%5BSolutions Manual%5D Fourier and Laplace Transform - Antwo

Answers to selected exercises for chapter 1

Apply cos(+ ) = cos cos sin sin, then1.1f1(t) + f2(t)= A1 cost cos1 A1 sint sin1 +A2 cost cos2 A2 sint sin2= (A1 cos1 +A2 cos2) cost (A1 sin1 +A2 sin2) sint= C1 cost C2 sint,

where C1 = A1 cos1 +A2 cos2 and C2 = A1 sin1 +A2 sin2. Put A =pC21 + C

22 and take such that cos = C1/A and sin = C2/A (this is

possible since (C1/A)2+(C2/A)

2 = 1). Now f1(t)+f2(t) = A(cost cossint sin) = A cos(t+ ).

Put c1 = A1ei1 and c2 = A2e

i2 , then f1(t) + f2(t) = (c1 + c2)eit. Let1.2

c = c1 + c2, then f1(t) + f2(t) = ceit. The signal f1(t) + f2(t) is again a

time-harmonic signal with amplitude | c | and initial phase arg c.The power P is given by1.5

P =

2pi

Z pi/pi/

A2 cos2(t+ 0) dt =A2

4pi

Z pi/pi/

(1 + cos(2t+ 20)) dt

=A2

2.

The energy-content is E =R0

e2t dt = 12.1.6

The power P is given by1.7

P =1

4

3Xn=0

| cos(npi/2) |2 = 12.

The energy-content is E =P

n=0 e2n, which is a geometric series with1.8

sum 1/(1 e2).a If u(t) is real, then the integral, and so y(t), is also real.1.9b Since Z

u() d

Z|u() | d,

it follows from the boundedness of u(t), so |u() | K for some constantK, that y(t) is also bounded.c The linearity follows immediately from the linearity of integration. Thetime-invariance follows from the substitution = t0 in the integralR tt1 u( t0) d representing the response to u(t t0).d Calculating

R tt1 cos() d gives the following response: (sin(t)

sin(t ))/ = 2 sin(/2) cos(t /2)/.e Calculating

R tt1 sin() d gives the following response: ( cos(t) +

cos(t ))/ = 2 sin(/2) sin(t /2)/.f From the response to cos(t) in d it follows that the amplitude responseis | 2 sin(/2)/ |.g From the response to cos(t) in d it follows that the phase responseis /2 if 2 sin(/2)/ 0 and /2 + pi if 2 sin(/2)/ < 0. From

1

2 Answers to selected exercises for chapter 1

phase and amplitude response the frequency response follows: H() =2 sin(/2)ei/2/.

a The frequency response of the cascade system is H1()H2(), since the1.11reponse to eit is first H1()e

it and then H1()H2()eit.

b The amplitude response is |H1()H2() | = A1()A2().c The phase response is arg(H1()H2()) = 1() + 2().

a The amplitude response is | 1 + i | e2i = 2.1.12b The input u[n] = 1 has frequency = 0, initial phase 0 and amplitude1. Since ein 7 H(ei)ein, the response is H(e0)1 = 1 + i for all n.c Since u[n] = (ein + ein)/2 we can use ein 7 H(ei)ein to obtainthat y[n] = (H(ei)ein+H(ei)ein)/2, so y[n] = (1+ i) cos((n2)).d Since u[n] = (1 + cos 4n)/2, we can use the same method as in b andc to obtain y[n] = (1 + i)(1 + cos(4(n 2)))/2.a The power is the integral of f2(t) over [pi/ | | , pi/ | |], times | | /2pi.1.13Now cos2(t + 0) integrated over [pi/ | | , pi/ | |] equals pi/ | | andcos(t) cos(t + 0) integrated over [pi/ | | , pi/ | |] is (pi/ | |) cos0.Hence, the power equals (A2 + 2AB cos(0) +B

2)/2.b The energy-content is

R 10sin2(pit) dt = 1/2.

The power is the integral of | f(t) |2 over [pi/ | | , pi/ | |], times | | /2pi,1.14which in this case equals | c |2.a The amplitude response is |H() | = 1/(1 + 2). The phase response1.16is argH() = .b The input has frequency = 1, so it follows from eit 7 H()eit thatthe response is H(1)ieit = iei(t+1)/2.

a The signal is not periodic since sin(2N) 6= 0 for all integer N .1.17b The frequency response H(ei) equals A(ei)eie

i

, hence, we obtainthat H(ei) = ei/(1 + 2). The response to u[n] = (e2in e2in)/2i isthen y[n] = (e2i(n+1) e2i(n+1))/(10i), so y[n] = (sin(2n + 2))/5. Theamplitude is thus 1/5 and the initial phase 2 pi/2.a If u(t) = 0 for t < 0, then the integral occurring in y(t) is equal to 0 for1.18t < 0. For t0 0 the expression u(t t0) is also causal. Hence, the systemis causal for t0 0.b It follows from the boundedness of u(t), so |u() | K for some con-stant K, that y(t) is also bounded (use the triangle inequality and theinequality from exercise 1.9b). Hence, the system is stable.c If u(t) is real, then the integral is real and so y(t) is real. Hence, thesystem is real.d The response is

y(t) = sin(pi(t t0)) +Z tt1

sin(pi) d = sin(pi(t t0)) 2(cospit)/pi.

a If u[n] = 0 for n < 0, then y[n] is also equal to 0 for n < 0 whenever1.19n0 0. Hence, the system is causal for n0 0.b It follows from the boundedness of u[n], so |u[n] | K for some constantK and all n, that y[n] is also bounded (use the triangle inequality):

| y[n] | |u[n n0] |+

nXl=n2

u[l]

K +

nXl=n2

|u[l] | K +nX

l=n2K,

Answers to selected exercises for chapter 1 3

which equals 4K. Hence, the system is stable.c If u[n] is real, then u[n n0] is real and also the sum in the expressionfor y[n] is real, hence, y[n] is real. This means that the system is real.d The response to u[n] = cospin = (1)n is

y[n] = (1)nn0 +nX

l=n2(1)l = (1)nn0 + (1)n(1 1 + 1)

= (1)n(1 + (1)n0).


a The absolute values follow frompx2 + y2 and are given by

2, 2, 3, 22.1

respectively. The arguments follow from standard angles and are given by3pi/4, pi/2, pi, 4pi/3 respectively.b Calculating modulus and argument gives 2+2i = 2

2epii/4, 3+ i =

2e5pii/6 and 3i = 3e3pii/2.In the proof of theorem 2.1 it was shown that |Re z | | z |, which implies2.2that | z | |Re z | | z |. Hence,| z w |2 = (z w)(z w) = zz zw wz + ww

= | z |2 2Re(zw) + |w |2 | z |2 2 | z | |w |+ |w |2 = (| z | |w |)2.

This shows that | z w |2 (| z | |w |)2.We have | z1 | = 4

2, | z2 | = 4 and arg z1 = 7pi/4, arg z2 = 2pi/3. Hence,2.4

| z1/z2 | = | z1 | / | z2 | =2 and arg(z1/z2) = arg(z1) arg(z2) = 13pi/12,

so z1/z2 =2e13pii/12. Similarly we obtain z21z

32 = 2048e

3pii/2 and z21/z32 =

12e3pii/2.

The solutions are given in a separate figure on the website.2.5

a The four solutions 1 i are obtained by using the standard technique2.6to solve this binomial equation (as in example 2.3).b As part a; we now obtain the six solutions 6

2(cos(pi/9 + kpi/3) +

i sin(pi/9 + kpi/3)) where k = 0, 1 . . . , 5.c By completing the square as in example 2.4 we obtain the two solutions1/5 7i/5.Write z5 z4 + z 1 as (z 1)(z4 + 1) and then solve z4 = 1 to find2.7the roots

2(1 i)/2. Combining linear factors with complex conjugate

roots we obtain z5 z4 + z 1 = (z 1)(z2 +2z + 1)(z2 2z + 1).Since 2i = 2epii/2 the solutions are z = ln 2 + i(pi/2 + 2kpi), where k Z.2.8Split F (z) as A/(z 1

2) + B/(z 2) and multiply by the denominator of2.9

F (z) to obtain the values A = 1/3 and B = 4/3 (as in example 2.6).a Split F (z) as A/(z + 1) + B/(z + 1)2 + C/(z + 3) and multiply by2.11the denominator of F (z) to obtain the values C = 9/4, B = 1/2 and, bycomparing the coefficient of z2, A = 5/4 (as in example 2.8).Trying the first few integers we find the zero z = 1 of the denominator. A2.12long division gives as denominator (z 1)(z2 2z+5). We then split F (z)as A/(z 1) + (Bz + C)/(z2 2z + 5). Multiplying by the denominatorof F (z) and comparing the coefficients of z0 = 1, z and z2 we obtain thatA = 2, B = 0 and C = 1.a Using the chain rule we obtain f (t) = i(1 + it)2.2.13Use integration by parts twice and the fact that a primitive of ei0t is2.14ei0t/i0. The given integral then equals 4pi(1 pii)/30 , since e2pii = 1.Since

1/(2 eit) = 1/ 2 eit and 2 eit 2 eit = 1, the result2.15

follows from R 1

0u(t) dt

R 1

0|u(t) | dt.

1


a Use that | an | = 1/n6 + 1 1/n3 and the fact that Pn=1 1/n3 con-2.16

verges (example 2.17).b Use that | an | 1/n2 and the fact that Pn=1 1/n2 converges.c Use that | an | = 1/

neneni

= 1/(nen) 1/en and the fact thatP

n=1 1/en converges since it is a geometric series with ratio 1/e.

a Use the ratio test to conclude that the series is convergent:2.17

limn

n!

(n+ 1)!

= lim

n1

n+ 1= 0.

b The series is convergent; proceed as in part a:

limn

2n+1 + 1

3n+1 + n+ 1

3n + n

2n + 1

= lim

n2 + 1/2n

3 + (n+ 1)/3n1 + n/3n

1 + 1/2n=

2

3.

Determine the radius of convergence as follows:2.19

limn

2n+1z2n+2

(n+ 1)2 + 1

n2 + 1

2nz2n

= lim

n2z2 1 + 1/n21 + 2/n+ 2/n2

= 2z2.

This is less than 1 ifz2< 1/2, that is, if | z | < 2/2. Hence, the radius

of convergence is2/2.

This is a geometric series with ratio zi and so it converges for | z i | < 1;2.20the sum is (1/(1 i))(1/(1 (z i))), so 1/(2 z(1 i)).b First solving w2 = 1 leads to z2 = 0 or z2 = 2i. The equation2.23z2 = 2i has solutions 1 + i and 1 i and z2 = 0 has solution 0 (withmultiplicity 2).c One has P (z) = z(z4 + 8z2 + 16) = z(z2 + 4)2 = z(z 2i)2(z + 2i)2, so0 is a simple zero and 2i are two zeroes of multiplicity 2.Split F (z) as (Az+B)/(z24z+5)+(Cz+D)/(z24z+5)2 and multiply2.25by the denominator of F (z). Comparing the coefficient of z0, z1, z2 and z3

leads to the values A = 0, B = 1, C = 2 and D = 2.Replace cos t by (eit+eit)/2, then we have to calculate

R 2pi0

(e2it+1)/2 dt,2.26which is pi.

a Using the ratio test we obtain as limit5/3. This is less than 1 and so2.27

the series converges.b Since (n+ in)/n2 = (1/n) + (in/n2) and the series

Pn=1 1/n diverges,

this series is divergent.

The seriesP

n=0 cn(z2)n converges for all z with

z2< R, so it has radius2.29

of convergenceR.

a Determine the radius of convergence as follows:2.30

limn

(1 + i)2n+2zn+1

n+ 2

n+ 1

(1 + i)2nzn

= lim

n| z | n+ 1

n+ 2

(1 + i)2

= 2 | z | .

This is less than 1 if | z | < 1/2, so the radius of convergence is 1/2.b Calculate f (z) by termwise differentiation of the series and multiplythis by z. It then follows that

zf (z) + f(z) =Xn=0

(1 + i)2nzn =

Xn=0

(2iz)n.

