paramesran and S. H. Ong, “Image analysis by Krawtchouk moments”, Image Processing, IEEE Transactions on, 12(11), 1367-1377, 2003.

XVII.S. Bayram, H.T. Sencar, N. Memon, “Improvements on source camera model identification based on cfainterpolation”, Advances in Digital Forensics II IFIP International Conference on Digital Forensics, pp. 289-299, 2006.

XVIII.S. Saito, Y. Tomioka and H. Kitazawa, “A Theoretical Framework for Estimating False Acceptance Rate of PRNU-Based Camera Identification,” in IEEE Transactions on Information Forensics and Security, vol. 12, no. 9, pp. 2026-2035, Sept. 2017.

XIX.T. Filler, J. Fridrich, M. Goljan, “Using sensor pattern noise for camera model identification”, Proc. FCIP 15th IEEE International Conference on Image Processing, pp. 1296-1299, 2008.

XX.TechnischeUniversität Dresden, Dresden, Germany. Dresden Image Database, accessed on May 1, 2015. [Online]. Available: http://forensics.inf.tu-dresden.de/ddimgdb.

XXI.X. Kang, Y. Li, Z. Qu and J. Huang, “Enhancing Source Camera Identification Performance with a Camera Reference Phase Sensor Pattern Noise”, in IEEE Transactions on Information Forensics and Security, vol. 7, no. 2, pp. 393-402, April 2012.

XXII.Y. Sutcu, S. Bayram, H. T. Sencar and N. Memon, “Improvements on Sensor Noise Based Source Camera Identification,” 2007 IEEE International Conference on Multimedia and Expo, Beijing, 2007, pp. 24-27.

XXIII.Yoichi Tomioka, Yuya Ito, and Hitoshi Kitazawa, “Robust Digital Camera Identification Based on Pairwise Magnitude Relations of Clustered Sensor Pattern Noise”, IEEE Transactions on Information Forensics and Security, Vol. 8, No. 12, December 2013.

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Abstract:

Weak clayey soil can cause premature failure in subgrade so their removal or proper treatment is necessary for the efficiency of structure. Soil stabilization is an excellent choice and economical in many circumstances for treatment and proper behavior of weak subgrade soil as recommended by many researchers. Lime is the oldest and well known additive for stabilization of many type of soils. This paper presents geotechnical investigation of low plastic clay soil being used as subgrade stabilized with lime. The low plastic clayey subgrade soil was stabilized with different percentages of lime and results show that soil can be satisfactorily stabilized with the addition of 6% lime. The Atterberg’s limit, compaction characteristics and strength tests including unconfined compressive strength (UCS) and California bearing ratio (CBR) tests were performed. Results indicate that addition of lime reduce plasticity index. An increase in OMC was observed with the decrease in maximum dry density (MDD). CBR and unconfined compressive strength of soil (qu)values improved significantly with the addition of lime.

Keywords:

Soil Stabilization,Lime,Subgrade Stabilization,Low Plastic Clay,

Refference:

I.Al-Rawas, A. A., Hago, A. W., & Al-Sarmi, H. (2005). Effect of lime, cement and Sarooj (artificial pozzolan) on the swelling potential of an expansive soil from Oman.Building and Environment,40(5), 681-687.

II.Eisazadeh, A., Kassim, K. A., & Nur, H. (2012). Solid-state NMR and FTIR studies of lime stabilized montmorillonitic and lateritic clays.Applied Clay Science,67, 5-10.

III.Ghobadi, M. H., Abdilor, Y., & Babazadeh, R. (2014). Stabilization of clay soils usinglime and effect of pH variations on shear strength parameters.Bulletin of Engineering Geology and the Environment,73(2), 611-619.

IV.Harichane, K., Ghrici, M., Kenai, S., & Grine, K. (2011). Use of natural pozzolana and lime for stabilization of cohesive soils.Geotechnical and geological engineering,29(5), 759-769.

V.Ingles, O. G., & Metcalf, J. B. (1972).Soil stabilization principles and practice(Vol. 11, No. Textbook).

VI.Little, D. N., Thompson, M. R., Terrell, R. L., Epps, J. A., & Barenberg, E. J. (1987).Soil stabilization for roadways and airfields. LITTLE (DALLAS N) AND ASSOCIATES BRYAN TX.

VII.Murthy, V. N. S. (2002).Geotechnical engineering: principles and practices of soil mechanics and foundation engineering. CRC press.

VIII. Muhmed, A., & Wanatowski, D. (2013). Effect of Lime Stabilisation on the Strength and Microstructure of Clay IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) ISSN: 2320-334X.Volume6, Issue3.

IX.Osinubi, K. J., Bafyau, V., & Eberemu, A. O. (2009). Bagasse ash stabilization of lateritic soil. InAppropriate Technologies for Environmental Protection in the Developing World(pp. 271-280). Springer, Dordrecht.

X.Rogers, C. D. F., Glendinning, S., & Roff, T. E. J. (1997, October). Lime modification of clay soils for construction expediency. InProceedings of the Institution of Civil Engineers: Geotechnical Engineering(Vol. 125, No. 4).

XI.Tuncer, E. R., & Basma, A. A. (1991). Strength and stress-strain characteristics of a lime-treated cohesive soil.Transportation Research Record, (1295).

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Abstract:

Ground granulated furnace slag is a waste material which is rich in Calcium. Aim of this study is to observe the effect of mixing of ground granulated blast furnace slag as a replacement of cement in concrete. The study is conducted on M-30 grade concrete. The cement is replaced partially by the ground granulated blast furnace slag to obtain a cost-effective mix. The concrete mixes are prepared by replacing the cement by 15%, 30%, 45%, 60% and 75 % ground granulated blast furnace slag. The tests are performed to know the compressive strength, flexural strength and workability of concrete. Non-destructive tests like rebound hammer test and ultrasonic pulse velocity tests are also performed to understand the post hardening characteristics of the concrete. It is found that the replacement of cement GGBFS reduces the initial strength of concrete but increases the ultimate strength if mixed in optimum amount. The optimum percentage of ground granulated furnace slag in M-30 concrete is found to be 45%. The workability increases as the amount of GGBFS is increased in the mix. The post hardening tests show the better performance of concrete at 30% and 45% mixing of GGBFS in concrete.

Keywords:

GGBFS,waste management,concrete,flexural strength,compression strength ,

Refference:

I.A. Islam, U.H. Alengaram, M.Z. Jumaat, I.I. Bashar, “The Development of Compressive Strength of Ground Granulated Blast Furnace Slag-Palm Oil Fuel Ash-Fly Ash Based Geo-polymer Mortar”, Materials and Design, Vol. 56, pp. 833-841, April 2014.

II.A.Venkatakrishnaiah, G.Sakthviel, “Bulk Utilization of Fly-ash in Self-compacting Concrete”, KSCE Journal of Civil Engineering, Vol. 19, No.7, pp. 2116-2120, November 2015.

III.F. Hogan, J. Meusel, “Evaluation for Durability and Strength Development of a Ground Granulated Blast Furnace Slag,” Cement, Concrete and Aggregates, Vol. 3, No. 1, pp. 40-52, 1981.

IV.H. Sethi, P.P. Bansal, R. Sharma, “Effectof Addition of GGBS and Glass Powder on the Properties of Geo-polymer Concrete”, Iranian Journal of Science and Technology, Transactions of Civil Engineering, pp. 1-11, November 2018.

V.H. Wang, J. Wang, X. Sun, W. Jin, “Improving Performance of Recycled Aggregate Concrete with Superfine Pozzolanic Powders”, Journal of Central South University, Vol. 20, No. 12, pp. 3715-3722, December 2013

VI.L. Black, P. Purnell, J. Hill, “Current Themes in Cement Research”, Advances in Ceramics, Vol. 109, No. 5, pp. 253-259, 2010.

VII.M.R. Antonyamaladhas, S. Chachithanantham, A. Ramaswamy, “Performance and Behaviour of Ground Granulated Blast Furnace Slag Imparted to Geopolymer Concrete Structural Elements and Analyzed with ANSYS”, Advances in Material Science and Engineering, Vol. 2016, pp. 1-9, August 2016.

VIII.M.Arizoumandi, S.A.Volz, “Effect of Fly Ash Replacement Level on the Fracture Behavior of Concrete”, Frontiers of Structural and Civil Engineering, Vol. 7, No. 4, pp. 411-418,December 2003.

IX.M. Elchalakani, T. Aly, E. Abu-Aisheh, “Sustainable concrete with high volume GGBFS to build Masdar City in the UAE”, Case Studies in Construction Materials, Vol. 1, pp. 10-24, December 2013.

X.O.Kayali,“Effect of High Volume Fly Ash on Mechanical Properties of Fiber Reinforced Concrete”, Materials and Structures, Vol. 37, No. 5, pp. 318-327, June 2004.

XI.O.M. Omar, G. D. AbdElhameed, M. A Sherif, H.A. Mohamadien, “Influence of limestone waste as partial replacement material for sand and marble powder in concrete properties”, HRBC Journal,Vol. 8, No. 3, pp. 193-203, December 2003.

XII.R. Siddique, D. Kaur, “Properties of Concrete Containing Ground Granulated Blast Furnace Slag (GGBFS) at Elevated Temperatures”, Journal of Advanced Research, Vol. 3, No. 1, pp. 45-51, January 2012.

XIII.S.A. Zareei, F.Ameri, F. Dorostkar, M. Ahmadi, “Rice Husk Ash as a Partial Replacement of Cement in High Strength Concrete containing Micro Silica: Evaluating Durability and Mechanical Properties”, Case Studies in Construction Materials, Vol. 7, pp. 73-81, December 2017.

XIV.S.P. Dunuweera, R.M.G. Rajapakse, “Cement Types, Composition, Uses and Advantages of Nano-cement, Environmental Impact on Cement Production, and Possible Solutions”, Advances in Material Science and Engineering, Vol. 2018, pp. 1-13, April 2018.

XV.S.V. Deo, “Parametric Study of Glass Fiber Reinforced Concrete”, Advances in Structural Engineering, In: Matsagar V. (eds), Springer, New Delhi, pp. 1909-1916, December 2004.

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Abstract:

In this study, a comparatively new technique named the generalized Kudryashov method (gKM) has been effectively implemented to explore the exact traveling wave solutions to some nonlinear evolution equations (NLEEs) in the field of nonlinear science and engineering. The effectiveness of the new functional method has been demonstrated by investigating single as well as coupled equations with arbitrary parameters explicitly the coupled Higgs field equation, the Benney-Luke equation, and the Drinfel'd-Sokolov-Wilson (DSW) equation. As a matter of fact, the solution attained in this article thrust into the abundant wave solutions which includes kink, singular kink, periodic and solitary wave solutions. Moreover, the characteristics of these analytic solutions are interpreted depicting some 2D and 3D graph by using computer symbolic programming Wolfram Mathematica. The computational work ascertained that the employed method is sturdy, simple, precise, and wider applicable. Also, the prominent competence of this current method ensures that practically capable to reducing the size of the computational task and can be solved several nonlinear types of new complex higher order partial differential equations that originating in applied mathematics, computational physics and engineering.

