Archive

Abstract:

Voltage instability is one of the major problems in the transmission line system it causes due to the dynamic load pattern and increasing load demand. Flexible AC transmission systems (FACTS) devices are used to maintain the voltage instability by controlling real and reactive power through the system. In transmission line system, the location and size of the FACTS devices are an important consideration to offer perfect real power flow in the bus system. In this paper, an optimal placement and sizing of the FACTS devices are carried out by combining the Kinetic Gas Molecular Optimization (KGMO) and Grey Wolf Optimization (GWO). There are three different FACTS devices are used in this research, such as Static VAR compensator (SVC), Thyristor Controlled Series Compensator (TCSC) and Unified Power Flow Controllers (UPFC). The objective functions considered for the proposed hybrid KGMO-GWO method are installation cost, Total Voltage Deviation (TVD), Line Loading (LL) and real power loss. Moreover, the optimal placement using the hybrid KGMO-GWO method is validated using IEEE 30 bus system. The performance of the hybrid KGMO-GWO method is analyzed by means of TVD, power loss, installation cost and line loading. Additionally, the hybrid KGMO-GWO method is compared with two existing technique named as QOCRO and hybrid KGMO-PSO. The TVD of the hybrid KGMO-GWO is 0.1007 p.u., it is less when compared to the QOCRO and hybrid KGMO-PSO.

Keywords:

Flexible AC Transmission Systems,Grey Wolf Optimization,Kinetic Gas Molecular Optimization,Static VAR Compensator,Thyristor Controlled Series Compensator,Unified Power Flow Controllers,

Refference:

I. Agrawal, R., Bharadwaj, S.K. and Kothari, D.P., “Population based evolutionary optimization techniques for optimal allocation and sizing of Thyristor Controlled Series Capacitor”, Journal of Electrical Systems and Information Technology, vol. 5, pp: 484-501,2018.
II. Balamurugan, K., Muralisachithanandam, R. and Dharmalingam, V., “Performance comparison of evolutionary programming and differential evolution approaches for social welfare maximization by placement of multi type FACTS devices in pool electricity market”, International Journal of Electrical Power & Energy Systems, vol. 67, pp: 517-528, 2015.
III. Canbing, L.I., Liwu, X.I.A.O., Yijia, C.A.O., Qianlong, Z.H.U., Baling, F.A.N.G., Yi, T.A.N. and Long, Z.E.N.G., “Optimal allocation of multi-type FACTS devices in power systems based on power flow entropy,” Journal of Modern Power Systems and Clean Energy, vol. 2, pp: 173-180, 2014.
IV. Sen, D., Ghatak, S.R. and Acharjee, P., “Optimal allocation of static VAR compensator by a hybrid algorithm”, Energy Systems, vol. 10, pp: 677-719, 2019.
V. Dash, S.P., Subhashini, K.R. and Satapathy, J.K., “Optimal location and parametric settings of FACTS devices based on JAYA blended moth flame optimization for transmission loss minimization in power systems. Microsystem Technologies, pp: 1-10, 2019.
VI. Dutta, S., Paul, S. and Roy, P.K., “Optimal allocation of SVC and TCSC using quasi-oppositional chemical reaction optimization for solving multi-objective ORPD problem,” Journal of Electrical Systems and Information Technology, vol. 5, pp: 83-98, 2018.
VII. Ersavas, C. and Karatepe, E., “Optimum allocation of FACTS devices under load uncertainty based on penalty functions with genetic algorithm”, Electrical Engineering, VOL. 99, pp: 73-84, 2017.
VIII. Gitizadeh, M., Khalilnezhad, H. and Hedayatzadeh, R., “TCSC allocation in power systems considering switching loss using MOABC algorithm”, Electrical Engineering, vol. 95, pp: 73-85, 2013.
IX. Ghahremani, E. and Kamwa, I., “Optimal placement of multiple-type FACTS devices to maximize power system loadability using a generic graphical user interface,” IEEE Transactions on Power Systems, vol. 28, pp.764-778, 2012.
X. Hemachandra Reddy K, P. Ram Kishore Kumar Reddy and V. Ganesh, “Optimal Allocation of Multiple Facts Devices with Hybrid Techniques for Improving Voltage Stability”, International Journal on Emerging Technologies,vol. 10,pp. 76-84, 2019.
XI. Mondal, D., Chakrabarti, A. and Sengupta, A., “Optimal placement and parameter setting of SVC and TCSC using PSO to mitigate small signal stability proble,”. International Journal of Electrical Power & Energy Systems, vol. 42, pp: 334-340,2012.
XII. Kavitha, K. and Neela, R. “Optimal allocation of multi-type FACTS devices and its effect in enhancing system security using BBO, WIPSO & PSO,” Journal of Electrical Systems and Information Technology, vol. 5, , pp.777-793, 2018.
XIII. Panda, S., Patil R. N., “Location of Shunt FACTS Controllers for Transient Stability Improvement Employing Genetic Algorithm”, Electric Power Components and Systems, vol. 135, pp: 189-203, 2007.
XIV. Packiasudha, M., Suja, S. and Jerome, J., “A new Cumulative Gravitational Search algorithm for optimal placement of FACT device to minimize system loss in the deregulated electrical power environment”, International Journal of Electrical Power & Energy Systems, vol. 84, pp: 34-46,2017.
XV. Rahimzadeh, and Bina, M.T. “Looking for optimal number and placement of FACTS devices to manage the transmission congestion,” Energy conversion and management, vol. 52, pp.437-446,2011.
XVI. Safari, A., Bagheri, M. and Shayeghi, H., “Optimal setting and placement of FACTS devices using strength Pareto multi-objective evolutionary algorithm” Journal of Central South University, vol. 24, p: 829-839, 2017.

View Download

Abstract:

This paper studies the effect of changing layers arrangement of composite materials (four samples consisting of 8 layers with a change in the arrangement of  layers and composite materials were used glass fibers, carbon fibers and perlon with lamina matrix) on the mechanical properties (yield stress, ultimate stress, Young's modulus, and Poisson's ratio) of the prosthetic below knee socket (BK) by using tensile test device. Also, calculate the (S-N) curves for these samples by using bending fatigue test device to calculate the fatigue life.       The pressure distribution between the (BK)socket and the residual lower limb using pressure sensor and the information on gait cycle was by using force plate on the case study patient with (BK) amputation.       The solid work program to drawn the socket and ANSYS workbench 14.5 was used to analyze and evaluate the fatigue characteristic by observing the maximum stress, total deformation and safety factor.       The results show that the yield stress in the samples 2, 3 and 4 is increased about 18 %, 95%, and 91% respectively more than the standard sample1. While the ultimate stress in the samples 2, 3 and 4 is increased 32%, 89%, and 68% respectively more than the standard sample1, the Young's modulus in the samples 2, 3 and 4 is increased about 5%, 18%, and 12% respectively more than the standard sample1, the Poisson's ratio is increased about 3%, 6%, and 7% respectively more than the standard sample1, and the fatigue life is increased about 23%, 73% and 29% in the samples 2, 3 and 4  respectively more than the standard sample 1.

Keywords:

Composite materials,mechanical properties,fatigue life,below knee socket,

Refference:

I. A. P. Irawan, I. Wayan S, “Tensile and Impact Strength of Bamboo Fiber Reinforced Epoxy Composite as Alternative Materials for above Prosthetic Socket”, International Conference on Sustainable Technology Development, Vol. 2, pp. 109-115, 2012.
II. A. Adawiya .Hamzah, “Vibrational Behavior of Three Floors Structure Equipped with Dampers”, International Journal of Mechanical and Mechatronics Engineering , Vol.17, No.04, 2017.
III. D .Popovic, T .Sinkjaer, “Control of Movement for the Physically Disabled”, Springer Verlag London, 2000.
IV. H. MajidFaidh-Allah, Mahmood W. Saeed and Adawiya A. Hamzah, “Experimental and Numerical Study the Effect of Materials Changing on Behavior of Dental Bur (Straight Fissure) under Static Stress Analysis” Innovative Systems Design and Engineering , Vol.6, No.2, 2015.
V. H .Majid. Faidh-Allah, Zainab A. Abdul Khalik, “Experimental and Numerical Stress Analysis of Involute Splined Shaft”, Journal of Engineering, College of Engineering, University of Baghdad, Vol.18 , No.4 , 2012.
VI. H. SaleelAbood, Majid H. Faidh-Allah, “Analysis of Prosthetic Running Plate of Limb Using Different Composite Materials”, Journal of Engineering, College of Engineering, University of Baghdad, Vol.25, No.12, pp.15-25, 2019.
VII. H. Majid .Faidh-Allah, Adawiya A. Hamzah, “Vibration Analysis of Aircraft Wing under Gust Load”, Journal of Engineering and Applied Sciences, Vol.14, No.11, pp.3571-3574, 2019.
VIII. H. Majid .Faidh-Allah, “Study the Steady-State and Dynamic Problems of the Rotating Blades”, International Journal of Mechanical Engineering and Technology, Vol.9, No.10, pp.548-558, 2018.
IX. H. Majid .Faidh-Allah, “The Temperature Distribution in Friction Clutch Disc under Successive Engagements”, Tribology in Industry, Vol.40, No.1, pp.92-99, 2018.
X. Hamill, J. and Knutzen, K., “Biomechanical Basis of Human Movement”, 2nd edition, Philadelphia: Lippincott Williams & Wilkins, 2003.
XI. K Sethi, P., “Technological Choices in Prosthetics and Orthotics for Developing Countries”, Prosthetic and Orthotics International ,Vol.13, pp.117-124,1989.
XII. Karen Junius, TomVerstraten, “Design of an Actuated Orthosis for Support of the Sound Leg of TransfemoralDysvascular Amputees”, Master Thesis in Department of Mechanical Engineering Vrije Universities Brussel Academic year: 2011 – 2012.
XIII. M. J. Jweeg , S. S. Hasan and J. S. Chiad , “Effects of Lamination Layers on the Mechanical Properties for above Knee Prosthetic Socket”, Eng. & Tech. Journal,Vol.27, No.4, 2009.
XIV. M .MuhammedA., “Experimental Investigation of Tensile and Fatigue Stresses for Orthotic / Prosthetic Composite Materials with Varying Fiber (Perlon, E-Glass and Carbon)”. Arpn Journal of Engineering and Applied Sciences, vol. 11, no. 21, November 2016.
XV. Standard, A. S. T. M. (2003), “Standard Test Method for Tensile Properties of Plastics”, ASTM International. Designation: D, 638, 1-13.
XVI. T. Mustafa Ismail ,Muhsin J. Jweeg and Kadhim K. Resan, “Study of Creep- Fatigue Interaction in the Prosthetic Socket below Knee”, Innovative Systems Design and Engineering , Vol.4, No.5, 2013.

