082-Engineering-Mathematics-Anthony-Croft-Robert-Davison-Martin-Hargreaves-James-Flint-Edisi-5-2017

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800 Chapter 24 The Fourier transformf(0, 0)f(124, 124)Figure24.21Source imageford.c.t.application.We use the transform given above to produce an N ×M matrix containing thereal d.c.t. values,F[k,l]. Figure 24.22 is a visualization ofF[k,l]. Dark pixels representlarge values, white pixels represent zero or close to zero elements. Notice theconcentration of largevalues close toF[0,0], which isinthe top-left corner.F(0, 0)Figure24.22Thed.c.t. matrix.F(124, 124)Inthenextstepwecompresstheimagebysettingthearrayelementstozeroattheright-handsideandbottom.Sincethecoefficientsaresmallinthisregiontheeffectson the image after inversion are minor, unless a significant number of values on theright-hand sideand bottom are set tozero.The results of applying the inverse transform are shown in Figure 24.23. We canseethattheimageontherightwhichcontainsthelowestnumberofd.c.t.coefficientshasmoredistortionduetothecompression.Suchdistortionsareoftentermedvisualartefacts. The advantage of having a highly compressed file is that it requires lessspace for data storage.In practice, the image is often broken into smaller blocks with the d.c.t. beingcarried out on these blocks. If a highly compressed image is viewed on a computeratalowresolutionitisoftenpossibletoseetheseblocks,whichtypicallymeasure8by 8 pixels.
24.15 Discrete convolution and correlation 801Figure24.23Effect ofsetting elements to zero in the d.c.t.matrix. Moving from left to right agreaternumber ofelements in the matrix are setto zero, resulting in more imageartefacts.24.15 DISCRETECONVOLUTIONANDCORRELATIONAs stated at the beginning of Section 24.8, convolution and correlation are importanttechniquesinsignalandimageprocessing.Inthissectionwewilldescribetheirdiscreterepresentations. Both ofthese can be implemented efficiently usingthe d.f.t.24.15.1 LinearconvolutionThe linear convolution of two real sequences f[n] andg[n] is another sequence,h[n]say, which we denote by f ∗g, which isdefined as follows:Linear convolution of f[n] andg[n]:∞∑h[n]=f∗g= f[m]g[n−m] forn=...−3,−2,−1,0,1,2,3,...m=−∞Notice the similaritybetween this definition and thatof convolution defined inthe contextof the continuous Fourier transform in Section 24.8. Notice also that in this formulathesequencegisfoldedandshifted.Thiswillbeillustratedintheexample whichfollows.Frequently the sequences being considered will be finite.Assume now that f[n] is a finite sequence ofN 1terms, so that all terms other thanf[0], f[1],..., f[N 1−1] arezero.Suppose also that g[n] is a finite sequence of N 2terms, with all terms other thang[0],g[1],...,g[N 2−1] being zero.Itcanbeshownthatthesequenceh[n]willhavelengthN 1+N 2−1,andtheconvolutionsum simplifies tothe following:The linear convolution of the two finite sequences f[n] andg[n] isdefined asn∑h[n]=f∗g= f[m]g[n−m] forn=0,1,2,3,...,(N 1+N 2−2)m=0
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ENGINEERINGMATHEMATICSA Foundation
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At Pearson, we have a simple missio
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PEARSONEDUCATIONLIMITEDEdinburghGat
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ContentsPrefaceAcknowledgementsxvii
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Contents ix8.3 Addition,subtraction
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Contents xiChapter17 Numericalinteg
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Contents xiiiChapter24 TheFouriertr
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Contents xvAppendixI Representing a
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PrefaceAudienceThis book has been w
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AcknowledgementsWearegratefultothef
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1 ReviewofalgebraictechniquesConten
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1.2 Laws of indices 3So6 2 6 3 =6 5
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1.2 Laws of indices 5(c)(d)x 9x 5 =
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1.2 Laws of indices 71.2.4 Multiple
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1.2 Laws of indices 9Similarly(2 1/
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1.3 Number bases 11Solutions1 (a) 8
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1.3 Number bases 13giving83=64+16+3
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1.3 Number bases 15Example1.16 Conv
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1.3 Number bases 17Table1.4isusedto
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1.3 Number bases 19The display show
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1.4 Polynomial equations 21Example1
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1.4 Polynomial equations 23We now i
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so thatβ=−13Bycomparing coeffici
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1.5.1 Properandimproperfractions1.5
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1.5 Algebraic fractions 29(b) Facto
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1.5 Algebraic fractions 31and4x +2
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1.6 Solution of inequalities 33(g)(
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1.6 Solution of inequalities 35Exam
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1.6 Solution of inequalities 37Case
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1.7 Partial fractions 391.7 PARTIAL
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1.7 Partial fractions 41Thus we hav
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1.7 Partial fractions 43Solution Th
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1.7 Partial fractions 45EXERCISES1.