Keywords:

Thegeneralized Kudryashov method,Coupled Higgs field equation,Benney-Luke equation,DSW equation, Traveling wave solution,Solitary wave solution,Exact solution,

Refference:

I.A. Bekir,A. Boz, “Applications of the He’s exp function method for nonlinear evolution equations”, Comput. Math. Appl., Vol.: 58, Issue: 11-12, pp.: 2286-2293, 2009.

II.A.H. Arnous, M. Mirzazadeh, M. Eslami,”Exact solution of the Drinfel’d Sokolov Wilson equation using Backlund transformation of Riccati equation and trial function approach”,Prama. J. Phys. Vol.: 86, Issue: 6,pp.: 1153-1160, 2016.

III.A. H. Arnous, M. Mirzazadeh, M. Eslami,”The Backlund transformation method of Riccati equation applied to Coupled Higgs field and Hamiltonian amplitude equations”,Comput. Methods Diff. Equat.,Vol.: 2, Issue: 4,pp.: 216-226, 2014.

IV.A. J. M. Jawad, M. D. Petkovic, A. Biswas,”Modified simple equation method for nonlinear evolution equations”, Appl. Math. Comput., Vol.: 217, Issue: 2,pp.: 869-877, 2010.

V.A. M. Wazwaz, “The extended tanh method for abundant solitary wave solutions of nonlinear wave equations”,Appl. Math. Comput., Vol.:187, Issue: 2,pp.: 1131-1142, 2007.

VI.D. Kumar, A. R. Seadawy, A. K. Joardar,”Modified Kudryashov method via new exact solution for some conformable fractional differential equations arising in mathematical biology”,Chin. J. Phys.,Vol.: 56, Isssue:1,pp.:75-85, 2018.

VII.D. Lu, D. Kang, B. Hong,”New exact solutions of the Drinfel’d SokolovWilson equation”,J. Informa. Comput. Sci., Vol.:18, pp.: 5955-5962, 2013.

VIII.E. Aksoy, M. Kaplan, A. Bekir,”Exponential rational function method for space-time fractional differential equations”,Waves Rand. Compl. Media, Vol.: 26, pp.: 142-151, 2016.

IX.E. Babolian, A. Azizi, J. saeidian,”Some notes on using the homotopy perturbation method for solving time-dependent differential equations”, Math. Comput. Model., Vol.; 50, Issue: 1-2, pp.: 213-224, 2009.

X.E. Fan,”Extended tanh method and its applications to nonlinear equation”. Phys. Lett. A, Vol.: 277, Issue: 4-5,pp.: 212-218, 2000.

XI.E. Fan, J. Zhang,”Applications of the Jacobi elliptic function method to special-type nonlinear equations”,Phys. Lett. A, Vol.:305, Issue: 6, pp.: 383-392, 2002

XII.E. M. E. Zayed,A. G. A. Nowehy,”The solitary wave ansatz method for finding the exact bright and dark soliton solutions of two nonlinear Schrodinger equations”,J. Assn. Arab Univ. Basic Appl. Sci., Vol.: 24, Issue:1, pp.:184-190, 2017.

XIII.F. Mahmud,M. Samsuzzoha, M. A. Akbar, “The generalized Kudryashov method to obtain exact traveling wave solutions of the PHI-four equation and the Fishers equation”,Res. Phys., Vol.: 7, pp.: 4296-4302, 2017.

XIV.G. Allah,R. Musa, Elzaki, M. Tarig, “Application of new homotopy perturbation method for solving partial differential equations”, J. Comput. Theor. Nanosci., Vol.: 15, Issue: 2, pp.: 500-508, 2018.

XV.H. Mao,Q. P. Liu, “Backlund-Darboux transformation and discretizations of 𝑁=2, 𝑎=−2supersymmetric KdV equation”,Phys. Lett. A, Vol.:382, Issue: 5, pp.: 253-258, 2018.

XVI.H. Naher,F. A. Abdullah, M. A. Akbar, “The exp function method for the new exact solution of the nonlinear partial differential equations”, Int. J. Phys. Sci., Vol.: 6, Issue: 29,pp.:6706-6716, 2011.

XVII.H. Triki,A. Yildirim, T. Hayat, O. M. Aldossary, A. Biswas, “Shockwave solution of Benney-Luke equation”,Romanian J. Phys., Vol.: 57, Issue: 7-8, pp.: 1029-1034, 2012.

XVIII.I. Hasim,”Adomian decomposition method for solving BVPs for fourth-order integrodifferential equations”,J. Comput. Appl. Math., Vol.: 193, Issue: 2,pp.:658-664, 2006.

XIX.J. H.He, “Homotopy perturbation technique”,Comput. Methods Appl. Mech. Eng., Vol.:178, Issue: 3-4,pp.:257-262, 1999.

XX.K. A. Gepreel, T. A. Nofal, A. A. Alasmari,”Exact solutions for nonlinear integro-partial differential equations using the generalized Kudryashov method”,J. Egypt. Math. Soc., Vol.: 25, pp.: 438-444, 2017.

XXI.K. Khan, M. A. Akbar, N. H. M. Ali,”The modified simple equation for exact and solitary wave solution of nonlinear evolution equation: the GZK-BBM equation and right-handed non-commutative Burgers equations”,ISRN Math. Phys., pp: 5, Article ID 146704, 2013.

XXII.K. R. Raslan,”The application of He’s exp function method for mKdV and Burgers equations with variable coefficients”,Int. J. Nonlinear Sci., Vol.: 7, Issue: 2, pp.: 174-181, 2009.

XXIII.L. Xu,”He’s parameter expanding methods for strongly nonlinear oscillators”,J. Comput. Appl. Math., Vol.: 207, Issue: 1, pp.: 148-154, 2007.

XXIV.M. A. Akbar, N. H. M. Ali,”The improved F-expansion method with the Riccati equation and its applications in mathematical physics”,Cogent Math. Vol.: 4, ID.: 1282577, 2017

XXV.M. A. Khater, A. R. Seadawy, D. Lu,”Dispersive solitary wave solutions of new coupled Konno-Ono,Higgs field and Maccari equations and their applications”,J. King Saud Univ. Sci., Vol.: 30, pp.: 417-423, 2018.

XXVI.M. Kaplan, A. Bekir, A. Akbulut, E. Aksoy,”The modified simple equation method for nonlinear fractional differential equations”,Romanian J. Phys., Vol.: 60, Issue: 9-10,pp.:1374-1383, 2015.

XXVII.M. K. Elboree,”The Jacobi elliptic function method and its application for two-component BKP hierarchy equations”,Comput. Math. Appl., Vol.: 62, Issue: 12,pp.: 4402-4414, 2011.

XXVIII.M. Koparan, M. Kaplan, A. Bekir, O. Guner,”A novel generalized Kudryashov method for exact solutions of nonlinear evolution equations”,AIP Con. Proc., Vol.: 1798, Issue: 1, 2017.

XXIX.M. M. Kabir, A. Khajeh, E. Aghdam, A. Y. Koma,”Modified Kudryashov method for finding exact solitarywave solutions of higher order nonlinear equations”,Math. Methods Appl. Sci., Vol.: 34, Issue: 2, pp.: 213-219, 2011.

XXX.M. S. Islam, K. Khan, M. A. Akbar,”Application of the improved F-expansion method with Riccati equation to find the exact solution of the nonlinear evolution equations”,J. Egypt. Math. Soc.,Vol.:25, pp.: 13-18, 2017.

XXXI.N. Ahmed, S. Bibi, U. Khan, S. T. Mohyud-din,”A new modification in the exponential rational function method for nonlinear fractional differential equations”,Eur. Phy. J. Plus, Vol.: 133, Issue: 45, 2018.

XXXII.N. Taghizadeh, M. Mirzazadeh,”The first integral method to some complex nonlinear partial differential equations”,J. Comput. Appl. Math., Vol.: 235,pp.:4871-4877, 2011.

XXXIII.O. A. Taiwo,”A parameter expansion method for two-point nonlinear singularly perturbed boundary value problems”,Int. J. Comput. Math., Vol.:55, Issue: 3-4, pp.: 189-196, 1995.

XXXIV.S. H. Dong,”The ansatz method for analyzing Schrodinger’s equation with three anharmonic potentials in D dimensions”,J. Genetic Counse., Vol.: 15, Issue: 4, pp.: 385-395, 2002.

XXXV.S. Kumar, K. Sing, R. K. Gupta,”Coupled Higgs field equations and Hamiltonian amplitude equation: Lie classical approach and (𝐺′/𝐺)-expansion method”,Prama. J. Phys., Vol.: 79, Issue: 1, pp.: 41-60, 2012.

XXXVI.S. Kutluay, A. Esen,”Exp function method for solving the general improved KdV equation”,Int. J. Nonlinear Sci. Numer. Simul., Vol.: 10, Issue: 6, pp.: 717-725, 2009

XXXVII.S. Sirisubtawee, S. koonprasert,”Exact traveling wave solution of certain nonlinearpartial differential equations using the (𝐺′𝐺2)-expansion method”,Advan. Math. Phys., Article ID 7628651, pp.:15, 2018.

XXXVIII.X. J. Yang, H. M. Srivastava, J. H. He, D. Baleanu,”Cantor-type cylindrical co-ordinate method for differential equations with local fractional derivatives”,Phys. Lett. A, Vol.: 377, Issue: 28-30, pp.: 1696-1700, 2013.

XXXIX.Y. C. Hon, E. G. Fan, “A series of the exact solution for coupled Higgs field equations and coupled Schrodinger-Boussinesq equations”, Nonlinear Anal., Theory Methods Appl., Vol.: 71, Issue: 7-8, pp.: 3501-3508, 2009.

XL.Z. Islam,M. M. Hossain, M. A. W. Seikh, “Exact traveling wave solution to Benney-Luke equation”,J. Bangladesh Math. Soc., Vol.: 37, pp.:1-14, 2017.

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Abstract:

This paper is based on the study of mutual funds in India which is understood to be one of the most vibrant in the money market. This paper analyses a set of representative schemes from heterogeneous group of different fund houses. There are well established criteria to judge their performance absolutely and also in relative terms. This paper deals with the analysis of risk-returns parameters of different mutual fund schemes and the relation between the risk preference of the investors and the risk adjusted performance (RAP) measure based on real time data. Various tests are applied to evaluate the performance of mutual funds based on well established measures and those tests have been used to rank the funds accordingly. Some hypotheses are constructed and tested to find out whether there are significant differences in their absolute and RAP. The paper also proposed an easy and practical path to solve an optimal portfolio problem containing the various mutual fund schemes. The analysis is carried out with the help of William Sharpe’s single index model and result could of use to substantial investors who are choosing an optimum portfolio of various mutual funds.