View Download

Abstract:

The incidences of diseases (morbidities) vary across geographic areas. Spatial statistical analysis concerning spread  and  direction  is useful to study  such diseases in the neighborhood. This helps the health provenance for reducing this disease and control spatially it. Many spatial interpolations have employed for predicting the risky diseases based on observed values. In this paper, two methods of the spatial interpolation have studied based on unmeasured values from the same characteristic of spatial data, area-to-point kriging and topological kriging. These methods exploit variogram structure to predict the unmeasured values, then they fit this variogram by one of the parametric variograms. The de-regularization or deconvolution method is iterative and search model of area that reduces the variation between the theoretical semivariogram model and the fitted model for irregular area data. However, it is an approximate method for different regions based on the concept of average distance between irregular areas. Then, area to point kriging method has used using back calculation for approximated irregular areas in topological kriging (top kriging) .The prediction results for top kriging is better than other method. Disease krige map explaining the embedding risk of effective disease from observed frequencies are summarizes and their performances have compared .The goal of this paper is  mapping and exploring the spatial variation and hot spots of district- level disease cases in Iraq country

Keywords:

Geostatistics ,Deconvolution,Change the support,Interpolatio,

Refference:

I. A. Soares et al. (eds.), geo ENV VI – Geostatistics for Environmental Applications, 3-22 , Springer Science& Business Media,vol .15 ,2008.
II. A .Minh, Muhajarine N, Janus M, Brownell M, Guhn M. A review of neighborhood effects and early child development: how, where, and for whom, do neighborhoods matter? Health & place;46:155 -74, 2017.
III. Atkinson, Peter, Jingxiong Zhang, and Michael F. Goodchild. Scale in spatial information and analysis. CRC Press, 2014.
IV. B .Ghosh, Random distances within a rectangle and between two rectangles.Bull. Calcutta Math. Soc. 43, 17- 24, 1951.
V. CA .Gotway, Young LJ , Combining incompatible spatial data. J Am Stat Assoc 97(459):632-648,2002.
VI. Duan, Qingyun, Soroosh Sorooshian, and Vijai Gupta. “Effective and efficient global optimization for conceptual rainfall-runoff models.” Water resources research 28.4: 1015-1031, 1992.
VII. Gotway Crawford C.A., Young L.J. , Change of support: an inter-disciplinary challenge. In: Geostatistics for Environmental Applications. Springer, Berlin, Heidelberg, 2005.
VIII. Gottschalk, Lars, Etienne Leblois, and Jon Olav Skøien. “Distance measures for hydrological data having a support.” Journal of hydrology 402.3-4 ,415-421,2011.
IX. G. Journel, A.., Huijbregts, C.J., Mining Geostatistics. Academic Press, London, UK, 1978.
X. H .Kupfersberger, Deutsch CV, Journel AG Deriving constraints on small-scale variograms due to variograms of large-scale data. Math Geol 30(7):837-852, 1998.
XI. Kyriakidis, P., A, geostatistical framework for area-to-point spatial interpolation. Geograph. Anal. 36 (3), 259 -289,2004 .
XII. N.Cressie, Statistics for Spatial Data. Wiley, a Wiley-Interscience-Publication, New York, NY, 1991.
XIII. O. Skøien, J., Merz, R., Blöschl, G., Top-kriging – geostatistics on stream network s. Hydrol. Earth Syst. Sci. 10, 277- 287, 2006 .
XIV. O .Skoien, J, Bloschl, G., Laaha, G., Pebesma, E., Parajka, J., Viglione, A., Rtop: An R package for interpolation of data with a variable spatial support, with an example from river networks. Computers & Geosciences, 67, 2014.
XV. P .Goovaerts,, “Kriging and semivariogram deconvolution in the presence of irregular geographical units”. Math. Geosci. 40 (1), 101 – 128 , 2008.
XVI. www.unicef.org

View Download

Abstract:

This paper study the effect of Heat Recovery Steam Generator (HRSG) type on the thermal effeicncy  of Integrated Solar Combined Power Plant. The aim of this work is to improve thermal effeicncy of Integrated Solar Combined Cycle System (ISCCS). In this plant, recovery the largest possible amount of thermal energy in flue gases of gas power plants, to produce steam, and adopting solar energy to produce hot water. The efficiency of Solar Integrated Steam Power Plant can be increased from 40%  for case A to 50% for case B, due to increased  the aviable heat of HRSG from 168.27 MW to306 MW. Also, thermal environmental pollution can reduced  from 148.36 ℃ to 68.97 ℃.

Keywords:

Heat Recovery Steam Generator,Solar Energy,Integrated Solar Combined Power Plant,

Refference:

I. AL-Zafaraniyah Gas Power Plant / Baghdad, Iraq.
II. Cavalcanti E. J. C.,” Exergoeconomic and exergoenvironmental analyses of an integrated solar combined cycle system” renewable and sustainable energy reviews, V.67, PP.507-519, 2017.
III. E.Kabalci ,” Design and analysis of a hybrid renewable energy plant with solar and wind power”, energy conversion and management, V.xxx, PP.xxx-xxx, 2013.
IV. F. Calise , Accadia M. D., Libertini L. and Vicidomini M. ,” Thermoeconomic analysis of an integrated solar combined cycle power plant”, energy conversion and management, V.171, PP.1038-1051, 2018.
V. G .Khankari, and Karmakar S., ” power generation from flue gas waste heat in a 500 MW subcritical coal-fired thermal power plant using solar-assisted Kalina cycle system 11“, applied thermal engineering, V.xxx, PP.xxx-xxx, 2018.
VI. G. Bonforte, Buchgeister J., Manfrida G. and Petela K., ” Exergoeconomic and Exergoenvironmental analysis of an integrated solar gas turbine/combined cycle power plant “, energy, V.152, PP.xxx-xxx, 2018.
VII. G. Manente, Rech S., and Lazzaretto A.,” optimum choice and placement of concentrating solar power technologies in integrated solar combined cycle systems “renewable energy, V.96, PP.172-189, 2016.
VIII. H .Nezammahalleh, F. Farhadi, and Tanhaemami M., ” conceptual design and techno-economic assessment of integrated solar combined cycle system with DSG technology “, solar energy, V.84, PP.1696-1705, 2010.
IX. H.Nezammahalleh, Farhadi F., and Tanhaemami M.,” conceptual design and techno-economic assessment of integrated solar combined cycle system with DSG technology “, solar energy, V.84, PP.1696-1705, 2010.
X. J .Potter ,”Power plant , Theory and design “, John wiley pub,1956.
XI. N. Khan M,and Tlili I., “ Innovative thermodynamic parametric investigation of gas and steam bottoming cycles with heat exchanger and heat recovery steam generator: Energy and exergy analysis”, Energy Reports, V4, PP. 497-506, 2018.
XII. Q .Yan, Hu E., Yang Y. and Zhai R., ” Evaluation of solar aided thermal power generation with various power plants”, International journal of energy research, V.35, PP.909-922, 2011.
XIII. S .Jamel M., Shamsuddin A.H., and Abd- Rahman A.,” advances in the integration of solar thermal energy with conventional and non-conventional power plants”, renewable and sustainable energy reviews, V20, PP.71-81, 2013.
XIV. Y. Li and Xiong Y., ” Thermo-economic analysis of a novel cascade integrated solar combined cycle system”, energy, V145, PP.116-127,2018.
XV. Y.Li and Xiong Y.,” Thermo-economic analysis of a novel cascade integrated solar combined cycle system”, energy, V145, PP.116-127, 2018.

View Download

Abstract:

In this paper we investigate some fundamental results on Jordan ideals, ∗-Jordan ideals, derivations and generalized derivations and hence establish some commutativity results for a certain class of semirings with involution

Keywords:

Inverse semirings,MA-semirings,Generalized derivations,*Jordanideals,

Refference:

I. B.E. Johnson, Continuity of derivations on commutative Banach algebras, Amer. J. Math.,vol. 91,pp: 1-10, 1969.
II. C. Lanski, Commutation with skew elements in rings with involution, Pacific J. Math., vol. 83, no. 2,pp: 393-399, 1979.
III. C.E. Rickart,Banach algebras with an adjoint operation, Ann. Math. (2), vol. 47, no. 3, pp:528-550, 1946.
IV. D.A. Jordan,On the ideals of a Lie algebra of derivations, J. London Math. Soc., vol. 33,no. 2,pp:33-39, 1986.
V. E.C. Posner, Derivations in prime rings, Proc. Amer. Math. Soc., vol. 8, pp:1093-1100, 1957.
VI. H.E. Bell, W. S. Martindale, Centralizing mappings of semiprime rings, Canad. Math. Bull., vol. 30, pp:92-101, 1987.
VII. H.J. Bandlet, M. Petrich, Subdirect products of rings and distributive attics, Proc. Edinburgh Math. Soc., vol. 25, pp:135-171, 1982.
VIII. I.E. Segal, Irreducible representations of operator algebras, Bull. Amer. Math. Soc., vol. 53, pp:73-88, 1947.
IX. I.N. Herstein, Topics in ring theory, Chicago lectures in mathematics, 1969.
X. I.N. Herstein,Rings with involution, University of Chicago, 1976.
XI. J. Berger, I.N. Herstein and J. W. Kerr, Lie ideals ana derivations of prime rings, J. Algebra, vol. 71, pp: 259-267, 1981.
XII. K.I. Beidar, W.S Martindale On functional identities in prime rings with involution, J. Algebra, vol. 203, no.2,pp:491-532, 1998.
XIII. L. Oukhtite, On Jordan ideals and derivations in rings with involution, Comment. Math. Univ. Carolin., 51(3) (2010), 389-395.
XIV. L. Oukhtite, A. Mamouni, Generalized derivations centralizing on Jordan ideals of rings with involution, Turk. J. Math., 38 (2014), 225-232.
XV. L. Oukhtite, A. Mamouni, Derivations satisfying certain algebraic identities on Jordan ideals, Arab. J. Math., vol. 1, no. 3, pp:341-346, 2012.
XVI. L. Oukhtite, Posner’s second theorem for Jordan ideals in rings with involution, Expos. Math., vol. 29, no. 4, pp:415-419, 2011.
XVII. Liaqat Ali, M. Aslam and Yaqoub Ahmed Khan, Commutativity of semirings with involution, Asian-European Journal of Mathematics, (2019) https://doi.org/10.1142/S1793557120501533
XVIII. Liaqat Ali, M. Aslam and Yaqoub Ahmed Khan, On Jordan ideals of inverse semirings with involution, Indian Journal of Science and Technology, vol. 13, no. 4, pp:430–438, 2020.
XIX. Liaqat Ali, M. Aslam and Yaqoub Ahmed Khan, On Posner’s second theorem for semirings with involution, Submitted.
XX. M. Bresar, On the distance of the composition of two derivations to the generalized derivations, Glasg. Math. J., vol. 33, no. 1, pp:89-93, 1991.
XXI. M.A. Javed, M. Aslam and M. Hussain, On condition (A2) of Bandlet and Petrich for inverse semirings, Int. Math. Forum, vol. 7, no. 59, pp:2903-2914, 2012.
XXII. M.N. Daif, Commutativity result for semiprime rings with derivations, Int. J. Math. Math. Sci., vol. 21, no. 3, pp:471-474, 1998.
XXIII. R. Awtar, Lie and Jordan structure in prime rings with derivations, Proc. Am.Math. Soc., vol. 41 ,pp:67-74, 1973.
XXIV. S.M.A. Zaidi, M. Ashraf, S. Ali, On Jordan ideals and left (θ,θ)-derivations in prime rings, Int. J. Math. Math. Sci. vol. 2004, no. 37, pp:1957-1964, 2004.
XXV. Sara Shafiq, M. Aslam, M.A Javed, On centralizer of semiprime inverse semiring, DiscussionesMathematicae, General Algebra andApplications, vol. 36, pp:71-84, 2016.
XXVI. T.K Lee, On derivations of prime rings with involution, Chin. J. Math., vol. 20, no. 2, pp:191-203, 1992.
XXVII. Yaqoub Ahmed Khan, M. Aslam and Liaqat Ali, Commutativity of additive inverse semirings through f(xy) = [x,f(y)], Thai J. of Math., Special Issue,pp:288-300, 2018.

View Download

Abstract:

Drilling in bone is an inevitable operation performed to join damaged bone during accidents. Drilling facilitates use of screws and plates and in this immobilisation of bone is achieved which is a primary requirement for natural bone growth and re-joining. To study bone drilling, threshold temperature [VI] has to be the prime concern and accordingly drilling parameters and specifications are to be selected otherwise irreversible[III] bone damage can occur. In this study, drilling process is conducted on a sheep bone and optimization of drilling parameters is suggested using Taguchi and ANOVA method, so that the cell damage can be on lower side. To control thermal necrosis an intelligent drilling machine is also proposed.

Keywords:

Bone drilling,threshold temperature,optimization,

Refference:

I. Augustin G, Davila S, Udiljak T, Vedrinal DS, Bagatin D. (2009) .Determination of spatial distribution of increase in bone temperature during drilling by infrared thermography: preliminary report. Archives of Orthopaedic and Trauma. Surgery,129(5):703–9.

II. Augustin G., S. Davila et al. (2008). Thermal osteonecrosis and bone drilling parameters revisited. Archives of Orthopaedic & Trauma Surgery.128(1): 71-77.

III. Bonfleld,W., Li,C.H.,. The temperature dependence of the deformation of bone J. Biomechanics.Vol I. pp. 323-329 PergamonPress..Printed in Great Britain.

IV. Brisman D.L., (1996).The effect of speed, pressure, and time on bone temperature during the drilling of implant sites. International Journal of Oral and Maxillofacial Implants, 11(1):35–7.

V. Davidson SRH, James D.F., (2000).Measurement of thermal conductivity of bovine cortical bone. Medical Engineering and Physics, 22(10):741–7.

VI. Eriksson, R. A. and Albrektsson,T . (1984).The effect of heat on bone regeneration: an experimental study in the rabbit using the bone growth chamber. Journal of Oral & Maxillofacial Surgery, 42(11): 705-711.

VI. Hillery M.T., Shuaib I. (1999). Temperature effects in the drilling of human and bovine bone. Journal of Materials Processing Technology. 92-93:302–8.

VIII. Jill E. Shea, (2002). Experimental Confirmation of the Sheep Model for Studying the Role of Calcified Fibrocartilage in Hip Fractures and Tendon Attachments,wiley-liss,inc.The anatomical record. 266:177–183,

IX. JoséCaeiroPotes, et.al, (2008).The Sheep as an Animal Model in Orthopaedic Research, Experimental pathology and health sciences;2(1):29-32,

X. Karaca, F., Aksakalb, B.,Komc,M.,. (2011). Influence of orthopaedic drilling parameters on temperature and histopathology of bovine tibia: An in vitro study, Medical Engineering & Physics, 33:1221– 1227.

XI. Lucia Martini, et.al, (2001). Sheep Model in Orthopaedic Research: A Literature Review,American Association for Laboratory Animal Science, August. vol.51.No. 4: Page 292-299.

XII. Lundskog,J., (1972).Heat and bone tissue, Scand. Journal of Plastic and Reconstructive Surgery, Sup.

XIII. Matthews LS, Green CA, Goldstein SA. (1984). The thermal effects of skeletal fixation pin insertion in bone. Journal of Bone and Joint Surgery. 66(7):1077–83.

XIV. Mortiz,A.R., Henerique,F.C., (1947).The relative importance of time and surface temperature in the causation of cutaneous burns, American Journal of Physiology. 23: 695-719.

XV. Nam O., Yu W., Choi M.Y., Kyung H.M. (2006), Monitoring of bone temperature during osseous preparation for orthodontic micro-screw implants: effect of motor speed and pressure. Key Engineering Materials, 321–323:1044–7.

XVI. Natali C., P. Ingle et al. (1996).Orthopaedic bone drills-can they be improved? Temperature changes near the drilling face. Journal of Bone and Joint Surgery.78-B (3): 357-362.

XVII. Ohashi H., Therin M., Meunier A., Christel P., (1994).The effect of drilling parameters on bone. Journal of Material Science: Materials in Medicine.5(4):225–31.
XVIII. Roy R., (2001).Design of experiments using the Taguchi approach: 16 steps to product and process improvement. John Wiley & Sons, New York, ISBN: 0471361011

XIX. Saha S, Pal S, Albright J. (1982), surgical drilling: design and performance of an improved drill. Transactions of ASME, Journal of Biomechanical Engineering.104(3):245–52.

XX. Sharawy M., Misch C.E., Weller N., Tehemar S., (2002). Heat generation during implant drilling: the significance of motor speed. Journal of Oral and Maxillofacial Surgery,; 60(10):1160–9.

XXI. Toews A. R., J. V. Bailey et al. (1999), Effect of feed rate and drill speed on temperatures in equine cortical bone. American Journal of Veterinary Research. 60(8): 942-944.

XXII. Tony M. Keaveny, Bone mechanics Source: standard handbook of biomedical engineering and design. Downloaded from Digital Engineering Library @ McGraw-Hill (www.digitalengineeringlibrary.com) Copyright © 2004

View Download

Abstract:

Nowadays water scarcity is a major threat to our society, in the name of development, depletion of water increases. The developing technologies had decreased the wealth of the soil. Advancement in agriculture brought artificial fertilizers to eradicate diseases, it turns the soil infertile. This could be overcome by an efficient method called “HYDROPONICS”. This plantation had brought smartness in agriculture. By this, we could achieve lesser space, less man power and 10% of water consumption compared to conventional method. The monitoring and control techniques could be  implemented using Internet of Things (IoT) for proper and advance maintenance.   The major parameters to be handled in Hydroponics are monitoring temperature, humidity, PH of water, water flow, nutrition level, pump motor speed and efficiency. The collected data are uploaded into cloud using IoT module. The data  can be processed in cloud or local server. Remote user can also control the system through Android/Web Application. The present work focused on the energy meter automation using Arduino. When the load is given to the energy meter the CAL led blinks and the blinking pulse is triggered using Opto coupler (4N35). The 5v impulse is given as digital HIGH input to any one of the Arduino digital pin. The pulse is counted in the Arduino and the power calculation  is processed in the program.

Keywords:

Cloud,Hydroponics,Internet of Things,PH,Web Application ,

Refference:

I. Abdur Rahim Biswas and RaffaeleGiaffreda, “IoT and Cloud Convergence: Opportunities and Challenges”, 2014 IEEE World Forum on Internet of Things (WF-IoT).
II. Asumadu, J.A., Smith, B., Dogan, N.S., Loretan, P.A., Aglan, H.,
Microprocessor-based instrument for hydroponic growth chambers used in ecological life support systems Instrumentation and Measurement Technology, IEEE Instrumentation and Measurement Technology Conference, June 4-6, 1996.
III. K. Kalovrektis, Ch. Lykas, I. Fountas, A. Gkotsinas, I.Lekakis,
Development and application embedded systems and wireless network of sensors to control of hydroponic greenhouses, International Journal of Agriculture and Forestry 2013, 3(5): pp. 198-202.
IV. Mamta D. Sardare, Shraddha V. Admane, A review on plant without soil hydroponics‘, IJRET: International Journal of Research in Engineering and Technology ISSN: 2319-1163, Volume: 02 Issue: 03, Mar-2013
V. NiveditaWagh, VijendraPokharkar, AvinashBastade, Priyanka
Surwase, UmeshBorole,PLC based automated hydroponic system‘, IJSTE International Journal of Science Technology & Engineering, Volume 2, Issue 10, April 2016.
VI. Rahul Nalwade, Mr.Tushar Mote, “Hydroponics Farming”, pg: 647, International Conference on Trends in Electronics and Informatics ICEI 2017
VII. Rajeev lochan Mishra1 and Preet Jain, ―Design and implementation of automatic hydroponics system using ARM processor, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 4,Issue 8, August 2015.
VIII. S.S.Kalamkar, “Urbanisation and Agricultural Growth in India”, Indian Journal Of Agri. Econ. Vol. 64, No.3, July-Sept. 2009.

