On Metric Dimension of Two Constructed Families from Antiprism Graph

Size: px
Start display at page:

Download "On Metric Dimension of Two Constructed Families from Antiprism Graph"

Transcription

1 Mah S Le 2, No, ) Mahemaal Sees Leers A Ieraoal 203 NSP Naural Sees Publhg Cor O Mer Dmeso of Two Cosrued Famles from Aprm Graph M Al,2, G Al,2 ad M T Rahm 2 Cere for Mahemaal Imagg Tehques ad Dep Mah Sees, Uversy of Lverpool, UK 2 Dep of Mahemas, Naoal Uversy of Compuer & Emergg Sees, Peshawar, Paa Emal: muraza_psh@yahooom, arqrahm@uedup, goharal@uedup Reeved: Ja 202, Reved: 3 May 202, Aeped: 2 Jul 202 Publhed ole: Ja 203 Absra: I h paper we ompue he mer dmeso of wo famles of graphs osrued from aprm graph Keywords: Mers dmeso, bas, resolvg se, aprm Iroduo For a oeed graph G, he dae d u, v) bewee wo veres u, v V G) he legh of a shores pah bewee hem G Le W w, w,, w } be a ordered se of veres of G ad le v 2 be a verex of G, he represeao of he verex v wh respe o W, deoed by r v W) he - uple d v, w ), d v, w ),, d v, w )) If d veres of G have d represeaos wh 2 respe o W, he W alled a resolvg se or loag se for G [2] A resolvg se of mmum ardaly alled a mer bas for G ad h ardaly he mer dmeso of G dm G), For a gve ordered se of veres W w, w,, w } of a graph G, he h ompoe of r v W) 2 0 f ad oly f v w Thus, o show ha W a resolvg se suffes o verfy ha r x W) r y W) for eah par of d veres x, y V G) \ W Movaed by he problem of uquely deermg he loao of a ruder a ewor, he oep of mer dmeso was rodued by Slaer [0,] ad suded depedely by Harary ad Meler [3] Applaos of h vara o he avgao of robos ewors are dussed [8] ad applaos o hemry [2] whle applaos o problems of paer reogo ad mage proessg, some of whh volve he use of herarhal daa sruures are gve [9] I [4,5,6] Imra e al proved he mer dmeso of some famles of ovex polyopes I [2] Charrad e al proved ha a graph has mer dmeso f ad oly f a pah, hee pahs o veres osue a famly of graphs wh osa mer dmeso Smlarly, yle wh 3 veres also osue suh a famly of graphs as her mer dmeso 2 I [] J Caeres e al proved ha: dm p m C 2, ) 3, f, odd; oher we

2 2 M Al e al: O mer dmeso of wo osrued famles Se prms D are he rvale plae graphs obaed by he aresa produ of he pah P 2 wh a yle C ; hey also osue a famly of 3 - regular graphs wh osa mer dmeso Javad e al proved [7] ha he graph of aprm A osues a famly of regular graphs wh osa mer dmeso ad dm A ) 3 for every 5 I h paper, we exed h sudy by osderg wo famles of graph whh are osrued from aprm The aprm A, 3, oss of a ouer -yle a a2 a, a er -yle b b2 b, ad a se of spoes b ad b a,,2,3,, where ae modulo a The graph H osrued from he graph A as follows: We delee he edges a a from A For eah,2,,, we rodue ew veres ad d for a ad b respevely For eah,2,,, rodue ew edges b, a d, d ad b, where ae modulo The graph R osrued from he graph,2,,, we rodue ew veres ad rodue ew edges b, a d, d, d d ad 2 Ma Resuls d for a ad b where A as follows: We delee he edges a a from A For eah b respevely For eah,2,, ae modulo Theorem: Le 6 be a eger he dm H )3 Proof We dguh wo ases Case ): 2, 3, IN We osder W b, b, b } V H ) We show ha W a 2 resolvg se for V H ) For h we fd he represeaos of he veres of V H )\W wh respe o W The represeaos of he veres are as follows;, 2, ), for 3 ; r b W ) 2,2 2, ), 2 r r a,2, ), W ),, 2 ), 2 2,3 2, ),,, ),,, ), W ),,), 2,2 2, ), for ; for 2 ; for 2 ; 2 ; ; 2 2,2, ), ;,, 2 ), 2 ; r d W ),,2), ; 2 2,2 3, ), 2 Noe ha here are o wo veres havg he same represeaos mplyg ha dm ) 3 H

3 M Al e al: O mer dmeso of wo osrued famles 3 Now we show ha dm H ) 3, by provg ha here o resolvg se W wh W 2 We have he followg possbles; ) Boh veres belog o b :,2,, } V H ) Whou loss of geeraly we suppose ha oe resolvg verex b ad he oher b, 2 ) For 2 r b, b }) r a b, b }), ) ad for +, r a b, b }) r a b, b }),), a orado 2) Boh veres belog o :,2,, } V H ) Whou loss of geeraly we suppose ha oe resolvg verex, ad he oher, 2 ) The for 2 r b, }) r a, }) 2, ) for r a, }) r a, }),), a orado 3) Boh veres belog o a :,2,, } V H) We suppose ha oe resolvg verex a ad he oher a, 2 ) The for 2 r a, a }) r b a, a }) 2, ) d :,2,, } V H We suppose ha oe resolvg verex For 2 r a d, d}) r b d, d}) 3, 2) r d, d}) r d, d}), a orado :,2,, } V H ad for, r b a, a }) r b a, a }),), a orado 4) Boh veres belog o ) d ad he oher d, 2 ) ad for, 2,) 5) Oe verex belog o b ) ad aoher verex belog o :,2,, } V H) Whou loss of geeraly we suppose ha oe resolvg verex b ad he oher, ) For r a b, }) r b b, }), ) ad for, r a b, }) r a b, }),2), a orado 6) Oe verex belog o b :,2,, } V H ) ad aoher verex belog o a :,2,, } V H) Whou loss of geeraly we suppose ha oe resolvg verex b ad he oher a, ) The for r a b, a}) r b, a}), ) ad for, r b b, a }) r a b, a }),2), a orado 2 :,2,, } V H 7) Oe verex belog o ) ad aoher verex belog o a :,2,, } V H) Whou loss of geeraly we suppose ha oe resolvg verex ad he oher a, ) For r a, a}) r b, a}) 2, ) ad for, r a, a}) r, a}),2), smlarly for +, r a, a }) r, a }),2), a orado 2 8) Oe verex belog o :,2,, } V H) o :,2,, } V H) he oher d, ) For r a a, d}) r b a, d}) 2, he represeao r a, d}) r 2 a, d}) 2,), a orado 9) Oe verex belog o :,2,, } V H) :,2,, } V H) a ad aoher verex belog d Whou loss of geeraly we suppose ha oe resolvg verex a ad 2) ad for b ad aoher verex belog o d Whou loss of geeraly we suppose ha oe resolvg verex b ad he oher d, ) For r a b, d}) r b b, d}), 2) ad for he represeao