Keywords:

Mutual fund,Risk adjusted performance,Sharp index,Optimal portfolio,

Refference:

I.A. Shah, S. Thomas, M. Gorham, India‟s Financial Market: An Insider‟s Guide, How the Markets Work, Academic Publishers, 2008.

II.B. Roy and S. S. Deb, “Conditional Alpha and Performance Persistence for Indian Mutual Funds: Empirical Evidence”, ICFAI Journal of Applied Finance, pp. 30-48, January, 2004.

III.E.Thanou,“Mutual Fund Evaluation During Up and Down Market Conditions: The Case of Greek Equity Mutual Funds”, International Research Journal of Finance and Economics, Vol.:13, pp. 84-93, 2008.

IV.G. Elton, G. Brown, “Modern portfolio theory and investment analysis”, 7th edition, John Wiley & Sons, Inc, 2007.

V.J. A. Haslem, Mutual funds: risk and performance analysis for decision making. John Wiley & Sons, 2009.

VI.J. D. Jobson, and B. Korkie, “Performance Hypothesis Testing with the Sharpe and Treynor Measures”, Journal of Finance, 36, 888-908, 1981.

VII.K. Daniel, M. Grinblatt, S. Titman and R. Wermers, “Measuring mutual fund performance with characteristic-based benchmarks”, Journalof Finance 52, 1035–1058, 1997.

VIII.L. Chan, H. Chen and J. Lakonishok, “On Mutual Fund Investment Styles”, The Review of Financial Studies, Vol.: 15, Issue: 5, pp. 1407-1437, 2002.

IX.M. C.Jensen, “The performance of mutual funds in the period 1945–1964”, The Journal of finance, Vol.: 23, Issue: 2, pp. 389-416, 1968.

X.M. Jayadev, “Mutual Fund Performance: An Analysis of Monthly Returns”, Finance India, Vol.: X, No.: 1, pp. 73–84, 1996

XI.N. D.Rao, “Investment Styles and Performance of Equity Mutual Funds in India”, available at SSRN http://ssrn.com/abstract=922595, 2006.

XII.P. K. Muthappan and E. Damodharan, “Risk-Adjusted Performance of Indian Mutual Funds Schemes”,Finance India,Vol.: 20, Issue: 3, 2006.

XIII.R. Bahadur, P. S. Koirala, “Application of Markowitz and Sharpe Models in Nepalese Stock Market”, The Journal of Nepalese Business Studies, Vol.: III, No.: 1, 2006.

XIV.S. D. Groot, and A. Plantinga, Risk-Adjusted Performance Measures and Implied Risk-Attitudes”, available at http://ssrn.com/abstract=289193, Nov 2001.

XV.S. H. Thomas and A. P. Ralph, “Equity Mutual Fund Historical Performance Ratings as Predictors of Future Performance”, Journal of Financial and Strategic Decisions, Vol.: 9, No.: 1, 1996.

XVI.S. Lee, and S. Stevenson, “Testing the Statistical Significance of Sector and Regional Diversification. Journal of Property Investment, and Finance, Vol.: 23, Issue: 5, pp. 394–411, 2005.

XVII.S. Sankaran, Indian Mutual Funds Handbook , A Guide For Industry Professionals And Intelligent Investors, 2nd ed., Vision Books, 2008.

XVIII.W. F. Sharpe, “The Sharpe Ratio”, Journal of Portfolio Management, Vol.: 21, 1994.

XIX.W. Sharpe, G. J. Alexander, J. W. Bailey, Investment, PHI (2006).

XX.Y. Ali, “Simplifying the Portfolio Optimization Process via Single Index Model”, available http://www.iems.northwestern.edu/docs/undergraduate/honors/Ali.pdf, 2008.

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Abstract:

Cloud computing is the evolving paradigm that provides the services in which cloud consumers can remotely save their data into the cloud and access the on-demand high-quality applications. In the existing technique explained an Extendable Access Control System procedure supposed that the authority is the trusted party, but in many cases, they may perform an illegal action which causes the data loss. The proposed work encrypted the data through Uncrackable Cipher Dynamic Double Encryption Standard (UCDDES). Generally, the UCDDES contains the key length of 32, 40 and 48. To randomly select the key length reduced the data security issues. After dynamically selecting the key length the data governor sent the key request to the authority. Then based on the obtained key length the data governor generated the partial secret key. It is further used to decrypt the data and stored in the cloud storage. The results improve the security of cloud and access control. It reduces the issue of unauthorized user/ hackers accessing data. It increases the cloud security and prevents from dictionary attacks, brute force attacks, collision attacks, and so on.

Keywords:

Cloud computing,data security issues,UCDDES based data encryption,cloud network security,

Refference:

I.Cui, H., Deng, R. H., & Li, Y. (2018). Attribute-based cloud storage with secure provenance over encrypted data.Future Generation Computer Systems,79, 461-472.

II.Di Vimercati, S. D. C., Foresti, S., Jajodia, S., Paraboschi, S., &Samarati, P. (2007, November). A data outsourcing architecturecombining cryptography and access control. InProceedings of the 2007 ACM workshop on Computer security architecture(pp. 63-69). ACM.

III.Divya, S. V., Shaji, R. S., &Venkadesh, P. (2018). An Efficient Data Storage and Forwarding Mechanism Using Fragmentation-Replication and DADR Protocol for Enhancing the Security in Cloud. Journal of Computational and Theoretical Nanoscience,15(1), 111-120.

IV.Goyal, V., Pandey, O., Sahai, A., & Waters, B. (2006, October). Attribute-based encryptionfor fine-grained access control of encrypted data. InProceedings of the 13th ACM conference on Computer and communications security(pp. 89-98). Acm.

V.Hur, J. (2013). Improving security and efficiency in attribute-based data sharing.IEEE transactions on knowledge and data engineering,25(10), 2271-2282.

VI.Iyapparaja, M., Navaneethan, C., Meenatchi, S., Kumar, P. J., &Suganya, P. (2017). A Privacy-Preserving Secure Access Control Mechanism in Cloud.

VII.Kumar, K., & Lu, Y. H. (2010). Cloud computing for mobile users: Can offloading computation save energy?.Computer,43(4), 51-56.

VIII.Mell, Peter, and Tim Grance. “The NIST definition of cloud computing.” (2011).

IX.Ning, J., Cao, Z., Dong, X., Liang, K., Wei, L., & Choo, K. K. R. (2018). CryptCloud+: Secure and Expressive Data Access Control for Cloud Storage.IEEE Transactions on Services Computing.

X.Patil, P., Narayankar, P., Narayan, D. G., &Meena, S. M. (2016). A comprehensive evaluation of cryptographic algorithms: DES, 3DES, AES, RSA, andBlowfish.Procedia Computer Science,78, 617-624.

XI.Qiu, M., Gai, K., Thuraisingham, B., Tao, L., &Zhao, H. (2018). Proactive user-centric secure data scheme using attribute-based semantic access controls for mobile clouds in financialindustry.Future Generation Computer Systems,80, 421-429.

XII.Sahai, A., & Waters, B. (2005, May). Fuzzy identity-based encryption. InAnnual International Conference on the Theory and Applications of Cryptographic Techniques(pp. 457-473). Springer, Berlin, Heidelberg.

XIII.Shiraz, M., Sookhak, M., Gani, A., & Shah, S. A. A. (2015). A study on the critical analysis of computational offloading frameworks for mobile cloud computing.Journal of Network and Computer Applications,47, 47-60.

XIV.Sookhak, M., Akhunzada, A., Gani, A., Khurram Khan, M., &Anuar, N. B. (2014). Towards dynamic remote data auditing in computational clouds.The Scientific World Journal,2014.

XV.Sookhak, M., Gani, A., Khan, M. K., &Buyya, R. (2017). Dynamic remote data auditing for securing big data storage in cloud computing.Information Sciences,380, 101-116.

XVI.Sookhak, M., Gani, A., Talebian, H., Akhunzada, A., Khan, S. U., Buyya, R., &Zomaya, A. Y. (2015). Remote data auditing in cloud computing environments: a survey, taxonomy, and open issues.ACM Computing Surveys (CSUR),47(4), 65.

XVII.Sookhak, M., Talebian, H., Ahmed, E., Gani, A., & Khan, M. K. (2014). A review on remote data auditing in single cloud server: Taxonomy and open issues.Journal of Network and Computer Applications,43, 121-141.

XVIII.Sookhak, M., Yu, F. R., Khan, M. K., Xiang, Y., &Buyya, R. (2017). Attribute-based data access control in mobile cloud computing: Taxonomy and open issues.Future Generation Computer Systems,72, 273-287.

XIX.Srinivasan, S., & Raja, K. (2018). An Advanced Dynamic Authentic Security Method for Cloud Computing. InCyber Security: Proceedings of CSI 2015(pp. 143-152).Springer Singapore.

XX.Tang, H., Sun, Q. T., Yang, X., & Long, K. (2018). A Network Coding and DES Based Dynamic Encryption Scheme for Moving Target Defense.IEEE Access,6, 26059-26068.

XXI.Wang, C., Ren, K., Lou, W., & Li, J. (2010). Toward publicly auditable secure cloud data storage services.IEEE Network,24(4).

XXII.Whaiduzzaman, M., Sookhak, M., Gani, A., &Buyya, R. (2014). A survey on vehicular cloud computing.Journal of Network and Computer Applications,40, 325-344.

XXIII.Yuan, D., Song, X., Xu, Q., Zhao, M., Wei, X., Wang, H., & Jiang, H. (2018). An ORAM-based privacy-preservingdata sharing scheme for cloud storage.Journal of information security and applications,39, 1-9.

XXIV.Zhou, Z., & Huang, D. (2012, October). Efficient and secure data storage operations for mobile cloud computing. InProceedings of the 8th International Conference on Network and Service Management(pp. 37-45). International Federation for Information Processing.

XXV.Zuo, C., Shao, J., Liu, J. K., Wei, G.,& Ling, Y. (2018). Fine-Grained Two-Factor Protection Mechanism for Data Sharing in Cloud Storage.IEEE Transactions on Information Forensics and Security,13(1), 186-196.