View Download

Abstract:

This paper proposes an approach to study the effect of faulty array element on the accuracy of the parameter estimation of direction of arrival of the plain waves and their amplitudes from sources that are considered to be far field sources. In this approach we require only one snapshot. The cost function is developed for heuristic computation using genetic algorithm (GA). Cost function is based on  norm of the difference between actual observation vector and the constructed vector plus the correlation between the two normalized vectors. The results have been given for different length of array i.e. 10, 15 and 20.Longer array is able to minimize the effect of faulty array element.

Keywords:

Direction of Arrival,Uniform Linear Array,Parameter Estimation,Faulty Array,

Refference:

I. B. Ottersten and T. Kailath, “Direction-of-arrival estimation for wide-band signals using the ESPRIT algorithm,” IEEE Trans. Acoust., vol. 38, no. 2, pp. 317–327, 1990.
II. F. Zaman, I. M. Qureshi, A. Naveed, and Z. U. Khan, “Real time direction of arrival estimation in noisy environment using particle swarm optimization with single snapshot,” Res. J. Appl. Sci. Eng. Technol., vol. 4, no. 13, pp. 1949–1952, 2012.
III. F. Zaman, I. M. Qureshi, A. Naveed, and Z. U. Khan, “Joint estimation of amplitude, direction of arrival and range of near field sources using memetic computing,” Prog. Electromagn. Res. C, vol. 31, pp. 199–213, 2012.
IV. F. Zaman, I. M. Qureshi, A. Naveed, J. A. Khan, and R. M. A. Zahoor, “Amplitude and directional of arrival estimation: comparison between different techniques,” Prog. Electromagn. Res. B, vol. 39, pp. 319–335, 2012.
V. F. Zaman, J. A. Khan, Z. U. Khan, and I. M. Qureshi, “An application of hybrid computing to estimate jointly the amplitude and direction of arrival with single snapshot,” in Proceedings of 2013 10th International Bhurban Conference on Applied Sciences & Technology (IBCAST), 2013, pp. 364–368.
VI. J. A. Khan, M. A. Z. Raja, and I. M. Qureshi, “Numerical treatment of nonlinear Emden–Fowler equation using stochastic technique,” Ann. Math. Artif. Intell., vol. 63, no. 2, pp. 185–207, 2011.
VII. M. Mouhamadou, P. Vaudon, and M. Rammal, “Smart antenna array patterns synthesis: Null steering and multi-user beamforming by phase control,” Prog. Electromagn. Res., vol. 60, pp. 95–106, 2006.
VIII. M. Mukhopadhyay, B. K. Sarkar, and A. Chakraborty, “Augmentation of anti-jam gps system using smart antenna with a simple doa estimation algorithm,” Prog. Electromagn. Res., vol. 67, pp. 231–249, 2007.
IX. M. A. Ur Rehman, F. Zaman, I. M. Qureshi, and Y. A. Sheikh, “Null and sidelobes adjustment of damaged array using hybrid computing,” Proc. – 2012 Int. Conf. Emerg. Technol. ICET 2012, pp. 386–389, 2012.
X. Cheng and Y. Hua, “Further study of the pencil-MUSIC algorithm,” IEEE Trans. Aerosp. Electron. Syst., vol. 32, no. 1, pp. 284–299, 1996.
XI. Y. Hua, T. K. Sarkar, and D. Weiner, “L-shaped array for estimating 2-D directions of wave arrival,” in Proceedings of the 32nd Midwest Symposium on Circuits and Systems, 1989, pp. 390–393.
XII. V. S. Kedia and B. Chandna, “A new algorithm for 2-D DOA estimation,” Signal Processing, vol. 60, no. 3, pp. 325–332, 1997.
XIII. Y. A. Sheikh, F. Zaman, I. M. Qureshi, and M. Atique-ur-Rehman, “Amplitude and direction of arrival estimation using differential evolution,” in 2012 International Conference on Emerging Technologies, 2012, pp. 1–4.
XIV. Y. Wu, G. Liao, and H.-C. So, “A fast algorithm for 2-D direction-of-arrival estimation,” Signal Processing, vol. 83, no. 8, pp. 1827–1831, 2003.
XV. Z. U. Khan, A. Naveed, I. M. Qureshi, and F. Zaman, “Independent null steering by decoupling complex weights,” IEICE Electron. Express, vol. 8, no. 13, pp. 1008–1013, 2011.

View Download

Abstract:

This work aims on improving the dynamic time response of closed-loop Bootstrap controlled SVM inverter (BSVMI) with PI, FOPID and FLC. In this work the simulink model of FLC based ZVS bootstrap SVM inverter system is discussed. Bootstrap converter is a popular device within the family of power Electronics device. The SVM inverter is used with voltage source inverter (VSI) and the switching pulses are given using FLC controller. The ZVS bootstrap converter is used for reduction of switching losses. The simulation results are presented to find the effect of BSVMI using FLC. The simulation results with PI, FOPID and FLC Controller based BSVMI are compared and the consequent time-domain parameters are presented. The results specify that FLC Controller system has enhanced response than PI and FOPID controlled system

Keywords:

FLC,Bootstrap,SVM,Cloased Loop,Dynamic reponse,

Refference:

I. Ayyanar R and Mohan N “Novel soft-switching DC-DC converter with full ZVS-range and reduced filter requirement. I: Regulated-output applications,” IEEE Trans. Power Electron., Vol. 16, No. 2, pp. 184-192, Mar. 2001.
II. Cavalcanti M.C, E.R.C. da Silva, A.M.N Lima, C.B. Jacobina, R.N.C.Alves ; “Reducing losses in three-phase PWM pulsed DC-link voltagetype inverter systems,” IEEE Transactions on Industry Applications, Vol. 38 , No.4 , pp. 1114 – 1122, 2002.
III. CelanovicN. and D. Boroyevich, “A fast space vector modulation algorithm for multilevel three phase converters,” IEEE Trans. Ind. Appl., Vol. 37, No. 2, pp. 637 – 641, Feb. 2001.
IV. Chu E. H, X. T. Hou, H. G Zhang, M. Y. Wu, and X. C. Liu, “Novel zero-voltage and zero-current switching (ZVZCS) PWM three-level DC/DC converter using output coupled inductor,” IEEE Trans. Power Electron., Vol. 29, No. 3, pp. 1082-1093, Mar. 2014.
V. Chen T. F and S. Cheng, “A novel zero-voltage zero-current switching full-bridge PWM converter using improved secondary active clamp,” IEEE International Symposium on Industrial Electronics, Montreal, pp. 1683-1687, 2006.
VI. Govindaraj T and B.Gokulakrishnan, “Simulation of PWM based AC/DC Converter control to improve Power Quality,” International Journal of Advanced and Innovative Research.ISSN: 2278-7844, Dec-2012, pp524-533.
VII. Govindaraj T, RasilaR,”Development of Fuzzy Logic Controller for DC – DC Buck Converters”, International Journal of Engineering TechsciVol 2(2), 192-198, 2010.
VIII. Gupta A. K, and A. M. Khambadkone, “A space vector PWM scheme for multilevel inverters based on two-level space vector PWM,” IEEE Trans. Ind. Electron., vol. 53, no. 5, pp. 1631–1639, Oct. 2006.
IX. Halasz S, B.T. Huu, A. Zakharov ;“Two-phase modulation technique for three-level inverter-fed AC drives,” IEEE Transactions on Industrial Electronics, Vol. 47, No.6, pp.1200 – 1211, 2000.
X. Hideaki Fujita, Ryo Suzuki : “A three-phase solar power conditioner using a single-phase PWM control method,” IEEJ Trans. IA, Vol. 130, No.2, pp.173-180, 2010.
XI. Haifeng Lu, WenlongQu, Xiaomeng Cheng, Yang Fan, Xing Zhang : “A Novel PWM Technique With Two-Phase Modulation,” IEEE Trans. On Power Electronics, Vol. 22, No.6, pp.2403-2409, 2007.
XII. Jin K., X. Ruan, and F. Liu, “An improved ZVS PWM three-level converter,” IEEE Trans. Ind. Electron., vol. 54, no. 1, pp. 319–329, Feb. 2007.
XIII. LingaSwamy. R and Satish Kumar. P (2008), ‘Speed control of space vectored modulated inverter driven induction motor’, proceedings of the International Multi conference of engineers and computer scientist, vol.2.
XIV. MousaviA and G. Moschopoulos, “A new ZCS-PWM fullbridge DC–DC converter with simple auxiliary circuits,” IEEE Trans. Power Electron., Vol. 29, No. 3, pp. 1321-1330, Mar. 2014.
XV. Ruan X. and Y. Yan, “A novel zero-voltage and zero current- switching PWM full-bridge converter using two diodes in series with the lagging leg,” IEEE Trans. Ind. Electron., Vol. 48, No. 4, pp. 777-785, Aug. 2001.
XVI. Szychta E., “ZVS operation region of multi resonant DC/DC boost converter”, Journal of Advances in Electrical and Electronic Engineering, Faculty of Electrical Engineering, Vol.6, No.2, 2007, Zilina University, pp. 60-62.
XVII. Sefa I., N. Altin, S. Ozdemi, and O. Kaplan, “Fuzzy PI controlled inverter for grid interactive renewable energy systems,” IET RenewablePower Generation, vol. 9, no. 7, pp. 729-738, 2015.
XVIII. Tabisz W.A., Lee F.C., ”DC analysis and design of zero-voltage switched multi-resonant converters”, IEEE 20th Annual Power Electronics Specialists Conference, PESC ’89, vol. 1, 1989, p. 243 – 251.
XIX. Tattiwong K. and C. Bunlaksananusorn, “Analysis design and experimental verification of a quadratic boost converter,” in TENCON 2014 – 2014 IEEE Region 10 Conference, Oct 2014, pp. 1–6.
XX. Taniguchi K., H. Irie ; “Trapezoidal modulating signal for three-phase pwm inverter,” IEEE Transactions on Industrial Electronics, Vol. 33 , No. 2, 193 – 200, 1986.
XXI. Tao C.W. and J.-H. Taur, “Design of fuzzy controllers with adaptive rule insertion,” IEEE Trans. Syst., Man, and Cyber., Part B: Cyber., vol. 29, no. 3, pp. 389-397, 1999.
XXII. Wai R.-J., M.-W. Chen, and Y.-K. Liu, “Design of adaptive control and fuzzy neural network control for single-stage boost inverter,” IEEE Trans. Ind. Electron., vol. 62, no. 9, pp. 5434-5445, 2015.
XXIII. Zhou K, D. Wang,(2002),‘Relationship between Space Vector Modulation and three phase carrier-based PWM: A comprehensive analysis’, IEEE Trans. Ind. Elec. Vol. 49,pp 186-196.