This is a geometric series with ratio 2iz and so it has sum 1/(1 2iz).

3a

2

b

4 5

c

1

d

3

e

32

1 + 2i

2

f

2

1

g

2 3

2 12

0


A trigonometric polynomial can be written as3.2

f(t) =a02

+

kXm=1

(am cos(m0t) + bm sin(m0t)).

Now substitute this for f(t) in the right-hand side of (3.4) and use thefact that all the integrals in the resulting expression are zero, except for

the integralR T/2T/2 sin(m0t) sin(n0t) dt with m = n, which equals T/2.

Hence, one obtains bn.

The function g(t) = f(t) cos(n0t) has period T , so3.4 Z T0

g(t)dt =

Z TT/2

g(t)dt+

Z T/20

g(t)dt

=

Z TT/2

g(t T )dt+Z T/20

g(t)dt =

Z 0T/2

g()d +

Z T/20

g(t)dt

=

Z T/2T/2

g(t)dt.

Multiplying by 2/T gives an.

From a sketch of the periodic function with period 2pi given by f(t) = | t |3.6for t (pi, pi) we obtain

cn =1

2pi

Z 0pi

(t)eint dt+ 12pi

Z pi0

teint dt.

As in example 3.2 these integrals can be calculated using integration byparts for n 6= 0. Calculating c0 separately (again as in example 3.2) weobtain

c0 =pi

2, cn =

(1)n 1n2pi

Substituting these values of cn in (3.10) we obtain the Fourier series. Onecan also write this as a Fourier cosine series:

pi

2 4

pi

Xk=0

cos((2k + 1)t)

(2k + 1)2.

From the description of the function we obtain that3.7

cn =1

2

Z 10

e(1+inpi)t dt.

This integral can be evaluated immediately and leads to

cn =inpi 1

2(n2pi2 + 1)

`(1)ne1 1 .

The Fourier series follows from (3.10) by substituting cn.

The Fourier coefficients are calculated by splitting the integrals into a real3.9and an imaginary part. For c0 this becomes:

3


c0 =1

2

Z 11

t2 dt+i

2

Z 11

t dt =1

3.

For n 6= 0 we have that

cn =1

2

Z 11

t2einpit dt+i

2

Z 11

teinpit dt.

The second integral can be calculated using integration by parts. To cal-culate the first integral we apply integration by parts twice. Adding theresults and simplifying somewhat we obtain the Fourier coefficients (andthus the Fourier series):

cn =(1)n(2 npi)

n2pi2.

From the values of the coefficients cn calculated earlier in exercises 3.6, 3.73.10and 3.9, one can immediately obtain the amplitude spectrum | cn | and thephase spectrum arg cn (note e.g. that arg cn = pi if cn > 0, arg cn = pi ifcn < 0, arg cn = pi/2 if cn = iy with y > 0 and arg cn = pi/2 if cn = iywith y < 0). This results in three figures that are given separately on thewebsite.

a By substituting a = T/4 in (3.14) it follows that3.11

cn =sin(npi/4)

npifor n 6= 0, c0 = 1

4.

b As in a, but now a = T and we obtain

c0 = 1, cn = 0 for n 6= 0.Hence, the Fourier series is 1 (!). This is no surprise, since the function is1 for all t.

By substituting a = T/2 in (3.15) it follows that3.12

c0 =1

2, cn = 0 for n 6= 0 even, cn = 2

n2pi2for n odd.

We have that f(t) = 2p2,4(t) q1,4(t) and so the Fourier coefficients follow3.14by linearity from table 1:c0 = 3/4, cn = (2npi sin(npi/2) 4 sin2(npi/4))/(n2pi2) for n 6= 0.Note that f(t) can be obtained from the sawtooth z(t) by multiplying the3.15shifted version z(t T/2) by the factor T/2 and then adding T/2, thatis, f(t) = T

2z(t T

2) + T

2. Now use the Fourier coefficients of z(t) (table 1

e.g.) and the properties from table 2 to obtain that

c0 =T

2, cn =

iT

2pinfor all n 6= 0.

Shifts over a period T (use the shift property and the fact that e2piin = 13.17for all n).

In order to determine the Fourier sine series we extend the function to an3.19odd function of period 8. We calculate the coefficients bn as follows (thean are 0):

bn =1

4

Z 24

(2 sin(npit/4)) dt+ 14

Z 22

t sin(npit/4) dt


+1

4

Z 42

2 sin(npit/4) dt.

The second integral can be calculated by an integration by parts and onethen obtains that

bn =8

n2pi2sin(npi/2) 4

npicos(npi),

which gives the Fourier sine series. For the Fourier cosine series we extendthe function to an even function of period 8. As above one can calculatethe coefficients an and a0 (the bn are 0). The result is

a0 = 3, an =8

n2pi2(cos(npi/2) 1) for all n 6= 0.

In order to determine the Fourier cosine series we extend the function to3.21an even function of period 8. We calculate the coefficients an and a0 asfollows (the bn are 0):

a0 =1

2

Z 40

(x2 4x) dx = 163,

while for n 1 we have

an =1

4

Z 04

(x2 + 4x) cos(npix/4) dx+1

4

Z 40

(x2 4x) cos(npix/4) dx

=1

2

Z 40

x2 cos(npix/4) dx 2Z 40

x cos(npix/4) dx.

The first integral can be calculated by applying integration by parts twice;the second integral can be calculated by integration by parts. Combiningthe results one then obtains that

an =64(1)nn2pi2

32((1)n 1)

n2pi2=

32((1)n + 1)n2pi2

,

which also gives the Fourier cosine series. One can write this series as

83+

16

pi2

Xn=1

1

n2cos(npix/2).

For the Fourier sine series we extend the function to an odd function ofperiod 8. As above one can calculate the coefficients bn (the an are 0). Theresult is

bn =64((1)n 1)

n3pi3for all n 1.

If f is real and the cn are real, then it follows from (3.13) that bn =3.240. A function whose Fourier coefficients bn are all 0 has a Fourier seriescontaining cosine functions only. Hence, the Fourier series will be even. If,on the other hand, f is real and the cn are purely imaginary, then (3.13)shows that an = 0. The Fourier series then contains sine functions onlyand is thus odd.

Since sin(0t) = (ei0t ei0t)/2i we have3.25

cn =1

2iT

Z T/20

ei(1n)0t dt 12iT

Z T/20

ei(1+n)0t dt.


The first integral equals T/2 for n = 1 while for n 6= 1 it equals i((1)n +1)/((1 n)0). The second integral equals T/2 for n = 1 while forn 6= 1 it equals i((1)n+1 1)/((1 + n)0). The Fourier coefficients arethus c1 = 1/(4i), c1 = 1/(4i) and ((1)n+1)/(2(1n2)pi) for n 6= 1,1;the Fourier series follows immediately from this.

b The even extension has period 2a, but it has period a as well. We can3.27thus calculate the coefficients an and a0 as follows (the bn are 0):

a0 =2

a

Z a/20

2bt/a dt 2a

Z 0a/2

2bt/a dt = b.

while for n 0 we obtain from an integration by parts that

an =2

a

Z a/20

(2bt/a) cos(2npit/a) dt 2a

Z 0a/2

(2bt/a) cos(2npit/a) dt

=2b((1)n 1)

n2pi2,

which gives the Fourier cosine series. It can also be determined using theresult of exercise 3.6 by applying a multiplication and a scaling.

The odd extension has period 2a and the coefficients bn are given by (thean are 0):

bn =1

a

Z a/2a

(2bta

2b) sin(npit/a) dt+ 1a

Z a/2a/2

2bt

asin(npit/a) dt

+1

a

Z aa/2

(2bta

+ 2b) sin(npit/a) dt

=8b

n2pi2sin(npi/2),

where we used integration by parts.

0 n2 4

a

24 0 n

pi

2 4

b

24

pi/2

0 n2 4

a

24 0 n2 4

b

24

pi2

pi2

12

0 n2 4

a

24 0 n2 4

b

24

12

pi


a The periodic block function from section 3.4.1 is a continuous function4.1on [T/2, T/2], except at t = a/2. At these points f(t+) and f(t) exist.Also f (t) = 0 for t 6= a/2, while f (t+) = 0 for t = a/2 and t = T/2and f (t) = 0 for t = a/2 and t = T/2. Hence f is piecewise continuousand so the periodic block function is piecewise smooth. Existence of theFourier coefficients has already been shown in section 3.4.1. The periodictriangle function is treated analogously.b For the periodic block function we have

Xn=

| cn |2 a2

T 2+

8

T 220

Xn=1

1

n2

since sin2(n0a/2) 1. The series Pn=1 1n2 converges, so Pn= | cn |2converges. The periodic triangle function is treated analogously.

This follows immediately from (3.11) (for part a) and (3.8) (for part b).4.2

Take t = T/2 in the Fourier series of the sawtooth from example 4.2 and4.4use that sin(n0T/2) = sin(npi) = 0 for all n. Since (f(t+)+f(t))/2 = 0,this agrees with the fundamental theorem.

a If we sketch the function, then we see that it is a shifted block function.4.6Using the shift property we obtain

c0 =1

2, cn = 0 even n 6= 0, cn = i

npiodd n.

The Fourier series follows by substituting the cn. One can write the serieswith sines only (split the sum in two pieces: one from n = 1 to andanother from n = 1 to ; change from n to n in the latter):1

2+

2

pi

Xk=0

sin(2k + 1)t

2k + 1.

b The function is piecewise smooth and it thus satisfies the conditions ofthe fundamental theorem. At t = pi/2 the function f is continuous, so theseries converges to f(pi/2) = 1. Since sin((2k + 1)pi/2) = (1)k, formula(4.11) follows:

Xk=0

(1)k2k + 1

=pi

4.

a We have that c0 = (2pi)1 R pi

0t dt = pi/4, while the Fourier coefficients4.7

for n 6= 0 follow from an integration by parts:

cn =1

2pi

Z pi0

teint dt =(1)ni2n

+(1)n 1

2n2pi.

The Fourier series follows by substituting these cn:

pi

4+

1

2

Xn=,n6=0

(1)ni

n+

(1)n 1n2pi

eint.

12


b From the fundamental theorem it follows that the series will convergeto 1

2(f(pi+) + f(pi)) = pi/2 at t = pi (note that at pi there is a jump). If

we substitute t = pi into the Fourier series, take pi4to the other side of the

=-sign, then multiply by 2, and finally split the sum into a sum from n = 1to and a sum from n = 1 to , then it follows thatpi

2= 2

Xn=1

(1)n 1n2pi

(1)n

(the terms with (1)ni/n cancel each other). For even n we have (1)n 1 = 0 while for odd n this will equal 2, so (4.10) results:pi2

8=

Xk=1

1

(2k 1)2 .

a From f(0+) = 0 = f(0) and f(1) = 0 = f((1)+) it follows that f4.9is continuous. We have that f (t) = 2t+1 for1 < t < 0 and f (t) = 2t+1for 0 < t < 1. Calculating the defining limits for f from below and fromabove at t = 0 we see that f (0) = 1 and since f (0+) = 1 = f (0)it follows that f is continuous at t = 0. Similarly it follows that f iscontinuous at t = 1. Since f (t) = 2 for 1 < t < 0 and f (t) = 2 for0 < t < 1 we see that f is discontinuous.b The function f is the sum of g and h with period 2 defined for 1 < t 1 by g(t) = t and h(t) = t2 for 1 < t 0 and h(t) = t2 for 0 < t 1.Since g is a sawtooth, the Fourier coefficients are cn = (1)ni/pin (seesection 3.4.3). The function h is the odd extension of t2 on (0, 1] andits Fourier coefficients have been determined in the first example of section3.6. By linearity one obtains the Fourier coefficients of f . In terms of thean and bn they become an = 0 and bn = 4(1 (1)n)/pi3n3. Hence, theydecrease as 1/n3.c Use e.g. the fundamental theorem for odd functions to obtain

f(t) =8

pi3

Xk=0

sin(2k + 1)pit

(2k + 1)3.