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Abstract:

Deep learning is a rapidly growing field of machine learning which finds the application of its methods to provide solutions to numerous problems related to computer vision, speech recognition, natural language processing, and others. This paper gives a comparative analysis of the five deep learning tools on the grounds of training time and accuracy. Evaluation includes classifying digits from the MNIST data set making use of a fully connected neural network architecture (FCNN). Here we have selected five frameworks— Torch ,Deeplearning4j, TensorFlow, Caffe & Theano (with Keras), to evaluate their performance and accuracy. In order to enhance the comparison of the frameworks, the standard MNIST data set of handwritten digits was chosen for the classification task. When working with the data set, our goal was to identify the digits (0–9) using a fully connected neural network architecture. All computations were executed on a GPU. The key metrics addressed were training speed, classification speed, and accuracy.

Keywords:

Deep Learning, Feedforward MLP,Keras,Tensorflow,Theano,Caffe,Deeplearning4j,Torch,

Refference:

I.Anuj Dutt, AashiDutt. “Handwritten Digit Recognition Using Deep Learning. ” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 6, Issue 7, July 2017.

II.Alexander K. Seewald, “On the Brittleness of Handwritten Digit Recognition Models,”ISRN Machine Vision, vol. 2012, Article ID 834127, 2012.

III.Li DengMicrosoft Research, Redmond, Washington USA. ” The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]” IEEE Signal Processing Magazine(Volume: 29,Issue: 6, Nov. 2012).

IV.Muhammad Ramzan, Shahid Mehmood Awan,Hikmat Ullah Khan , Waseem Akhtar, Ammara Zamir,Mahwish Ilyas. “A Survey on using Neural Network based Algorithms for Hand Written Digit.” International Journal of Advanced Computer Science and Applications, Vol. 9, No. 9, 2018.

V.Subhransu Maji and Jitendra Malik EECS Department University of California, Berkeley Technical Report No. UCB/EECS-2009-159 November 25, 2009http://www2.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-159.pdf

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Abstract:

This study is targeted for reducing the power losses for a branch of Peshawar Electric Supply Company (PESCO), a small electric power grid in Pakistan, starting from Shahibagh and ending at Hayatabad substation. This study evaluates the current configuration of the transmission network, and then by using Particle Swarm Optimization, the best possible configuration that will ensure maximum throughput and minimum transmission and distribution losses is determined. The study is verified using Newton Raphson Method. Newton Raphson method is used to find the state of the mentioned network and then after the new configuration is proposed, the state estimation is done again to evaluate various parameters of the network and confirm its feasibility. The reconfiguration resulted from the PSO and NR methods have shown electric power losses minimization of the selected grid with 15.021%, amounting to a total of 0.3MW power loss minimization.

Keywords:

Power systems, Power system measurements, Power grids,Power system planning,Power transmission,

Refference:

I.Cui-Ru Wang et al., “A modified particle swarm optimization algorithm and its application in optimal power flow problem,” in 2005 International Conference on Machine Learning and Cybernetics, 2005, vol. 5, no. August, p. 2885–2889 Vol. 5.

II.F. R. Zaro and M. A. Abido, “Multi-objective particle swarm optimization for optimal power flow in a deregulated environment of power systems,” 2011 11th International Conference on Intelligent Systems Design and Applications. IEEE, 2011.

III.H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama, and Y. Nakanishi, “A particle swarm optimization for reactive power and voltage control considering voltage security assessment,” IEEE Trans. Power Syst., vol.15, no. 4, pp. 1232–1239, 2000.

IV.I. M. Malik and D. Srinivasan, “Optimum power flow using flexible genetic algorithm model in practical power systems,” 2010 Conference Proceedings IPEC. IEEE, 2010.

V.J. A. J. A. Momoh, S. X. X. Guo, E. C. C. Ogbuobiri, andR. Adapa, “the Quadratic Interior Point Method Solving Power System Optimization Problems,” IEEE Trans. Power Syst., vol. 9, no. 3, pp. 1327–1336, 1994.

VI.L. L. Lai et al., “Particle Swarm Optimization for Economic Dispatch of Units with Non-Smooth Input-Output Characteristic Functions,” Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems. IEEE.

VII.M. A. Abido, “Multiobjective Particle Swarm Optimization for OptimalPower Flow Problem,” in 12th International Middle-East Power System Conference, 2008. MEPCON 2008., 2008, pp. 392–396.

VIII.Muhammad Aamir Aman, 2Muhammad Zulqarnain Abbasi, 3Akhtar Khan, 4Waleed Jan, 5Mehr-e-Munir.Department of Electrical Engineering, IQRA National University, Peshawar, Pakistan. Power Generator Automation, Monitoring and Protection System. J.Mech.Cont.& Math. Sci., Vol. -13, No. -4, September-October (2018) Pages 122 –133.

IX.Muhammad Aamir Aman, 2Muhammad Zulqarnain Abbasi, 3Hamza Umar Afridi, 4KhushalMuhammad, 5Mehr-e-Munir.. Department of Electrical Engineering, IQRA National University, Peshawar, Pakistan. Prevailing Pakistan’s Energy Crises. J.Mech.Cont.& Math. Sci., Vol. -13, No. -4, September-October (2018) Pages 147-154.

X.N.P. Padhy, M. A. Abdel-Moamen, and B. J. Praveen Kumar, “Optimal location and initial parameter settings of multiple TCSCs for reactive power planning using genetic algorithms,” IEEE Power Eng. Soc. Gen. Meet. 2004., vol. 2, pp. 1110–1114, 2004.

XI.Weibing Liu, Min Li, and Xianjia Wang, “An improved particle swarm optimization algorithm for optimal power flow,” 2009 IEEE 6th International Power Electronics and Motion Control Conference. IEEE, pp. 2448–2450, 2009.

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Abstract:

3D-printing technology is otherwise called added substance assembling or fast prototyping, is an advanced manufacturing technique which builds 3D parts directly in layer by layer from the computer aided plan model in raster way with minimal wastage of material. Rather than in conventional manufacturing process where material is removed by the hard tool to bring the 3D component in desired model, 3D printing is completely contrast to it where material is added in sequence parts are built in layer by layer, it doesn’t require any post processing as in conventional process. 3D printed parts are more performing under different loading conditions and easy to build and repair parts any stage of design cycle. Due its flexibility of manufacturing, it shows its applications in auto ancillaries, aerospace and medical filed. 3D printing technology showing it influencing in making medical implants. Manufacturing of medical implants in conventional process is very expensive. As these implants vary patient to patient, and it is difficult to make tailor made implants in conventional manufacturing processes. Hence 3D printing technology can overcome this issue with minimal cost for making tailor made implants for individual patients

Keywords:

Additive manufacturing,bio-materials,medical implants,

Refference:

I.C. Nastase-Dan, P. Doru Dumitru, G. Gheorghe Ion, and P. Sanda, “Innovative technology through selective laser sintering in mechatronics, biomedical engineering and industry,” Incas Bull., vol. 3, no. 1, pp. 31–37, 2011.

II.D. T. R. S. G. Pham, “A Comparsion of RP Technologies.pdf.

III.D. V Mahindru, P. Mahendru, V. Mahindru, and P. Mahendru, “Review of Rapid Prototyping-Technology for the Future,” Glob. J. Comput. Sci. Technol. Graph. {&} Vis., vol. 13, no. 4, pp. 27–38, 2013.

IV.F. P. W. Melchels, J. Feijen, and D. W. Grijpma, “A review on stereolithography and its applications in biomedical engineering,” Biomaterials, vol. 31, no. 24, pp. 6121–6130, 2010.

V.G. Suresh and K. L. Narayana, “3D Printing: Breakthroughs in Research and Practice,” in 3D Printing, IGI Global, 2016, pp. 1–21.

VI.G. Suresh and K. L. Narayana, “A Review on Fabricating Procedures in Rapid Prototyping,” Int. J. Manuf. Mater. Mech. Eng., vol. 6, no. 2, 2016.

VII.G. Suresh, K. L. Narayana, and M. K. Mallik, “A Review on Development of Medical Implants by Rapid PrototypingTechnology,” Int. J. Pure Appl. Math., vol. 117, no. 21, pp. 257–276, 2017.

VIII.Ganzi Suresh, K L Narayana and M. Kedar Mallik., “Bio-Compatible Processing of LENSTM Deposited Co-Cr-W alloy for Medical Applications”. International Journal of Engineering and Technology (UAE). 7 (2.20) (2018) 362-366. DOI:10.14419/ijet.v7i2.20.16734.

IX.Ganzi Suresh, K L Narayana, M. Kedar Mallik, V. Srinivas and G. Jagan Reddy., “Processing & Characterization of LENSTM Deposited Co-Cr-W Alloy for Bio-Medical Applications”. International Journal of Pharmaceutical Research (IJPR) Volume 10, Issue-1, 2018, 276-285.

X.Ganzi Suresh, K L Narayana, M. Kedar Mallik, V. Srinivas, G. Jagan Reddy and I.Gurappa.,“Electro Chemical Corrosion Behavior of LENSTM Deposited Co-Cr-W Alloy for Bio-Medical Applications”. International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) Special Issue, Jun 2018, 41-5.

XI.Hangobo Lan, “Web-based rapid prototyping and manufacturing systems: A review,” vol. 60, pp. 643–656, 2009.

XII.I.Palčič, M. Balažic, M. Milfelner, and B. Buchmeister, “Potential of laser engineered net shaping (LENS) technology,” Mater. Manuf. Process., vol. 24, no. 7–8, pp. 750–753, 2009.

XIII.Kumar, G. N. S. and A. Srinath. 2018. “An Ergonomical conditions of Pedestrians on Accelerating Moving Walkway: A People Mover System.” International Journal of Mechanical and Production Engineering Research and Development 8 (Special Issue 7): 1376-1381. www.scopus.com.

XIV.Kumar, Gurram Narendra Santosh, and A. Srinath. “Exploration of Accelerating Moving Walkway for Futuristic Transport System in Congested and Traffical Areas.” (2018): 616-624.

XV.L. Villalpando, H.Eiliat, and R. J. Urbanic, “An optimization approach for components built by fused deposition modeling with parametric internal structures,” Procedia CIRP, vol. 17, pp. 800–805, 2014.

XVII.M. E. W. M. Johnson, M. Rowell, B. Deason, “BENCHMARKING EVALUATION OF AN OPEN SOURCE FUSED DEPOSITION,” pp. 197–211, 1997.

XVIII.M. L. Griffith et al., “Free Form Fabrication of Metallic Components Using Laser Engineered Net Shaping (LENS),” Proc. 7th Solid Free. Fabr. Symp., pp. 125–132, 1996.

XIX.M. Montero, S. Roundy, and D. Odell, “Material characterization of fused deposition modeling (FDM) ABS by designed experiments,” Proc. Rapid Prototyp. Manuf. Conf., pp. 1–21, 2001.

XX.P. B. Klosterman D, Chartoff R, Graves G, Osborne N, “Interfacial characteristics of composites fabricated by laminated object manufacturing,” Compos Part A, vol. 29A, p. 1165–74., 1998.