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1.8 Summation notation 47Engineerin
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1.8 Summation notation 49thecircuit
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Review exercises 1 513 Removethe br
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Review exercises 1 53[ ((d) 3 z −
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2.2 Numbers and intervals 55in orde
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2.3 Basic concepts of functions 57f
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2.3 Basic concepts of functions 59s
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2.3 Basic concepts of functions 61b
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2.3 Basic concepts of functions 63E
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2.3 Basic concepts of functions 65f
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2.3 Basic concepts of functions 67T
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2.3 Basic concepts of functions 691
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2.4 Review of some common engineeri
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2.4.2 Rationalfunctions2.4 Review o
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Table2.2Values ofa x fora = 0.5, 2
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2.4.4 Logarithmfunctions2.4 Review
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Review exercises 2 113REVIEWEXERCIS
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3 ThetrigonometricfunctionsContents
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Note thattanθ = BCAB = BCAC × ACA
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3.3 The trigonometric ratios 119yyB
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3.4 The sine, cosine and tangent fu
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3.5 The sincxfunction 123EXERCISES3
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3.6 Trigonometric identities 125ors
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3.6 Trigonometric identities 127Exa
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3.6 Trigonometric identities 129Exa
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3.7 Modelling waves using sint and
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3.8 Trigonometric equations 145sin
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3.8 Trigonometric equations 147tan
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Usingascientific calculator wehavez
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Review exercises 3 1516 Simplify th
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Review exercises 3 153(d) 1.581sin(
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4.2 Cartesian coordinate system --
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4.3 Cartesian coordinate system --
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4.4 Polar coordinates 1594.4 POLARC
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4.4 Polar coordinates 161yOurxFigur
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4.5 Some simple polar curves 163EXE
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4.5 Some simple polar curves 165uAn
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4.6 Cylindrical polar coordinates 1
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4.6 Cylindrical polar coordinates 1
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4.7 Spherical polar coordinates 171
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Review exercises 4 173zzFeeding lin
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5 DiscretemathematicsContents 5.1 I
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5.2 Set theory 177Example5.1 Useset
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5.2 Set theory 179EMNM fNfFigure5.2
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5.2 Set theory 181someelementsinA.W
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5.3 Logic 183CDCD(a)(b)CDCD(c)(d)(e
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5.4 Boolean algebra 185ABA . BFigur
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5.4 Boolean algebra 187Table5.12Tru
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5.4 Boolean algebra 189However, we
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5.4 Boolean algebra 191S0Stage0A0 B
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5.4 Boolean algebra 193V DDPMOSQ1DD
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5.4 Boolean algebra 195(d) A ·B·(
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Review exercises 5 19712 (a) A·B+A
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Review exercises 5 1997 (a) (A·B)
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6.2 Sequences 2016.2 SEQUENCESAsequ
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6.2 Sequences 203sin t1x[k]1- 3p-2p
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6.2 Sequences 205Ageneralgeometricp
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6.2 Sequences 207can sensibly letk
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6.3 Series 20913 ±12814 315 016 (a
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6.3.2 Sumofafinitegeometricseries6.
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6.3 Series 213whereS ∞is known as
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6.4 The binomial theorem 215Example
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6.4 The binomial theorem 217EXERCIS
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6.6 Sequences arising from the iter
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6.6 Sequences arising from the iter
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Review exercises 6 22315 (a) Write
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7.2 Vectors and scalars: basic conc
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yjOirFigure7.19Thex--y plane withpo
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7.3 Cartesian components 235Solutio
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7.3 Cartesian components 237Enginee
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7.3 Cartesian components 2393 Ifa=4
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7.5 The scalar product 241OEFigure7
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7.5 The scalar product 243Example7.