Now substitute t = 1/2 and use that f(1/2) = 1/4 and sin((2k + 1)pi/2) =(1)n to obtain the required result.Use (3.8) to write the right-hand side of (4.14) as a20/4+

12

Pn=1(a

2n+ b

2n).4.10

a The Fourier coefficients of f and g are (see table 1 or section 3.4.1),4.12respectively, fn = (sinna)/npi for n 6= 0 and f0 = a/pi and gn = (sinnb)/npifor n 6= 0 and g0 = b/pi. Substitute into Parseval (4.13) and calculate theintegral (1/pi)

R a/2a/2 1 dt (note that a b). Take all constants together and

then again (as in exercise 4.7) split the sum into a sum from n = 1 to and a sum from n = 1 to . The required result then follows.b Use that sin2(npi/2) = 1 for n odd and 0 for n even, then (4.10) follows.

a The Fourier coefficients are (see table 1 or section 3.4.2 and use that4.13sin2(npi/2) = 1 for n odd and 0 for n even): cn = 2/n

2pi2 for n odd, 0 forn 6= 0 even and c0 = 1/2. From Parseval for f = g, so from (4.14), it thenfollows that (calculate the integral occurring in this formula):

1

3=

1

4+

8

pi2

Xk=1

1

(2k 1)4


(again we split the sum in a part from n = 1 to and from n = 1 to). Take all constants together and multiply by pi2/8, then the requiredresult follows.b Since

S =

Xn=1

1

n4=

Xk=1

1

(2k)4+

Xk=0

1

(2k + 1)4,

it follows from part a that

S =1

16

Xk=1

1

k4+

pi4

96=

1

16S +

pi4

96.

Solving for S we obtainP

n=11n4

= pi4

90.

SinceR baf(t) dt =

R bT/2 f(t) dt

R aT/2 f(t) dt, we can apply theorem 4.94.15

twice. Two of the infinite sums cancel out (the ones representing h0 intheorem 4.9), the other two can be taken together and lead to the desiredresult.

This follows from exercise 4.15 by using (3.8), so cn = (an ibn)/2 and4.16cn = (an + ibn)/2 (n N).a The Fourier series is given by4.17

4

pi

Xn=0

sin(2n+ 1)t

2n+ 1.

b SinceZ tpi

sin(2n+ 1) d = cos(2n+ 1)t2n+ 1

12n+ 1

,

the integrated series becomes

4pi

Xn=0

1

(2n+ 1)2 4

pi

Xn=0

cos(2n+ 1)t

(2n+ 1)2.

From (4.10) we see that the constant in this series equals pi/2.c The series in part b represents the function

R tpi f() d (theorem 4.9 or

better still, exercise 4.16). Calculating this integral we obtain the functiong(t) with period 2pi given for pi < t pi by g(t) = | t | pi.d Subtracting pi from the Fourier series of | t | in exercise 3.6 we obtain aFourier series for g(t) which is in accordance with the result from part b.

This again follows as in exercise 4.16 from (3.8).4.19

Since f is piecewise smooth, f is piecewise continuous and so the Fourier4.20coefficients cn of f

exist. Since f is continuous, we can apply integrationby parts, as in the proof of theorem 4.10. It then follows that cn = in0c

n,

where cn are the Fourier coefficients of f. But cn = in0cn by theorem

4.10, so cn = n220cn. Now apply the Riemann-Lebesgue lemma to cn,then it follows that limn n2cn = 0.

a The Fourier coefficients have been determined in exercise 3.25: c1 =4.221/(4i), c1 = 1/(4i) and ((1)n + 1)/(2(1 n2)pi) for n 6= 1,1. Tak-ing positive and negative n in the series together, we obtain the followingFourier series:


1

2sin t+

1

pi+

2

pi

Xk=1

1

1 4k2 cos 2kt.

b The derivative f exists for all t 6= npi (n Z) and is piecewise smooth.According to theorem 4.10 we may thus differentiate f by differentiatingits Fourier series for t 6= npi:

f (t) =1

2cos t 4

pi

Xk=1

k

1 4k2 sin 2kt.

At t = npi the differentiated series converges to (f (t+)+ f (t))/2, whichequals 1/2 for t = 0, while it equals 1/2 for t = pi. Hence, the differen-tiated series is a periodic function with period 2pi which is given by 0 forpi < t < 0, 1

2for t = 0, cos t for 0 < t < pi, 1

2for t = pi.

Write down the expression for Si(x) and change from the variable t to4.25t, then it follows that Si(x) = Si(x).a From the definition of Si(x) it follows that Si(x) = sinx/x. So Si(x) =4.260 if sinx/x = 0. For x > 0 we thus have Si(x) = 0 for x = kpi with k N.A candidate for the first maximum is thus x = pi. Since sinx/x > 0 for0 < x < pi and sinx/x < 0 for pi < x < 2pi, it follows that Si(x) indeed hasits first maximum at x = pi.b The value at the first maximum is Si(pi). Since Si(pi) = 1.852 . . . andpi/2 = 1.570 . . ., the overshoot is 0.281 . . .. The jump of f at x = 0 ispi = 3.141 . . ., so the overshoot is 8.95 . . .%, so about 9%.

a The function f is continuous for t 6= (2k + 1)pi (k Z) and it then4.28converges to f(t), which is 2t/pi for 0 | t | < pi/2, 1 for pi/2 t < pi and 1for pi < t pi/2. For t = (2k+1)pi it converges to (f(t+)+f(t))/2 = 0.b Since f is odd we have an = 0 for all n. The bn can be found using anintegration by parts:

bn =4

pi2

Z pi/20

t sinnt dt+2

pi

Z pipi/2

sinnt dt =4

n2pi2sin(npi/2) 2(1)

n

npi.

Since sin(npi/2) = 0 if n even and (1)k if n = 2k+1, the Fourier series is

2pi

Xn=1

(1)nn

sinnt+4

pi2

Xn=0

(1)n(2n+ 1)2

sin(2n+ 1)t.

Substituting t = 0 and t = pi it is easy to verify the fundamental theoremfor these values.c We cannot differentiate the series; the resulting series is divergent be-cause limn(1)n cosnt 6= 0. Note that theorem 4.10 doesnt apply sincef is not continuous.d We can integrate the series since theorem 4.9 can be applied (note thatc0 = 0). If we put g(t) =

R tpi f() d , then g is even, periodic with period

2pi and given by (t2/pi)(3pi/4) for 0 t < pi/2 and by tpi for pi/2 t pi.Use table 1 to obtain the Fourier coefficients and then apply Parseval, that4.29is, (4.13). Calculating the integral in Parsevals identity will then give thefirst result; choosing a = pi/2 gives the second result.

a The Fourier series has been determined in the last example of section4.303.6. Since f is continuous (and piecewise smooth), the Fourier series con-verges to f(t) for all t R:


f(t) =2

pi 4

pi

Xn=1

1

4n2 1 cos 2nt.

b First substitute t = 0 in the Fourier series; since f(0) = 0 and cos 2nt =1 for all n, the first result follows. Next substitute t = pi/2 in the Fourierseries; since f(pi/2) = 1 and cos 2nt = (1)n for all n, the second resultfollows.c One should recognize the squares of the Fourier coefficients here. Hencewe have to apply Parsevals identity (4.14), or the alternative form givenin exercise 4.10. This leads to

1

2pi

Z pipi

sin2 t dt =4

pi2+

1

2pi2

Xn=1

16

(4n2 1)2 .

SinceR pipi sin

2 t dt = pi, the result follows.

a Since f1 is odd it follows that4.31

(f1 f2)(t) = 1T

Z T/2T/2

f1(t+ )f2() d.

Now change the variable from to and use that f2 is odd, then itfollows that (f1 f2)(t) = (f1 f2)(t).b The convolution product equals

(f f)(t) = 12

Z 11

f(t ) d.

Since f is odd, part a implies that f f is even. It is also periodic withperiod 2, so it is sufficient to calculate (f f)(t) for 0 t 1. First notethat f is given by f(t) = t 2 for 1 < t 2. Since 1 1 and0 t 1 we see that t 1 t t + 1. From 0 t 1 it followsthat 1 t 1 0, and so close to = 1 the function f(t ) is givenby t . Since 1 t + 1 2, the function f(t ) is given by t 2close to = 1. Hence, we have to split the integral precisely at the pointwhere t gets larger than 1, because precisely then the function changesfrom t to t 2. But t 1 precisely when t 1, and so wehave to split the integral at t 1:

(f f)(t) = 12

Z t11

(t 2) d + 12

Z 1t1

(t ) d.

It is now straightforward to calculate the convolution product. The resultis (f f)(t) = t2/2 + t 1/3.c From section 3.4.3 or table 1 we obtain the Fourier coefficients cn ofthe sawtooth f and applying the convolution theorem gives the Fouriercoefficients of (f f)(t), namely c20 = 0 and c2n = 1/pi2n2 (n 6= 0).d Take t = 0 in part c; since f is odd and real-valued we can write(f f)(0) = 1

2

R 11 | f() |2 d , and so we indeed obtain (4.13).

e For 1 < t < 0 we have (f f)(t) = t 1, while for 0 < t < 1we have (f f)(t) = t + 1. Since f f is given by t2/2 + t 1/3 for0 < t < 2, (f f)(t) is continuous at t = 1. Only at t = 0 we have thatf f is not differentiable. So theorem 4.10 implies that the differentiatedseries represents the function (f f)(t) on [1, 1], except at t = 0. Att = 0 the differentiated series converges to ((f f)(0+)+(f f)(0))/2 =(1 1)/2 = 0.f The zeroth Fourier coefficient of f f is given by


1

2

Z 11

(f f)(t) dt =Z 10

(t2/2 + t 1/3) dt = 0.

This is in agreement with the result in part c since c20 = 0. Since thiscoefficient is 0, we can apply theorem 4.9. The function represented by theintegrated series is given by the (periodic) function

R t1(f f)() d . It is

also odd, since f is even and for 0 t 1 it equalsZ 01

(2/2 1/3) d +Z t0

(2/2 + 1/3) d = t(t 1)(t 2)/6.


For a stable LTC-system the real parts of the zeroes of the characteristic5.1polynomial are negative. Fundamental solutions of the homogeneous equa-tions are of the form x(t) = tlest, where s is such a zero and l 0some integer. Since

tlest

= | t |l e(Re s)t and Re s < 0 we have that

limt x(t) = 0. Any homogeneous solution is a linear combination ofthe fundamental solutions.

The Fourier coefficients of u are5.2

u0 =1

2, u2k = 0, u2k+1 =

(1)k(2k + 1)pi

(u = ppi,2pi, so use table 1 and the fact that sin(npi/2) = (1)k for n = 2k+1odd and 0 for n even). Since H() = 1/(i + 1) and yn = H(n0)un =H(n)un it then follows that

y0 =1

2, y2k = 0, y2k+1 =

(1)k(1 + (2k + 1)i)(2k + 1)pi

.

a The frequency response is not a rational function, so the system cannot5.3be described by a differential equation (5.3).b Since H(n0) = H(n) = 0 for |n | 4 (because 4 > pi), we only need toconsider the Fourier coefficients of y with |n | 3. From Parseval it thenfollows that P =

P3n=3 | yn |2 with yn as calculated in exercise 5.2. This

sum is equal to P = 14+ 20

9pi2.

Note that u has period pi and that the integral to be calculated is thus the5.4zeroth Fourier coefficient of y. Since y0 = H(00)u0 = H(0)u0 and H(0) =1 (see example 5.6 for H()), it follows that y0 = u0 = 1pi

R pi0u(t) dt =

2pi.

a According to (5.4) the frequency response is given by5.5

H() =2 + 1

2 + 4 + 2i .

Since H() = 0 for = 1, the frequencies blocked by the system are = 1.b Write u(t) = e4it/4 eit/2i + 1/2 + eit/2i + e4it/4. It thus followsthat the Fourier coefficients unequal to 0 are given by u4 = u4 = 1/4,u1 = 1/2i, u1 = 1/2i and u0 = 1/2. Since yn = H(n0)un = H(n)unand H(1) = H(1) = 0 we thus obtain thaty(t) = y4e

4it + y1eit + y0 + y1e

it + y4e4it

=15

12 + 8i 14e4it +

1

4 12+

15

12 8i 1

4e4it.