XXI.P. Chennakesava and Y. S. Narayan, “Fused Deposition Modeling -Insights,” Int. Conf. Adv. Des. Manuf., pp. 1345–1350, 2014.

XXII.P. Rochus, J. Plesseria, M. Van Elsen, J. Kruth, R. Carrus, and T. Dormal, “New applications of rapid prototyping and rapid manufacturing ( RP / RM ) technologies for space instrumentation,” vol. 61, pp. 352–359, 2007.

XXIII.Q. Wei et al., “Selective laser melting of stainless-steel/nano-hydroxyapatite composites for medical applications: Microstructure, element distribution, crack and mechanical properties,” J. Mater. Process. Technol., vol. 222, pp. 444–453, 2015.

XXIV.Rama ChandraManohar, K et al. Modeling and Analysis of Kaplan Turbine Blade Using CFD.International Journal of Engineering & Technology, [S.l.], v. 7, n. 3.12, p. 1086-1089, july 2018. ISSN 2227-524X. Available at: https://www.sciencepubco.com/index.php/ijet/article/view/17766>. Date accessed: 05 jan. 2019. doi:http://dx.doi.org/10.14419/ijet.v7i3.12.17766.

XXV.Sk.Hasane Ahammad,V.Rajesh, “Image Processing based segmentation for spinal cord in MRI”,Indian Journal of Public Health Research and Development 9(6), pp.317-323XVI.M. Domingo-espin, I. Engineering, and U. Ramon, “A methodology to choose the best building direction for Fused Deposition Modeling end-use parts.

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Abstract:

Limitations of Wireless nodes are the battery power and storage capacity, while plotting a MANET, these are to be considered. By improvising battery life, the energy used by nodes shall be increased such that network is operational. To move data packets efficiently the network, MANET uses smallest Hop Count routing protocol. Most power is used by data transmission process. Key challenges in Ad Hoc networks are the recurring changes in network topology. Network topology changes happen due to motility and finite battery power of the mobile devices. Mostly links are not available in the network as depletion of power source may cause early unavailability of nodes. This paper discusses about the protocol that incorporates link failure prediction at network layer and Power Control Protocol at MAC layer to improve network performance. Performance enhancement in regards to total power transmission, energy regulation and consumption per node along with throughput of our proposed cross layer routing protocol is shown by simulation results when compared to AODV.

Keywords:

MANET,MAC Protocol,Cross layer,AODV,RDSR, LBP-AOMSV,LP-PCP,

Refference:

I.Abdule.S.M etHassan.S, “Divert Failure Route Protocol Based on AODV”, In Network Applications Protocols and Services (NETAPPS), 2010 Second International Conference on. IEEE, 2010.

II.Aman Kumar and Rahul Hans, ”Performance Analysis of DSDV, I-DSDV, OLSR, ZRP Proactive Routing Protocol in Mobile Ad Hoc Networks in IPv6”, International Journal of Advanced Science and Technology Vol.77,pp.25-36, 2015. III.Chakrabarti. S and Mishra. A, “Quality of service challenges for wireless mobile Ad hoc networks”, Wiley J. Wireless Communication and Mobile Computing, vol. 4, n°12, p. 29-153, 2004.

IV.Chang.R and Leu.J, “Long-lived path routing with received signal strength for ad hoc networks”, In Wireless Pervasive Computing, 1stInternational Symposium on. IEEE, 2006.

V.Crawley .E, Nair R., Rajagopalan. B and Sandick. H, “A Framework for QoS-based Routing in the Internet IETF RFC2386”, 2002.

VI.Frank Aune, “Cross-Layer Tutorial”,NTNU 2014.

VII.F. Sophia Pearlin and G. Rekha,“ Performance Comparison of AODV, DSDV and DSR Protocols in Mobile Networks using NS-2”, Indian Journal of Science and Technology, Vol 9(8), February 2016.

VIII.Hwang.YetVarshney.P, “An adaptive QoS routing protocol with dispersity for ad-hoc networks”, chez System Sciences, 2003. Proceedings of the 36th Annual Hawaii International Conference on.IEEE,2003.

IX.John Novatnack , Lloyd Greenwald and Harpreet Arora, “Evaluating ad hoc routing protocols with respect to quality of service”, Wireless And Mobile Computing, Networking And Communications, Volume 3, pp. 205-212,Aug.2005.

X.L. Qin and T. Kunz, “Proactive Route Maintenance in DSR”, SIGMOBILE Mob. Comput. Commun. Rev., Vol. 6, No. 3, pp. 79–89, 2002.

XI.M. Al-Shurman, S.-M.Yoo, and S. Park, “A Performance Simulation for Route Maintenance in Wireless Ad Hoc Networks”, in ACM-SE 42:Proceedings of the 42nd annual Southeast regional conference, New York, USA: ACM, pp. 25–30, 2004.

XII.Mamoun Hussein Mamoun, “Location Aided Hybrid Routing Algorithm for MANET,” Int. Journal of Engineering& Technology IJET/IJENS, Vol. 11, No. 01, pp. 51-57, Feb. 2011.

XIII.Mamoun Hussein Mamoun,”A Proposed Route Selection Technique in DSR Routing Protocol for MANET”, International Journal of Engineering & Technology IJET-IJENS, Vol. 11, No. 02, April 2011.

XIV.MerlindaDrini and Tarek Saadawi, ”Modeling Wireless Channel for Ad-Hoc Network Routing Protocol”, ISCC MarakechMarocco, pp. 549-555, July 2008.

XV.M. F. Sjaugi, M. Othman, and M. F. A. Rasid,“A New Distance Based Route Maintenance Strategy for Dynamic Source Routing Protocol”, Journal of Computer Science, Vol. 4, No. 3, pp. 172–180, 2008.

XVI.M. Tsai, N. Wisitpongphan, and O.K. Tonguz, “Link-Quality Aware AODV Protocol”, in Proc. IEEE International Symposium on Wireless Pervasive Computing (ISWPC) 2006, Phuket, Thailand, January 2006.

XVII.Perkins.C, Belding.E ,Royer and Das.S, “Ad hoc on-demand distance vector routing”, RFC 3561, IETF, 2003.

XVIII.P. Srinivasan and K. Kamalkkannan, “Signal Strength And Energy Aware Reliable Route Discovery in Manet”, International Journal of Communication Network Security, Vol. 1, Issue 4, 2012.

XIX.QoS Forum, July 1999. [On line]. Available: http://www.qosforum.com.

XX.RjabHajlaoui, Sami Touil and Wissemachour,” O-DSR: Optimized DSR Routing Protocol For Mobile Ad Hoc Network”, International Journal of Wireless & Mobile Networks (IJWMN) Vol. 7, No. 4, August 2015.

XXI.RAJESHKUMAR, P.SIVAKUMAR, ”Comparative Study of AODV, DSDV and DSR Routing Protocols in MANET Using NetworkSimulator-2”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 12, December 2013.

XXII.Rajeshwar Singh, Dharmendra K Singh and Lalan Kumar,” Performance Evaluation of DSR and DSDV Routing Protocols for Wireless Ad Hoc Networks”, Int. J. Advanced Networking and Applications 732 Volume: 02, Issue: 04, pp. 732-737 2011.

XXIII.Ravneetkaur, Dr.Neeraj Sharma, “Dynamic node recovery in MANET for high recovery probability”, International Journal of Computer Networks and Applications (IJCNA), Vol 2, Issue 4, July -August 2015.

XXIV.Rohan Gupta, Harbhajan Singh and Gurpreet Singh,” Performance Evaluation of Routing Protocols for Mobile AdhocNetworks ”, Indian Journal of Science and Technology, Vol 10, No. 31, August 2017.

XXV.Rupinder Kaur, Paramdeep Singh et al, ” Performance Enhancement of AODV with Distributed-DSR Routing Protocol in Manet”, Indian Journal of Science and Technology, Vol. 8, No. 28, October 2015.

XXVI.Sarma.N and Nandi.S, “Route stability based QoS routing in mobile AdHoc networks”, Wireless Personal Communications , vol. 54, n° 11,pp. 203-224, 2010.

XXVII.S. Wu, S. Ni, Y. Tseng, and J. Sheu, “Route Maintenance in a Wireless Mobile Ad Hoc Network”, 33rd Hawaii International Conference onSystem Sciences, Maui, 2000.

XXVIII.VivekSoi,and Dr. B.S. Dhaliwal,“ Performance comparison of DSR and AODV Routing Protocol in Mobile Ad hoc Networks”, International Journal of Computational Intelligence Research Volume 13, No. 7, pp. 1605-1616, 2017.

XXIX.Y. Ramesh, Usha Ch. andJagadishGurrala,” CBR based Performance Evaluation on FSR, DSR,STAR-LORA, DYMO Routing Protocols in MANET”, International Journal of Engineering Research and Development, Vol. 2, Issue 9, PP. 17-27, (August 2012).

XXX.ZekiBilgin, Bilal Khan, “A Dynamic Route Optimization Mechanism for AODV in MANETs”, Journal of Computer Science, 2014.

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Abstract:

The venturi scrubber has been used as air pollution controlling device. These scrubbers are promising device for cleaning the contaminated gases. It is found in the literature that the performance of venturi scrubber (i.e. collection efficiency), is significantly influenced by droplet distribution, pressure drop, disintegration of liquid, droplet sizes and injection methods. Effect of submergence height, multi-stage injection, position of the orifice, diameter of orifice, throat length and angle of convergence and divergence of venturi scrubber is found scarce and these parameters are affecting collection efficiency drastically. Therefore, it is necessary to study their effect to improve the performance of self-priming venturi scrubber. This article is the review of numerical and experimental study of the performance in venturi scrubber.

Keywords:

Venturi Scrubber,Self-Priming,CFD Modelling,Collection efficiency,

Refference:

I.A.Rahimi,J.Fathikalajahi,andM.Taheri,“ANewMethodofEddyDiffusivityCalculationforDropletsofaVenturiScrubber,”vol.84,no.February,pp.310–315,2006.

II.A.Moharana,P.Goel,andA.K.Nayak,“N12:Performanceestimationofaventuriscrubberanditsapplicationtoself-primingoperationindecontaminatingaerosolparticulates,”Nucl.Eng.Des.,vol.320,pp.165–182,2017.

III.A.M.Silva,J.C.F.Teixeira,andS.F.C.F.Teixeira,“Experimentsinlargescaleventuriscrubber.PartII.Dropletsize,”Chem.Eng.Process.ProcessIntensif.,vol.48,no.1,pp.424–431,2009.

IV.A.SharifiandA.Mohebbi,“AcombinedCFDmodelingwithpopulationbalanceequationtopredictpressuredropinventuriscrubbers,”2013.

V.A.M.Silva,J.C.F.Teixeira,andS.F.C.F.Teixeira,“Experimentsinalarge-scaleventuriscrubber.PartI:Pressuredrop,”Chem.Eng.Process.ProcessIntensif.,vol.48,no.1,pp.59–67,2009.