4 4 M Al e al: O mer dmeso of wo osrued famles r a b, d}) r b, d}),), smlarly for, r a b, d }) r b, d }),), a orado 2 :,2,, } V H :,2,, } V H) 0) Oe verex belog o ) ad aoher verex belog o d Whou loss of geeraly we suppose ha oe resolvg verex ad he oher d, ) For r a, d}) r b, d}) 2, 2) ad for, r a, d}) r, d}),), smlarly for, r a, d }) r, d }),), a orado 2 Hee, from above follows ha here o resolvg se wh wo veres for V H ) mplyg ha dm H) 3 Case): 2, 3, IN Le W b, b, b } V H ) We show ha W a 2 2 resolvg se for V H) For h we fd he represeaos of veres of V H ) \ W wh respe o W The represeaos of he veres are as follow;,, ), for ; r a W ),, 2 ), for 2 ; 2 2,3 2, ), for 2 r b r, 2,2 ), W ) 2 2,2 3, 2 ),,2, ),,, 3 ), W ),,), 2 3,2 4, ), for 3 ; 3 ; 2 ; 2; 3 2,2, 2), for ; r d W ),, 3 ), for 2 ; 2 3,4 2, ), for 2 Proeedg o same le as ase) we oe ha here are o wo veres havg he same represeaos, mplyg ha dm H ) 3 Also as ase), a be show ha here o se W wh W 2, suh ha W a resolvg se for V H ) for 6 ad odd Thus, dm ) 3 Hee dm H) 3 From ase) ad ase) we ge dm H) 3 Theorem: Le 6 be a eger he dm R ) 3 Proof We dguh wo ases: Case) 2, 3, IN Suppose W b, b, b } V R ) We show ha W a resolvg se H 2 for V R ) For h we fd he represeaos of he veres of V R ) \ W wh respe o W The represeaos of he veres are as follows;

5 M Al e al: O mer dmeso of wo osrued famles 5 r b r, 2, ), W ) 2,2 2, ),,2, ), W ),, 2 ), 2 2,3 2, ), for 3 ; 2 for ; for 2 ; for 2 r a,, ),,, ), W ),,), 2,2 2, ), ; 2 ; ; 2 2,2, ), ;,, 2 ), 2 ; r d W ),,2), ; 2 2,2 3, ), 2 We oe ha here are o wo veres havg he same represeaos mplyg ha dm ) 3 Now we show ha dm R ) 3, by provg ha here o resolvg se W wh W 2 We have he followg possbles, ) Boh veres belog o b :,2,, } V R ) Whou loss of geeraly we suppose he resolvg veres b ad b, 2 ) For 2 r b, b }) r a b, b }), ) ad for, r a b, b }) r a b, b }),), a orado 2) Boh veres belog o :,2,, } V R ) We suppose ha oe resolvg verex ad he oher, 2 ) For 2 r b, }) r a, }) 2, ) ad for, r b, }) r a, }) 2, ), a orado :,2,, } V R 3) Boh veres belog o a ) We suppose ha oe resolvg verex a ad he oher a, 2 ) For 2 r a, a }) r b a, a}) 2, ) ad for r b a, a }) r b a, a }),), a orado 4) Boh veres belog o d :,2,, } V R ) We suppose ha oe resolvg verex d ad he oher d, 2 ) For 2 r a d, d}) r b d, d}) 3, 2) ad for, r d, d }) r d, d }) 2,), a orado :,2,, } V R 5) Oe verex belog o b ) ad aoher belog o :,2,, } V R ) R Whou loss of geeraly we suppose ha oe resolvg verex b ad he oher, ) For r a b, }) r b b, }), ) ad for +, r a b, }) r a b, }),2), a orado 6) Oe verex belog o b :,2,, } V R ) ad aoher belog o a :,2,, } V R ) Whou loss of geeraly we suppose ha oe resolvg verex b ad he oher a, )

6 6 M Al e al: O mer dmeso of wo osrued famles For r r a b, a}) r b, a}), ) ad for b, a }) r a b, a }),2), a orado b 2 :,2,, } V R 7) Oe verex belog o ) ad aoher belog o a :,2,, } V R ) Whou loss of geeraly we suppose ha oe resolvg verex ad he oher a, ) For r a, a}) r b, a}) 2, ) ad for, r a, a}) r, a}),2), smlarly for r a, a }) r, a }),2), a orado 2 :,2,, } V R 8) Oe verex belog o a ) ad aoher belog o d :,2,, } V R ) Whou loss of geeraly we suppose ha oe resolvg verex a ad he oher d, ) For r a a, d}) r b a, d}) 2, 2) ad for, r a, d }) r a, d }) 2,), a orado 2 :,2,, } V R 9) Oe verex belog o b ) ad aoher belog o d :,2,, } V R ) Whou loss of geeraly we suppose ha oe resolvg verex b ad he oher d, ) For r a, }), }), b d r b b d 2) ad for r a b, d}) r b, d}),), smlarly for ha r a b, d }) r b, d }),), a orado 2 :,2,, } V R 0) Oe verex belog o ) ad aoher belog o d :,2,, } V R ) Whou loss of geeraly we suppose ha oe resolvg verex ad he oher d, ) For r a, d}) r b, d}) 2, 2) ad for r a, d}) r, d}),), smlarly for ha r a, d }) r, d }),), a orado 2 Hee, from above follows ha here o resolvg se wh wo veres for V R ) mplyg ha dm R ) 3 Case ): 2, 3, IN Cosder W b, b, b } V R ) We show ha W a 2 2 resolvg se for V R ) For h we fd he represeaos of veres of V R ) \ W wh respe o W The represeaos of he veres are as follow;,, ), for ; r a W ),, 2 ), for 2 ; 2 2,3 2, ), for 2 r b r, 2,2 ), W ) 2 2,2 3, 2 ),,2, ),,, 3 ), W ),,), 2 3,2 4, ), for 3 ; 3 ; 2 ; 2; 3

7 M Al e al: O mer dmeso of wo osrued famles 7 2,2, 2), for ; r d W ),, 3 ), for 2 ; 2 3,4 2, ), for 2 Proeedg o same le as ase) we observe ha here are o wo veres havg he same represeaos, mplyg ha dm R ) 3 Also as ase), a be show ha here o se W wh W 2, suh ha W a resolvg se for V R ) for 6 ad odd Thus, dm ) 3 Hee dm R ) 3 From ase) ad ase) we ge dm R ) 3 3 Coluso I h paper suded he mer dmeso of wo famles of graphs whh are he exeso of he aprm graph We have see ha he mer dmeso of hese graphs fe ad does o deped o he order of he graph ad oly hree veres appropraely hose suffe o resolve all he veres of hese graphs R Referees [] J Caeres, C Herado, M Mora, I M Pelayo, M L Pueras, C Seara, D R Wood, O he mer dmeso of aresa produ of graphs, SIAM J D Mah ) [2] G Charrad, L Eroh, M A Johso, O R Oellerma, Resolvably graphs ad mer dmeso of a graph, D Appl Mah ) 99-3 [3] F Harary, R A Meler, O he mer dmeso of a graph, Ars Comb 2 976) 9-95 [4] M Imra, A Q Bag, A Ahmad, Famles of plae graphs wh osa mer dmeso, o appear Ulas Mah [5] M Imra, S A Bohary, A Q Bag, O famles of ovex polyopes wh osa mer dmeso, Compu Mah Appl ) [6] M Imra, A Q Bag, M K Shafq, Adrea Feovova, Classes of ovex polyopes wh osa mer dmeso, Ulas, Mah, press [7] I Javad, M T Rahm, K Al, Famles of regular graphs wh osa mer dmeso, Ulas Mah ) 2-33 [8] K Karlraj, J V Verold, O equaable olorg of helm ad gear graphs, Ieraoal J Mah Comb, 4 200) [9] R A Meler, I Tomesu, Mer bases dgal geomery, Compuer Vo, Graphs, ad Image Proessg, ) 3-2 [0] P J Slaer, Leaves of rees, Cogress Numer 4 975) [] P J Slaer, Domag ad referee ses graphs, J Mah Phys S )

Cyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles

Cyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles Ope Joural of Dsree Mahemas 2017 7 200-217 hp://wwwsrporg/joural/ojdm ISSN Ole: 2161-7643 ISSN Pr: 2161-7635 Cylally Ierval Toal Colorgs of Cyles Mddle Graphs of Cyles Yogqag Zhao 1 Shju Su 2 1 Shool of

More information

Chebyshev Polynomials for Solving a Class of Singular Integral Equations

Chebyshev Polynomials for Solving a Class of Singular Integral Equations Appled Mahemas, 4, 5, 75-764 Publshed Ole Marh 4 SRes. hp://www.srp.org/joural/am hp://d.do.org/.46/am.4.547 Chebyshev Polyomals for Solvg a Class of Sgular Iegral Equaos Samah M. Dardery, Mohamed M. Alla

More information

ASYMPTOTIC BEHAVIOR OF SOLUTIONS OF DISCRETE EQUATIONS ON DISCRETE REAL TIME SCALES

ASYMPTOTIC BEHAVIOR OF SOLUTIONS OF DISCRETE EQUATIONS ON DISCRETE REAL TIME SCALES ASYPTOTI BEHAVIOR OF SOLUTIONS OF DISRETE EQUATIONS ON DISRETE REAL TIE SALES J. Dlí B. Válvíová 2 Bro Uversy of Tehology Bro zeh Repul 2 Deprme of heml Alyss d Appled hems Fuly of See Uversy of Zl Žl

More information

Solution. The straightforward approach is surprisingly difficult because one has to be careful about the limits.

Solution. The straightforward approach is surprisingly difficult because one has to be careful about the limits. ose ad Varably Homewor # (8), aswers Q: Power spera of some smple oses A Posso ose A Posso ose () s a sequee of dela-fuo pulses, eah ourrg depedely, a some rae r (More formally, s a sum of pulses of wdh

More information

Complementary Tree Paired Domination in Graphs

Complementary Tree Paired Domination in Graphs IOSR Joural of Mahemacs (IOSR-JM) e-issn: 2278-5728, p-issn: 239-765X Volume 2, Issue 6 Ver II (Nov - Dec206), PP 26-3 wwwosrjouralsorg Complemeary Tree Pared Domao Graphs A Meeaksh, J Baskar Babujee 2

More information

INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY

INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY [Mjuh, : Jury, 0] ISSN: -96 Scefc Jourl Impc Fcr: 9 ISRA, Impc Fcr: IJESRT INTERNATIONAL JOURNAL OF ENINEERIN SCIENCES & RESEARCH TECHNOLOY HAMILTONIAN LACEABILITY IN MIDDLE RAPHS Mjuh*, MurlR, B Shmukh

More information

Continuous Indexed Variable Systems

Continuous Indexed Variable Systems Ieraoal Joural o Compuaoal cece ad Mahemacs. IN 0974-389 Volume 3, Number 4 (20), pp. 40-409 Ieraoal Research Publcao House hp://www.rphouse.com Couous Idexed Varable ysems. Pouhassa ad F. Mohammad ghjeh

More information

Key words: Fractional difference equation, oscillatory solutions,

Key words: Fractional difference equation, oscillatory solutions, OSCILLATION PROPERTIES OF SOLUTIONS OF FRACTIONAL DIFFERENCE EQUATIONS Musafa BAYRAM * ad Ayd SECER * Deparme of Compuer Egeerg, Isabul Gelsm Uversy Deparme of Mahemacal Egeerg, Yldz Techcal Uversy * Correspodg

More information

The Poisson Process Properties of the Poisson Process

The Poisson Process Properties of the Poisson Process Posso Processes Summary The Posso Process Properes of he Posso Process Ierarrval mes Memoryless propery ad he resdual lfeme paradox Superposo of Posso processes Radom seleco of Posso Pos Bulk Arrvals ad

More information

A note on Turán number Tk ( 1, kn, )

A note on Turán number Tk ( 1, kn, ) A oe o Turá umber T (,, ) L A-Pg Beg 00085, P.R. Cha apl000@sa.com Absrac: Turá umber s oe of prmary opcs he combaorcs of fe ses, hs paper, we wll prese a ew upper boud for Turá umber T (,, ). . Iroduco

More information

Solution to Some Open Problems on E-super Vertex Magic Total Labeling of Graphs

Solution to Some Open Problems on E-super Vertex Magic Total Labeling of Graphs Aalable a hp://paed/aa Appl Appl Mah ISS: 9-9466 Vol 0 Isse (Deceber 0) pp 04- Applcaos ad Appled Maheacs: A Ieraoal Joral (AAM) Solo o Soe Ope Probles o E-sper Verex Magc Toal Labelg o Graphs G Marh MS

More information

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER 2

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER 2 Joh Rley Novembe ANSWERS O ODD NUMBERED EXERCISES IN CHAPER Seo Eese -: asvy (a) Se y ad y z follows fom asvy ha z Ehe z o z We suppose he lae ad seek a oado he z Se y follows by asvy ha z y Bu hs oads

More information

A Theoretical Framework for Selecting the Cost Function for Source Routing

A Theoretical Framework for Selecting the Cost Function for Source Routing A Theoreal Framework for Seleg he Cos Fuo for Soure Roug Gag Cheg ad Nrwa Asar Seor ember IEEE Absra Fdg a feasble pah sube o mulple osras a ework s a NP-omplee problem ad has bee exesvely suded ay proposed

More information

Efficient Estimators for Population Variance using Auxiliary Information

Efficient Estimators for Population Variance using Auxiliary Information Global Joural of Mahemacal cece: Theor ad Praccal. IN 97-3 Volume 3, Number (), pp. 39-37 Ieraoal Reearch Publcao Houe hp://www.rphoue.com Effce Emaor for Populao Varace ug Aular Iformao ubhah Kumar Yadav

More information

Calculating Exact Transitive Closure for a Normalized Affine Integer Tuple Relation