It is a good exercise to write this with real terms only:

y(t) =45

104cos 4t+

30

104sin 4t+

1

8.

We have that5.6

H() =1

2 + 20.

18


Since |0 | is not an integer, there are no homogeneous solutions havingperiod 2pi, while u does have period 2pi. There is thus a uniquely determinedperiodic solution y corresponding to u. Since u(t) = piqpi,2pi(t) the Fouriercoefficients of u follow immediately from table 1:

u0 =pi

2, u2k = 0(k 6= 0), u2k+1 = 2

(2k + 1)2pi2.

Since yn = H(n0)un = H(n)un =1

n2+20un, the line spectrum of y

follows.

For the thin rod the heat equation (5.8) holds on (0, L), with initial condi-5.7tion (5.9). This leads to the fundamental solutions (5.15), from which thesuperposition (5.16) is build. The initial condition leads to a Fourier serieswith coefficients

An =2

L

Z L/20

x sin(npix/L) dx+2

L

Z LL/2

(L x) sin(npix/L) dx,

which can be calculated using an integration by parts. The result is: An =(4L/n2pi2) sin(npi/2) (which is 0 for n even). We thus obtain the (formal)solution

u(x, t) =4L

pi2

Xn=0

(1)n(2n+ 1)2

e(2n+1)2pi2kt/L2 sin((2n+ 1)pix/L).

a The heat equation and initial conditions are as follows:5.9

ut = kuxx for 0 < x < L, t > 0,ux(0, t) = 0, u(L, t) = 0 for t 0,u(x, 0) = 7 cos(5pix/2L) for 0 x L.b Separation of variables leads to (5.12) and (5.13). The function X(x)should satisfy X (x) cX(x) = 0 for 0 < x < L, X (0) = 0 and X(L) = 0.For c = 0 we obtain the trivial solution. For c 6= 0 the characteristicequation s2 c = 0 has two distinct roots s1. The general solution isthen X(x) = es1x + es1x, so X (x) = s1es1x s1es1x. The firstboundary condition X (0) = 0 gives s1( ) = 0, so = . Nextwe obtain from the second boundary condition X(L) = 0 the equation(es1L + es1L) = 0. For = 0 we get the trivial solution. So we musthave es1L + es1L = 0, implying that e2s1L = 1. From this it followsthat s1 = i(2n+ 1)pi/2L. This gives us eigenfunctions Xn(x) = cos((2n+1)pix/2L) (n = 0, 1, 2, 3, . . .). Since Tn(t) remains as in the textbook (forother parameters), we have thus found the fundamental solutions

un(x, t) = e(2n+1)2pi2kt/4L2 cos((2n+ 1)pix/2L).

Superposition gives

u(x, t) =

Xn=0

Ane(2n+1)2pi2kt/4L2 cos((2n+ 1)pix/2L).

Substituting t = 0 (and using the remaining initial condition) leads to

u(x, 0) =

Xn=0

An cos((2n+ 1)pix/2L) = 7 cos(5pix/2L).


Since the right-hand side consists of one harmonic only, it follows thatA2 = 7 and An = 0 for all n 6= 2. The solution is thus u(x, t) =7e25pi

2kt/4L2 cos(5pix/2L).

a The equations are5.11

ut = kuxx for 0 < x < L, t > 0,u(0, t) = 0, ux(L, t) = 0 for t 0,u(x, 0) = f(x) for 0 x L.b Going through the steps one obtains the same fundamental solutions asin exercise 5.9. The coefficients An cannot be determined explicitly here,since f(x) is not given explicitly.

The equations are given by (5.17) - (5.20), where we only need to substitute5.12the given initial condition in (5.19), so u(x, 0) = 0.05 sin(4pix/L) for 0 x L. All steps to be taken are the same as in section 5.2.2 of the textbookand lead to the solution

u(x, t) =

Xn=1

An cos(npiat/L) sin(npix/L).

Substituting t = 0 (and using the remaining initial condition) gives

u(x, 0) =

Xn=1

An sin(npix/L) = 0.05 sin(4pix/L).

Since the right-hand side consists of one harmonic only, it follows thatA4 = 0.05 and An = 0 for all n 6= 4. The solution is thus u(x, t) =0.05 cos(4piat/L) sin(4pix/L).

Separation of variables leads to X (x) cX(x) = 0 for 0 < x < pi, X (0) =5.15X (pi) = 0. For c = 0 we obtain the constant solution, so c = 0 is aneigenvalue with eigenfunction X(x) = 1. For c 6= 0 the characteristicequation s2 c = 0 has two distinct roots s1. The general solutionis then X(x) = es1x + es1x, so X (x) = s1es1x s1es1x. Theboundary condition X (0) = 0 gives s1( ) = 0, so = . Fromthe boundary condition X (pi) = 0 we obtain s1(es1pi es1pi) = 0. For = 0 we get the trivial solution. So we must have es1pi es1pi = 0,implying that e2s1pi = 1. From this it follows that s1 = ni. This gives useigenfunctions Xn(x) = cos(nx) (n = 0, 1, 2, 3, . . .). For T (t) we get theequation T (t) + n2a2T (t) = 0. From the initial condition ut(x, 0) = 0we obtain T (0) = 0. The non-trivial solution are Tn(t) = cos(nat) (n =0, 1, 2, 3, . . .) and we have thus found the fundamental solutions

un(x, t) = cos(nat) cos(nx).

Superposition gives

u(x, t) =

Xn=0

An cos(nat) cos(nx).

Substituting t = 0 (and using the remaining initial condition) leads to

u(x, 0) =

Xn=0

An cos(nx) = kx for 0 < x < pi.


We have A0 = (2/pi)R pi0kx dx = kpi and An = (2/pi)

R pi0kx cos(nx) dx for

n 6= 0, which can be calculated by an integration by parts: An = 0 for neven (n 6= 0) and An = 4k/n2pi for n odd. The solution is thus

u(x, t) =kpi

2 4k

pi

Xn=0

1

(2n+ 1)2cos((2n+ 1)at) cos((2n+ 1)x).

a From H() = H() and yn = H(n0)un follows that the response5.16y(t) to a real signal u(t) is real: since un = un we also have yn = yn.b Since we can write sin0t = (e

i0t ei0t)/2i, the response is equalto (H(0)e

i0t H(0)ei0t)/2i, which is ((1 e2i0)2ei0t (1 e2i0)2ei0t)/2i. This can be rewritten as sin0t 2 sin(0(t 2)) +sin(0(t 4)).c A signal with period 1 has Fourier series of the form

Pn= une

2piint.

The response isP

n=H(2pin)une2piint, which is 0 since H(2pin) = 0 for

all n.

a The characteristic equation is s3 + s2 + 4s + 4 = (s2 + 4)(s + 1) = 05.18and has zeroes s = 1 and s = 2i. The zeroes on the imaginary axiscorrespond to periodic eigenfrequencies with period pi and so the responseto a periodic signal is not always uniquely determined. But see part b!b Since here the input has period 2pi/3, we do have a unique response.From Parseval and the relation yn = H(n0)un we obtain that the poweris given by

P =3

2pi

Z 2pi/30

| y(t) |2 dt =X

n=| yn |2 =

Xn=

|H(n0)un |2 .

We have that

H() =1 + i

4 2 + i(4 2) .

Now use that only u3 = u3 = 12 and that all other un are 0, then it followsthat P = 1/50.

For the rod we have equations (5.8) - (5.10), where we have to take f(x) =5.19u0 in (5.10). The solution is thus given by (5.16), where now the An are theFourier coefficients of the function u0 on [0, L]. These are easy te determine(either by hand or using tables 1 and 2): An = 0 for n even, An = 4u0/npifor n odd. This gives

u(x, t) =4u0pi

Xn=0

1

(2n+ 1)2e(2n+1)

2pi2kt/L2 sin((2n+ 1)pix/L).

Substituting x = L/2 in the x-derivative and using the fact that cos((2n+1)pi/2) = 0 for all n leads to ux(L/2, t) = 0.

a As in the previous exercise the solution is given by (5.16). The An are5.20

given by (2/L)R L/20

a sin(npix/L) dx = 2a(1 cos(npi/2))/npi, which givesthe (formal) solution

u(x, t) =2a

pi

Xn=1

1

n(1 cos(npi/2))en2pi2kt/L2 sin(npix/L).


b The two rods together form one rod and so part a can be applied withL = 40, k = 0.15 and a = 100. Substituting t = 600 in u(x, t) from parta then gives the temperature distribution. On the boundary between therods we have x = 20, so we have to calculate u(20, 600); using only thecontibution from the terms n = 1, 2, 3, 4 we obtain u(20, 600) 36.4.c Take k = 0.005, a = 100, L = 40, substitute x = 20 in u(x, t) frompart a, and now use only the first two terms of the series to obtain theequation u(20, t) 63.662e0.0000308t = 36 (terms of the series tend to 0very rapidly, so two terms suffice). We then obtain 18509 seconds, whichis approximately 5 hours.


We have to calculate (the improper integral)R e

it dt. Proceed as in6.1

eaxample 6.1, but we now have to determine limB eiB . This limitdoes not exist.

a We have to calculate G() =R0

e(a+i)t dt, which can be done pre-6.2cisely as in section 6.3.3 if we write a = + i and use that e(a+i)R =eRei(+)R. If we let R then this tends to 0 since > 0.b The imaginary part of G() is /(a2+2) and applying the substitu-tion rule gives

R/(a2 + 2) d = 1

2ln(a2 + 2), so this improper integral,

which is the Fourier integral for t = 0, does not exits (limA ln(a2 +A2)does not exist e.g.).c We have lima0 g(t) = lima0 (t)eat = (t), while for 6= 0 we havethat lima0G() = i/.To calculate the spectrum we split the integral at t = 0:6.4

G() =

Z 10

teit dtZ 01

teit dt.

Changing from the variable t to t in the second integral we obtain thatG() = 2

R 10t cost dt, which can be calculated for 6= 0 using an integ-

ration by parts. The result is:

G() =2 sin

+

2(cos 1)2

.

For = 0 we have that G(0) = 2R 10t dt = 1. Since lim0 sin/ = 1 and

lim0(cos 1)/2 = 12 (use e.g. De lHopitals rule), we obtain thatlim0G() = G(0), so G is continuous.

a Calculating the integral we have that6.5

F () = 2icos(a/2) 1

for 6= 0, F (0) = 0.

b Using Taylor or De lHopital it follows that lim0 F () = 0 = F (0),so F is continuous.

From the linearity and table 3 it follows that6.7

F () =12

4 + 2+ 8i

sin2(a/2)

a2.

Use (6.17) and table 3 for the spectrum of e7| t |, then6.8

F () =7

49 + ( pi)2 +7

49 + ( + pi)2.

a From the shift property in the frequency domain (and linearity) it fol-6.9lows that the spectrum of f(t) sin at is F ( a)/2i F ( + a)/2i.b Write f(t) = p2pi(t) sin t, obtain the spectrum of p2pi(t) from table 3 andapply part a (and use the fact that sin(pi pi) = sin(pi)), then

F () =2i sin(pi)

2 1 .

23


Use section 6.3.3 (or exercise 6.2) and the modulation theorem 6.17, and6.10write the result as one fraction, then

(F(t)eat cos bt)() = a+ i(a+ i)2 + b2

.

Similarly it follows from section 6.3.3 (or exercise 6.2) and exercise 6.9athat

(F(t)eat sin bt)() = b(a+ i)2 + b2

.

Write6.12

F () =

Z 0

f(t)eit dt+Z 0

f(t)eit dt

and change from t to t in the second integral, then it follows that F () =2i R

0f(t) sint dt.

a We have F () = F () and F () is even, so F () = F (), and thus6.13F () is real.b We have F () = F () (by part a) and since |F () | = (F ()F ())1/2,it follows that |F () | = |F () |.Calculate the spectrum in a direct way using exactly the same techniques6.14as in example 6.3.3 (or use (6.20) and twice an integration by parts):

F () =2i1 + 2

.

