Chapter 15: Fourier Series

Size: px
Start display at page:

Download "Chapter 15: Fourier Series"

Transcription

1 Chapter 5: Fourier Series Ex Ex Ex. 5.- f(t) K is a Fourier Series. he coefficiets are a K; a b for. f(t) AcosZ t is a Fourier Series. a A ad all other coefficiets are zero. Set origi at t, so have a odd fuctio; the a for,,... 8 ote: Also, f(t) is half wave symmetric, the b for eve b f(t) si f t dt si f t dt + si f t dt so f(t) si t; odd ad rad s where f si f t dt rad s (cos f), 3, 5,... f Ex. 5.-, a, a for all odd fuctio quarter wave symmetric b eve t < t < 8 6 b f(t) si t dt where f(t) 6 t < 6 hus b si 3 so f(t) si si (t) odd 3 Ex a) f(t) is either eve or odd. f(t) will cotai both sie ad cosie terms b) wave symmetry o eve harmoics c) average value of f(t) a 65

2 Ex Odd fuctio, C jt jt j t C f(t)e dt e dt e dt j j j j e + + e e (e ) j j odd j eve so f(t) e e e... e j jt j3t j5t jt j3t j5t e e Ex Ex Eve fuctio: a, a for eve, rad s t a cos t dt C j e t dt! odd so f (t) cos t+ cos 3t+ cos 5t C % & K ' K j j e t j / j / e e j ( ) ( ) odd eve, " $ # 66

3 Ex MathCad Spreadsheet Settig up the idex: :,..3 Settig the costraits: A : K : : he various values of delta: he coefficiets: Similarily, ow plottig, usig Cc Cc d : ad d : 3 8 Ad si (x ) d : where x: 8 also φ : arg (Cc ) x Ad si (x ) d : with x: φ : arg (Cc) 8 x : Ex v t si s() 3. si t V si si t 3 3 V 3 si si t V 5 si si t 5 5 RC s, rad s 67

4 use also V V s + (Rc ) V at.6 & φ ta (6) 86 V s +( 57 ) V3 9.3 & φ ta (.) 88.8 V s + (.) (or 3.7% of fudametal) So use oly ad 3 terms v 3. (.6) si (t 86 ) 3.(.3) si (t 89 ). si (t86 ).75 si (t89 ) Ex Ex 5.- at f() t e u() t ( ) a+ j t + jt at jt e F( ) f ( t) e dt e e dt a+ j a+ j ( ) ( ) jt { f at } f at e dt ( ) τ Let τ at t a jτ a τ j( a) τ { f ( at) } f ( τ) e d f ( τ) e dτ F a a a a Ex 5.- Ex jt f () t ( δ ( ) A) e dt ( δ ( ) A) dt A jt e dt e { δ( ) } δ( ) ake the Fourier rasform of both sides to get: jt ( e ) δ ( ) ( ) ( ) ( ) jt jt e + e A jt jt A { Acost} A ( e ) + ( e ) δ + δ + Aδ + Aδ + jt ( ) ( ) ( ) 68

5 Ex 5.- a.) V ( ) V ( ) i i + j b.) W W i out ta d 3 J ta d 6.3 J Wout 6.3 η % %.5% W 3 i Ex 5.3- () at () at ( ) f t te + f t te f t te at F () s ad F () s s+ a s+ a + ( ) ( ) he F F s + F s + + ( ) ( ) () s j s j ( s+ a) ( s+ a) s j s j ja ( a+ j) ( a j) ( a + ) 69

6 Problems Sectio 5.3: he Fourier Series P5.3- f(t) t for t a t dt 3 a t cost dt 6 b t si t dt ft + cos 6 t si 3 t P5.3- Eve fuctio:., 5 rad s % A cos t t. f(t) &K. t <.3 'K A cos t.3 t. Choose period. t. 3 for itegral a. Acos t A. a. Acost cos t dt. A so a 5A cos t dt a 5A cos t cos t dt 5A [ cos 5 (+)t+cos 5 ()t ] dt A cos ( /) ad b 7

7 P a cos t dt + cos t dt si t si t (si ) + (si ) si ( + ) si odd eve + b si t dt + si t dt cos t cos t (cos ( ) ) cos 3 is odd,6,,!,8,,! P5.3- f(t) A(t ) t P A A( ) ( ) + ( ( )) Sice A si t A cos t A cos t f (t)f t cos t a A e d ( ) + A A( ) + cos (t ) ( ) + A A( ) + ( ) A A cos( t) cos t ( ) ( ) his is the Fourier Series of a eve fuctio (Why?) deed, f (t) is eve. a A( t )cos t dt > b A( t ) si t dt A f(t ) A + A si t e 7