Calculating Exact Transitive Closure for a Normalized Affine Integer Tuple Relation Clulg E Trsve Closure for Normlzed Affe Ieger Tuple elo W Bele*, T Klme*, KTrfuov** *Fuly of Compuer See, Tehl Uversy of Szze, lme@wpspl, bele@wpspl ** INIA Sly d Prs-Sud Uversy, ordrfuov@rfr Absr: A pproh

More information

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF EDA/DIT6 Real-Tme Sysems, Chalmers/GU, 0/0 ecure # Updaed February, 0 Real-Tme Sysems Specfcao Problem: Assume a sysem wh asks accordg o he fgure below The mg properes of he asks are gve he able Ivesgae

More information

CS344: Introduction to Artificial Intelligence

CS344: Introduction to Artificial Intelligence C344: Iroduco o Arfcal Iellgece Puhpa Bhaacharyya CE Dep. IIT Bombay Lecure 3 3 32 33: Forward ad bacward; Baum elch 9 h ad 2 March ad 2 d Aprl 203 Lecure 27 28 29 were o EM; dae 2 h March o 8 h March

More information

Available online Journal of Scientific and Engineering Research, 2014, 1(1): Research Article

Available online  Journal of Scientific and Engineering Research, 2014, 1(1): Research Article Avalable ole wwwjsaercom Joural o Scec ad Egeerg Research, 0, ():0-9 Research Arcle ISSN: 39-630 CODEN(USA): JSERBR NEW INFORMATION INEUALITIES ON DIFFERENCE OF GENERALIZED DIVERGENCES AND ITS APPLICATION

More information

The ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3.

The ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3. C. Trael me cures for mulple reflecors The ray pahs ad rael mes for mulple layers ca be compued usg ray-racg, as demosraed Lab. MATLAB scrp reflec_layers_.m performs smple ray racg. (m) ref(ms) ref(ms)

More information

Learning of Graphical Models Parameter Estimation and Structure Learning

Learning of Graphical Models Parameter Estimation and Structure Learning Learg of Grahal Models Parameer Esmao ad Sruure Learg e Fukumzu he Isue of Sasal Mahemas Comuaoal Mehodology Sasal Iferee II Work wh Grahal Models Deermg sruure Sruure gve by modelg d e.g. Mxure model

More information

Determination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction

Determination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction refeed Soluos for R&D o Desg Deermao of oe Equao arameers Soluos for R&D o Desg December 4, 0 refeed orporao Yosho Kumagae refeed Iroduco hyscal propery daa s exremely mpora for performg process desg ad

More information

(1) Cov(, ) E[( E( ))( E( ))]

(1) Cov(, ) E[( E( ))( E( ))] Impac of Auocorrelao o OLS Esmaes ECON 3033/Evas Cosder a smple bvarae me-seres model of he form: y 0 x The four key assumpos abou ε hs model are ) E(ε ) = E[ε x ]=0 ) Var(ε ) =Var(ε x ) = ) Cov(ε, ε )

More information

AML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending

AML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending CUIC SLINE CURVES Cubc Sples Marx formulao Normalsed cubc sples Alerae ed codos arabolc bledg AML7 CAD LECTURE CUIC SLINE The ame sple comes from he physcal srume sple drafsme use o produce curves A geeral

More information

General Complex Fuzzy Transformation Semigroups in Automata

General Complex Fuzzy Transformation Semigroups in Automata Joural of Advaces Compuer Research Quarerly pissn: 345-606x eissn: 345-6078 Sar Brach Islamc Azad Uversy Sar IRIra Vol 7 No May 06 Pages: 7-37 wwwacrausaracr Geeral Complex uzzy Trasformao Semgroups Auomaa

More information

Collocation Method for Nonlinear Volterra-Fredholm Integral Equations

Collocation Method for Nonlinear Volterra-Fredholm Integral Equations Ope Joural of Appled Sees 5- do:436/oapps6 Publshed Ole Jue (hp://wwwsrporg/oural/oapps) Colloao Mehod for olear Volerra-Fredhol Iegral Equaos Jafar Ahad Shal Parvz Daraa Al Asgar Jodayree Akbarfa Depare

More information

The algebraic immunity of a class of correlation immune H Boolean functions

The algebraic immunity of a class of correlation immune H Boolean functions Ieraoal Coferece o Advaced Elecroc Scece ad Techology (AEST 06) The algebrac mmuy of a class of correlao mmue H Boolea fucos a Jgla Huag ad Zhuo Wag School of Elecrcal Egeerg Norhwes Uversy for Naoales

More information

Fuzzy Possibility Clustering Algorithm Based on Complete Mahalanobis Distances

Fuzzy Possibility Clustering Algorithm Based on Complete Mahalanobis Distances Ieraoal Joural of Sef Egeerg ad See Volue Issue. 38-43 7. ISSN (Ole): 456-736 Fuzzy Possbly Cluserg Algorh Based o Colee ahalaobs Dsaes Sue-Fe Huag Deare of Dgal Gae ad Aao Desg Tae Uvey of are Tehology

More information

Ensemble Of Image Segmentation With Generalized Entropy Based Fuzzy Clustering

Ensemble Of Image Segmentation With Generalized Entropy Based Fuzzy Clustering Ieraoal Joural of Copuer ad Iforao Tehology (ISSN: 79 0764) Volue 03 Issue 05, Sepeber 014 Eseble Of Iage Segeao Wh Geeralzed Eropy Based Fuzzy Cluserg Ka L *, Zhx Guo Hebe Uversy College of Maheas ad

More information

Lecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination

Lecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination Lecure 3 Topc : Drbuo, hypohe eg, ad ample ze deermao The Sude - drbuo Coder a repeaed drawg of ample of ze from a ormal drbuo of mea. For each ample, compue,,, ad aoher ac,, where: The ac he devao of

More information

N! AND THE GAMMA FUNCTION

N! AND THE GAMMA FUNCTION N! AND THE GAMMA FUNCTION Cosider he produc of he firs posiive iegers- 3 4 5 6 (-) =! Oe calls his produc he facorial ad has ha produc of he firs five iegers equals 5!=0. Direcly relaed o he discree! fucio

More information

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall 8. Queueg sysems lec8. S-38.45 - Iroduco o Teleraffc Theory - Fall 8. Queueg sysems Coes Refresher: Smle eleraffc model M/M/ server wag laces M/M/ servers wag laces 8. Queueg sysems Smle eleraffc model

More information

The Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting

The Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting Appled Mahemacs 4 5 466-477 Publshed Ole February 4 (hp//wwwscrporg/oural/am hp//dxdoorg/436/am45346 The Mea Resdual Lfeme of ( + -ou-of- Sysems Dscree Seg Maryam Torab Sahboom Deparme of Sascs Scece ad

More information

Continuous Time Markov Chains

Continuous Time Markov Chains Couous me Markov chas have seay sae probably soluos f a oly f hey are ergoc, us lke scree me Markov chas. Fg he seay sae probably vecor for a couous me Markov cha s o more ffcul ha s he scree me case,

More information

Department of Mathematical and Statistical Sciences University of Alberta

Department of Mathematical and Statistical Sciences University of Alberta MATH 4 (R) Wier 008 Iermediae Calculus I Soluios o Problem Se # Due: Friday Jauary 8, 008 Deparme of Mahemaical ad Saisical Scieces Uiversiy of Albera Quesio. [Sec.., #] Fid a formula for he geeral erm

More information

Midterm Exam. Tuesday, September hour, 15 minutes

Midterm Exam. Tuesday, September hour, 15 minutes Ecoomcs of Growh, ECON560 Sa Fracsco Sae Uvers Mchael Bar Fall 203 Mderm Exam Tuesda, Sepember 24 hour, 5 mues Name: Isrucos. Ths s closed boo, closed oes exam. 2. No calculaors of a d are allowed. 3.