The spectrum is given byR a/2a/2 te

it dt, which can be calculated using an6.16integration by parts. The result is indeed equal to the formula given inexample 6.3.

a From the differentiation rule (and differentiating the Fourier transform6.17

of the Gauss function, of course) it follows that ipie2/4a/(2aa) isthe spectrum of tf(t).b If we divide the Fourier transform of f (t) by 2a, then we indeedobtain the same result as in part a.

Two examples are the constant function f(t) = 0 (k arbitrary), and the6.18

Gauss function et2/2 with k =

2pi. Using exercise 6.17a we obtain the

function tet2/2 with k = i2pi.

Use table 3 for (t)eat and then apply the differentiation rule in the fre-6.19quency domain, then the result follows: (a+i)2. (Differentiate (a+i)1

just as one would differentiate a real function.)

The function ea| t | is not differentiable at t = 0. The function t3(1+ t2)16.20e.g. is not bounded.

Use the fact that limx xaex = 0 for all a R and change to the variable6.21x = at2 in tk/eat

2(separate the cases t 0 and t < 0). Then part a follows

and, hence, part b also follows since we have a finite sum of these terms.

Apply the product rule repeatedly to get an expression in terms of the de-6.22rivatives of f and g (this involves the binomial coefficients and is sometimescalled Leibniz rule). Since f and g belong to S, tn(f(t)g(t))(m) will be asum of terms belonging to S, and so the result follows.


We have that ( )(t) = R0

(t ) d . Now treat the cases t > 0 and6.23t 0 separately, then it follows that ( )(t) = (t)t. (If t 0, thent < 0 for > 0 and so (t ) = 0; if t > 0 then (t ) = 0 for > t and the integral

R t01 d = t remains.) Since (t)t is not absolutely

integrable, the function ( )(t) is not absolutely integrable.From the causality of f it follows that (f g)(t) = R

0f()g(t ) d . For6.25

t < 0 this is 0. For t 0 it equals R t0f()g(t ) d .

a We use the definition of convolution and then split the integral at = 0:6.26

(e| v | e| v |)(t) =Z 0

ee| t | d +Z 0

ee| t | d.

First we take t 0. Then | t | = t for < 0. Furthermore we havefor > t that | t | = t and for 0 < t that | t | = t.Hence,

(e| v | e| v |)(t) =Z t0

et d +Z t

et2 d +Z 0

e2t d.

A straightforward calculation of these integrals gives (1 + t)et.Next we take t < 0. Then | t | = t for > 0. Furthermore we havefor < t that | t | = t and for t < 0 that | t | = t .Hence,

(e| v | e| v |)(t) =Z 0

et2 d +Z 0t

et d +

Z t

e2t d.

A straightforward calculation of these integrals gives (1 t)et.b Use the result from section 6.3.3 and the convolution theorem to obtainthe spectrum (2(1 + 2)1)2 = 4/(1 + 2)2.c Since (1 + | t |)e| t | = e| t | + | t | e| t | and the spectrum of e| t | is2(1 + 2)1, we only need to determine the spectrum of f(t) = | t | e| t |.But f(t) = tg(t) with g(t) the function from exercise 6.14, whose spec-trum weve already determined: G() = 2i(1 + 2)1. Apply theorem6.8 (differentiation rule in the frequency domain): the spectrum of f(t)is G()/i. Calculating this and taking the results together we obtain4/(1 + 2)2, in agreement with part b.

a From the differentiation rule in the frequency domain we obtain that6.28

the spectrum of tg(t) is iG() = i2pie2/2. Since (Ftg(t))(0) = 0,we may apply the integration rule to obtain that F1() =

2pie

2/2.b Apply the differentiation rule in the frequency domain with n = 2, then

F2() =2pi(1 2)e2/2.

c Since f3(t) = f2(t 1), it follows from the shift property that F3() =eiF2().d From part a and exercise 6.9 it follows that F4() = (

2pie(4)

2/2+2pie(+4)

2/2)/2i.e Use the scaling property from table 4 with c = 4, then F5() =G(/4)/4.

b Since p1() = 0 for | | > 12 and 1 for | | < 12 , we have6.29

(p1 p3)(t) =Z 1/21/2

p3(t ) d.


Here p3(t ) 6= 0 only if t 3/2 t + 3/2. Moreover, we havethat 1/2 1/2, and so we have to separate the cases as indicatedin the textbook: if t > 2, then (p1 p3)(t) = 0; if t < 2, then also(p1 p3)(t) = 0; if 1 t 1, then (p1 p3)(t) =

R 1/21/2 1 d = 1; if

1 < t 2, then (p1 p3)(t) =R 1/2t3/2 1 d = 2 t; finally, if 2 t < 1,

then (p1 p3)(t) =R t+3/21/2 1 d = 2 + t.

c Apply the convolution theorem to T (t) = (p1p3)(t), then the spectrumof T (t) follows: 4 sin(/2) sin(3/2)/2.

Answers to selected exer cises for chapter 7

From the spectra calculated in exerices 6.2 to 6.5 it follows immediately7.1that the limits for are indeed 0: they are all fractions with abounded numerator and a denominator that tends to . As an examplewe have from exercise 6.2 that lim 1/(a+ i) = 0.

Use table 3 with a = 2A and substitute = s t.7.2Take C > 0, then it follows by first changing from the variable Au to v and7.3then applying (7.3) that

limA

Z C0

sinAu

udu = lim

A

Z AC0

sin v

vdv =

pi

2.

Split 1/(a+i) into the real part 1/(1+2) and the imaginary part /(1+7.42). The limit of A of the integrals over [A,A] of these parts giveslimA 2 arctanA = pi for the real part and limA(ln(1 + A2) ln(1 +(A)2)) = 0 for the imaginary part.a In exercise 6.9b it was shown that F () = 2i sin(pi)/(2 1). The7.6function f(t) is absolutely integrable since

R | f(t) | dt =

R pipi | sin t | dt 0 for > 2). The integral in theright-hand side is convergent.b Apply the fundamental theorem, then

f(t) =1

2pi

Z

2i sin(pi)

2 1 eit d

for all t R (f is continuous). Now use that F () is an odd function andthat 2 sinpi sint = cos(pi t) cos(pi


and hence, f [0] = 0, f [1] = 0, f [2] = 0, f [3] = 2, that is, f [n] = 24[n 3].a On [0, 2pi] the periodic function f is given by16.10

f(t) = 1 | t pi |pi

=

( tpi

for 0 t pi,2 t

pifor pi t 2pi.

Since f(t + pi) = 1 t/pi for 0 < t pi and f(t + pi) = f(t pi) = t/pi 1for pi t 2pi we obtain that f(t) + f(t+ pi) = 1 for all t.b Since f [n] = f(2pin/N) we obtain from part a that f [n]+f [n+N/2] = 1.c Apply the N -point DFT to f [n] + f [n + N/2] = 1, using the shiftrule in the n-domain. Since 1 NN [k] (table 11), we the obtain thatF [k] + e2piiNk/2NF [k] = NN [k], hence (1 + e

piik)F [k] = NN [k]. When kis even and not a multiple of N then 2F [k] = NN [k] = 0, so F [k] = 0.When k is a multiple of N then N [k] = 1, hence, F [k] = N/2.

The signal is real, so F [k] = F [k], which gives F [3] = F [1] = F [1] = i.16.12Applying the inverse DFT leads to f [n] = (1 + in+1 + (i)n+1)/4.Apply the definition of the cyclical convolution and use theorem 15.2, then16.13one obtains that (f f)[n] = f [n]+f [n1] = N [n]+2N [n1]+N [n2].Use the convolution theorem: first determine the functions f1 and f2 with16.14f1 cos(2pik/N) = (e2piik/N + e2piik/N )/2 and f2 sin(4pik/N) =(e4piik/N e4piik/N )/2i and then calculate f [n] = (f1 f2)[n]. SinceN [n m] e2piimk/N (table 11 and table 12, shift in the n-domain),we have f1[n] = (N [n 1] + N [n + 1])/2 and f2[n] = (N [n + 2] N [n 2])/2i. From the convolution theorem and theorem 15.2 it thenfollows that f [n] = (f1 f2)[n] = (f2[n + 1] + f2[n 1])/2, which equals(N [n+ 3] + N [n+ 1] N [n 1] N [n 3])/4i.From table 11 we have that N [n] 1 and so (table 12, shift in the n-16.16domain) N [n l] e2piilk/N . Since cos2(pik/N) = (1+cos(2pik/N))/2 =(2+epiik/N +epiik/N )/4 it follows that f [n] = (2N [n]+N [n1]+N [n+1])/4. The power equals

1

N

N1Xn=0

| f [n] |2 .

Now | f [n] |2 = (4N [n] + N [n 1] + N [n+ 1])/16 since N [n]N [n+ 1] =N [n]N [n1] = N [n1]N [n+1] = 0. Also note that N [n+1] = N [n(N 1)] and hence, | f [0] |2 = 1/4, | f [1] |2 = 1/16, | f [N 1] |2 = 1/16,while all other values are 0. This means that

P =1

N

1

4+

1

16+

1

16

=

3

8N.

a We calculate G[k] from the expression for the 5-point DFT. Note that16.18g[3] = g[2] = c2 = 1 and g[4] = g[1] = c1 = 2. Hence,G[k] = g[0] + g[1]e2piik/5 + g[2]e4piik/5 + g[3]e6piik/5 + g[4]e8piik/5

= 1 + 2e2piik/5 + e4piik/5 + e6piik/5 + 2e8piik/5

= 1 + 4 cos(2pik/5) + 2 cos(4pik/5).

b Since the Fourier coefficients of f are known, we can express f as aFourier series: f(t) = c0 + c1e

i0t + c1ei0t + c2e2i0t + c2e2i0t.Hence,

f (2pim/50)


= g[0] + g[1]e2piim/5 + g[2]e4piim/5 + g[3]e6piim/5 + g[4]e8piim/5 = G[m].c First use Parseval for Fourier series:

1

T

Z T0

| f(t) |2 dt =X

n=| cn |2 .

Since ck = 0 for | k | 3 we obtain thatP

n= | cn |2 =P2

n=2 | g[n] |2 =P4n=0 | g[n] |2. Applying Parseval for the DFT one obtains the result.

a From table 11 we have that N [n] 1 and so (table 12, shift in the16.19n-domain) f [n] e2piik/N 1 + e2piik/N = 2 cos(2pik/N) 1.b Calculate the convolution using (16.14) and theorem 15.2, then

(f g)[n] =N1Xl=0

f [l]g[n l] = g[n+ 1] g[n] + g[n 1].

c First write g[n] as complex exponentials and then determine the N -point DFT using table 11 and the shift rule in the k-domain:

G[k] =N

2(N [k 2] + N [k + 2]).

Finally apply (16.19) to calculate the power:

1

N

N1Xn=0

| g[n] |2 = 1N2

N1Xk=0

N

2| N [k 2] + N [k + 2] |

2=

1

2.

a Since f(t) is real and even, the sampling f [n] is real and even since16.20f [n] = f(nT/5) = f(nT/5) = f [n].b Apply the inverse DFT, where the values of F [3] and F [4] are calcu-lated using the fact that F has period 5 and is even. Hence, f [n] = (1 +2e2piin/5+2e8piin/5+ e4piin/5+ e6piin/5)/5, which equals (1+4 cos(2pin/5)+2 cos(4pin/5))/5.c The function f is band-limited with band-width 10pi/T . The Fouriercoefficients ck of f contribute to the frequencies k0 = 2pik/T . Hence,these are 0 for | k | 3. This means that f(t) is equal to the Fourier seriesc0 + c1e

i0t + c1ei0t + c2e2i0t + c2e2i0t. Substituting t = nT/5(and rearranging) we obtain that f [n] = c0 + c1e

2piin/5 + c2e4piin/5 +

c2e6piin/5 + c1e8piin/5. But this is precisely the expression for the in-verse DFT, which implies that c0 = F [0]/5, c1 = F [1]/5, c2 = F [2]/5,c2 = F [3]/5 = F [2]/5, c1 = F [4]/5 = F [1]/5. Hence ck = F [k]/5 for| k | 2. Since F [k] has period 5 and ck = 0 for | k | > 2 we do not haveck = F [k]/5 for | k | > 2.