View Download

Abstract:

The present investigation adoptsComputationalFluid Dynamics CFD to analyze the problem of forced convection cooling of electronic equipment equipped with a heat sink, including inclination and vibration effects. Two fans were usedto circulate the air inside the computer chassis. Three main components on the motherboard were used;CentralProcessorUnit (CPU), North Bridge, and South Bridge.These components generate heat at the rate of 3750, 2500, and 2222.22kW/ respectively. Three different types of heat sink were used for CPU, these are: plate heat sink, radial heat sink without core,and radial heat sink with core.Theother two main components on the motherboardused the same standard heat sink. The two fans are operated with different cases to specify the suitable operation. Inclination for the computer chassis and motherboard with vibration influence was also investigated. The power dissipation, fan flow rate, and ambient temperature are fixed. The results show that the radial heat sink with core enhances the heat transfer by reducing the temperature of the CPU. Also the influence of vibration has more effect in case of without heat sink, for other cases the influence of vibration is not affected in the investigated range. The effect of inclination angle for computer chassis also is not affected, just when the mother board inclination by  from top edge with vertical plane, the temperature reduction approximately 18  in case without heat sink,  4.8 with plate heat sink on CPU, 1  in case with radial heat sink. The CFD analysis was validated with a thermal profile for real operation CPU, the results show good agreement with a mean deviation of (0.023). A radial heat sink with core reduce the temperature more than 114.5 compared without heat sink on CPU case.

Keywords:

CFD,Forced Convection,Inclination and Vibration,Electronic Equipment Cooling,Heat Sink,

Refference:

I. ANSYS FLUENT, version 14.5, ANSYS Inc. 2013, “fluent 14.5 users guide”, 2013.

II. C.B.Baxi, and A.RamachAndran, “effect of vibration on heat transfer from spheres”, journal of heat transfer, 2016.

III. Cengel Y.A. Heat transfer a practical approach (MGH, 2002)

IV. EmreOzturk, ”CFD analysis of heat sinks for CPU cooling with fluent”, thesis submitted to the graduate school of natural and applied sciences, Middle East technical university, 2004.

V. Farouq Ali S. GDHAIDH, “heat transfer characteristics of natural convection within an enclosure using liquid cooling system”, submitted for the degree of doctor of philosophy, 2015.

VI. Fluent user services center, www.fluentusers.com accessed on septemper,2019.

VII. GeorgiosBalafas, “polyhedral mesh generation for CFD-analysis of complex structures”, master thesis for the master of science program computational mechanics,2014.

VIII. HibaMudhafarHashim , Ihsan Y. Hussain, ” Natural Convection Cooling of PCB Equipped with Perforated Fins Heat Sink including Inclination and Vibration Effects”,JMCMS,2019.

IX. Intel Celeron, D processor in the 775-Land LGA package for embedded applications data sheets.

X. J. M. Jalil, E.H.Ali and H.H.Kurdi, “numerical and experimental study of CPU cooling with finned heat sink and different P.C.Air passages configurations”, Al-Nahrain journal for engineering sciences, vol.21, No.1, pp.99-107, 2018.

XI. J.S Chiang, S.H.Chuang, Y.K.Wu, H.J Lee, “numerical simulation of heat transfer in desktop computer with heat generating components”, international communication in heat and mass transfer 32 (2005) 184-191.

XII. Jalal M. Jalil, Ekbal H. Ali and Hiba H. kurdi, “numerical and experimental study of cooling in desktop computer with block heat sink”, engineering and technology journal, vol.36, part A, No.4, PP.430-438, 2018.

XIII. K. Sreenivasan and A.Ramachandran, “effect of vibration on heat transfer from a horizontal cylinder to a normal air stream”, int.J.Heat mass transfer, Vol.3, pp.60-67, pergamon press, 1961.

XIV. MarcinSosnowski, JaroslawKrzywanski, Karolina G rabowska and RenataGnatowska, “polyhedral meshing in numerical analysis of conjugate heat transfer”, EPJ web of conferences 180, 02096 (2018).

XV. N.D etal,”heat induced vibration of a rectangular plate” journal of engineering for industry, 1974.

XVI. P.K.Nag and A.Bhattacharya, “effect of vibration on natural convection heat transfer from vertical fin arrays”, letters in heat and mass transfer, vol.9, pp.487-498, 1982.

XVII. Robert Lemlich, “effect of vibration on natural convective heat transfer”, industrial and engineering chemistry, 1955.

XVIII. S. Kong Wang, Juin Haw Hu and Chun-HsienKuo, “passive enhancement of heat dissipation of desktop computer chassis”, engineering applications of computational fluid mechanics, vol.4, No.1, pp.139-149, 2010.

XIX. SelinArodag, UtkuOlgun, FatihAkturk and BurcuBasibuyuk,”CFD analysis of cooling of electronic equipment as an undergraduate design project”,2009

XX. Wu- Shung Fu, and Bao-Hong Tong, “numerical investigation of heat transfer from a heated oscillating cylinder in cross flow”, international journal of heat and mass transfer, 45(2002) 3033-3043.

XXI. Wu-Shung Fu, and Chien-Ping Huang, “effects of a vibrational heat surface on natural convection in a vertical channel flow”, international journal of heat and mass transfer 49(2006) 1340-1349.

View Download

Abstract:

Artificial Intelligence (AI) technology has been adopted rapidly in the industry. Various research initiatives have been carried out to innate the AI system characteristics as humans. In our concept paper [VI] we disclosed the “Reflex layer” to mimic human systems. A reflex layer would have the ability to differentiate the repetitive stimuli, its related responses and ability to process this through a separate layer. We discussed the key characteristics of reflex features of the following AI capabilities:

• The vision interface
• The audio interface
• The kinematic interface
• The sheath interface
• The core layer

   In this paper we baseline the scope to core and kinematic interface; elaborate key characteristics, provide solutions and results.  

Keywords:

Artificial Intelligence,Distributed Artificial Intelligence,Reflex AI,

Refference:

I. Anna Melamed. “Distributed Systems Management on Wall Street- AI Technology needs”
II. B Thuraisingham, J Larson “ AI applications in Distributed System Design issues”
III. D Verma, G Bent “Policy Enabled Caching for Distributed AI”. 2017 IEEE International Conference on Big Data (BIGDATA)
IV. Du-Mim Yoon, Joo-Seon Lee, Hyun-SuSeon, Jeong-Hyeon Kim, Kyung-Joong Kim*. “Optimization of Angry Birds AI Controllers with Distributed Computing”. IEEE CIG 2015, Tainan, Taiwan August 31, 2015 – September 2, 2015
V. E Oyekanlu, K Scoles “Towards Low-Cost, Real-Time, Distributed Signal and Data Processing for Artificial Intelligence Applications at Edges of Large Industrial and Internet Networks”. 2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)
VI. Gandhi Sivakumar, G. Arumugam, “The Facets of Distributed And Artificial Intelligence”, Volume8, Issue 12, December 2019 Edition, International Journal of Scientific & Technology Research
VII. JSR Jang, “Self- learning fuzzy controllers based on temporal back propagation”
VIII. Sean Martin, Andrew slade “A methodology for distributed AI and its applicability for Data fusion applications”
IX. YAO Ping-xiang, FENG Wen-xian “DESIGN OF DISTRIBUTED MARINE AISCOMMUNICATION MECHANISM AND INTEGRATED MONITORING PLATFORM”
X. Z. A. Shah1, T. AbdulQayyum2, Dr. S. I. A. Shah “A Distributed Approach for Solving the Position Inverse Kinematics of Mobile Parallel Manipulators”, 2014 International Conference on Robotics and Emerging Allied Technologies in Engineering (iCREATE) Islamabad, Pakistan, April 22-24, 2014

View Download

Abstract:

Human activity recognition through smartphones is now beneficial for humans to recognize their daily activities. Many of the researches are introduced for recognition of activities but somehow the performance of the classifiers is low because of different problems with the data or the classifiers. This research study offers a method to achieve the best performing classifiers. The comparative analysis held between the supervised and ensemble learning classifiers. Based on the best performing classifier, a system is also introduced in this study. We evaluate the method by using two publicly available datasets of human activities recognition acquired from UCI Machine Learning repository. One is UCI-Human Activity Recognition and the second is Smartphone-Based Recognition of Human Activities and Postural Transitions. The activities selected for this research study are Walking, Standing, Sitting, Laying, Downstairs and Upstairs. These input signals are a 3-dimensional raw form of data that was difficult to handle. The Principle Component Analysis (PCA) technique is used to reduce the dimensionalities of the data features and extract the most substantial data features for the classification of human activities. A comparison is performed between the different supervised and ensemble machine learning classifiers on the selected datasets. The supervised learning classifiers that we used are Gaussian Naïve Bayes, K-Nearest Neighbor, and Logistic Regression while the ensemble learning classifiers are Random Forest and Gradient Boosting. The achieved result shows that the Logistic Regression is more accurate as compared to other selected classifiers in this study for human activity recognition. The higher accuracy rate of Logistic Regression is 96.1% for UCI-HAR and 94.5% for HAPT dataset among all the compared classifiers.

Keywords:

UCI-HAR dataset,HAPT dataset,Smartphones,Accelerometer and gyroscope Sensors,Classifiers,HAR,

Refference:

I. [vanSmeden, Maarten, et al. “No rationale for 1 variable per 10 events criterion for binary logistic regression analysis.” BMC Medical Research Methodology 16.1 (2016): 163]

II. Anguita, D., Ghio, A., Oneto, L., Parra, X., & Reyes-Ortiz, J. L. (2013, April). A public domain the dataset for human activity recognition using smartphones. In ESANN.