More information

International Journal Of Engineering And Computer Science ISSN: Volume 5 Issue 12 Dec. 2016, Page No.

International Journal Of Engineering And Computer Science ISSN: Volume 5 Issue 12 Dec. 2016, Page No. www.jecs. Ieraoal Joural Of Egeerg Ad Compuer Scece ISSN: 19-74 Volume 5 Issue 1 Dec. 16, Page No. 196-1974 Sofware Relably Model whe mulple errors occur a a me cludg a faul correco process K. Harshchadra

More information

Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations

Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations Joural of aheacs ad copuer Scece (4 39-38 Soluo of Ipulsve Dffereal Equaos wh Boudary Codos Ters of Iegral Equaos Arcle hsory: Receved Ocober 3 Acceped February 4 Avalable ole July 4 ohse Rabba Depare

More information

Real-time Classification of Large Data Sets using Binary Knapsack

Real-time Classification of Large Data Sets using Binary Knapsack Real-me Classfcao of Large Daa Ses usg Bary Kapsack Reao Bru bru@ds.uroma. Uversy of Roma La Sapeza AIRO 004-35h ANNUAL CONFERENCE OF THE ITALIAN OPERATIONS RESEARCH Sepember 7-0, 004, Lecce, Ialy Oule

More information

Fermat Numbers in Multinomial Coefficients

Fermat Numbers in Multinomial Coefficients 1 3 47 6 3 11 Joural of Ieger Sequeces, Vol. 17 (014, Aricle 14.3. Ferma Numbers i Muliomial Coefficies Shae Cher Deparme of Mahemaics Zhejiag Uiversiy Hagzhou, 31007 Chia chexiaohag9@gmail.com Absrac

More information

14. Poisson Processes

14. Poisson Processes 4. Posso Processes I Lecure 4 we roduced Posso arrvals as he lmg behavor of Bomal radom varables. Refer o Posso approxmao of Bomal radom varables. From he dscusso here see 4-6-4-8 Lecure 4 " arrvals occur

More information

Some Improved Estimators for Population Variance Using Two Auxiliary Variables in Double Sampling

Some Improved Estimators for Population Variance Using Two Auxiliary Variables in Double Sampling Vplav Kumar gh Rajeh gh Deparme of ac Baara Hdu Uver Varaa-00 Ida Flore maradache Uver of ew Meco Gallup UA ome Improved Emaor for Populao Varace Ug Two Aular Varable Double amplg Publhed : Rajeh gh Flore

More information

Ruled surfaces are one of the most important topics of differential geometry. The

Ruled surfaces are one of the most important topics of differential geometry. The CONSTANT ANGLE RULED SURFACES IN EUCLIDEAN SPACES Yuuf YAYLI Ere ZIPLAR Deparme of Mahemaic Faculy of Sciece Uieriy of Aara Tadoğa Aara Turey yayli@cieceaaraedur Deparme of Mahemaic Faculy of Sciece Uieriy

More information

Section 2:00 ~ 2:50 pm Thursday in Maryland 202 Sep. 29, 2005

Section 2:00 ~ 2:50 pm Thursday in Maryland 202 Sep. 29, 2005 Seto 2:00 ~ 2:50 pm Thursday Marylad 202 Sep. 29, 2005. Homework assgmets set ad 2 revews: Set : P. A box otas 3 marbles, red, gree, ad blue. Cosder a expermet that ossts of takg marble from the box, the

More information

A Mean- maximum Deviation Portfolio Optimization Model

A Mean- maximum Deviation Portfolio Optimization Model A Mea- mamum Devato Portfolo Optmzato Model Wu Jwe Shool of Eoom ad Maagemet, South Cha Normal Uversty Guagzhou 56, Cha Tel: 86-8-99-6 E-mal: wujwe@9om Abstrat The essay maes a thorough ad systemat study

More information

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters Leas Squares Fg LSQF wh a complcaed fuco Theeampleswehavelookedasofarhavebeelearheparameers ha we have bee rg o deerme e.g. slope, ercep. For he case where he fuco s lear he parameers we ca fd a aalc soluo

More information

Spring Ammar Abu-Hudrouss Islamic University Gaza

Spring Ammar Abu-Hudrouss Islamic University Gaza ١ ١ Chapter Chapter 4 Cyl Blo Cyl Blo Codes Codes Ammar Abu-Hudrouss Islam Uversty Gaza Spr 9 Slde ٢ Chael Cod Theory Cyl Blo Codes A yl ode s haraterzed as a lear blo ode B( d wth the addtoal property

More information

Math 315: Linear Algebra Solutions to Assignment 6

Math 315: Linear Algebra Solutions to Assignment 6 Mah 35: Linear Algebra s o Assignmen 6 # Which of he following ses of vecors are bases for R 2? {2,, 3, }, {4,, 7, 8}, {,,, 3}, {3, 9, 4, 2}. Explain your answer. To generae he whole R 2, wo linearly independen

More information

Tracking Algorithm for Estimating the Orientation Angles of the Object Based on the Signals of Satellite Radio Navigation System

Tracking Algorithm for Estimating the Orientation Angles of the Object Based on the Signals of Satellite Radio Navigation System mera Joural of ppled Sees Orgal Researh Paper rag lgorhm for Esmag he Oreao gles of he Obje Based o he Sgals of Saelle Rado Navgao Sysem lexadr Perov Mosow Power Egeerg Isue Naoal Researh Uversy Mosow

More information

Packing of graphs with small product of sizes

Packing of graphs with small product of sizes Joural of Combatoral Theory, Seres B 98 (008) 4 45 www.elsever.com/locate/jctb Note Packg of graphs wth small product of szes Alexadr V. Kostochka a,b,,gexyu c, a Departmet of Mathematcs, Uversty of Illos,

More information

EE 6885 Statistical Pattern Recognition

EE 6885 Statistical Pattern Recognition EE 6885 Sascal Paer Recogo Fall 005 Prof. Shh-Fu Chag hp://.ee.columba.edu/~sfchag Lecure 8 (/8/05 8- Readg Feaure Dmeso Reduco PCA, ICA, LDA, Chaper 3.8, 0.3 ICA Tuoral: Fal Exam Aapo Hyväre ad Erkk Oja,

More information

Maximum Flow in Planar Graphs

Maximum Flow in Planar Graphs Maximum Flow in Planar Graph Planar Graph and i Dual Dualiy i defined for direced planar graph a well Minimum - cu in undireced planar graph An - cu (undireced graph) An - cu The dual o he cu Cu/Cycle