We can write (17.2) for N = 5 as follows: F [k] = f [0] + f [1]wk5 + +17.1f [4]w4k5 where w5 = e

2pii/5 = w. Hence,

f [0] + f [1] + + f [4] = F [0],f [0] + f [1]w1 + + f [4]w4 = F [1],f [0] + f [1]w2 + + f [4]w8 = F [2],f [0] + f [1]w3 + + f [4]w12 = F [3],f [0] + f [1]w4 + + f [4]w16 = F [4].Using that w5 = 1 we then obtain a system that is equal to the systemarising from the matrix representation.

As in exercise 17.1 we can write the formula for the inverse DFT in matrix17.2form:

1

5

[email protected] 1 1 1 11 w w2 w3 w4

1 w2 w4 w w3

1 w3 w w4 w2

1 w4 w3 w2 w

[email protected] [0]F [1]F [2]F [3]F [4]

1CCCA [email protected] [0]f [1]f [2]f [3]f [4]

1CCCA .The matrix in the left-hand side is thus the inverse of the matrix in exercise17.1.

Take N1 = 2 and N2 = 2. Note that f [3] = f [1]. The matrix Mf now17.4looks as follows:

Mf =

f [0] f [2]f [1] f [3]

=

2 20 1

.

The 2-point DFT of the rows of this matrix gives

C =

4 01 1

.

Multiplying this by the twiddle factors w4 with w4 = epii/2 = i gives

Ct =

4 01 i

.

Now calculate the 2-point DFT of the columns of this matrix to get the4-point DFT:

MF =

5 i3 i

.

Hence, F [0] = 5, F [1] = i, F [2] = 3, F [3] = i.The matrix Mf looks as follows:17.6

Mf =

f [0] f [2] . . . f [N 2]1 1 . . . 1

.

The N/2-point DFT of the first row of this matrix is A[k], while the N/2-point DFT of the second row follows from table 11. This gives the matrixC:

71


C =

A[0] A[1] . . . A[N/2 1]N/2 0 . . . 0

.

Multiplying this by the twiddle factors will not change this matrix becauseof the zeroes in the second row of C and hence Ct = C. Now calculate the2-point DFT of the columns of Ct = C to get the matrix MF and, hence,the required N -point DFT of f [n]:

MF =

A[0] +N/2 A[1] . . . A[N/2 1]A[0]N/2 A[1] . . . A[N/2 1]

=

F [0] F [1] . . . F [N/2 1]

F [N/2] F [N/2 + 1] . . . F [N 1].

Let A1[k] be the 2N -point DFT of f [2n] and B1[k] the 2N -point DFT of17.7f [2n+ 1]. Then we have for the 4N -point DFT of f [n] (see (17.14)):

F [] = A1[] + w4NB1[],

F [2N + ] = A1[] w4NB1[],where = 0, 1, . . . , 2N1. The 2N -point DFT of f [2n] follows analogouslyfrom the N -point DFT of f [4n] and f [4n+ 1]:

A1[] = A[] + w2NC[],

A1[N + ] = A[] w2NC[],where = 0, 1, . . . , N 1. Also, the 2N -point DFT of f [2n + 1] followsfrom the N -point DFT of f [4n+ 1] and f [4n+ 3]:

B1[] = B[] + w2ND[],

B1[N + ] = B[] w2ND[],where = 0, 1, . . . , N 1. Combining these results leads to the 4N -pointDFT of f [n]:

F [] = A[] + w2NC[] + w4NB[] + w

34N D[],

F [N + ] = A[] w2NC[] + w4NB[] w34N D[],F [2N + ] = A[] + w2NC[] w4NB[] w34N D[],F [3N + ] = A[] w2NC[] w4NB[] + w34N D[],where = 0, 1, . . . , N 1.Since f(t) is causal we have FT () =

R T0f(t)eit dt. Apply the trapezium17.8

rule to the integral and substitute = (2k + 1)pi/T , then it follows that

FT ((2k + 1)pi/T ) TN

N1Xn=0

epiin/Nf [n]e2piin/N .

This shows that the spectrum at the frequencies = (2k + 1)pi/T can beapproximated by the N -point DFT of epiin/Nf [n].

Let F () be the Fourier transform of f(t). Applying the trapezium rule to17.9 R T0f(t)eit dt and to

R T0f(t)eit dt leads to (T/N)F [k] and (T/N)F [k]

respectively, where F [k] denotes the N -point DFT of f [n]. Adding thisgives

F (2pik

T) T

N(F [k] + F [k]) for | k | < N/2.


This means that we can efficiently approximate the spectrum of f at thefrequencies 2pik/T with | k | < N/2 by using an N -point DFT.First we determine the DFTs of the signals using table 11. Since f1[n] =17.11N [n] + N [n 1] F1[k] = 1+ e2piik/N and f2[n] = N [n] + N [n+1]F2[k] = 1 + e

2piik/N we obtain the DFT of the cross-correlation as follows:

12 F1[k]F2[k] = (1 + e2piik/N )2 = 1 + 2e2piik/N + e4piik/N .Applying the inverse transform gives the cross-correlation 12 = N [n] +2N [n+ 1] + N [n+ 2].

Let P (z) = f [0] + f [1]z + f [2]z2 and w3 = e2pii/3 = (1 + i3)/2 = 1/w.17.13

Then F [k] = P (wk3 ) = P (wk) = f [0]+f [1]wk+f [2]w2k = f [0]+wk(f [1]+

wkf [2]).

Let Mf be the 3N -matrix given by17.14

Mf =

[email protected] f [0] f [3] . . . f [3N 3]f [1] f [4] . . . f [3N 2]f [2] f [5] . . . f [3N 1]

1A .The N -point DFT of the rows of this matrix are given by A[k], B[k] andC[k] respectively. The matrix C is then given by

C =

[email protected][0] A[1] . . . A[N 1]B[0] B[1] . . . B[N 1]C[0] C[1] . . . C[N 1]

1A .Multiplying this by the twiddle factors w3N gives the matrix Ct. The3-point DFT of the columns of Ct will give us the matrix MF :

MF =

[email protected] F [0] F [1] . . . F [N 1]F [N ] F [N + 1] . . . F [2N 1]F [2N ] F [2N + 1] . . . F [3N 1]

1A .Since the 3-point DFT of g[n] is given by G[k] = g[0] + g[1]wk3 + g[2]w

2k3

we conclude that

F [N + ] = A[] + w(+N)3N B[] + w

2(+N)3N C[].

Take N1 = 3 and N2 = 3m1 and consider the N1 N2-matrix17.15

Mf =

[email protected] f [0] f [3] . . . f [N 3]f [1] f [4] . . . f [N 2]f [2] f [5] . . . f [N 1]

1A .Let the N2-point DFT of the rows f [3n], f [3n+ 1] and f [3n+ 2] be givenby A[k], B[k] and C[k], then the matrix C is given by

C =

[email protected][0] A[1] . . . A[N2 1]B[0] B[1] . . . B[N2 1]C[0] C[1] . . . C[N2 1]

1A .Multiplying this by the twiddle factors and then applying the 3-point DFTof the columns gives the matrix MF containing the N -point DFT of f [n]:

MF =

[email protected] F [0] F [1] . . . F [N2 1]F [N2] F [N2 + 1] . . . F [2N2 1]F [2N2] F [2N2 + 1] . . . F [N 1]

1A .


To calculate the 3-point DFT we need 6 additions and 4 multiplications,hence 10 elementary operations. The N2-point DFT of f [3n], of f [3n +1], and of f [3n + 2] can be determined by repeatedly splitting this into(multiples of 3), (multiples of 3)+1, and (multiples of 3)+2. We thus onlyhave to determine 3-point DFTs.

Let h[n] =PN

l=0 f [l]g[n l]. Since g[n] is causal and g[n] = 0 for n > N17.16we have that h[n] = 0 for n < 0 or n > 2N . Hence, it suffices to calculateh[n] for n = 0, 1, . . . , 2N . Now let fp[n] and gp[n] be two periodic signalswith period 2N + 1 and such that fp[n] = f [n] and gp[n] = g[n] for n =0, 1, . . . , 2N . Then

h[n] =

2NXl=0

fp[l]gp[n l] = (fp gp)[n] for n = 0, 1, . . . , 2N .

Now let fp[n] Fp[k] and gp[n] Gp[k]. Then we have

h[n] =1

2N + 1

2NXk=0

Fp[k]Gp[k]e2piink/(2N+1)

for n = 0, 1, . . . , 2N . Although these are really 2N + 1-point DFTs, wecontinue for convenience our argument with 2N . If we assume that cal-culating a 2N -point DFT using the FFT requires 2N(2 logN) elementaryoperations, then the number of elementary operations to calculate h[n] will(approximately) equal 2N(2 logN)+2N(2 logN)+(2N+1)+2N(2 logN) =6N(2 logN) + 2N + 1. A direct calculation would require in the order N2

operations, which for large N is much less efficient.

Let P (z) = f [0] + f [1]z + f [2]z2 + f [3]z3 and w = e2pii/4 = i. Then17.17F [k] = P (wk), hence, F [0] = P (1), F [1] = P (i), F [2] = P (1), F [3] =P (i). Now f [n] is even and so f [3] = f [1] = f [1]. Then F [k] is alsoeven, hence F [3] = F [1]. This gives F [0] = f [0] + f [1] + f [2] + f [3] =f [0] + 2f [1] + f [2], F [1] = f [0] if [1] f [2] + if [3] = f [0] f [2], andF [2] = f [0] f [1] + f [2] f [3] = f [0] 2f [1] + f [2].


When f [n] = 0 for |n | > N for some N > 0, then18.1

F (z) =

Xn=

f [n]zn =1X

n=Nf [n]zn +

NXn=0

f [n]zn.

The anti-causal part converges for all z, while the causal part convergesfor all z 6= 0. The region of convergence is thus given by 0 < | z | < . Iff [n] = 0 for n 1 then the z-transform converges for all z C.The anti-causal part converges for all z. The causal part can be written as18.2

Xn=0

1

2z

n+

1

3z

n.

Hence, the z-transform converges for | 2z | > 1 and | 3z | > 1, that is, for| z | > 1/2.a A direct calculation of F (z) gives18.3

Xn=0

z2

n+z3

n.

This series converges for | z/2 | < 1 and | z/3 | < 1, hence, for | z | < 2.b The z-transform is given by

F (z) =

Xn=0

cos(pin/2)zn.

Since | cos(pin/2) | 1 for all n and sincePn=0 | z |n converges for | z | > 1,the z-transform also converges for | z | > 1. Moreover, Pn=0 cos(pin/2)diverges since limn cos(pin/2) 6= 0. We conclude that the z-transformconverges for | z | > 1.c From parts a and b it follows immediately that the region of convergenceis the ring 1 < | z | < 2.a We rewrite f [n] in order to apply a shift in the n-domain: f [n] =18.42(n 2)[n 2] + 4[n 2]. Since [n] z/(z 1) for | z | > 1, we obtainfrom the shift rule that 4[n 2] 4z1/(z 1). It also follows from[n] z/(z 1) and the differentiation rule that n[n] z/(z 1)2 andapplying the shift rule we then obtain that (n 2)[n 2] z1/(z 1)2.Combining these results gives the z-transform F (z) of f [n]:

F (z) =2z1

(z 1)2 +4z1

z 1 =4z 2

z(z 1)2 for | z | > 1.

b We rewrite f [n] in order to apply a shift in the n-domain: f [n] =2(n+ 2)[(n+ 2)] 4[(n+ 2)]. From [n] z/(z 1) we obtain fromtime reversal that [n] (1/z)/((1/z)1) = 1/(1z) for | z | > 1. Fromthe shift rule it follows that 4[(n+2)] 4z2/(1z). It also follows from[n] 1/(1 z) and the differentiation rule that n[n] z/(1 z)2and applying the shift rule we then obtain that 2(n + 2)[(n + 2)] 2z3/(1 z)2. Combining these results gives

75


F (z) =2z3

(1 z)2 4z2

1 z =2z3 4z2(1 z)2 .

c We can, for example, calculate this z-transform in a direct way:

f [n] = (1)n[n]Xn=0

(1)nzn = 11 + z

for | z | < 1.

d Again we can, for example, calculate this z-transform in a direct way:

f [n] = [4 n]X

n=4zn =

z4

1 z for | z | < 1.