8 P5.3-6 MathCad Spreadsheet dex of summatio, : : 5 :,.. Defie parameters: : : Defie icremet of time. Set up idex to ru over to of the sigal. : dt: i :,.. t i : dt i Eter the formulas for the Fourier Series: a : t dt a : t cos( t) dt b : t si ( t) dt a a b : ( si( ) + cos( ) ) Eter the Fourier Series : f(i) : a + b si( t ) i Plot the periodic sigal : Sectio 5-: Symmetry of the Fuctio f(t) P5.- Choose t, average a b f t si t dt a sice have odd fuctio 6 b t si t t si t d! " b +! $# b b 3 3 t cos t f() t t < t < 7

9 P5.- Odd fuctio, half-wave symmetry, from able 5- a a b for eve for eve 9 a cos t dt odd a for all b 9si t dt cos " t 6 35,,,...! $ # ft 6 si t ; odd P5.-3 8s, eve fuctio b ( ) a average 8 a () 8 3 f t cos t dt cos t dt cos t dt 3 si si a.7, a.955, a.66 P5.-, a a b A A cost dt A cost cos t dt ( ) ( ) ( ) ( ) A si t si + t + + A si( ) si( + ) + + A A A + si ( ) cos( ) ( ) ( ) si ( ) ( ) due to symmetry ( ) ( ) ( ) 73

10 P5.-5 a because the average value is zero ext P5.-6 a because the fuctio is odd b for eve due to wave symmetry b t si t dt 6 Refer to able ake A si cos % & K ' K 8 8 for,5,9,... for 3,7,,... ad a average value of f t Also so ft + si t + si t + si 3t + si 5t rad s Sectio 5.5: Expoetial Form of the Fourier Series P5.5- MathCad Spredsheet: dex of summatio, : : 5 :,.. m :,.. Defie parameters :A : : : : Defie icremet of time. Set up idex to ru over two periods of the sigal. : dt : i :,.. t i: dt i Eter the formulas for the Fourier Series: cos si C i A cos m m si m Cm i A m Eter the Fourier Series: 6 i6 m i6 m fi: C exp j t + C exp j m t Plot the periodic sigal: 7

11 P5.5- MathCad Spreadsheet: dex of summatio, : : 5 :,.. m,.. Defie parameters : A : : 8 : 785. Defie icremet of time. Set up idex to ru over two periods of the sigal. : dt: i:,.. ti : dt i Eter the formulas for the Fourier Series: C : exp j t dt+ exp j t dt+ exp j t dt C m : exp j m t dt+ exp j m t dt+ exp j m t dt Eter the Fourier Series: 6 i6 m i6 m fi: C expj t + C expj m t Plot the periodic sigal: P5.5-3 ; C average value jt C f6 t e dt ! + jt jt 5 5 j j j j j j e jt dt e dt + 5. e dt. 5 e 5 e 3 e 5 e e e j " $ # 75

12 P5.5- MathCad Spreadsheet: dex of summatio: : 5 :,.. m :,.. Defie parameters: A : 6 : Fudametal frequecy: : 57. Defie icremet of time. Set up idex to ru over two periods of the sigal. : dt: i:,.. t i: dt i Eter the formulas for the Fourier Series: Aj C : m 6 6 Eter the Fourier Series: Aj C m: m 6 i 6 i 6 m fi: 6+ C expj t+ + C exp jm t+ Plot the periodic sigal: <he Fourier coefficiets foud usig able 5.5- he 6 shifts the plot vertically, while the (t+) shifts the plot horizotally. P5.5-5 C C ( t) exp( j t) dt ( exp j j ( ) + ) ( ) cos jsi ( ) ( ) j ( ) j f iteger, the C ( ) j 76

13 P5.5-6 MathCad Spreadsheet: Creatig the sigal: x:,... y:,... f(x): exp x. y g(y) : 5 exp. dex of summatio, : : 5 :,.. m:,.. Defie parameters: A: : Fudametal frequecy: : : 3. Defie icremet of time. Set up idex to ru over two periods of the sigal. : dt: i:,.. t i : dt i Eter the formulas for the Fourier Series: C : exp C m : exp t. t. t exp ( j t) dt+ 5 exp. exp( j t) dt t exp( j m t) dt + 5 exp exp( j m t) dt. Eter the Fourier Series: Plot the periodic sigal: f(i): C exp j t + C exp j m t + 5. * Solvig yields C for odd C, eve i6 m i6 m 5 j65+ j6 * 77