More information

On subsets of the hypercube with prescribed Hamming distances

On subsets of the hypercube with prescribed Hamming distances O subses of he hypercube wh prescrbed Hammg dsaces Hao Huag Oleksy Klurma Cosm Pohoaa Absrac A celebraed heorem of Klema exremal combaorcs saes ha a colleco of bary vecors {0, 1} wh dameer d has cardaly

More information

New approach for numerical solution of Fredholm integral equations system of the second kind by using an expansion method

New approach for numerical solution of Fredholm integral equations system of the second kind by using an expansion method Ieraoal Reearch Joural o Appled ad Bac Scece Avalable ole a wwwrabcom ISSN 5-88X / Vol : 8- Scece xplorer Publcao New approach or umercal oluo o Fredholm eral equao yem o he ecod d by u a expao mehod Nare

More information

Big O Notation for Time Complexity of Algorithms

Big O Notation for Time Complexity of Algorithms BRONX COMMUNITY COLLEGE of he Ciy Uiversiy of New York DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE CSI 33 Secio E01 Hadou 1 Fall 2014 Sepember 3, 2014 Big O Noaio for Time Complexiy of Algorihms Time

More information

ECON 8105 FALL 2017 ANSWERS TO MIDTERM EXAMINATION

ECON 8105 FALL 2017 ANSWERS TO MIDTERM EXAMINATION MACROECONOMIC THEORY T. J. KEHOE ECON 85 FALL 7 ANSWERS TO MIDTERM EXAMINATION. (a) Wh an Arrow-Debreu markes sruure fuures markes for goods are open n perod. Consumers rade fuures onras among hemselves.

More information

An AGV-Routing Algorithm in the Mesh Topology with Random Partial Permutation

An AGV-Routing Algorithm in the Mesh Topology with Random Partial Permutation A AGV-Rou Alorhm he Mesh Topoloy wh Radom aral ermuao Ze Jaya, Hsu We-J ad Vee Voo Yee ere for Advaed Iformao Sysems, Shool of ompuer eer Naya Teholoal Uversy, Sapore 69798 {p8589, hsu, ASVYV}@uedus Absra

More information

TEACHERS ASSESS STUDENT S MATHEMATICAL CREATIVITY COMPETENCE IN HIGH SCHOOL

TEACHERS ASSESS STUDENT S MATHEMATICAL CREATIVITY COMPETENCE IN HIGH SCHOOL Jourl o See d rs Yer 5, No., pp. 5-, 5 ORIGINL PPER TECHERS SSESS STUDENT S MTHEMTICL CRETIVITY COMPETENCE IN HIGH SCHOOL TRN TRUNG TINH Musrp reeved: 9..5; eped pper:..5; Pulsed ole:..5. sr. ssessme s

More information

Fundamentals of Speech Recognition Suggested Project The Hidden Markov Model

Fundamentals of Speech Recognition Suggested Project The Hidden Markov Model . Projec Iroduco Fudameals of Speech Recogo Suggesed Projec The Hdde Markov Model For hs projec, s proposed ha you desg ad mpleme a hdde Markov model (HMM) ha opmally maches he behavor of a se of rag sequeces

More information

QR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA

QR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA QR facorzao Ay x real marx ca be wre as AQR, where Q s orhogoal ad R s upper ragular. To oba Q ad R, we use he Householder rasformao as follows: Le P, P, P -, be marces such ha P P... PPA ( R s upper ragular.

More information

Quantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state)

Quantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state) Pro. O. B. Wrgh, Auum Quaum Mechacs II Lecure Tme-depede perurbao heory Tme-depede perurbao heory (degeerae or o-degeerae sarg sae) Cosder a sgle parcle whch, s uperurbed codo wh Hamloa H, ca exs a superposo

More information

Fresnel Dragging Explained

Fresnel Dragging Explained Fresel Draggig Explaied 07/05/008 Decla Traill Decla@espace.e.au The Fresel Draggig Coefficie required o explai he resul of he Fizeau experime ca be easily explaied by usig he priciples of Eergy Field

More information

M. Sc. MATHEMATICS MAL-521 (ADVANCE ABSTRACT ALGEBRA)

M. Sc. MATHEMATICS MAL-521 (ADVANCE ABSTRACT ALGEBRA) . Sc. ATHEATICS AL-52 (ADVANCE ABSTRACT ALGEBRA) Lesso No &Lesso Name Wrer Veer Lear Trasformaos Dr. Pakaj Kumar Dr. Nawee Hooda 2 Caocal Trasformaos Dr. Pakaj Kumar Dr. Nawee Hooda 3 odules I Dr. Pakaj

More information

Speech, NLP and the Web

Speech, NLP and the Web peech NL ad he Web uhpak Bhaacharyya CE Dep. IIT Bombay Lecure 38: Uuperved learg HMM CFG; Baum Welch lecure 37 wa o cogve NL by Abh Mhra Baum Welch uhpak Bhaacharyya roblem HMM arg emac ar of peech Taggg

More information

Review Answers for E&CE 700T02

Review Answers for E&CE 700T02 Review Aswers for E&CE 700T0 . Deermie he curre soluio, all possible direcios, ad sepsizes wheher improvig or o for he simple able below: 4 b ma c 0 0 0-4 6 0 - B N B N ^0 0 0 curre sol =, = Ch for - -

More information

The Linear Regression Of Weighted Segments

The Linear Regression Of Weighted Segments The Lear Regresso Of Weghed Segmes George Dael Maeescu Absrac. We proposed a regresso model where he depede varable s made o up of pos bu segmes. Ths suao correspods o he markes hroughou he da are observed

More information

Algorithmic Discrete Mathematics 6. Exercise Sheet

Algorithmic Discrete Mathematics 6. Exercise Sheet Algorihmic Dicree Mahemaic. Exercie Shee Deparmen of Mahemaic SS 0 PD Dr. Ulf Lorenz 7. and 8. Juni 0 Dipl.-Mah. David Meffer Verion of June, 0 Groupwork Exercie G (Heap-Sor) Ue Heap-Sor wih a min-heap

More information

FROM THE BCS EQUATIONS TO THE ANISOTROPIC SUPERCONDUCTIVITY EQUATIONS. Luis A. PérezP. Chumin Wang

FROM THE BCS EQUATIONS TO THE ANISOTROPIC SUPERCONDUCTIVITY EQUATIONS. Luis A. PérezP. Chumin Wang FROM THE BCS EQUATIONS TO THE ANISOTROPIC SUPERCONDUCTIVITY EQUATIONS J. Samuel Mllá Faulad de Igeería Uversdad Auóoma del Carme Méxo. M Lus A. PérezP Isuo de Físa F UNAM MéxoM xo. Chum Wag Isuo de Ivesgaoes

More information

Comparison of the Bayesian and Maximum Likelihood Estimation for Weibull Distribution

Comparison of the Bayesian and Maximum Likelihood Estimation for Weibull Distribution Joural of Mahemacs ad Sascs 6 (2): 1-14, 21 ISSN 1549-3644 21 Scece Publcaos Comarso of he Bayesa ad Maxmum Lkelhood Esmao for Webull Dsrbuo Al Omar Mohammed Ahmed, Hadeel Salm Al-Kuub ad Noor Akma Ibrahm

More information

S n. = n. Sum of first n terms of an A. P is

S n. = n. Sum of first n terms of an A. P is PROGREION I his secio we discuss hree impora series amely ) Arihmeic Progressio (A.P), ) Geomeric Progressio (G.P), ad 3) Harmoic Progressio (H.P) Which are very widely used i biological scieces ad humaiies.