III. B. Yuan and J. Herbert, “Context-aware Hybrid Reasoning Framework for Pervasive Healthcare,” Pervasive Ubiquitous Computing., vol. 18, no. 4, pp.865–881, 2013.

IV. Bayat, Akram, Marc Pomplun, and Duc A. Tran. “A study on human activity recognition using accelerometer data from smartphones.” Procedia Computer Science 34 (2014): 450-457.

V. Campos, Guilherme O.; Zimek, Arthur; Sander, Jörg; Campello, Ricardo J. G. B.; Micenková,Barbora; Schubert, Erich; Assent, Ira; Houle, Michael E. (2016). “On the evaluation ofunsupervised outlier detection: measures, datasets, and an empirical study”. Data Mining andKnowledge Discovery. 30 (4): 891–927. doi:10.1007/s10618-015-0444-8. ISSN 1384-5810

VI. D. Figo, P. C. Diniz, D. R. Ferreira, and J. M. P.Cardoso, “Preprocessing Techniques for Context Recognition from Accelerometer Data,” PervasiveUbiquitous Computing., vol. 14, no. 7, pp. 645–662,2010.

VII. Deshmukh, R., Aware, S., Picha, A., Agrawal, A., &Wable, S. D. (2018). Human ActivityRecognition using Embedded Smartphone Sensors.

VIII. Elith, Jane. “A working guide to boosted regression trees”. British Ecological Society. British Ecological Society. Retrieved 31 August 2018.

IX. F. Attal, S. Mohammed, M. Dedabrishvili, F. Chamroukhi, L. Oukhellou, and Y.Amirat, “Physical Human Activity Recognition Using Wearable Sensors,” Sensors, vol. 15, no.12, pp. 31314–31338, 2015.

X. Friedman, Jerome. “Multiple Additive Regression Trees with Application in Epidemiology”Thestatisticin Medicine. Wiley. Retrieved 31 August 2018.

XI. Fu, B., Kirchbuchner, F., Kuijper, A., Braun, A., &VaithyalingamGangatharan, D. (2018, June). Fitness Activity Recognition on Smartphones Using Doppler Measurements. In Informatics (Vol. 5, No. 2, p. 24). Multidisciplinary Digital Publishing Institute.

XII. G. Vavoulas, M. Pediaditis, E. G. Spanakis, and M.Tsiknakis, “The MobiFall dataset: An Initial Evaluation of Fall Detection Algorithms using Smartphones,” 13thIEEE Int. Conf. Bioinforma. Bioeng., no. November,pp. 1–4, 2013.

XIII. I. Farkas and E. Doran, “Activity Recognition from Acceleration Data Collected with a Tri-axial Accelerometer,” Acta Tech. Napocensis – Electron.Telecommun., vol. 52, no. 2, pp. 38–43, 2011.

XIV. Inoue, Masaya, Sozo Inoue, and Takeshi Nishida. “Deep recurrent neural network for mobilehuman activity recognition with high throughput.” Artificial Life and Robotics 23.2 (2018): 173- 185.

XV. J. Fu, C. Liu, Y. Hsu, and L. Fu, “Recognizing Context-aware Activities of Daily Living using RGBD Sensor,” Iros2013, pp. 2222–2227, 2013.

XVI. K. G. ManoshaChathuramali and R. Rodrigo, “Faster Human Activity Recognition with SVM,” in International Conference on Advances in ICT for Emerging Regions, ICTer 2012 – Conference Proceedings, 2012, pp. 197–203.

XVII. Le, Tuan Dinh, and Chung Van Nguyen. “Human activity recognition by smartphone. “Information and Computer Science (NICS), 2015 2nd National Foundation for Science andTechnology Development Conference on. IEEE, 2015.

XVIII. Li, F., Shirahama, K., Nisar, M. A., Köping, L., &Grzegorzek, M. (2018). Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors. Sensors, 18(2), 679.

XIX. M. N. S. Zainuddin, N. Sulaiman, N. Mustapha, and T. Perumal, “ActivityRecognitionbasedonAccelerometerSensorusingCombinational Classifiers,” pp. 68–73, 2015.

XX. Machado, Inês P., et al. “Human activity data discovery from triaxial accelerometer sensor:Non-supervised learning sensitivity to feature extraction parametrization.” InformationProcessing & Management 51.2 (2015): 204-214.

XXI. Milenkoski, Martin, et al. “Real-time human activity recognition on smartphones using LSTMNetworks.” 2018 41st International Convention on Information and CommunicationTechnology, Electronics and Microelectronics (MIPRO). IEEE, 2018.

XXII. R. Cilla, M. A. Patricio, J. García, A. Berlanga, and J.M. Molina, “Recognizing Human Activities from Sensors using Hidden Markov Models Constructed by Feature Selection Techniques,” Algorithms, vol. 2, no. 1, pp. 282–300, 2009.

XXIII. Ronao, C. A., & Cho, S. B. (2016). Human activity recognition with smartphone sensors using deep learning neural networks. Expert Systems with Applications, 59, 235-244.

XXIV. S. Chernbumroong, A. S. Atkins, and H. Yu, “Perception of Smart Home Technologies to Assist Elderly People,” 4th Int. Conf. Software, Knowledge,Inf. Manag. Appl. (SKIMA 2010),
no. March 2016, pp.1–7, 2010.

XXV. Shah, Muhammad Arif, et al. “Ensembling Artificial Bee Colony with Analogy-Based Estimation to Improve Software Development Effort Prediction.” IEEE Access (2020).

XXVI. Shah, Muhammad Arif, et al. “Communication management guidelines for software organizations in Pakistan with clients from Afghanistan.” IOP Conference Series: Materials Science and Engineering. Vol. 160. No. 1. IOP Publishing, 2016.

XXVII. Shoaib, M., Bosch, S., Incel, O. D., Scholten, H., &Havinga, P. J. (2016). Complex humanactivity recognition using a smartphone and wrist-worn motion sensors. Sensors, 16(4), 426.

XXVIII. Sukor, AS Abdul, A. Zakaria, and N. Abdul Rahim. “Activity recognition using accelerometer sensor and machine learning classifiers.” Signal Processing & Its Applications (CSPA), 2018 IEEE 14th International Colloquium on. IEEE, 2018.

XXIX. T. Shi, X. Sun, Z. Xia, L. Chen, and J. Liu, “Fall Detection Algorithm Based on TriaxialAccelerometer and Magnetometer,” no.2, May 2016.

XXX. T. Sztyler, “On-body Localization of Wearable Devices : An Investigation of Position-AwareActivity Recognition,” 2016.

XXXI. Vellampalli, Haritha. “Physical Human Activity Recognition Using Machine LearningAlgorithms.” (2017).

XXXII. W. Xiao and Y. Lu, “Daily Human Physical Activity Recognition Based on KernelDiscriminant Analysis and Extreme Learning Machine,” Math. Probl. Eng., vol. 2015, 2015.

View Download

Abstract:

Face analysis is a requisite notion for dissimilar appeal allied to artificial intelligence has made possible for Classification of Gender. Facial Data images are still an arduous task for biometric systems due to diverse expressions, dimensions, pose, illustrations and age in facial and other affiliated images includes dissimilar object label classifications. In this paper, SIFT Probabilistic Fuzzy C-means Clustering Approach (SPFCA) proposed to intensify the stratification methodology in object classification for dissimilar images using GSVM. This approach extremely used for recognition and classification of an object due to its fundamental properties which make decorous contrasting object classification in divergent types of robust in facial and other related images. SPFCA is robust clustering approach to diminish uproar insensitivity and assists to group the vicinity ages, male, female and objects. It also assists to find a solution for coinciding cluster complications which may face preceding clustering approaches. Consequently the proficiency can also be used to increase the comprehensive robustness of face recognition and multi-label object classification system and the result increases its invariance and make it a reliably passable biometric.

Keywords:

Object classification,fuzzy c-means clustering,Eigenvalues,shape,corner,wavelet transform,face recognition ,principal component analysis,

Refference:

I. Chang Huang ,Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, “CNN-RNN: A Unified Framework for Multi-label Image Classification”, Neural Networks and Learning Systems, IEEE Transactions on,2014.
II. Chih-Chin Lai, Chih-Hung Wu1, Shing-Tai Pan, Shie-Jue Lee, and Bor-Haur Lin, “Gender Recognition Using Local Block Difference Pattern”, Advances in Intelligent Information Hiding and Multimedia Signal Processing, page:45-54,Springer International Publishing AG 2017.
III. Chunyu Zhang , Hui Ding ,Yuanyuan Shang et.al , Gender Classification Based on Multiscale Facial Fusion Feature,Hindawi Mathematical Problems in Engineering Volume 2018, ISSN: 1563-5147.
IV. Dai D Q, Yuen P C. Wavelet-based discriminant analysis for face recognition. Applied Mathematics and Computation, 175,1(2006):307-318.
V. Dogucan Yaman1,FevziyeIremEyiokur,NurdanSezgin,”Age and Gender Classification from Ear Images”ACCEPTED FOR IAPR/IEEE IWBF 2018.
VI. D. Ramanan ,T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, P. Doll´ar, and C. L. Zitnick. Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014, pages 740–755. Springer, 2014
VII. G. Ding ,Z. Lin, M. Hu, Y. Lin and S. S. Ge. Image tag completion via dual-view linear sparse reconstructions. Computer Vision and Image Understanding, 124:42–60, 2014.
VIII. G. D. K. Kishore, M.Babu Reddy, Analysis and Prototype Sequences of Face Recognition Techniques in Real-Time Picture Processing, Intelligent Engineering Informatics, Advances in Intelligent Systems and Computing 695, Springer Nature Singapore Pte Ltd-2018.pp.323-335,2018.
IX. G. D. K. Kishore, M.Babu Reddy,” Gender classification based on similarity features through SURF and SVM”,Int. J. Knowledge Engineering and Data Mining, pg No:89-104, Vol. 6, No. 1, 2019.
X. G.D.K. Kishore, M.Babu Reddy, “ Detecting Human and classification of Gender using Facial Images MSIFT Features Based GSVM”, International Journal of Recent Technology and Engineering(IJRTE), pg No:1466-1471, Vol.8, Issue3, Sep2019.
XI. I.Daubechies, Ten lectures on wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics, vol. 61, SIAM Press, Philadelphia, 1992.
XII. J.Bezdek, “A convergence theorem for the fuzzy ISODATA clustering algorithms,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-2, no.1, pp. 1–8, Jan. 1981
XIII. J.Kittler, M. Hatef, R.P. Duin, J.G. Matas, On combining classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (3) (1998) 226–239.
XIV. J.Mao, W. Xu, Y. Yang, J. Wang, and A. Yuille. Deep captioning with multimodal recurrent neural networks (m-rnn). In ICLR, 2015.
XV. J.Sullivan ,A. S. Razavian, H. Azizpour, and S. Carlsson. Cnn features off-the-shelf: an astounding baseline for recognition. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on, pages 512–519.IEEE, 2014.
XVI. K.W. Wong, K. M. Lam, and W. C. Siu, “An efficient algorithm for face detection and facial feature extraction under different conditions,” Pattern Recognit., vol. 34, no. 10, pp. 1993–2004, 2001.
XVII. LI Xiao-Fei, “Wavelet Transform for Face Recognition Based on Improved Fuzzy C-Means”, Applied Mechanics and Materials Vols. 602-605 (2014) pp 2170-2173.
XVIII. Mingyuan Xin, Yong Wang “Research on image classification model based on deep convolution neural network”, EURASIP Journal on Image and Video Processing, Springer, page no:2-11,2019.
XIX. MinjSalen Kujur1, Prof. Prashant Jain, “Performance Parameter Analysis of Face Recognition Based On Fuzzy C-Means Clustering, SIFTDetection”, MinjSalenKujur et al. Int. Journal of Engineering Research and Applications” ISSN: 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.515-520.
XX. P.J. Phillips, H. Moon, S.A. Rizvi, P.J. Rauss, The FERET evaluation methodology for face-recognition algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (10) (2000) 1090–1104.
XXI. PuQ,Gao C B, Zhou J L. Theory of fractional covariance matrix and its applications in PCA and 2D-PCA. Expert systems with applications, 40(2013):5395-5401.
XXII. Sandeep Kumar,Sukhwinder Singh,Jagdish Kumar,”A Study on Face Recognition Techniques with Age and Gender Classification” IEEE conference, at Greater Noida, May 2017.
XXIII. S J Raudys, A K Jain. Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Trans. Pattern Anal. Machine Intell. 13 (1991):252–264.
XXIV. T. Sim, S. Baker, M. Bsat, TheCMUpose, illumination, and expression (PIE) database, Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, May 2002.
XXV. W J Krzanowski, P Jonathan, W V McCarthy, M R Thomas. iscriminant analysis with singular covariance matrices: methods and applications to spectroscopic data, Appl. Statist. 44 (1995) :101–115.
XXVI. W. Pedrycz, “Conditional fuzzy clustering in the design of radial basis function neural networks,” IEEE Trans. Neural Netw., vol. 9, no. 4, pp.601–612, Jul. 1998.
XXVII. Yang, J, Zhang D. Two-dimensional PCA: A new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26,1( 2004):131–137.

View Download

Abstract:

Joining of heat-treated alloys(AA6061-T6) by Welding process often results a deterioration of mechanical properties because of the coarsening and dissolution of the strengthening precipitates(Mg₂Si,Al₃FeSi,Al₁₂FeSi) at the weld nugget. However, its scares the applications of AA6061-T6 alloy. In order to enhance mechanical properties of Friction stir welded(FSW) AA6061-T6 alloy and to minimize the loss of T6 condition , four butt joints (FSW-SiC, FSW- B₄C, FSW- Zn and FSW- Al₂O₃)were fabricate with the addition of harder reinforcement materials such as SiC, B₄C,Zn and Al₂O₃ particles. In this study, the microstructure, tensile strength and  hardness of reinforced friction stir welded AA6061-T6 alloy joints were investigated, while the base metal and the welded joint prepared without reinforcement material were utilized as reference to control the process. The grains refinement ,which had been the reason for improved mechanical properties was increased with the addition of reinforced particles in the weld region. Due to the high density of homogeneous dispersion of harder reinforcement particles and  considerably increased grain refinement in the entire welded joints, all the reinforced welded joints resulted improvements over the unreinforced joint in terms of strength and hardness. The addition of SiC, B₄C,Zn and Al₂O₃ reinforcements  particles increases the tensile strength by 24.2% ,1.79%,32.46 and 10.83% respectively, whereas the elongation decreased as compared to unreinforced welded. Due to extremely high hardness value and homogeneous dispersion of B₄C particles in the FSW- B₄C joint .It showed the highest percentage of hardness enhancement that was about 54.9% followed by Al₂O₃, SiC and Zn with improved hardness percentage as 50.37% 40.9%, and 23.2% respectively.

Keywords:

Friction Stir welding (FSW) AA 6061-T6 Hardness Reinforcement particles Microstructure,

Refference:

I. Ahmadnia .M, Seidanloo .A, Teimouri .R, Rostamiyan .Y, Titrashi.k.G,:Determining influence of ultrasonic-assisted friction stir welding parameters on mechanical and tribological properties of AA6061 joints. Int. J. Adv. Manuf. Technol, Vol.78 (9e12),pp.2009-2024,2015.
II. Bahrami .M, Dehghani.K, MKB Givi , A novel approach to develop aluminum matrix nano-composite employing friction stir welding technique, Materials & Design .Vol. 53, pp. 217-225, 2014.
III. Bodaghi M, Dehgani K. Friction stir welding of AA5052: the effects of SiC nano-particles addition. Int J Adv Manuf Technol ,Vol.88,pp.2651–60,2017.
IV. Cavaliere.P, SantisA.D, Panella.F, A. Squillace, Effect of welding parameters on mechanical and microstructural properties of dissimilar AA6082–AA2024 joints produced by friction stir welding, Mater. Des. pp.609–616,2009.
V. Dinaharan.I, Murugan .N,: Effect of friction stir welding on microstructure, mechanical and wear properties of AA6061/ZrB2 in situ cast composites Materials Science and Engineering: Vol. 543, pp. 257-266, 2012.
VI. Dixitm M, Newkirk. J.W, Mishra. R.S, :Properties of friction stir-processed Al1100-NiTi composite, Scr. Mater. Vol.56,pp. 541–544,2007.
VII. Dong.Y.Y, Ai-P.W, Juergen.S,Qing.Y.S,: Predicting residual distortion of aluminum alloy stiffened sheet after friction stir welding by numerical simulation Materials & Design ,Vol. 32, pp. 2284-2291 ,l 2011
VIII. Dursun.T, Soutis. C, : Recent developments in advanced aircraft aluminium alloys. Materials & Design. Vol. 56, pp. 862–871, 2013
IX. Elangovan .K, Balasubramanian. V,: Influences of post-weld heat treatment on tensile properties of friction stir-welded AA6061 aluminum alloy joints: M a t e r i a l s c h a r a c t e r i z a t i o n ,Vol.5 9 ,pp. 1 1 6 8 – 1 1 7 7, 2 0 0 8 .
X. Elangovan. K, Balasubramanian. V, Babu. S,: Predicting tensile strength of friction stir welded AA6061 aluminium alloy joints by a mathematical model. Materials &Design,Vol.30, , pp. 188-193 , 2009.
XI. Faraji.G, Asadi.P, :Studying microstructure and mechanical properties of SiC-incorporated AZ31 joints fabricated through FSW: the effects of rotational and traveling speeds,Mater. Sci. Eng. A,Vol. 527 ,pp.2320–2330,2011.
XII. XII.Ghamdi. K.A.Al, Hussain .G, Hashemi.R,: Fabrication ofmetal-matrixAL7075T651/TiN nano composite employing friction stir process, Proc. IME B J. Eng. Manufact. Vol.231 (8), 1319-1331,2017.
XIII. Gopalakrishnan.S, Murugan .N,: Prediction of tensile strength of friction stir welded aluminium matrix TiCp particulate reinforced composite J. Mater Des, Vol.32,pp. 462–467 ,2011.

XIV. Guo.J, Gougeon .P, Chen X.G, :Microstructure evolution and mechanical propertiesof dissimilar friction stir welded joints between AA1100-B4C MMC and AA6063 alloy, Mater. Sci. Eng. A.,Vol. 553,pp.149–156,2012.
XV. Heydarian. Arash, Dehghani Kamran &SlamkishTaymor. Optimizing powder distribution in production of surface nano-composite via friction stir processing. Metallurgical and Materials Transactions .B,Vol. 45, pp.821-826 ,2014.

XVI. Khodaverdizadeh H, Heidarzadeh A &Saeid T. Effect of tool pin profile on microstructure and mechanical properties of friction stir welded pure copper joints. Materials &Design,Vol. 45, pp.265-270,2013.

XVII. Li. Y, Liu. Y, Liu. C, Li. C, Ma. Z, Huang .Y, Wang. Z, Li. W, Microstructure evolution and mechanical properties of linear friction welded S31042 heat-resistant steel, J. Mater. Sci. Technol. Vol.34 (4),pp.653–659, 2018.
XVIII. Morisada Y, Fujii H, Nagaoka T &Fukusumi M. Effect of friction stir processing with SiC particles on microstructure and hardness of AZ31. Materials Science and Engineering: A .Vol.433, pp.50-54,2006.

XIX. Reimann. M, Gartner .T, Suhuddin. U, Göbel. J, dos Santos JF, :Keyhole closure using friction spot welding in aluminum alloy 6061-T6. J Mater Process Technol,Vol.237,pp .12–8,2016.
XX. Rhodes .C.G, Mahoney .M.W, Bingel .W.H, Spurling .R.A, Bampton .C.C, : Effects of friction stir welding on microstructure of 7075 aluminium. Scr Mater.Vol.36,pp.69–75,1997.