14 Sectio 5-6: he Fourier Spectrum P5.6- Average value a half wave symmetry A A a t cos t dt ( cos( ) ) f(t) ad A A b t si t dt cos 67 C a b θ ta 59. A A A A 88. b a P5.6- MathCad Spreadsheet: Determiig the Fourier Coefficiets: Settig the idex : 5 :,.. m :,.. Parameters: : wo : j : Fidig the coefficiets of the expoetial Fourier Series. Split the fuctio ito four regios. 6 C : t 3 exp jwot)dt C : ( si t exp( j wo t)dt C3 : 6 3 t exp( j t)dt C : si t exp( jwot)dt + 6 C : C + C + C3 + C 3 78

15 Verify that these coefficiets are ideed correct by usig them to plot the fuctio: Defie icremet of time. Set up idex to ru over two periods of the sigal. dt : i:,.. t i : dt i Eter the Fourier Series: f(i): C cos wo t + arg C Plot the periodic sigal: 7 i 6 Here are the coefficiets: C.6.7i.388i i.77.79i i.9.77i i.5.i i P5.6-3 From P5.5-3 C

16 P5.6- MathCad Spreadsheet: dex. of summatio: : :,.. m:,.. idexes pos, m idexes eg, Defie parameters: A: : as the summatios should be ru from to + Defie icremet of time. Set up idex to ru over two periods of the sigal. : Eter the formulas for the Fourier Series:! dt: i:,.. t i : dt i 6 6 " $! 6 6 C : t exp j t dt # C m: t exp j m t dt Eter the Fourier Series: " $ # f(i): C exp j t + C exp j m t + ow to get the Fourier Spectrum: 6 6. i m i 5 m 8 : φ : arg(c ) C φ

17 P5.6-5 MathCad Spreadsheet dex. of summatio: :5 :,,.. m:,.. idexes pos, m idexes eg, Defie parameters: A : : as the summatios should be ru from to + Defie icremet of time. Set up idex to ru over two periods of the sigal. : dt: i :,.. t i: dt i Eter the formulas for the Fourier Series: C: exp C : m exp f(i): C exp j t + C exp j m t + 5 Eter the Fourier Series: t. t t exp j t dt+ exp exp j t dt 5. t exp j m t dt+ exp exp j m t dt 5. i6 m i6. m 8 ow to get the Fourier SP ectrum: : φ: arg C 6 Cotiued C φ i i i i i i i i i i i i i i

18 Sectio 5.8: Circuits ad Fourier Series P5.8- Refer to able 5.-. ake A 5, ad a 5. he v(t) s o 5+ Let k k si k t k v(t) s 5+ sit 5+ sit+ si6t+ sit odd o 67 he trasfer fuctio of the circuit is ( ) ( ) V s6 6 6 () so 5 5 i(t) + + j + j odd + + e 6 ta 6 9 si tta P5.8- V(s) V(s) s V(s) V(s) s We require Z p (s) where z p (s) Z (s) + Z (s) s p s LC R L R C s R L LC RLLC RL + jr C L ad Z (s) R + jl. After some algebra s R 8 + LC R LC or L C R + R L L µ H 8

19 P5.8-3 Rather tha fid the Fourier Series of v(t) directly, cosider the sigal v(t) show above. hese two sigals are related by v(t) v (t) 6 sice v(t) is delayed by ms ad shifted dow by 6 V. For example, at t ms v (ms) 3 V v( ms) V he Fourier series of v (t) is obtaied as follows ms radias rad/ms ms average value of v (t) a because v (t) is a odd fuctio. b 63t6si tdt 3 3 si t dt t si tdt " cos t 3 3 si t tcos t! $ # cos 6 si 6cos 67 so v (t) si t he v(t) 6+ si t6 6+ si t where t is i ms. Equivaletly v(t) si t where t is i s 83

20 so he ad ext, the trasfer fuctio of the circuit is R R (s) L s Cs Ls R R s L s LC ( ) () R j L LC + Fially, v t 5 j 9 R L j 8 + j j j + 3 si t + 9 ta + e 9 j 9 ta P5.8- Rather tha fid the Fourier Series of v(t) directly, cosider the sigal v(t) ˆ show below. ( t ) hese two sigals are related by v(t) vˆ Let's calculate the Fourier Series of v(t), ˆ takig advatage of its symmetry. 6ms O rad rad ms 6ms 3 3. a o average value of v(t) ˆ V 6 b because v(t) ˆ is a eve fuctio ( t ) a 33 cos tdt 6 3 8