More information

K3 p K2 p Kp 0 p 2 p 3 p

K3 p K2 p Kp 0 p 2 p 3 p Mah 80-00 Mo Ar 0 Chaer 9 Fourier Series ad alicaios o differeial equaios (ad arial differeial equaios) 9.-9. Fourier series defiiio ad covergece. The idea of Fourier series is relaed o he liear algebra

More information

Cyclone. Anti-cyclone

Cyclone. Anti-cyclone Adveco Cycloe A-cycloe Lorez (963) Low dmesoal aracors. Uclear f hey are a good aalogy o he rue clmae sysem, bu hey have some appealg characerscs. Dscusso Is he al codo balaced? Is here a al adjusme

More information

An interesting result about subset sums. Nitu Kitchloo. Lior Pachter. November 27, Abstract

An interesting result about subset sums. Nitu Kitchloo. Lior Pachter. November 27, Abstract A ieresig resul abou subse sums Niu Kichloo Lior Pacher November 27, 1993 Absrac We cosider he problem of deermiig he umber of subses B f1; 2; : : :; g such ha P b2b b k mod, where k is a residue class

More information

)-interval valued fuzzy ideals in BF-algebras. Some properties of (, ) -interval valued fuzzy ideals in BF-algebra, where

)-interval valued fuzzy ideals in BF-algebras. Some properties of (, ) -interval valued fuzzy ideals in BF-algebra, where Inernaonal Journal of Engneerng Advaned Researh Tehnology (IJEART) ISSN: 454-990, Volume-, Issue-4, Oober 05 Some properes of (, )-nerval valued fuzzy deals n BF-algebras M. Idrees, A. Rehman, M. Zulfqar,

More information

k-remainder Cordial Graphs

k-remainder Cordial Graphs Journal of Algorihms and Compuaion journal homepage: hp://jac.u.ac.ir k-remainder Cordial Graphs R. Ponraj 1, K. Annahurai and R. Kala 3 1 Deparmen of Mahemaics, Sri Paramakalyani College, Alwarkurichi

More information

RATIO ESTIMATORS USING CHARACTERISTICS OF POISSON DISTRIBUTION WITH APPLICATION TO EARTHQUAKE DATA

RATIO ESTIMATORS USING CHARACTERISTICS OF POISSON DISTRIBUTION WITH APPLICATION TO EARTHQUAKE DATA The 7 h Ieraoal as of Sascs ad Ecoomcs Prague Sepember 9-0 Absrac RATIO ESTIMATORS USING HARATERISTIS OF POISSON ISTRIBUTION WITH APPLIATION TO EARTHQUAKE ATA Gamze Özel Naural pulaos bolog geecs educao

More information

Economics 8723 Macroeconomic Theory Problem Set 2 Professor Sanjay Chugh Spring 2017

Economics 8723 Macroeconomic Theory Problem Set 2 Professor Sanjay Chugh Spring 2017 Deparme of Ecoomics The Ohio Sae Uiversiy Ecoomics 8723 Macroecoomic Theory Problem Se 2 Professor Sajay Chugh Sprig 207 Labor Icome Taxes, Nash-Bargaied Wages, ad Proporioally-Bargaied Wages. I a ecoomy

More information

BEST PATTERN OF MULTIPLE LINEAR REGRESSION

BEST PATTERN OF MULTIPLE LINEAR REGRESSION ERI COADA GERMAY GEERAL M.R. SEFAIK AIR FORCE ACADEMY ARMED FORCES ACADEMY ROMAIA SLOVAK REPUBLIC IERAIOAL COFERECE of SCIEIFIC PAPER AFASES Brov 6-8 M BES PAER OF MULIPLE LIEAR REGRESSIO Corel GABER PEROLEUM-GAS

More information

COMPUTER SCIENCE 349A SAMPLE EXAM QUESTIONS WITH SOLUTIONS PARTS 1, 2

COMPUTER SCIENCE 349A SAMPLE EXAM QUESTIONS WITH SOLUTIONS PARTS 1, 2 COMPUTE SCIENCE 49A SAMPLE EXAM QUESTIONS WITH SOLUTIONS PATS, PAT.. a Dene he erm ll-ondoned problem. b Gve an eample o a polynomal ha has ll-ondoned zeros.. Consder evaluaon o anh, where e e anh. e e

More information

More Digital Logic. t p output. Low-to-high and high-to-low transitions could have different t p. V in (t)

More Digital Logic. t p output. Low-to-high and high-to-low transitions could have different t p. V in (t) EECS 4 Spring 23 Lecure 2 EECS 4 Spring 23 Lecure 2 More igial Logic Gae delay and signal propagaion Clocked circui elemens (flip-flop) Wriing a word o memory Simplifying digial circuis: Karnaugh maps

More information

Competitive Facility Location Problem with Demands Depending on the Facilities

Competitive Facility Location Problem with Demands Depending on the Facilities Aa Pacc Maageme Revew 4) 009) 5-5 Compeve Facl Locao Problem wh Demad Depedg o he Facle Shogo Shode a* Kuag-Yh Yeh b Hao-Chg Ha c a Facul of Bue Admrao Kobe Gau Uver Japa bc Urba Plag Deparme Naoal Cheg

More information

Improved Approximate Solutions for Nonlinear Evolutions Equations in Mathematical Physics Using the Reduced Differential Transform Method

Improved Approximate Solutions for Nonlinear Evolutions Equations in Mathematical Physics Using the Reduced Differential Transform Method Journal of Applied Mahemaics & Bioinformaics, vol., no., 01, 1-14 ISSN: 179-660 (prin), 179-699 (online) Scienpress Ld, 01 Improved Approimae Soluions for Nonlinear Evoluions Equaions in Mahemaical Physics

More information

Suppose we have observed values t 1, t 2, t n of a random variable T.

Suppose we have observed values t 1, t 2, t n of a random variable T. Sppose we have obseved vales, 2, of a adom vaable T. The dsbo of T s ow o belog o a cea ype (e.g., expoeal, omal, ec.) b he veco θ ( θ, θ2, θp ) of ow paamees assocaed wh s ow (whee p s he mbe of ow paamees).

More information

Strong metric dimension of rooted product graphs

Strong metric dimension of rooted product graphs Srong meric dimension of rooed produc graphs Doroa Kuziak (1), Ismael G. Yero () and Juan A. Rodríguez-Velázquez (1) (1) Deparamen d Enginyeria Informàica i Maemàiques, arxiv:1309.0643v1 [mah.co] 3 Sep

More information

Extremal colorings and independent sets

Extremal colorings and independent sets Exremal colorings and independen ses John Engbers Aysel Erey Ocober 17, 2017 Absrac We consider several exremal problems of maximizing he number of colorings and independen ses in some graph families wih

More information

FALL HOMEWORK NO. 6 - SOLUTION Problem 1.: Use the Storage-Indication Method to route the Input hydrograph tabulated below.