VI.A.Majid,Y.Changqi,S.Zhongning,W.Jianjun,andG.Haifeng,“CFDsimulationofdustparticleremovalefficiencyofaventuriscrubberinCFX,”Nucl.Eng.Des.,vol.256,pp.169–177,2013.

VII.A.Majid,C.Yan,S.Zhongning,J.Wang,andA.Rasool,“N6:CFDSimulationofThroatPressureinVenturiScrubberMajidAli,”Appl.Mech.Mater.,vol.173,pp.3630–3634,2012.

VIII.A.Rahimi,A.Niksiar,andM.Mobasheri,“Consideringrolesofheatandmasstransferforincreasingtheabilityofpressuredropmodelsinventuriscrubbers,”Chem.Eng.Process.ProcessIntensif.,vol.50,no.1,pp.104–112,2011.

IX.C.Goniva,Z.Tukovic,C.Feilmayr,T.Bürgler,andS.Pirker,“SimulationofoffgasscrubbingbyacombinedEulerian-Lagrangianmodel,”SeventhInt.Conf.CFDMiner.ProcessInd.,no.December,pp.1–7,2009.

X.D.B.RobertsandJ.C.Hill,“Atomizationinaventuriscrubber,”Chem.Eng.Commun.,vol.12,no.1–3,pp.33–68,1981.

XI.D.FernándezAlonso,J.A.S.Gonçalves,B.J.Azzopardi,andJ.R.Coury,“DropsizemeasurementsinVenturiscrubbers,”Chem.Eng.Sci.,vol.56,no.16,pp.4901–4911,2001.

XII.F.AhmadvandandM.R.Talaie,“CFDmodelingofdropletdispersioninaVenturiscrubber,”Chem.Eng.J.,vol.160,no.2,pp.423–431,2010.

XIII.H.E.Hesketh,“FineParticleCollectionEfficiencyRelatedtoPressureDrop,ScrubbantandParticleProperties,andContactMechanism,”J.AirPollut.ControlAssoc.,vol.24,no.10,pp.939–942,1974.

XIV.H.Haller,E.Muschelknautz,andT.Schultz,“VenturiScrubberCalculationandOptimization,”vol.12,pp.188–195,1989.

XV.H.SunandB.J.Azzopardi,“Modellinggas-liquidflowinVenturiscrubbersathighpressure,”ProcessSaf.Environ.Prot.Trans.Inst.Chem.Eng.PartB,vol.81,no.4,pp.250–256,2003.

XVI.J.R.Coury,G.Guerra,R.Be,andJ.A.S.Gonc,“PressureDropandLiquidDistributioninaVenturiScrubber:ExperimentalDataandCFDSimulationVad,”2012.

XVII.J.Fathikalajahi,M.Taheri,andM.R.Talaie,“Theoreticalstudyofnonuniformdropletsconcentrationdistributiononventuriscrubberperformance,”Part.Sci.Technol.,vol.14,no.2,pp.153–164,1996.

XVIII.J.F.andM.R.Talaie,“THEEFFECTOFDROPLETSIZEDISTRIBUTIONONLIQUIDDISPERSIONINAVENTURISCRUBBER,”J.AerosolSci.Vol.,vol.28,no.1,pp.291–292,1997.

XIX.J.A.S.Gonçalves,M.A.M.Costa,M.L.Aguiar,andJ.R.Coury,“AtomizationofliquidsinaPease-AnthonyVenturiscrubber:PartII.Dropletdispersion,”J.Hazard.Mater.,vol.116,no.1–2,pp.147–157,2004.

XX.J.A.S.Gonçalves,D.F.Alonso,M.A.M.Costa,B.J.Azzopardi,andJ.R.Coury,“Evaluationofthemodelsavailableforthepredictionofpressuredropinventuriscrubbers,”J.Hazard.Mater.,vol.81,no.1–2,pp.123–140,2001.

XXI.K.C.GoalandK.G.T.Hollands,“AGeneralMethodforPredictingParticulateCollectionEfficiencyofVenturiScrubbers,”Ind.Eng.Chem.Fundam.,vol.16,no.2,pp.186–193,1977.

XXII.M.TaheriandA.Mohebbi,“N3:Designofartificialneuralnetworksusingageneticalgorithmtopredictcollectionefficiencyinventuriscrubbers,”J.Hazard.Mater.,vol.157,no.1,pp.122–129,2008.

XXIII.M.TaheriandG.F.Haines,“Optimizationoffactorsaffectingscrubberperformance,”J.AirPollut.ControlAssoc.,vol.19,no.6,pp.427–431,1969.

XXIV.M.Lehner,“AerosolSeparationEfficiencyofaVenturiScrubberWorkinginSelf-PrimingMode,”AerosolSci.Technol.,vol.28,no.5,pp.389–402,1998.

XXV.M.A.M.Costa,P.R.Henrique,J.A.S.Gonçalves,andJ.R.Coury,“DropletsizeinarectangularVenturiscrubber,”BrazilianJ.Chem.Eng.,vol.21,no.2,pp.335–343,2004.

XXVI.M.Ali,C.Q.Yan,Z.N.Sun,J.J.Wang,andK.Mehboob,“N5:CFDSimulationofPredictionofPressureDropinVenturiScrubber,”Appl.Mech.Mater.,vol.166–169,pp.3008–3011,2012.

XXVII.M.Costa,A.Riberio,E.Tognetti,M.Aguiar,J.Gonclaves,andJ.Coury,“Performanceofaventuriscrubberintheremovaloffinepowderfromaconfinedgasstream,”Mater.Res.,vol.18,no.2,pp.177–179,2005.

XXVIII.M.M.Toledo-Melchoretal.,“NumericalsimulationofflowbehaviourwithinaVenturiscrubber,”Math.Probl.Eng.,vol.2014,pp.1–8,2014.

XXIX.M.BalandB.C.Meikap,“N10:PredictionofhydrodynamiccharacteristicsofaventuriscrubberbyusingCFDsimulation,”SouthAfricanJ.Chem.Eng.,vol.24,pp.222–231,2017.

XXX.N.V.AnanthanarayananandS.Viswanathan,“EffectofnozzlearrangementonVenturiscrubberperformance,”Ind.Eng.Chem.Res.,vol.38,no.12,pp.4889–4900,1999.

XXXI.N.P.Gulhane,A.D.Landge,D.S.Shukla,andS.S.Kale,“Experimentalstudyofiodineremovalefficiencyinself-primingventuriscrubber,”Ann.Nucl.Energy,vol.78,pp.152–159,2015.

XXXII.N.Horiguchi,H.Yoshida,andY.Abe,“N9:Numericalsimulationoftwo-phaseflowbehaviorinVenturiscrubberbyinterfacetrackingmethod,”Nucl.Eng.Des.,vol.310,pp.580–586,2016.

XXXIII.N.Horiguchi,H.Yoshida,S.Uesawa,A.Kaneko,andY.Abe,“Icone21-16287FilterVenting:PreliminaryAnalysisandObservationofHydraulic,”pp.1–6,2013.

XXXIV.P.Goel,A.Moharana,andA.K.Nayak,“Experimentalstudyofpressuredropinself-primingandsubmergedventuriscrubber,”pp.14–17.

XXXV.P.Goel,A.Moharana,andA.K.Nayak,“Measurementofscrubbingbehaviourofsimulatedradionuclideinasubmergedventuriscrubber,”Nucl.Eng.Des.,vol.327,no.December2017,pp.92–99,2018.

XXXVI.R.H.Boll,“ParticleCollectionandPressureDropinVenturiScrubbers,”Ind.Eng.Chem.Fundam.,vol.12,no.1,pp.40–50,1973.

XXXVII.R.W.K.AllenandA.VanSanten,“DesigningforpressuredropinVenturiscrubbers:Theimportanceofdrypressuredrop,”Chem.Eng.J.Biochem.Eng.J.,vol.61,no.3,pp.203–211,1996.

XXXVIII.S.Nasseh,A.Mohebbi,Z.Jeirani,andA.Sarrafi,“N2:Predictingpressuredropinventuriscrubberswithartificialneuralnetworks,”J.Hazard.Mater.,vol.143,no.1–2,pp.144–149,2007.

XXXIX.S.CalvertandD.Lundgren,“ParticleCollectioninaVenturiScrubber,”J.AirPollut.ControlAssoc.,vol.18,no.10,pp.677–678,1968.

XL.S.Viswanathan,C.C.St.Pierre,andA.W.Gnyp,“Jetpenetrationmeasurementsinaventuriscrubber,”Can.J.Chem.Eng.,vol.61,no.4,pp.504–508,1983.

XLI.S.I.PakandK.S.Chang,“N1:PerformanceestimationofaVenturiscrubberusingacomputationalmodelforcapturingdustparticleswithliquidspray,”J.Hazard.Mater.,vol.138,no.3,pp.560–573,2006.

XLII.S.Calvert,“VenturiandOtherAtomizingScrubbersEfficiencyandPressureDrop,”AIChE,vol.16,no.3,pp.392–396,1970.

XLIII.S.C.Yung,H.F.Barbarika,andS.Calvert,“Pressurelossinventuriscrubbers,”J.AirPollut.ControlAssoc.,vol.27,no.4,pp.348–351,1977.

XLIV.S.Nasseh,A.Mohebbi,A.Sarrafi,andM.Taheri,“N4:Estimationofpressuredropinventuriscrubbersbasedonannulartwo-phaseflowmodel,artificialneuralnetworksandgeneticalgorithm,”Chem.Eng.J.,vol.150,no.1,pp.131–138,2009.

XLV.S.IlKim,J.B.Lee,J.H.Jung,K.S.Ha,H.Y.Kim,andJ.H.Song,“IntroductionoffilteredcontainmentventingsystemexperimentalfacilityinKAERIandresultsofaerosoltest,”Nucl.Eng.Des.,vol.326,no.November2017,pp.344–353,2018.

XLVI.T.J.OvercampandS.R.Bowen,“EffectofThroatLengthandDiffuserAngleonPressureLossAcrossaVenturiScrubber,”J.AirPollut.ControlAssoc.,vol.33,no.6,pp.600–604,1983.

XLVII.V.Sekar,A.W.Gnyp,andC.C.S.Pierre,“ExaminationofGas-LiquidFlowinaVenturiScrubber,”Ind.Eng.Chem.Fundam.,vol.23,no.3,pp.303–308,1984.

XLVIII.V.G.Guerra,M.A.F.Daher,M.V.Rodrigues,J.A.S.Gonçalves,andJ.R.Coury,“DropletInteractionintheLiquidInjectionbyMultipleOrificesinthePerformanceofaVenturiScrubber,”Mater.Sci.Forum,vol.591–593,pp.896–901,2008.