21 a cos t dt t cos t dt 3 3 si 3 cos t tsi t si cos si cos So 8 ˆv( t) + cos cos t v() t vˆ ( t) + cos cos t where t is i ms. Equivaletly v(t) + 8 t cos cos 3 where t is i secods. ext we calculate the trasfer fuctio of the circuit: ( ) R + j CR j CR R +jr jr C + C + jc 6 6 j j + j ta + ta he output voltage is v6 t At t ms v t cos cos 9 ta ta cos cos 9 ta ta

22 P 5.9- Let () at at g t e u() t e u( t). otice that f () t g() t ext ( ) lim. a ( a+ j) t ( aj) t at jt at jt e e G e e dt e e dt + F j limg lim a a a + j Fially ( ) ( ) ( a j) ( a j) j ( a j) ( a j) + a + P 5.9- ( ) ( ) ( a+ j ) t at jt at jt Ae A A F Ae u t e dt Ae e dt a+ j a+ j a+ j ( ) ( ) P First otice that A A he, from lie 6 of able 5.-: { f() t } Sa Sa { } () d A Also, from lie 7 of able 5.-: { f() t } f() t j { f t } j Sa dt si A A his ca be writte as: { f() t } j si j 86

23 P 5.9- First otice that: ( ) { } ( ) jt j t δ δ e d e j t e j5t j5t δ. ext, cos 5t 5 e + 5 e. herefore { } ( ) j5t j5t herefore { } { } { } cos 5t 5 e + 5 e δ ( 5) + δ ( + 5). P5.9-5 jt e F e dt e e j j j j j jt j j ( ) ( ) (( cos si ) ( cos si ) ) P j + ( cos cos ) ( si si ) B j t ( ) ( ) ( ) j B B A j t A e A e ( ) F t e dt jt jb B B j B jb jb A Be e B + j P jt jt e e F e dt e dt e e e e j j j j jt jt j j j j ( ) ( ) ( ) si si ( ) P5.- s () sigum() i t t s H ( ) ( ) 8 j j + j s ( ) ( ) 8 ( ) H( ) s ( ) + j j j + j t () sigum() () i t t e u t 87

24 P5.- () cos 3 A s ( ) δ( 3) + δ( + 3) ( ) ( ) s ( ) + j δ ( 3) + δ ( + 3) ( ) i t t s H + j ( 3) + δ ( + 3) j3t j3t δ jt e e i() t e d 5 j + + j3 + j3 ( ) ( ) e + e cos j t j t ( t ) P 5.-3 () cos t ( ) δ ( + ) + δ ( ) v t V Y ( ) + j ( + ) + δ ( ) δ ( ) Y( ) V( ) + j ( + ) + δ ( ) jt jt δ jt e e i() t e d 5 j + + j + j ( 5) ( 5) ( ) j t j t 5 e e + 5 cos t5 A 88

25 P5.- t () ( ) + () v t eu t u t ( j ) t t t jt t jt e { ( )} ( ) eu t eu t e dt ee dt j j + j { u() t} δ ( ) V ( ) + δ( ) + j j j + j, H ( ) j j j + j V o 3 δ ( ) j j j 3+ j j j 3+ j ( ) δ( ) ( ) δ ( ) δ jt e d 3+ j 3+ j 6 3t t vo () t e u() t + eu( t) + sigum() t P5.-5 5t 5 vs () t 5e u() t V V( ) 5 + j ( ) ( ) 5t 5t ( ()) ( ) W 5e u t dt 5e dt.5 J H s jc RC R+ + j jc RC C µ F. ry R k Ω. he V o 5 + j 5 + j Wo d d 5 J + j 5 + j

26 P 5.-6 H ( ) + j 8 8 Vs ( ) { 8u( t) 8u( t ) } 8δ( ) + 8δ( ) + e j j 8 ( ) ( j) sice j Vs e δ( ) e δ( ) j 8 j Vo ( ) ( e ) e + j j j + j j + j ext use V o + δ j j ( ) δ ( ) ( ) 8 δ ( ) δ ( ) 8 δ ( ) δ ( ) t () () () ( ) to write e j + j j + j ( t) ( ( )) j j δ ( ) 8 + δ ( ) e j + j j + j v t 8u t 8e u t 8u t 8e u t o 8( e t ( ) ) () 8( t u t e ) u( t) V j j 9