FALL HOMEWORK NO. 6 - SOLUTION Problem 1.: Use the Storage-Indication Method to route the Input hydrograph tabulated below. Jorge A. Ramírez HOMEWORK NO. 6 - SOLUTION Problem 1.: Use he Sorage-Idcao Mehod o roue he Ipu hydrograph abulaed below. Tme (h) Ipu Hydrograph (m 3 /s) Tme (h) Ipu Hydrograph (m 3 /s) 0 0 90 450 6 50

More information

Non-uniform Turán-type problems

Non-uniform Turán-type problems Joural of Combatoral Theory, Seres A 111 2005 106 110 wwwelsevercomlocatecta No-uform Turá-type problems DhruvMubay 1, Y Zhao 2 Departmet of Mathematcs, Statstcs, ad Computer Scece, Uversty of Illos at

More information

JORIND 9(2) December, ISSN

JORIND 9(2) December, ISSN JORIND 9() December, 011. ISSN 1596 8308. www.rascampus.org., www.ajol.o/jourals/jord THE EXONENTIAL DISTRIBUTION AND THE ALICATION TO MARKOV MODELS Usma Yusu Abubakar Deparme o Mahemacs/Sascs Federal

More information

OLS bias for econometric models with errors-in-variables. The Lucas-critique Supplementary note to Lecture 17

OLS bias for econometric models with errors-in-variables. The Lucas-critique Supplementary note to Lecture 17 OLS bias for ecoomeric models wih errors-i-variables. The Lucas-criique Supplemeary oe o Lecure 7 RNy May 6, 03 Properies of OLS i RE models I Lecure 7 we discussed he followig example of a raioal expecaios

More information

Notes for Lecture 17-18

Notes for Lecture 17-18 U.C. Berkeley CS278: Compuaional Complexiy Handou N7-8 Professor Luca Trevisan April 3-8, 2008 Noes for Lecure 7-8 In hese wo lecures we prove he firs half of he PCP Theorem, he Amplificaion Lemma, up

More information

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD Leas squares ad moo uo Vascocelos ECE Deparme UCSD Pla for oda oda we wll dscuss moo esmao hs s eresg wo was moo s ver useful as a cue for recogo segmeao compresso ec. s a grea eample of leas squares problem

More information

Two Coupled Oscillators / Normal Modes

Two Coupled Oscillators / Normal Modes Lecure 3 Phys 3750 Two Coupled Oscillaors / Normal Modes Overview and Moivaion: Today we ake a small, bu significan, sep owards wave moion. We will no ye observe waves, bu his sep is imporan in is own

More information

EGR 544 Communication Theory

EGR 544 Communication Theory EGR 544 Commuicaio heory 7. Represeaio of Digially Modulaed Sigals II Z. Aliyazicioglu Elecrical ad Compuer Egieerig Deparme Cal Poly Pomoa Represeaio of Digial Modulaio wih Memory Liear Digial Modulaio

More information

Comparison between Fourier and Corrected Fourier Series Methods

Comparison between Fourier and Corrected Fourier Series Methods Malaysia Joural of Mahemaical Scieces 7(): 73-8 (13) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES Joural homepage: hp://eispem.upm.edu.my/oural Compariso bewee Fourier ad Correced Fourier Series Mehods 1

More information

Redundancy System Fault Sampling Under Imperfect Maintenance

Redundancy System Fault Sampling Under Imperfect Maintenance A publcao of CHEMICAL EGIEERIG TRASACTIOS VOL. 33, 03 Gues Edors: Erco Zo, Pero Barald Copyrgh 03, AIDIC Servz S.r.l., ISB 978-88-95608-4-; ISS 974-979 The Iala Assocao of Chemcal Egeerg Ole a: www.adc./ce

More information

Approximation of Controllable Set by Semidefinite Programming for Open-Loop Unstable Systems with Input Saturation

Approximation of Controllable Set by Semidefinite Programming for Open-Loop Unstable Systems with Input Saturation Egeerg eers 7:4 E_7_4_ Approxao of Corollable Se by Sedefe Prograg for Ope-oop Usable Syses wh Ipu Saurao Abraha W.. Wag Meber IAENG ad Ye-Mg Che Absra I order o es he effey of sedefe prograg (SDP we apply

More information

We just finished the Erdős-Stone Theorem, and ex(n, F ) (1 1/(χ(F ) 1)) ( n

We just finished the Erdős-Stone Theorem, and ex(n, F ) (1 1/(χ(F ) 1)) ( n Lecure 3 - Kövari-Sós-Turán Theorem Jacques Versraëe jacques@ucsd.edu We jus finished he Erdős-Sone Theorem, and ex(n, F ) ( /(χ(f ) )) ( n 2). So we have asympoics when χ(f ) 3 bu no when χ(f ) = 2 i.e.

More information

Exercises for Square-Congruence Modulo n ver 11

Exercises for Square-Congruence Modulo n ver 11 Exercses for Square-Cogruece Modulo ver Let ad ab,.. Mark True or False. a. 3S 30 b. 3S 90 c. 3S 3 d. 3S 4 e. 4S f. 5S g. 0S 55 h. 8S 57. 9S 58 j. S 76 k. 6S 304 l. 47S 5347. Fd the equvalece classes duced

More information

MAHALAKSHMI ENGINEERING COLLEGE TIRUCHIRAPALLI

MAHALAKSHMI ENGINEERING COLLEGE TIRUCHIRAPALLI MAHALAKSHMI EGIEERIG COLLEGE TIRUCHIRAALLI 6 QUESTIO BAK - ASWERS -SEMESTER: V MA 6 - ROBABILITY AD QUEUEIG THEORY UIT IV:QUEUEIG THEORY ART-A Quesio : AUC M / J Wha are he haraerisis of a queueig heory?

More information

Reliability Analysis of Sparsely Connected Consecutive-k Systems: GERT Approach

Reliability Analysis of Sparsely Connected Consecutive-k Systems: GERT Approach Relably Aalyss of Sparsely Coece Cosecuve- Sysems: GERT Approach Pooa Moha RMSI Pv. L Noa-2131 poalovely@yahoo.com Mau Agarwal Deparme of Operaoal Research Uversy of Delh Delh-117, Ia Agarwal_maulaa@yahoo.com

More information

Survival Prediction Based on Compound Covariate under Cox Proportional Hazard Models

Survival Prediction Based on Compound Covariate under Cox Proportional Hazard Models Ieraoal Bomerc Coferece 22/8/3, Kobe JAPAN Survval Predco Based o Compoud Covarae uder Co Proporoal Hazard Models PLoS ONE 7. do:.37/oural.poe.47627. hp://d.plos.org/.37/oural.poe.47627 Takesh Emura Graduae

More information