XXI. Safeen W, Hussain S, Wasim A, Jahanzaib M, Aziz H, AbdallaH. Predicting the tensile strength, impact toughness and hardness of friction stir-welded AA6061-T6 using response surface methodology. Int J Adv Manuf Technol ,Vol.87,pp.1765–81,2016.
XXII. Sahraeinejad ,Izad.H, Meysam.H. Fabrication of metal matrix composites by friction stir processing with different Particles and processing parameters. Materials Science and Engineering A•Vol. 626,pp.505-513, 2015.
XXIII. Scialpi .D.F, Cuomo.P, Summa.P.D,: Micro-friction stir welding of 2024–6082 aluminium alloys, Weld. Int.Vol. 21 (1),pp. 16–22,2008.
XXIV. Senthil.V, Balasubramanian. E,Effect of tool pin profile on corrosion behavior of friction stir processed LM25AA 10% SiCp metal matrix composites, Metal Powder Report, 2019

XXV. Singh. T, Tiwari.S.K, Shukla.D.K, Friction stir welding of AA6061-T6: the effects of Al2O3nano-particles addition, Results in Materials .1. 100005,2019.
XXVI. Sun. Y.F ,Fujii. H, :The effect of SiC particles on the microstructure and mechanical properties of friction stir welded pure copper joints. Mater SciEng .A.Vol.528,pp. 5470–5475,2011.
XXVII. T.E. Abioyea,b, H. Zuhailawatia,∗, A.S. Anasyidaa, S.A. Yahayac, B.K. Dhindaw,:Investigation of the microstructure, mechanical and wear properties of AA6061-T6 friction stir weldments with different particulate reinforcements addition Journal of Materials Research and Technology.Vol. 8(5) , 2019
XXVIII. Tutunchilar S, Haghpanahi M, Givi MK Besharati, Asadi P &Bahemmat P. Simulation of material flow in friction stir processing of a cast Al–Si alloy. Materials &Design,Vol. 40,pp. 415-426,2012.
XXIX. Yokoyama T, Nakai K, Katoh K. Tensile properties of 6061-T6 friction stir welds and constitutive modelling in transverse and longitudinal orientations. Weld Int ,Vol.32,pp.161–71,2018.
XXX. Yuvaraj. N, Aravindan S, Vipin. Fabrication of Al5083/B4C surface composite by friction stir processing and its tribological characterisation. J Mater Res Technol ,Vol.4(4),pp.398–410,2015.
XXXI. Zhang.C, Cui.L , Wang.D, Liu.Y, Liu.C, Li.H, The heterogeneous microstructure of heat affect zone and its effect on creep resistance for friction stir joints on 9Cr–1.5W heat resistant steel, Scr. Mater. Vol.158 ,pp.6–10, 2019.
XXXII. Zhang .H.J, Liu .H.J, Huang .Y.X, Yu. L,: Effect of water cooling on performance of friction stir welded heat affected zone, J. Mater. Eng. Perform,Vol. 21,pp.1182-1187,2012.

View Download

Abstract:

Texture in images can be utilized as a cue for different computer vision tasks as object identification and classification. This paper proposes CSTC-Mel Identification Model for texture classification, the feature representation which is low dimensional and training free, robust in nature for the texture description. The proposed technique is implemented in 3 phases such as ULL responses, feature computation, Feature encoding and the representation of image. Feature Computation is generated to categorize the texture structures and their connection by implementing linear and non-linear operators on the ULL responses of Gaussian Filter in the scale space, which is established based on steerable filters. Feature encoding through more than one level of thresholding or binary can be adopted to compute these feature computation into texture. Two encoding methods are designed which is robust in nature to the illumination changes and image rotation. The feature representation is explored to combine the discrete texture into the histogram representation. Our proposed model is tested on PH² dataset. By comparing the experimental outcomes of proposed CSTC-Mel Identification Model with existing models, we can observe t at the proposed CSTC-Mel Identification Model identifies the skin cancer with accuracy of 93.81%.

Keywords:

Texture Classification,Steerable Filter,Gaussian Filter,Feature Computation,Feature Encoding,

Refference:

I. A. Madooei, M. S. Drew and H. Hajimirsadeghi, “Learning to Detect Blue–White Structures in Dermoscopy Images With Weak Supervision,” in IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 2, pp. 779-786, March 2019.

II. A. Madooei, M. S. Drew, M. Sadeghi, and M. S. Atkins, “Automatic detection of blue-white veil by discrete colour matching in dermoscopy images,” in Medical Image Computing and Computer-Assisted Intervention MICCAI, ser. Lecture Notes in Computer Science, K. Mori, I. Sakuma, Y. Sato, C. Barillot, and N. Navab, Eds. Springer Berlin Heidelberg, 2013, no. 8151, pp. 453–460.

III. EbtihalAlmansour and M. ArfanJaffar, “Classification of Dermoscopic Skin Cancer Images Using Color and Hybrid Texture Features”, IJCSNS International Journal of Computer Science and Network Security, VOL.16 No.4, April 2016.

IV. Fatima, R and Khan, Mohammed Zafar Ali and A, Govardhan and Dhruve, K P (2012) Computer Aided Multi-Parameter Extraction System to Aid Early Detection of Skin Cancer Melanoma. International Journal of Computer Science and Network Security, 12 (10). pp. 74-86. ISSN 1738-7906

V. G. A. S. Saroja and C. H. Sulochana, “Texture analysis of non-uniform images using GLCM,” 2013 IEEE Conference on Information & Communication Technologies, Thuckalay, Tamil Nadu, India, 2013, pp. 1319-1322.

VI. H. Ganster, A. Pinz, R. Rohrer, and E. W. ¨ et al., “Automated melanoma recognition,” Medical Imaging, IEEE Transactions on, vol. 20, no. 3, pp. 233–239, 2001. [11] I. Maglogiannis, S. Pavlopoulos, and D.
VII. H. Kittler, H. Pehamberger, K. Wolff, and M. Binder, “Diagnostic accuracy of dermoscopy,” The lancet oncology, vol. 3, pp. 159-165, 2002.

VIII. J. C. Kavitha and A. Suruliandi, “Texture and color feature extraction for classification of melanoma using SVM,” 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE’16), Kovilpatti, 2016, pp. 1-6.

IX. Koutsouris, “An integrated computer supported acquisition, handling, and characterization system for pigmented skin lesions in dermatological images,” IEEE Transactions on Information Technology in Biomedicine, pp. 86–98, 2005.

X. Löfstedt, T., Brynolfsson, P., Asklund, T., Nyholm, T., &Garpebring, A. (2019). Gray-level invariant Haralick texture features. PLOS ONE, 14(2), e0212110.

XI. M. E. Celebi, H. Iyatomi, W. V. Stoecker, R. H. Moss, H. S. Rabinovitz, G. Argenziano, and H. P. Soyer, “Automatic detection of blue-white veil and related structures in dermoscopy images,” Computerized Medical Imaging and Graphics, vol. 32, no. 8, pp. 670–677, 2008.

XII. M. Moncrieff, S. Cotton, P. Hall, R. Schiffner, U. Lepski, and E. Claridge, “SIAscopy assists in the diagnosis of melanoma by utilizing computer vision techniques to visualise the internal structure of the skin,” Med Image Understanding Analysis, pp. 53-56, 2001.

XIII. M. Rademaker and A. Oakley, “Digital monitoring by whole body photography and sequential digital dermoscopy detects thinner melanomas,” J Prim Health Care, vol. 2, pp. 268-72, 2010.

XIV. O. Abuzaghleh, B. D. Barkana, and M. Faezipour, “Automated skin lesion analysis based on color and shape geometry feature set for melanoma early detection and prevention,” in Systems, Applications and Technology Conference (LISAT), 2014 IEEE Long Island, 2014, pp. 1-6.

XV. O. Abuzaghleh, M. Faezipour and B. D. Barkana, “A comparison of feature sets for an automated skin lesion analysis system for melanoma early detection and prevention,” 2015 Long Island Systems, Applications and Technology, Farmingdale, NY, 2015, pp1-6.

XVI. S. Joseph and J. R. Panicker, “Skin lesion analysis system for melanoma detection with an effective hair segmentation method,” 2016 International Conference on Information Science (ICIS), Kochi, 2016, pp. 91-96.

XVII. S. K. Vengalil, N. Sinha and G. S. Raghavan, “Modified oriented Gaussian derivative filter based texture detection algorithm and parameter estimation,” 2015 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, 2015, pp. 1-6.

XVIII. S.Suer, S.Kockara, and M.Mete, “An improved border detection in dermoscopy images for density based clustering”, BMC bioinformatics.,vol.12, No.10,p.S12,2011.

XIX. Saha, Sujaya and Dr. Rajat Gupta. “An Automated Skin Lesion Diagnosis by using Image Processing Techniques.” (2014).
XX. Sreelatha, T., Subramanyam, M.V. & Prasad, M.N.G., “Early detection of skin cancer using Melanoma Segmentation Technique”, Journal of Medical Systems (May 2019) 43:190.

XXI. T. Mendonca, P. Ferreira, J. Marques, A. Marcal, and J. Rozeira, “PH2- a dermoscopic image database for research and benchmarking,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS). IEEE, 2013, pp. 5437–5440.

XXII. T. Wadhawan, N. Situ, K. Lancaster, X. Yuan, and G. Zouridakis, “SkinScan©: A portable library for melanoma detection on handheld devices,” in Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, 2011, pp. 133-136l.

XXIII. T. Y. Satheesha, D. Satyanarayana, M. N. G. Prasad and K. D. Dhruve, “Melanoma Is Skin Deep: A 3D Reconstruction Technique for Computerized Dermoscopic Skin Lesion Classification,” in IEEE Journal of Translational Engineering in Health and Medicine, vol. 5, pp. 1-17, 2017, Art no. 4300117.

XXIV. TammineniSreelatha, M.V.Subramanyam, M.N.Giri Prasad. A Survey work on Early Detection methods of Melanoma Skin Cancer. Research J.Pharm. and Tech. 2019; 12(5): 2589-2596.

XXV. Torkashvand, Fatemeh and Mehdi Fartash. “Automatic Segmentation Of Skin Lesion Using Markov Random Field.” (2015).

XXVI. W. Freeman and E. Adelson, “The design and use of steerable filters,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 9, pp. 891–906, Sep. 1991.

XXVII. W.Stoecker, M.Wronkiwwiez, R.Chowdhury, R. J. Stanley, J. Xu, A. Bangert, B. Shrestha, D.A. Calcara, H.S. Rabinovitz, M. Oliviero, F. Ahmed, L.A. Perry and R. Drugge, “Detection of Granularity in dermoscopy images of malignant melanoma using color and texture features”, Compu.Med Imaging Graph, Vol.32.N0.8, pp 670-677, Dec.2008.

View Download