27 PSpice Problems SP 5- i pulse ( ) R.tra four.595 v().probe.ed FOURER COMPOES OF RASE RESPOSE V () DC COMPOE.88355E- HARMOC FREQUECY FOURER ORMALZED PHASE ORMALZED O (HZ) COMPOE COMPOE (DEG) PHASE (DEG).59E-.E+.E+ -.78E-.E+ 3.83E-.E+ 5.3E- -.8E+ -.8E E E E- -3.3E- -.53E E- 5.3E-.56E- -.8E+ -.8E E-.6E-.8E E- -.9E E E-.676E- -.8E+ -.8E+ 7.E+.88E-.E E- -6.5E- 8.73E+.56E-.63E- -.8E+ -.8E+ 9.3E+.5E-.6E- -9.6E- -8.5E- SP 5- i pulse ( ) R.tra..four V().probe.ed FOURER COMPOES OF RASE RESPOSE V() DC COMPOE E- HARMOC FREQUECY FOURER ORMALZED PHASE ORMALZED O (HZ) COMPOE COMPOE (DEG) PHASE (DEG).E+ 3.8E-.E+ -.77E+.E+.E+.59E-.996E- -.7E+.895E+ 3 3.E+.59E- 3.37E- -.73E E+.E+ 7.95E-.9E- -.68E E+ 5 5.E+ 6.36E-.988E E+.56E+ 6 6.E+ 5.57E-.65E- -.67E+.E+ 7 7.E+.9E-.E E+.73E+ 8 8.E+ 3.9E-.3E E+.7E+ 9 9.E+ 3.6E-.88E- -.5E+.33E+ 9

28 SP 5-3 Vi pulse (.5m.5m m) R.tra u m.four k v().probe.ed FOURER COMPOES OF RASE RESPOSE V() DC COMPOE E- HARMOC FREQUECY FOURER ORMALZED PHASE ORMALZED O (HZ) COMPOE COMPOE (DEG) PHASE (DEG).E E+.E E+.E+.E+3.6E- 3.E E+.796E+ 3 3.E+3.698E E- 8.89E+.793E+.E+3.6E- 3.E-3-9.E+ -.8E+ 5 5.E+3.9E+.E- -9.8E+ -.E+ 6 6.E+3.6E- 3.E E+.78E+ 7 7.E+3 7.7E+.8E- 8.78E+.778E+ 8 8.E+3.6E- 3.E E+ -.5E+ 9 9.E E+.E- -9.3E+ -.88E+ SP 5- Vi pulse (5 5 5) R.tra. 5.four. v().probe.ed FOURER COMPOES OF RASE RESPOSE V() DC COMPOE 8.96E+ HARMOC FREQUECY FOURER ORMALZED PHASE ORMALZED O (HZ) COMPOE COMPOE (DEG) PHASE(DEG).E- 7.9E+.E+.53E+.E+.E- 6.3E+ 8.7E-.66E+ 3.58E+ 3 6.E-.6E+ 5.73E- -.6E+ -.89E+ 8.E-.935E+.69E- -.89E+ -.5E+ 5.E+ 8.E-.78E E+ -.89E+ 6.E+.8E+.593E-.7E+ -3.6E+ 7.E+.7E+.97E-.57E+ 3.68E+ 8.6E+.537E+.7E E+ -.93E+ 9.8E+ 8.95E.7E- -.35E E+ 9

29 SP 5-5 Vi pulse ( ) R.tra..four v().probe.ed FOURER COMPOES OF RASE RESPOSE V() DC COMPOE.9937E- HARMOC FREQUECY FOURER ORMALZED PHASE ORMALZED O (HZ) COMPOE COMPOE (DEG) PHASE (DEG).E+ 6.36E-.E E+.E+.E+ 3.8E-.996E-.679E+.83E+ 3 3.E+.7E- 3.36E- -.73E+.68E+.E+.585E-.9E E+.87E+ 5 5.E+.6E-.987E E E+ 6 6.E+.5E-.65E-.7E+.97E+ 7 7.E+ 8.97E-.E E+.9E+ 8 8.E+ 7.87E-.8E-.88E+.965E+ 9 9.E+ 6.96E-.87E E+.883E+ Verificatio Problems VP 5- f(t) + cos t a, a ad all other coefficiets are zero. he computer pritout is correct. VP 5- able 5. - shows that the average value of a full wave rectified siewave is A () where A is the amplitude of the siewave. this case a 55. Ufortuately the report says, "half - wave rectified." he report is ot correct. 93