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----------7.5 The scalar product 24
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7.6 The vector product 247a 3 bubPl
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7.6 The vector product 249The k com
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7.6 The vector product 251Engineeri
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7.7 Vectors ofndimensions 253andH =
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Review exercises 7 255EXERCISES7.71
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8 MatrixalgebraContents 8.1 Introdu
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8.3 Addition, subtraction and multi
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Example8.8 IfB =( ) 123andC=456⎛
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8.4 Using matrices in the translati
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8.4 Using matrices in the translati
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8.5 Some special matrices 271V new=
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Example8.18 IfA =Solution We haveA
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8.6.1 FindingtheinverseofamatrixFor
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8.6 The inverse of a 2 × 2 matrix
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8.7 Determinants 279In addition to
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8.8 The inverse of a 3 × 3 matrix
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8.9 Application to the solution of
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8.9 Application to the solution of
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Solution Firstconsider the equation
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8.10 Gaussian elimination 289anythi
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Eliminating the unwanted values int
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⎛R 1R 2→R 2−6R 1⎝R 3→R 3
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8.11 Eigenvalues and eigenvectors 2
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Itfollows thatso that(2−λ)(4−
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8.12 Analysis of electrical network
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8.13 Iterative techniques for the s
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8.14 Computer solutions of matrix p
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Review exercises 8 321V = [3;−6;4
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Review exercises 8 3234 (a) −8⎛
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9.2 Complex numbers 3259.2 COMPLEXN
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9.2 Complex numbers 3279.2.1 Thecom
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Similarly, wefind, by equating imag
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9.3 Operations with complex numbers
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9.5 Polar form of a complex number
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9.5.1 Multiplicationanddivisioninpo
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9.7 The exponential form of a compl
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9.7 The exponential form of a compl
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9.8 Phasors 341example, in the case
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9.8 Phasors 343Therefore,Ṽ S=ṼR
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which isclearly true,and the theore
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9.9 De Moivre’s theorem 347y25p
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9.9 De Moivre’s theorem 349Now, w
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9.10 Loci and regions of the comple
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9.10 Loci and regions of the comple
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Review exercises 9 3559 Sketch the
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10.2 Graphical approach to differen
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10.3 Limits and continuity 359f (t)
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10.3 Limits and continuity 361Theli
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10.4 Rate of change at a specific p
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10.5 Rate of change at a general po
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10.6 Existence of derivatives 371x
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10.7 Common derivatives 373Table10.
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10.8 Differentiation as a linear op
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Review exercises 10 385REVIEWEXERCI
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11.2 Rules of differentiation 387Th
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11.2 Rules of differentiation 389Th
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(b) Ify = ln(1 −t),theny ′ =
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11.3 Parametric, implicit and logar
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11.4 Higher derivatives 401Example1
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11.4 Higher derivatives 403yCByCAyB
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Review exercises 11 405Solutions1 (
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12.2 Maximum points and minimum poi
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12.3 Points of inflexion 415Solutio
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12.3 Points of inflexion 417y ′ =
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12.4 The Newton--Raphson method for
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12.4 The Newton--Raphson method for
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12.5 Differentiation of vectors 423
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Solution (a) Ifa = 3t 2 i +cos2tj,
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13.2 Elementary integration 42913.2
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13.2 Elementary integration 431Tabl
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Solution (a)sinx+cosx(j)2(k) 2t −
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13.2 Elementary integration 435Engi
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13.2 Elementary integration 4374.00
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Therefore,∫∫ 1sinmtsinnt dt = {
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13.2 Elementary integration 4412 (a
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13.3 Definite and indefinite integr
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Review exercises 13 4536716438 (a)
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Review exercises 13 45522 Calculate
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14 TechniquesofintegrationContents
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14.2 Integration by parts 459Usingt
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Usingintegration by parts we have
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14.3 Integration by substitution 46
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14.3 Integration by substitution 46
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14.4 Integration using partial frac
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Review exercises 14 469∫ π/3(b)
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15 ApplicationsofintegrationContent
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15.2 Average value of a function 47
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15.3 Root mean square value of a fu
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15.3 Root mean square value of a fu
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Review exercises 15 479(d) 0.7071(e
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[Example16.1 Show that f (x) =xandg
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Solution (a) We use the trigonometr
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16.3 Improper integrals 485iC 1 C 2
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∫ 2either of the integrals failst
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16.4 Integral properties of the del
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16.5 Integration of piecewise conti
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16.6 Integration of vectors 49316.6
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Review exercises 16 49512 Given14 G
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17.2 Trapezium rule 497y{ {y 0 y ny
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17.2 Trapezium rule 499The differen
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yABCDE17.3 Simpson’s rule 501quad
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17.3 Simpson’s rule 503Table17.6T
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Review Exercises 17 505EXERCISES17.