XLIX.V.G.Guerra,J.A.S.Gon??alves,andJ.R.Coury,“ExperimentalinvestigationontheeffectofliquidinjectionbymultipleorificesintheformationofdropletsinaVenturiscrubber,”J.Hazard.Mater.,vol.161,no.1,pp.351–359,2009.

L.V.G.Guerra,J.A.S.Gonçalves,andJ.R.Coury,“ExperimentalverificationoftheeffectofliquiddepositionondropletsizemeasuredinarectangularVenturiscrubber,”Chem.Eng.Process.ProcessIntensif.,vol.50,no.11–12,pp.1137–1142,2011.

LI.X.Gamisans,M.Sarrà,F.J.Lafuente,andB.J.Azzopardi,“Thehydrodynamicsofejector-Venturiscrubbersandtheirmodellingbyanannularflow/boundarylayermodel,”Chem.Eng.Sci.,vol.57,no.14,pp.2707–2718,2002.

LII.Y.Zhou,Z.Sun,H.Gu,andZ.Miao,“Structuredesignonimprovinginjectionperformanceforventuriscrubberworkinginself-primingmode,”Prog.Nucl.Energy,vol.80,pp.7–16,2015.

LIII.Yung,“venturiscrubberperformancemodel.PDF,”vol.7,no.9,pp.10–13,1978.

LIV.Y.Zhou,Z.Sun,H.Gu,andZ.Miao,“Experimentalresearchonaerosolscollectionperformanceofself-primingventuriscrubberinFCVS,”Prog.Nucl.Energy,vol.85,pp.771–777,2015.

LV.Y.Zhou,Z.Sun,H.Gu,andZ.Miao,“Performanceofiodidevapourabsorptionintheventuriscrubberworkinginself-primingmode,”Ann.Nucl.Energy,vol.87,pp.426–434,2016.

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Abstract:

The most challenging task is searching Telugu script from the database because of difficulty in differentiating the Characteristics of the Telugu word or scripts. In this, we introduced robust approach for Telugu script retrieval using transformation-based methodology. Non-subsampled contourlet transform (NSCT) is utilized for texture classification which will function based on Non-subsampled pyramid filter bank (NSPFB) and Non-subsampled directional filter bank (NSDFB). Spatial dependence matrix is utilized to extract the texture features. In addition, image statistics is computed to enhance the retrieval performance further. Finally, hamming similarity metric is calculated which calculates the distance between trained and test word templates, which an effective distance metric over conventional Euclidean distance. In order to test, missing segment, noisy, corrupted and occlusion effected words are used as an input and taken into consideration multi conjunct vowel consonant clustered word images for showing the robustness of presented algorithm. In the substantial simulation analysis gives the presented technique finds most similar word images from database although if it is under testing conditions. Our presented scheme has superior performance compared to the traditional approaches described in the literature with respect to mean Average Precision (mAP) and mean Average Recall (mAR).

Keywords:

Telugu script,texture features,statistical properties,non-subsampled contourlet transform,statistical parameters,feature vector and hammingdistance metric,

Refference:

I.Arthur L. da Cunha, Jianping Zhou,and Minh N. Do, “The Non-subsampled Contourlet Transform: Theory, Design and Applications”, IEEE Transaction on Image Processing, Vol. 15, No. 10, pp. 3089-3100, 2006.

II.B. Verma, M. Blumenstein, S. Kulkarni, “Recent achievements in off-line handwriting recognition systems”, School of Information Technology, Griffith University, Gold Coast Campus.

III.C. V. Jawahar and A. Kumar, “Content-level Annotation of Large Collection of Printed Document Images”, In: Proc. of International Conf. on Document Analysis and Recognition, Parana, Brazil, 2007.

IV.C. V. Jawahar, M. N. S. S. K. Pavan Kumar, S. S. Ravi Kiran, “A Bilingual OCR for Hindi-Telugu Documents and its Applications”, Centre for Visual Information Technology, International Institute of Information Technology, Hyderabad.

V.D. G. Lowe, “Distinctive Image Features from Scale-Invariant Key points,” International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2004.

VI.Danish Nadeem and Saleha Rizvi, “Character recognition using template matching”, Department of Computer Science, JamiaMilliaIslamia, New Delhi, 2015.

VII.E Candes and D. Donoho, “Curvelets –a surprisingly effective nonadaptive representation for objects with edges.” In: A. Cohen, C. Rabut and L. Schumaker, Editors,Curves and Surface Fitting: Saint-Malo 1999, Vanderbilt University Press, Nashville, pp. 105–120, 2000.

VIII.E. Kreyszig, Advanced Engineering Mathematics, J. Willey & Sons Inc. 2011.

IX.E. Kreyszig, Advanced Engineering Mathematics, J. Willey & Sons Inc. 2011.

X.I. Z. Yalniz and R. Manmatha, “An Efficient Framework for Searching Text in Noisy Document Images”, IAPS International Workshop on Document Analysis Systems, Gold Cost, QLD, Australia, pp. 48-52, 2012.

XI.J. van Gemert, C. J. Veenman, A. W. M. Smeulders, and J.-M. Geusebroek, “Visual Word Ambiguity”, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 32, No. 7, pp.1271-1283, 2010.

XII.J. van Gemert, J.-M. Geusebroek, C. J. Veenman, and A. W. M. Smeulders, “Kernel Codebooks for Scene Categorization”, In: Proc. Of European Conf. on Computer Vision, Berlin, Heidelberg, pp. 696-709, 2008.

XIII.Jangala. Sasi Kiran, N. Vijaya Kumar, N. SashiPrabha and M. Kavya, “A Literature Survey on Digital Image Processing technique in character recognition of Indian languages”, International Journal of Computer Science and Information Technologies, Vol. 6, No. 3, pp. 2065-2069, 2015.

XIV.Jatin M Patil and Ashok P. Mane, “Multi Font and Size Optical Character Recognition Using Template Matching”, International Journal of Emerging Technology and Advanced Engineering, Vol. 3, No. 1, pp. 504-506, 2013.

XV.K Mohana Lakshmi and T RangaBabu, “Searching for Telugu Script in Noisy Images using SURF Descriptors”, IEEE 6th International Conference on Advance Computing, pp: 480-483, 2016.

XVI.K. Takeda, K. Kise, and M. Iwamura, “Real-time document image retrieval for a 10 Million pages database with a memory efficient and stability improved LLAH”, International Conf. on Document Analysis and Recognition, Beijing, China, pp. 1054-1058, 2011.

XVII.K.Mohana Lakshmi, Dr.T.Ranga Babu, “A Novel Telugu Script Recognition and Retrieval Approach Based on Hash Coded Hamming , ICCCPE(Springer LNS), 978-981-13-0211-4, 2018.

XVIII.KesanaMohana Lakshmi and TummalaRangaBabu, “A New Hybrid Algorithm for Telugu Word Retrieval and Recognition”, International Journal of Intelligent Engineering and Systems, Vol. 11, No. 4, pp.117-127, 2018.

XIX.M N Do and M Vetterli, “The contourlet transform: an efficient directional multiresolution image representation”, IEEE Transactions on Image Processing, Vol. 14, No. 12, pp. 2091-2106, 2005.

XX.M. J. Shensa, “The discrete wavelet transform: Wedding the àtrous and Mallat algorithms,” IEEE Trans. Signal Process., Vol. 40, No. 10, pp. 2464–2482, 1992.

XXI.M. Wenying and D. Zuchun, “A Digital Character Recognition Algorithm Based on the Template Weighted Match Degree”, International Journal of Smart Home, Vol.7, No. 3, pp. 53-60, 2013.

XXII.Md. Mahbubar Rahman, M. A. H. Akhand, Shahidul Islam, Pintu Chandra Shill and M. M. Hafizur Rahman, “Bangla Handwritten Character Recognition using Convolutional Neural Network”, International Journal of Image, Graphics and Signal Processing, Vol. 7, No. 8, pp. 42-49, 2015.

XXIII.N. Sharma, S. Chanda, U. Pal and M. Blumenstein, “Word-wise Script Identification from Video Frames”, In: Proc. of International Conf.on Document Analysis and Recognition, Washington, DC, USA, pp.867-871, 2013.

XXIV.N. Shobha Rani Vasudev T and Pradeep C.H. “A Performance Efficient Technique for Recognition of Telugu Script Using Template Matching”, International Journal of Image, Graphics and Signal Processing, Vol. 8, No. 3, pp.15-23, 2016. XXV.N. Shobha Rani, T. Vasudev, “A Generic Line Elimination Methodology using Circular Masks for Printed and Handwritten Document Images”, Emerging research in computing, information, communication and applications ELSEVIER science and technology, Vol. 3, No. 1, pp. 589-594, 2014.

XXVI.N.sharma, U.Pal, and M. Blumenstein, “A Study on Word Level Multi-script Identification from Video Frames”, In: Proc. of International Joint Conf. on Neural Networks, Beijing, China, pp.1827-1833, 2014.

XXVII.Nagasudha D and Y MadhaviLatha, “Keyword Spotting using HMM in Printed Telugu Documents”, In: Proc. of International Conf. on Signal Processing, Communication, Power and Embedded Systems, Paralakhemundi, India, pp: 1997-2000, 2016.

XXVIII.Nikhil Rajiv Pai and Vijaykumar S. Kolkure, “Design and implementation of optical character recognition using template matching for multi fonts size”, International Journal of Research in Engineering and Technology, Vol. 4, No. 2, pp. 398-400, 2015.

XXIX.P. Shivakumara, N. Sharma, U. Pal, M. Blumenstein, and C. L. Tan, “Gradient-Angular-Features for Word-wise Video Script Identification”, In: Proc. of International Conf. on Pattern Recognition, Stockholm, Sweden, pp.3098-3103, 2014.

XXX.R. Shekhar and C. V. Jawahar, “Word Image Retrieval Using Bag of Visual Words”, IAPS International Workshop on Document Analysis Systems, Gold Cost, QLD, Australia, pp. 297-301, 2012.

XXXI.Ravi Shekhar and C V Jawahar, “Word Image Retrieval Using Bag-of-Visual Words”, In: Proc. of IAPR International Workshop on Document Analysis Systems, Gold Cost, QLD,

XXXII.Rinki Singh, Manideep Kaur, “OCR for Telugu Script Using Back-Propagation Based Classifier”, International Journal of Information Technology and Knowledge Management, Vol. 2, No. 2, pp. 639-643, 2010.

XXXIII.S. Lazebnik, C. Schmid, and J. Ponce, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories”, In: Proc. of IEEE Computer Society Conf. onComputer Vision and Pattern Recognition, New York, USA, 2006.

XXXIV.Soumendu Das and Sreeparna Banerjee, “An Algorithm for Japanese Character Recognition”, International Journal of Image, Graphics and Signal Processing, Vol. 7, No. 1, pp. 9-15, 2014.