One can also use [n] 1/(1 z) and apply a shift rule.e From example 18.6 it follows that (n2 n)[n] 2z/(z 1)3 andn[n] z/(z 1)2. Adding these results gives

n2[n] z(z + 1)(z 1)3 for | z | > 1.

From example 18.2 we obtain that 4n[n] z/(z 4) for | z | > 4. Thedifferentiation rule implies that n4n[n] 4z/(z4)2 for | z | > 4. Togetherthese results give

F (z) =z(z + 1)

(z 1)3 +4z

(z 4)2 for | z | > 4.

a A direct calculation of the z-transform gives (for | z | > 1):18.5

F (z) =

Xn=0

cos(npi/2)zn = 1 z2 + z4 = 11 + z2

=z2

1 + z2.

b A direct calculation of the z-transform gives (for | z | > 1):

F (z) =

Xn=0

sin(npi/2)zn = z1 z3 + = z1

1 + z2=

z

1 + z2.

c A direct calculation of the z-transform of ein[n] gives (for | z | > 1):Xn=0

einzn = 1 + eiz1 + e2iz2 + = 11 ei/z =

z

z ei .

A direct calculation of the z-transform of 2nein[n] gives (for | z | < 2):0X

n=2neinzn =

Xn=0

2neinzn =1

1 eiz/2 .

Hence we get for 1 < | z | < 2:

F (z) =z

z ei +1

1 eiz/2 .

One could apply a partial fraction expansion here (see e.g. exercise 18.10).18.8However, in this case it is easy to obtain the z-transform in a direct wayby developing F (z) in a series expansion. Since the z-transform has toconverge for | z | > 2 (there are poles at z = 2i and f [n] has a finiteswitch-on time) we develop F (z) as follows:

F (z) =1

z2 + 4=

1

z2

1 4

z2+

16

z4+

.


From the series we now obtain that f [n] = 0 for n 0 and that f [2n] =(1)n122n2, f [2n+ 1] = 0, for n > 0.As in the previous exercise we obtain the z-transform in a direct way by18.9developing F (z) in a series expansion. Since the unit circle | z | = 1 has tobelong to the region of convergence (f [n] has to be absolute convergent)we develop F (z) for | z | < 2 as follows:

F (z) =1

4(1 + z2/4)=

1

4

1 z

2

4+

z4

16+

.

From the series we obtain that f [n] = 0 for n 1 and f [2n] = (1)n22n2,f [2n 1] = 0, for n 0.The poles are at z = 1/2 and z = 3; the signal f [n] must have a finite18.10switch-on time, hence, the z-transform has to converge for | z | > 3. Apartial fraction expansion of F (z)/z gives

F (z)

z= 1 +

1/10

z + 1/2 18/5

z + 3.

Applying (18.14) to the expansion of F (z) gives

(1/2)n[n] zz + 1/2

for | z | > 1/2, (3)n[n] zz + 3

for | z | > 3.

Combining this gives f [n] = [n+ 1] + ((1/2)n[n] 36(3)n[n])/10.In exercise 18.10 we obtained the partial fraction expansion. Now the18.11signal f [n] has to be absolutely convergent, which means that | z | = 1 hasto belong to the region of convergence. Applying (18.14) to z/(z + 1/2)gives

(1/2)n[n] zz + 1/2

for | z | > 1/2,

while applying (18.15) to z/(z + 3) gives

(3)n[n 1] zz + 3

for | z | < 3.

Combining this gives f [n] = [n+1]+((1/2)n[n]+36(3)n[n1])/10.A direct application of the definition of the convolution product gives18.12

(f g)[n] =M1Xl=N1

f [l]g[n l]

= f [N1]g[nN1] + f [N1 + 1]g[nN1 1] + + f [M1 1]g[nM1 + 1] + f [M1]g[nM1].

Since g[n N1] = 0 for n < N1 + N2 and, hence, also g[n N1 1] = 0,g[nN1 2] = 0, etc., we have that (f g)[n] = 0 for n < N1 +N2. Thismeans that the switch-on time is N1 +N2.Since g[nM1] = 0 for n > M1 +M2 and, hence, also g[nM1 + 1] = 0,g[nM1+2] = 0, etc., we have that (f g)[n] = 0 for n > M1+M2. Thismeans that the switch-off time is M1 +M2.

a The signal is causal and F (z) has poles at z = i. The z-transform18.13converges for | z | > 1. Write F (z) as the sum of a geometric series (or usea partial fraction expansion):


F (z) =z

z2 + 1=

1

z

1 1

z2+

1

z4+

.

From the series we obtain that f [n] = 0 for n < 0 and f [2n+ 1] = (1)n,f [2n] = 0, for n 0.b The convolution theorem gives (f f)[n] F 2(z). Hence,

h[n] = (f f)[n] =X

l=f [l]f [n l] =

nXl=0

f [l]f [n l]![n].

From this we obtain that h[n] = 0 for n < 0 while for m 0 we have:h[2m+ 1] = f [0]f [2m+ 1] + f [1]f [2m] + + f [2m+ 1]f [0] = 0,h[2m] = f [0]f [2m] + f [1]f [2m 1] + + f [2m]f [0]= 0 + (1)0(1)m1 + 0 + + (1)m1(1)0 + 0 = m(1)m1.

Apply the definition of the convolution product and use that [n l] = 018.14for l > n and [n l] = 1 for l n.Use that f [n] F (z) and [n] 1 and apply the convolution theorem18.15(assuming that the intersection of the regions of convergence is non-empty)then (f )[n] F (z) 1 = F (z). Hence, f [n] = (f )[n].Define a discrete-time signal h[n] by h[n] =

Pnl= 2

lnf [l], which is the18.17convolution product of f [n] with 2n[n]. Now 2n[n] z/(z 1/2) for| z | > 1/2 and f [n] F (z). Hence, h[n] zF (z)/(z 1/2). Assumingthat | z | = 1 belongs to the region of convergence of the z-transform of f [n]we get

eiF (ei)

ei 1/2 =X

n=h[n]ein.

Using (18.22) to determine h[n] gives

h[n] =1

pi

Z pipi

eiF (ei)ein

2ei 1 d.

Applying (18.31) gives18.19

Xn=

| f [n] |2 = 12pi

Z pipi

F (ei)

2d =

1

2pi

Z pipi

cos2 d =1

2.

(One can also determine f [n] first and then calculateP

n= | f [n] |2 dir-ectly.)

We have that [n] = (g f)[n] with g[l] = f [l]. The convolution theorem18.20and property (18.25) (or table 15, entry 2) imply that the spectrum of [n]is given by G(ei)F (ei). But G(ei) = F (ei) (combine table 15, entries

2 and 5) and hence [n] F (ei)F (ei) = F (ei) 2.a The signal f [n] is causal. The region of convergence is the exterior of a18.22circle. Applying the shift rule to 2n[n] z/(z1/2) for | z | > 1/2 gives2(n+2)[n+2] z3/(z1/2) for | z | > 1/2. Hence, F (z) = 4z3/(z1/2)for | z | > 1/2.b One could write g[n] = (f )[n], apply the convolution theorem andthen a partial fraction expansion. However, it is easier to do a directcalculation: g[n] =

Pnl= 2

l[l+2] and hence, g[n] = 0 for n < 2 whileg[n] =

Pnl=2 2

l = 8(1 2n3) for n 2 (it is a geometric series). We


thus obtain that g[n] = (8 2n)[n+ 2].c The z-transform of f [n] converges for | z | > 1/2, which contains theunit circle | z | = 1. Hence, the Fourier transform of f [n] equals F (ei) =4e3i/(ei 1/2).a The signal f [n] is absolutely summable. The z-transform has one pole18.23at z = 2 and therefore the region of convergence is | z | < 2, since itmust contain | z | = 1. Since F (z)/z = 1 2/(z + 2) we have F (z) =z 2z/(z + 2) for | z | < 2. The inverse transform of this gives f [n] =[n+ 1] (2)n+1[n 1].b The Fourier transform of f [n] equals F (ei) = e2i/(ei + 2).c We have f [n] F (z) for | z | < 2. The scaling property (table 14, entry5) gives 2nf [n] F (z/2) for | z | < 4. The spectrum of 2nf [n] is thus equalto F (ei/2) = e2i/(2ei + 8).

a The signal f [n] is causal. The poles of F (z) are at i/2 and at 0. The18.24region of convergence is the exterior of the circle | z | = 1/2. This containsthe unit circle and so the signal is absolutely summable. The spectrum off [n] equals F (ei) = 1/(ei(4e2i + 1)).b First apply a partial fraction expansion to F (z)/z (the denominatorequals z2(2z + i)(2z i)):F (z)

z=

1

z2+

i

z i/2 i

z + i/2.

Applying (18.14) to F (z) gives f [n] = [n 1] + (i(i/2)n i(i/2)n)[n].c If F (ei) = F (ei), then the signal is real. Since

F (ei) = 1/(ei(4e2i + 1)) = F (ei),

the signal is indeed real. (One can also write (i(i/2)n i(i/2)n) =in+12n(1 (1)n), which equals 0 for n = 2k and 21n(1)k+1 forn = 2k + 1, showing clearly that it is real.)

For n < 0 we have thatPn

l=0 g[l]g[n l] = 0 since g[n] is causal. Thus f [n]18.25is also causal. Since g[l] = 0 for l < 0 and g[n l] = 0 for l > n we canwrite

f [n] =

Xl=

g[l]g[n l] = (g g)[n].

This implies that F (z) = G(z)2, so we need to determine G(z). To do so,we write G(ei) = 1/(4 + cos 2) as a function of ei:

1

4 + cos 2=

1

4 + 12(e2i + e2i)

=2e2i

e4i + 8e2i + 1.

Taking z = ei we find that G(z) = 2z2/(z4+8z2+1), which gives F (z) =G(z)2.


a Substituting [n] for u[n] we find that19.2

h[n] =

n1Xl=

2ln[l] = 2n[n 1].

Use the definition of [n] and [n] to verify (19.3):

y[n] =

n1Xl=

2lnu[l] =X

l=2ln[n 1 l]u[l] =

Xl=

h[n l]u[l].

b As in part a we obtain that h[n] = ([n+ 1] + [n 1])/2 andX

l=h[n l]u[l] = 1

2

Xl=

u[l] ([n l + 1] + [n l 1])

=1

2(u[n+ 1] + u[n 1]) = y[n].

c We now have h[n] =P

l=n 2ln[l] = 2n[n] and

Xl=

h[n l]u[l] =X

l=u[l]2ln[l n] =

Xl=n

u[l]2ln = y[n].

a An LTD-system is causal if and only if the impulse response is a causal19.3signal. So this system is causal. The system is stable if and only ifthe impulse response is absolutely summable. Since

Pn= |h[n] | =P

n=1 2n = 1


We now calculate the first sum; the second one then follows by replacing nby n 1.X

l=a[l]u[n l] =

Xl=

2l[l] 3l[l 1]

u[n l]

=

Xl=0

2l4ln[n l]Xl=1

3l4ln[n l]

=

4n

nXl=0

2l![n] 4n

nXl=1

(4/3)l[n 1]

= 4n(2n+1 1)[n] 3 4n((4/3)n+1 (4/3))[n 1]= (21n 4n)[n] 4(3n 4n)[n 1].