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18 Taylorpolynomials,Taylorseriesan
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18.2 Linearization using first-orde
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18.2 Linearization using first-orde
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18.3 Second-order Taylor polynomial
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18.3 Second-order Taylor polynomial
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18.4 Taylor polynomials of the nth
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18.4. Taylor polynomials of the nth
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18.5 Taylor’s formula and the rem
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We know |x| < 1, and so 4x51518.5 T
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18.6 Taylor and Maclaurin series 52
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Review exercises 18 5337 (a) 0, 2,
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19.2 Basic definitions 535In engine
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19.2 Basic definitions 537Note also
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19.2 Basic definitions 539fromwhich
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19.3 First-order equations: simple
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19.4 First-order linear equations:
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and so, upon integrating,so that(co
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19.5 Second-order linear equations
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19.6 Constant coefficient equations
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thatis,β = 332The required particu
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19.7 Series solution of differentia
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19.8 Bessel’s equation and Bessel
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fromwhicha 5=− 124 a 3 = 1192 a 1
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n 5 2n 5 3n5 419.8 Bessel’s equat
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Review exercises 19 601quencies, kn
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20 OrdinarydifferentialequationsIIC
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20.2 Analogue simulation 605y iy i
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20.3 Higher order equations 607High
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20.4 State-space models 609Solution
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20.4 State-space models 611statevar
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20.4 State-space models 613R a L aT
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20.5 Numerical methods 615Combining
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20.6 Euler’s method 617and we can
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20.6 Euler’s method 619Whenx = 1,
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20.7 Improved Euler method 621Table
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20.8 Runge--Kutta method of order 4
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20.8.1 Higherorderequations20.8 Run
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21 TheLaplacetransformContents 21.1
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21.3 Laplace transforms of some com
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21.4 Properties of the Laplace tran
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(b) Usethe first shift theorem with
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21.5 Laplace transform of derivativ
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21.5 Laplace transform of derivativ
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21.6 Inverse Laplace transforms 639
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21.7 Using partial fractions to fin
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21.8 Finding the inverse Laplace tr
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21.8 Finding the inverse Laplace tr
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21.9 The convolution theorem 64721.
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21.9 Solving linear constant coeffi
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21.10 Solving linear constant coeff
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X(s)= s3 −3s 2 −4s+6s 2 (s 2
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21.11 Transfer functions 659(c) x
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21.11 Transfer functions 661R(s)G(s
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21.11 Transfer functions 663R(s) +-
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21.11 Transfer functions 665Fuel va
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21.11 Transfer functions 667u a (t)
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21.12 Poles, zeros and thesplane 66
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21.13 Laplace transforms of some sp
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21.13.2 Periodicfunctions21.13 Lapl
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Review exercises 21 6792 Find the L
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22 Differenceequationsandtheztransf
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22.2 Basic definitions 68322.2.2 Th
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22.2 Basic definitions 685an inhomo
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22.3 Rewriting difference equations
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22.4 Block diagram representation o
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22.4 Block diagram representation o
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22.5 Design of a discrete-time cont
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22.6 Numerical solution of differen
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22.6 Numerical solution of differen
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22.7 Definition of the z transform
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22.7 Definition of the z transform
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22.8 Sampling a continuous signal 7