XXXV.Suman V Patgar, Vasudev T, Murali S, “A system for detection of fabrication in photocopy document”, Journal of Computer Science & Information Technology, Vol. 5, No. 14, pp. 29–35, 2015.

XXXVI.T. M. Rath and R. Manmatha, “Word spotting for historical documents”, International Journal of Document Analysis and Research, Vol. 9, No. 2-4,pp.139-152, 2007.

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Abstract:

Transportation model provides a powerful framework to meet the Business challenges. In highly competitive market the pressure is increasing rapidly to the organizations to determine the better ways to deliver goods to the customers with minimum transportation cost. In this paper we proposed a new algorithm based on Least Cost Method(LCM)for finding Initial Basic Feasible Solution(IBFS) to minimize transportation cost .Our proposed algorithm provides a IBFS which is either optimal or near to the optimal value with minimum steps comparatively better than those obtain by traditional algorithm or method .For the validity of this algorithm we considered a numerical transportation problem and comparative study has been made minimum cost with graphically.

Keywords:

Transportation Cost, Least Cost Method, Supply,Demand, Initial Basic feasible Solution,Optimum solution,

Refference:

I.Ahuja, R.K.(1986). Algorithms for minimax transportation problem. Naval Research Logistics Quarterly.33 (4), 725-739. II.A.Gupta, S.Khanna and M. Puri, (1992), Paradoxical situations in transportation problems, Cahiers du Centre d’Etudes de RechercheOperationnell, 37–49.

III.Charnes, A. and Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming, 1, John Wiley & Sons, New York.

IV.Erlander S.B (2010) Cost-Minimizing Choice Behavior in Transportation Planning: A Theoretical. Page 8-10.

V.Goyal, S.K.(1984). Improving VAM for unbalanced transportation problem. Journal of Operational Research Society. 35(12), 1113-1114.

VI.Hadley, G., (1972). Linear Programming, Addition-Wesley Publishing Company, Massachusetts.

VII.Hemaida, R. & Kwak, N. K. (1994). A linear goal programming model for transshipment problems with flexible supply and demand constraints. Journalof Operational Research Society, 45(2), 1994, 215-224.

VIII.Hitchcock, F.L.(1941). The distribution of a product from several sources to numerous localities. Journal of Mathematics & Physics. 20, 224-230.

IX.Kvanli, A. (1980). Financial planning using goal programming. Omega, 8, 207-218.

X.Kwak, N.K. & Schniederjans, M.J.(1979) “A goal programming model for improved transportation problem solutions,” Omega, 12, 367-370.

XI.Lee, S.M., (1972). Goal Programming for Decision Analysis, Auerbach, Philadelphia.

XII.M.A .Hakim, M. A. Hossen, M. Sarif Uddin (2016),A credit policy approach of an inventory model for deteriorating item with price and time dependent demandaccepted for publication inJournal of Mechanics of Continua and Mathematical Sciences, ISSN 0973-8975,Volume -10 No. -2 .

XIII.Tolstoi, A.N. (1939). Methody ustraneniya neratsional’nykh perevozok pri planirovanii [Russian; Methods of removing irrational transportation in planning], Sotsialisticheskii Transport 9, 28-51 [also published a ‘pamphlet’: Methods of Removing Irrational Transportation in the construction of Operations Plans], Transzheldorizdat, Moscow, 1941.

XIV.V K Kapoor ,Operation Research (Problem and solution) ,sultan chand &sons, educational publishers ,new delhi.

XV.Veena Adlakha and Krzysztof Kowalski (2001), A heuristic method for more –for-less in distribution related problems, International Journal of Mathematical Education in Science and Technology, 32 61-71.

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Abstract:

This letter presents a single feed novel miniaturized patch antenna for WiMax applications and small wireless technologies. Antenna is fabricated on FR4 substrate with 1.6mm thickness and copper sheet of 0.035mm. The miniaturization of 82% is achieved by etching a Fork shape slot in ground plane as response is observed at 3.4GHz. Simulated and measured results shows acceptable gain of 3.4 to 3.6dB and efficiency ranging to 82% with 260MHz bandwidth. The proposed antenna is simulated in Computer Simulation Technology 2015. The measurement results demonstrate that the proposed antenna provides acceptable radiation performances with directional radiation patterns at desired frequency.

Keywords:

Miniaturization,Microstrip Patch Antenna (MPA),directivity,gain,bandwidth,Slots,Computer Simulation Technology (CST),

Refference:

I.Aguilar, Suzette M., Mudar A. Al-Joumayly, Matthew J. Burfeindt, Nader Behdad, and Susan C. Hagness. ”Multiband miniaturized patch antennas for a compact, shielded microwave breast imaging array.” IEEE transactions on antennas and propagation 62, no. 3 (2014): 1221-1231.

II.Ali, M. S. M., Rahim, S. K. A., Sabran, M. I., Abedian, M., Eteng, A., Islam, M. T. (2016). Dual band miniaturized microstrip slot antenna for WLAN applications. Microwave and Optical Technology Letters, 58(6), 1358-1362.

III.Amit K. Singh*, Mahesh P.Abegaonkar, and Shiban K. Koul, “Miniaturized Multiband Microstrip Patch Antenna Using Metamaterial Loading for Wireless Application” Progress In Electromagnetics Research C, Vol. 83, 71–82, 2018.

IV.Boukarkar, Abdelheq, Xian Qi Lin, Yuan Jiang, and Yi QiangYu. “Miniaturized single-feed multiband patch antennas.” IEEE Transactions on Antennas and Propagation 65, no. 2 (2017): 850-854.

V.Chen, Richard H., and Yi-Cheng Lin. “Miniaturized design of microstrip-fed slot antennas loaded with C-shaped rings.” IEEE Antennas and Wireless Propagation Letters 10 (2011): 203-206.

VI.Fritz-Andrade, E., Tirado-Mendez, J. A., Jardon-Aguilar, H., & Flores-Leal, R. (2017). Application of complementary split ring resonators for size reduction in patch antenna arrays. Journal of Electromagnetic Waves and Applications, 31(16), 1755-1768.

VII.Gupta, Ashish. “Miniaturized dual‐band metamaterial inspired antenna with modified SRR loading.” International Journal of RF and Microwave Computer‐Aided Engineering (2018): e21283.

VIII.Li, Ziyang, Leilei Liu, Pinyan Li, and Jian Wang. “Miniaturized design of CPW-Fed slot antennas using slits.” In 2017 Sixth Asia-Pacific Conference on Antennas and Propagation (APCAP), pp. 1-3. IEEE, 2017.

IX.M. M. Bait-Suwailam and H. M. Al-Rizzo, “Size reduction of microstrip patch antennas using slotted Complementary Split-Ring Resonators,” in Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2013 International Conference on, 2013, pp. 528-531.

X.Motevasselian, Alireza, and William G. Whittow. “Miniaturization of a Circular Patch Microstrip Antenna Using an Arc Projection.” IEEE Antennas and Wireless Propagation Letters 16 (2017): 517-520.

XI.Saad Hassan Kiani, Khalid Mahmood, Mehre Munir and Alex James Cole, “A Novel Design of Patch Antenna using U-Slotand Defected Ground Structure” International Journal of Advanced Computer Science and Applications(ijacsa),8(3),2017. http://dx.doi.org/10.14569/IJACSA.2017.080303E.

XII.Tirado‐Mendez, J. A., Jardon‐Aguilar, H., Flores‐Leal, R., & Rangel‐Merino, A. (2018). Multiband reduced‐size patch antenna by employing a modified DMS‐spur‐line combo technique. International Journal of RF and Microwave Computer‐Aided Engineering, 28(4), e21232.

XIII.Wang, Qian, Ning Mu, Linli Wang, Jingping Liu, and Ying Wang. “Miniaturization microstrip antenna design based on artificial electromagnetic structure.” In 2017 Sixth Asia-Pacific Conference on Antennas and Propagation (APCAP), pp. 1-3. IEEE, 2017.

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Abstract:

This study is a very good approach to find the solution of secure authentication for IOT based smart home environment and its appliances. The study aims to compare the different authentication methods with respect to smart home environment and trying to identify its limitation. After analyzing the existing authentication methods its limitation and core issues then targeted the message authentication for SHE. Presently SHE authentication is based on Exchange of six message authentication techniques in Enhance authentication and key establishment scheme 6LOWPAN (EAKES6Lo) which is advance version of secure authentication and key establishment scheme (SAKES). This authentication method cause much high end to end delay, energy consumption, overall throughput of the system, complexity and poor security approach. By simulation of EAKES6Lo and SAKES scheme found some results, in contrast to these results, there may be another solution to access any SHE lights, fans, refrigerators, air condition, geezer, door lock, microwave oven, television and water pump, HVAC control and security alarms etc remotely with better security, better complexity, minimum energy consumption, better key length, better throughput and minor end to end delay named two step authentication (TSA). The proposed model also helps to monitor accessing system by comparing security codes and its complexity.

Keywords:

Internet of Things(IOT),Smart Home Environment (SHE),Version 6 Low Power Wireless Personal Area Network (6LoWPAN),Enhanced Authentication and Key Establishment Scheme for 6LoWPAN (EAKES6Lo),Secure Authentication and Key Establishment Scheme(SAKES),Two Step Authentication(TSA),

Refference:

I.Atzori, L., Iera, Antonio,Morabito, Giacomo, The internet of things: A survey. Computer networks, 2010. 54(15): p. 2787-2805.

II.Commission, E., The alliance for internet of things innovation (AIOTI). 2016.

III.Costin Badic ̆ a ̆, M.B., Amelia Badic ̆, a ̆, An Overview of Smart Home Environments: Architectures, Technologies and Applications. 2017: p. 8.

IV.Ding, F.S., A.; Tong, E.;Li,J., A smart gateway architecture for improving effeciency of home network application. 2016.

V.Geoff Mulligan , M.y., Patrick Wetterwal, ColinPatrickO’Flyn, MakingsensornetworksIPv6ready. 2008.

VI.Huichen Lin, N.W.B., IoT Privacy and Security Challenges for Smart Home Environments. 2016(4 July 2016).

VII.Internet, ADVANCE AUTHENTICATION TECHNIQUES.

VIII.Kenji, I.M., T.; Toyoda, K.; Sasase, I, Secure parent node selection scheme in route construction to exclude attacking nodes from rpl network. 2015. 4: p.5.

IX.Komninos, N., Phillppou, E. & Pitsillides, A. , Survey in Smart Grid and Smart Home Security: Issues, Challenges and Countermeasures 2014.

X.Madakam, S., R. Ramaswamy, and S. Tripathi, Internet of Things (IoT): A Literature Review. IT Applications Group, 2015 3: p. 164-173.

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