30 Desig Problems DP 5- For siusoidal aalysis, shift horizotal axis to average, which is 6V. Have odd fuctio ad half -wave symmetry a, / eed third harmoic : b b 3 so v. si 6t. cos (6t 9 ) f(t) si t dt / si 6t dt / cos6t. 6 V. ( assume si iput ad output for ease ), Z / 6 trasfer fuctio H(j ) 6 j/6c V VH (. ) H θ.36 choose so H 3. requires C F so H j3 third harmoic of v.36 si (6t+6.9 ) V c j for third harmoic 6c DP 5- Refer to able 5.-. So here v (t) 36 6 s v(t) A A s cos (377t) or v (t)v + v (t) ad v (t)v + v (t) ripple. dc output max v (t). v or v (t). v but v v whe dc the L becomes a short V o R R+j L V s so s o o o o o so s o cos( t) 9

31 R V R+j L V + j377l V, but V s s s So V + j377l 6 3 but V v ad v v 36. o o s 6 the +(377) L Solvig for L yields L >.5mH 6 () 3 DP 5-3 V o V to dc rasfer Fuctio First harmoic: s Zp V Z +Z V where Z R s p L p + jrc R So V + jrc / LC V R s j L+ (j ) +(j ) + + jrc RC LC ad V Vo Vs /LC or + RC + 8 with R75 kω LC ad choosig L. mh yields C. F 95

5.1. Periodic Signals: A signal f(t) is periodic iff for some T 0 > 0,

5.1. Periodic Signals: A signal f(t) is periodic iff for some T 0 > 0, 5. Periodic Sigals: A sigal f(t) is periodic iff for some >, f () t = f ( t + ) i t he smallest value that satisfies the above coditios is called the period of f(t). Cosider a sigal examied over to 5 secods

More information

y[ n] = sin(2" # 3 # n) 50

y[ n] = sin(2 # 3 # n) 50 Period of a Discrete Siusoid y[ ] si( ) 5 T5 samples y[ ] y[ + 5] si() si() [ ] si( 3 ) 5 y[ ] y[ + T] T?? samples [iteger] 5/3 iteger y irratioal frequecy ysi(pisqrt()/5) - - TextEd si( t) T sec cotiuous

More information

EDEXCEL NATIONAL CERTIFICATE UNIT 4 MATHEMATICS FOR TECHNICIANS OUTCOME 4 - CALCULUS

EDEXCEL NATIONAL CERTIFICATE UNIT 4 MATHEMATICS FOR TECHNICIANS OUTCOME 4 - CALCULUS EDEXCEL NATIONAL CERTIFICATE UNIT 4 MATHEMATICS FOR TECHNICIANS OUTCOME 4 - CALCULUS TUTORIAL 1 - DIFFERENTIATION Use the elemetary rules of calculus arithmetic to solve problems that ivolve differetiatio

More information

Signal Processing in Mechatronics. Lecture 3, Convolution, Fourier Series and Fourier Transform

Signal Processing in Mechatronics. Lecture 3, Convolution, Fourier Series and Fourier Transform Sigal Processig i Mechatroics Summer semester, 1 Lecture 3, Covolutio, Fourier Series ad Fourier rasform Dr. Zhu K.P. AIS, UM 1 1. Covolutio Covolutio Descriptio of LI Systems he mai premise is that the

More information

Vibratory Motion. Prof. Zheng-yi Feng NCHU SWC. National CHung Hsing University, Department of Soil and Water Conservation

Vibratory Motion. Prof. Zheng-yi Feng NCHU SWC. National CHung Hsing University, Department of Soil and Water Conservation Vibratory Motio Prof. Zheg-yi Feg NCHU SWC 1 Types of vibratory motio Periodic motio Noperiodic motio See Fig. A1, p.58 Harmoic motio Periodic motio Trasiet motio impact Trasiet motio earthquake A powerful

More information

Mathematical Description of Discrete-Time Signals. 9/10/16 M. J. Roberts - All Rights Reserved 1

Mathematical Description of Discrete-Time Signals. 9/10/16 M. J. Roberts - All Rights Reserved 1 Mathematical Descriptio of Discrete-Time Sigals 9/10/16 M. J. Roberts - All Rights Reserved 1 Samplig ad Discrete Time Samplig is the acquisitio of the values of a cotiuous-time sigal at discrete poits