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22.9 The relationship between the z
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22.9 The relationship between the z
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22.10 Properties of the z transform
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22.11 Inversion of z transforms 715
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22.11 Inversion of z transforms 717
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22.12 The z transform and differenc
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Review exercises 22 721(e) q[k +3]
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23.2 Periodic waveforms 72323.2 PER
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23.2 Periodic waveforms 725f(t)1f(t
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23.3 Odd and even functions 727f(t)
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23.3 Odd and even functions 729f(t)
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23.3 Odd and even functions 731the
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23.5 Fourier series 733Table23.1Som
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23.5 Fourier series 735point out th
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23.5 Fourier series 737but cos(−n
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23.5 Fourier series 739Example23.16
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23.5 Fourier series 741y4- 3π -- -
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23.5 Fourier series 743We conclude
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23.6 Half-range series 745EXERCISES
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23.6 Half-range series 747Half-rang
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Page 779 and 780: 24.2 The Fourier transform -- defin
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Page 823 and 824: 24.15 Discrete convolution and corr
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Page 843 and 844: 25 FunctionsofseveralvariablesConte
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26.3 The gradient of a scalar field
Page 873 and 874:
(b) At (0,0,0), ∇φ =0i+3j+0k =3j
Page 875 and 876:
26.3 The gradient of a scalar field
Page 877 and 878:
26.4.1 Physicalinterpretationof ∇
Page 879 and 880:
26.5 The curl of a vector field 859
Page 881 and 882:
26.6 Combining the operators grad,
Page 883 and 884:
26.6 Combining the operators grad,
Page 885 and 886:
Equation3Review exercises 26 865cur
Page 887 and 888:
27 Lineintegralsandmultipleintegral
Page 889 and 890:
27.2 Line integrals 869SolutionThe
Page 891 and 892:
27.3 Evaluation of line integrals i
Page 893 and 894:
27.4 Evaluation of line integrals i
Page 895 and 896:
27.5 Conservative fields and potent
Page 897 and 898:
27.5 Conservative fields and potent
Page 899 and 900:
Substitutingy =x 2 and dy = 2xdx we
Page 901 and 902:
27.6 Double and triple integrals 88
Page 903 and 904:
Page 905 and 906:
Page 907 and 908:
y10GDR27.6 Double and triple integr
Page 909 and 910:
27.7 Some simple volume and surface
Page 911 and 912:
Page 913 and 914:
Page 915 and 916:
27.8 The divergence theorem and Sto
Page 917 and 918:
27.8 The divergence theorem and Sto
Page 919 and 920:
27.9 Maxwell’s equations in integ
Page 921 and 922:
Page 923 and 924:
28 ProbabilityContents 28.1 Introdu
Page 925 and 926:
28.2 Introducing probability 905Ifm
Page 927 and 928:
28.2 Introducing probability 907Eng
Page 929 and 930:
28.3 Mutually exclusive events: the
Page 931 and 932:
28.3 Mutually exclusive events: the
Page 933 and 934:
28.4 Complementary events 913EXERCI
Page 935 and 936:
28.5 Concepts from communication th
Page 937 and 938:
28.5 Concepts from communication th
Page 939 and 940:
28.6 Conditional probability: the m
Page 941 and 942:
28.6 Conditional probability: the m
Page 943 and 944:
P(A ∩C) =P(C ∩A) =P(C)P(A|C)P(A
Page 945 and 946:
28.7 Independent events 9255 Compon
Page 947 and 948:
28.7 Independent events 927Solution
Page 949 and 950:
28.7 Independent events 929E 1,E 2a
Page 951 and 952:
Review exercises 28 931(d) A compon
Page 953 and 954:
29 Statisticsandprobabilitydistribu
Page 955 and 956:
29.3 Probability distributions -- d
Page 957 and 958:
29.4 Probability density functions
Page 959 and 960:
29.5 Mean value 939Example29.3 Find
Page 961 and 962:
29.6 Standard deviation 94129.6 STA
Page 963 and 964:
29.7 Expected value of a random var
Page 965 and 966:
29.7 Expected value of a random var
Page 967 and 968:
29.8 Standard deviation of a random
Page 969 and 970:
29.9 Permutations and combinations
Page 971 and 972:
29.9 Permutations and combinations
Page 973 and 974:
29.10 The binomial distribution 953
Page 975 and 976:
29.10 The binomial distribution 955
Page 977 and 978:
29.11 The Poisson distribution 957I
Page 979 and 980:
Table29.6Theprobabilitiesforbinomia
Page 981 and 982:
29.12 The uniform distribution 9615
Page 983 and 984:
29.14 The normal distribution 963So
Page 985 and 986:
29.14 The normal distribution 965N(
Page 987 and 988:
29.14 The normal distribution 967Ta
Page 989 and 990:
29.14 The normal distribution 969EX
Page 991 and 992:
29.15 Reliability engineering 971in
Page 993 and 994:
29.15 Reliability engineering 973En
Page 995 and 996:
29.15 Reliability engineering 975k
Page 997 and 998:
Review exercises 29 977Solutions1 0
Page 999 and 1000:
AppendicesContents I Representingac
Page 1001 and 1002:
II The Greek alphabet 981f(t)T0 t 0
Page 1003 and 1004:
Indexabsolute quantity88absorption
Page 1005 and 1006:
Index 985chord 357--8circuital law
Page 1007 and 1008:
Index 987cycle ofsint 131cycles ofl
Page 1009 and 1010:
Index 989eigenvalues andeigenvector
Page 1011 and 1012:
Index 991spectra 766--8t-w duality
Page 1013 and 1014:
Index 993characteristic impedance o
Page 1015 and 1016:
Index 995exclusive OR gate 189NAND
Page 1017 and 1018:
Index 997second-order 829--30, 833,
Page 1019 and 1020:
Index 999remainder term,Taylor’s
Page 1021 and 1022:
Index 1001solenoidal vectorfield 85
Page 1023 and 1024:
Index 1003calculus 849--66curl859--
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082-Engineering-Mathematics-Anthony-Croft-Robert-Davison-Martin-Hargreaves-James-Flint-Edisi-5-2017

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