Hence, replacing n by n 1 and then taking terms together in the sum,y[n] = (21n4n)[n]4(3n+2n24n)[n1]4(31n41n)[n2].The transfer function follows from an[n] z/(z a) for | z | > | a |; this19.8is because we can write cosn = (ein + ein)/2, and hence

h[n] 12

z

z ei/2 +z

z ei/2

for | z | > ei/2 = 1/2. We thus obtain:H(z) =

z(z cos/2)z2 cosz + 1/4

for | z | > 1/2. The poles are at ei/2 and ei/2, which is inside the unitcircle. Therefore the system is stable.

a The impulse response h[n] can be determined by a partial fraction ex-19.9pansion of H(z)/z. Since

H(z)

z=

1

9

1

z + 1/3 1/3

(z + 1/3)2

we have

H(z) =1

9

z

z + 1/3 z/3

(z + 1/3)2

.

The system is stable, so the region of convergence must contain | z | = 1.This region is thus given by | z | > 1/3. Using table 13 we find thath[n] = ((1/3)n + n(1/3)n)[n]/9.b Write u[n] = (einpi/2 einpi/2)/2i and use (19.10): einpi/2 has re-sponse H(eipi/2)einpi/2, so the response to u[n] is (H(eipi/2)einpi/2 H(eipi/2)einpi/2)/2i, which is of the form (w w)/2i = Im (w). Hencethe response is

Im

19 + 6i+ 1e

inpi/2

= Im

8 + 6i

100(cos(npi/2) + i sin(npi/2))

,

which is (3 cos(npi/2) + 4 sin(npi/2))/50.

a The frequency response can be written as H(ei) = 1 + e2i + e2i.19.11SinceH(ei) =

Pn= h[n]e

in it follows that h[0] = 1, h[2] = h[2] = 1and h[n] = 0 for all other n. Hence, the impulse response is h[n] = [n] +[n 2]+ [n+2]. The input is u[n] = [n 2]. Since [n] 7 h[n], we have


u[n] 7 h[n 2], so the response to u[n] is y[n] = [n 2] + [n 4] + [n].b The impulse response is not causal, so the system is not causal.

From (19.15) and the fact that H(ei) is even we obtain that19.13

h[n] =1

2pi

Z pipi

H(ei) cos(n) d =1

pi

Z ba

cos(n) d

=1

npi(sinnb sinna),

which can be written as 2(sin 12n(ba) cos 12n(b+a))/(npi) for n 6= 0

and

h[0] =1

pi

Z ba

d =b a

pi.

The spectrum Y (ei) of y[n] is a periodic function with period 2pi. Apply19.14Parseval for periodic functions and substitute Y (ei) = H(ei)U(ei) toget the desired result.

a Apply the z-transform to the difference equation, using the shift rule19.15in the n-domain. We then obtain that (1 + 1

2z1)Y (z) = U(z). Since

H(z) = Y (z)/U(z) it follows that

H(z) =1

1 + z1/2=

z

z + 1/2.

From table 13 we get the impulse response h[n] = (1/2)n[n].b We could use z-transforms here: from Y (z) = H(z)U(z) we get Y (z) =z2/((z + 1/2)(z 1/2)) (use table 13) and applying a partial fraction ex-pansion to Y (z)/z then leads to y[n] (again use table 13). However, in thiscase it is easier to follow the direct way:

(h u)[n] =X

l=(1/2)l[l](1/2)nl[n l].

Now if n < 0 then this is 0, while if n 0 then it equals (1/2)nPnl=0(1)l =(1/2)n+1(1 + (1)n).c The transfer function H(z) has one pole at z = 1/2, which is insidethe unit circle, so the system is stable.

a From the difference equation we obtain that (1 z2/4)Y (z) = (1 +19.17z1)U(z) and hence

H(z) =1 + z1

1 z2/4 =z(z + 1)

z2 1/4 .

A partial fraction expansion of H(z)/z gives

H(z)

z=

1/2z + 1/2

+3/2

z 1/2 .

Hence, H(z) = (z/2)/(z + 1/2) + (3z/2)/(z 1/2). Using table 13 wefind that h[n] = ((1/2)n+1 + 3(1/2)n+1)[n].b The (rational) transfer function H(z) has poles at z = 1/2, which lieinside the unit circle, so the system is stable.c The z-transform of [n] is z/(z 1) and therefore the z-transform A(z)of the step response a[n] is given by A(z) = H(z)U(z) = z2(z + 1)/((z 1)(z + 1/2)(z 1/2)). A partial fraction expansion of A(z)/z gives


A(z)

z=

8/3

z 1 3/2

z 1/2 1/6

z + 1/2.

Multiply this by z and use table 13 to obtain a[n] = ((8/3) 3(1/2)n+1 (1/3)(1/2)n+1)[n].d Since u[n] = [n]+[n2] and [n] 7 a[n] we get u[n] 7 a[n]+a[n2].a To find the impulse response we first apply a partial fraction expansion19.18to H(z)/z, which gives

H(z)

z=

1

z 1z + 1/2

1/4(z + 1/2)2

.

Multiply this by z and use table 13 to obtain that h[n] = [n] ((1/2)n(1/2)n(1/2)n)[n].b According to (19.9) we have that zn 7 H(z)zn. Substituting z = 1we get the response to the input (1)n. Since H(1) = 0, the response isthe null-signal.c The (rational) transfer function H(z) has one pole at z = 1/2, whichis inside the unit circle, so the system is stable.d The impulse response is real, so the system is real.e Let u[n] 7 y[n], then Y (ei) = H(ei)U(ei). Furthermore we havethat U(ei) =

Pn= u[n]e

in. Comparing this with U(ei) = cos 2 =(e2i + e2i)/2 we may conclude that u[2] = u[2] = 1/2 and u[n] = 0for n 6= 2, hence, u[n] = ([n 2] + [n+ 2])/2. By superposition it thenfollows from [n] 7 h[n] that y[n] = (h[n 2] + h[n+ 2])/2.a The response to u[n+N ] is y[n+N ] (time-invariance), but also u[n] =19.19u[n+N ] for n Z, so y[n] = y[n+N ] for n Z.b In (16.7) (with f replaced by u) the input u[n] is written as superpos-ition of the signals e2piink/N . Since zn 7 H(z)zn, we have e2piink/N 7H(e2piik/N )e2piink/N . Hence we obtain from (16.7) that

y[n] =1

N

N1Xk=0

U [k]H(e2piik/N )e2piink/N .

On the other hand we have from the inverse DFT for y[n] that

y[n] =1

N

N1Xk=0

Y [k]e2piink/N ,

where Y [k] is the N -point DFT of y[n]. Hence, Y [k] = U [k]H(e2piik/N ).

a Since H(ei) = cos 2 = (e2i+e2i)/2 we see from definition (19.11)19.20of the frequency response that h[2] = h[2] = 1/2 and h[n] = 0 for n 6= 2.Hence, h[n] = ([n+ 2] + [n 2])/2.b Using the inverse DFT we can recover u[n] from the DFT F [k] of u[n]:

u[n] =1

4

3Xk=0

F [k]e2piink/4,

where F [k] is the 4-point DFT of u[n]. We have F [0] = 1, F [1] = 1,F [2] = 0, F [3] = 1 and since e2piink/4 7 H(e2piik/4)e2piink/4, it follow bysuperposition that y[n] = (1 + 2i sin(npi/2))/4.

a Since [n] = [n] [n 1] we have for the responses that h[n] =19.21a[n] a[n 1] and hence h[n] = n2(1/2)n[n] (n 1)2(1/2)n1[n


1]. For the z-transforms we have (use the shift rule in the n-domain)H(z) = A(z) A(z)/z = A(z)(z 1)/z. From table 13 it follows that(1/2)n[n] z/(z1/2), n(1/2)n[n] (z/2)/(z1/2)2, `n

2

(1/2)n[n]

(z/4)/(z 1/2)3 and since n2 = 2`n2

+ n it then follows that

A(z) =z/2

(z 1/2)3 +z/2

(z 1/2)2 =1

2

z(z + 1/2)

(z 1/2)3 .

This means that the transfer function H(z) equals

H(z) =1

2

(z 1)(z + 1/2)(z 1/2)3 .

b The impulse response is causal, so the system is causal.c From ein 7 H(ei)ein it follows that

y[n] =1

2

(ei 1)(ei + 1/2)(ei 1/2)3 e

in.

a From the difference equation we obtain that (1 z1/2)Y (z) = (z1 +19.22z2)U(z) and hence

H(z) =z1 + z2

1 z1/2 =z + 1

z2 z/2 .

Since [n] z/(z 1) = U(z) we have

a[n] A(z) = H(z)U(z) = z + 1(z 1/2)(z 1) .

A partial fraction expansion of A(z)/z gives

A(z)

z=

2

z+

4

z 1 6

z 1/2 .

Multiply this by z and use table 13 to obtain that a[n] = 2[n] + (4 6(1/2)n)[n].b The (rational) transfer function H(z) has poles at z = 0 and z = 1/2,which lie inside the unit circle, so the system is stable.

a From the difference equation we obtain that (6 5z1 + z2)Y (z) =19.23(6 6z2)U(z) and hence

H(z) =6 6z2

6 5z1 + z2 =6(z2 1)

6z2 5z + 1 .

A partial fraction expansion of H(z)/z (note that the denominator equalsz(2z 1)(3z 1)) givesH(z)

z= 6

z+

16

z 1/3 9

z 1/2 .

Multiply this by z and use table 13 to obtain that h[n] = 6[n] +(16(1/3)n 9(1/2)n)[n].b The frequency response H(ei) is obtained from the transfer functionH(z) by substituting z = ei, so H(ei) = 6(e2i 1)/(6e2i 5ei + 1).c From (19.10) we know that Y (ei) = H(ei)U(ei). Since y[n] isidentically 0, we know that Y (ei) = 0 for all frequencies . SinceH(ei) 6=0 for e2i 6= 1 we have U(ei) = 0 for e2i 6= 1. The frequencies = 0or = pi may still occur in the input. These frequencies correspond to


the time-harmonic signals ei0n = 1 and eipin = (1)n respectively. Hence,the input consists of a linear combination of 1 and (1)n, which meansthat the solution equals u[n] = A + B(1)n, where A and B are complexconstants.

a The frequency response can be written asH(ei) = 1+2 cos+cos 2 =19.241 + ei + ei + e2i/2 + e2i/2. Since H(ei) =

Pn= h[n]e

in itfollows that h[n] = [n] + [n+ 1] + [n 1] + [n+ 2]/2 + [n 2]/2.b If we write U(ei) = 1+sin+sin 2 as complex exponentials, as in parta, then it follows that u[0] = 1, u[1] = u[2] = i/2, u[1] = u[2] = i/2and so u[n] = [n]+ i([n1]+ [n2] [n+1] [n+2])/2. We have tocalculate E =

Pn= | y[n] |2. In this case it is easiest to do this directly

(and so not using Parseval). From the expression for u[n] it follows (bylinearity and time-invariance) that y[n] = h[n] + i(h[n 1] + h[n 2] h[n+1]h[n+2])/2. Substituting h[n] from part a we get y[n] as a linearcombination of [n 4], [n 3], . . . , [n+ 3], [n+ 4]. The coefficients inthis combination are not hard to determine. In fact, they are i/4, 3i/4,(1/2) i, 13i/4, 1, 1+3i/4, (1/2)+ i, 3i/4, i/4. Their contribution to Eis 1/16, 9/16, 20/16, 25/16, 1, 25/16, 20/16, 9/16, 1/16. The sum of thesecontributions is 126/16, hence, E = 7 7

8.

935_Ch1Answ.pdf936_Ch2Answ.pdf955_fig.02.06.pdf937_Ch3Answ.pdf956_fig.03.20.pdf957_fig.03.21.pdf958_fig.03.23.pdf938_Ch4Answ.pdf939_Ch5Answ.pdf940_Ch6Answ.pdf941_Ch7Answ.pdf942_Ch8Answ.pdf943_Ch9Answ.pdf944_Ch10Answ.pdf945_Ch11Answ.pdf946_Ch12Answ.pdf947_Ch13Answ.pdf948_Ch14Answ.pdf949_Ch15Answ.pdf950_Ch16Answ.pdf951_Ch17Answ.pdf952_Ch18Answ.pdf953_Ch19Answ.pdf

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