More information

Chapter 4 : Laplace Transform

Chapter 4 : Laplace Transform 4. Itroductio Laplace trasform is a alterative to solve the differetial equatio by the complex frequecy domai ( s = σ + jω), istead of the usual time domai. The DE ca be easily trasformed ito a algebraic

More information

Signals & Systems Chapter3

Signals & Systems Chapter3 Sigals & Systems Chapter3 1.2 Discrete-Time (D-T) Sigals Electroic systems do most of the processig of a sigal usig a computer. A computer ca t directly process a C-T sigal but istead eeds a stream of

More information

EE / EEE SAMPLE STUDY MATERIAL. GATE, IES & PSUs Signal System. Electrical Engineering. Postal Correspondence Course

EE / EEE SAMPLE STUDY MATERIAL. GATE, IES & PSUs Signal System. Electrical Engineering. Postal Correspondence Course Sigal-EE Postal Correspodece Course 1 SAMPLE STUDY MATERIAL Electrical Egieerig EE / EEE Postal Correspodece Course GATE, IES & PSUs Sigal System Sigal-EE Postal Correspodece Course CONTENTS 1. SIGNAL

More information

Solutions to Final Exam Review Problems

Solutions to Final Exam Review Problems . Let f(x) 4+x. Solutios to Fial Exam Review Problems Math 5C, Witer 2007 (a) Fid the Maclauri series for f(x), ad compute its radius of covergece. Solutio. f(x) 4( ( x/4)) ( x/4) ( ) 4 4 + x. Sice the

More information

(, ) (, ) (, ) ( ) ( )

(, ) (, ) (, ) ( ) ( ) PROBLEM ANSWER X Y x, x, rect, () X Y, otherwise D Fourier trasform is defied as ad i D case it ca be defied as We ca write give fuctio from Eq. () as It follows usig Eq. (3) it ( ) ( ) F f t e dt () i(

More information

The z-transform. 7.1 Introduction. 7.2 The z-transform Derivation of the z-transform: x[n] = z n LTI system, h[n] z = re j

The z-transform. 7.1 Introduction. 7.2 The z-transform Derivation of the z-transform: x[n] = z n LTI system, h[n] z = re j The -Trasform 7. Itroductio Geeralie the complex siusoidal represetatio offered by DTFT to a represetatio of complex expoetial sigals. Obtai more geeral characteristics for discrete-time LTI systems. 7.

More information

radians A function f ( x ) is called periodic if it is defined for all real x and if there is some positive number P such that:

radians A function f ( x ) is called periodic if it is defined for all real x and if there is some positive number P such that: Fourier Series. Graph of y Asix ad y Acos x Amplitude A ; period 36 radias. Harmoics y y six is the first harmoic y y six is the th harmoics 3. Periodic fuctio A fuctio f ( x ) is called periodic if it

More information

Appendix: The Laplace Transform

Appendix: The Laplace Transform Appedix: The Laplace Trasform The Laplace trasform is a powerful method that ca be used to solve differetial equatio, ad other mathematical problems. Its stregth lies i the fact that it allows the trasformatio

More information

School of Mechanical Engineering Purdue University. ME375 Frequency Response - 1

School of Mechanical Engineering Purdue University. ME375 Frequency Response - 1 Case Study ME375 Frequecy Respose - Case Study SUPPORT POWER WIRE DROPPERS Electric trai derives power through a patograph, which cotacts the power wire, which is suspeded from a cateary. Durig high-speed

More information

Fourier Series and the Wave Equation

Fourier Series and the Wave Equation Fourier Series ad the Wave Equatio We start with the oe-dimesioal wave equatio u u =, x u(, t) = u(, t) =, ux (,) = f( x), u ( x,) = This represets a vibratig strig, where u is the displacemet of the strig

More information

Z - Transform. It offers the techniques for digital filter design and frequency analysis of digital signals.

Z - Transform. It offers the techniques for digital filter design and frequency analysis of digital signals. Z - Trasform The -trasform is a very importat tool i describig ad aalyig digital systems. It offers the techiques for digital filter desig ad frequecy aalysis of digital sigals. Defiitio of -trasform:

More information

Math 113, Calculus II Winter 2007 Final Exam Solutions

Math 113, Calculus II Winter 2007 Final Exam Solutions Math, Calculus II Witer 7 Fial Exam Solutios (5 poits) Use the limit defiitio of the defiite itegral ad the sum formulas to compute x x + dx The check your aswer usig the Evaluatio Theorem Solutio: I this

More information

Chapter 4. Fourier Series

Chapter 4. Fourier Series Chapter 4. Fourier Series At this poit we are ready to ow cosider the caoical equatios. Cosider, for eample the heat equatio u t = u, < (4.) subject to u(, ) = si, u(, t) = u(, t) =. (4.) Here,

More information

The Z-Transform. (t-t 0 ) Figure 1: Simplified graph of an impulse function. For an impulse, it can be shown that (1)

The Z-Transform. (t-t 0 ) Figure 1: Simplified graph of an impulse function. For an impulse, it can be shown that (1) The Z-Trasform Sampled Data The geeralied fuctio (t) (also kow as the impulse fuctio) is useful i the defiitio ad aalysis of sampled-data sigals. Figure below shows a simplified graph of a impulse. (t-t

More information

FREE VIBRATION RESPONSE OF A SYSTEM WITH COULOMB DAMPING

FREE VIBRATION RESPONSE OF A SYSTEM WITH COULOMB DAMPING Mechaical Vibratios FREE VIBRATION RESPONSE OF A SYSTEM WITH COULOMB DAMPING A commo dampig mechaism occurrig i machies is caused by slidig frictio or dry frictio ad is called Coulomb dampig. Coulomb dampig

More information

EE Midterm Test 1 - Solutions

EE Midterm Test 1 - Solutions EE35 - Midterm Test - Solutios Total Poits: 5+ 6 Bous Poits Time: hour. ( poits) Cosider the parallel itercoectio of the two causal systems, System ad System 2, show below. System x[] + y[] System 2 The

More information

Chapter 7: The z-transform. Chih-Wei Liu

Chapter 7: The z-transform. Chih-Wei Liu Chapter 7: The -Trasform Chih-Wei Liu Outlie Itroductio The -Trasform Properties of the Regio of Covergece Properties of the -Trasform Iversio of the -Trasform The Trasfer Fuctio Causality ad Stability

More information

Mechatronics. Time Response & Frequency Response 2 nd -Order Dynamic System 2-Pole, Low-Pass, Active Filter

Mechatronics. Time Response & Frequency Response 2 nd -Order Dynamic System 2-Pole, Low-Pass, Active Filter Time Respose & Frequecy Respose d -Order Dyamic System -Pole, Low-Pass, Active Filter R 4 R 7 C 5 e i R 1 C R 3 - + R 6 - + e out Assigmet: Perform a Complete Dyamic System Ivestigatio of the Two-Pole,

More information

Finite-length Discrete Transforms. Chapter 5, Sections

Finite-length Discrete Transforms. Chapter 5, Sections Fiite-legth Discrete Trasforms Chapter 5, Sectios 5.2-50 5.0 Dr. Iyad djafar Outlie The Discrete Fourier Trasform (DFT) Matrix Represetatio of DFT Fiite-legth Sequeces Circular Covolutio DFT Symmetry Properties

More information

Sinusoidal Steady-state Analysis

Sinusoidal Steady-state Analysis Siusoidal Steady-state Aalysis Complex umber reviews Phasors ad ordiary differetial equatios Complete respose ad siusoidal steady-state respose Cocepts of impedace ad admittace Siusoidal steady-state aalysis

More information

Module 18 Discrete Time Signals and Z-Transforms Objective: Introduction : Description: Discrete Time Signal representation

Module 18 Discrete Time Signals and Z-Transforms Objective: Introduction : Description: Discrete Time Signal representation Module 8 Discrete Time Sigals ad Z-Trasforms Objective:To uderstad represetig discrete time sigals, apply z trasform for aalyzigdiscrete time sigals ad to uderstad the relatio to Fourier trasform Itroductio

More information

Solutions - Homework # 1

Solutions - Homework # 1 ECE-4: Sigals ad Systems Summer Solutios - Homework # PROBLEM A cotiuous time sigal is show i the figure. Carefully sketch each of the followig sigals: x(t) a) x(t-) b) x(-t) c) x(t+) d) x( - t/) e) x(t)*(

More information

Olli Simula T / Chapter 1 3. Olli Simula T / Chapter 1 5

Olli Simula T / Chapter 1 3. Olli Simula T / Chapter 1 5 Sigals ad Systems Sigals ad Systems Sigals are variables that carry iformatio Systemstake sigals as iputs ad produce sigals as outputs The course deals with the passage of sigals through systems T-6.4

More information

Zeros of Polynomials

Zeros of Polynomials Math 160 www.timetodare.com 4.5 4.6 Zeros of Polyomials I these sectios we will study polyomials algebraically. Most of our work will be cocered with fidig the solutios of polyomial equatios of ay degree

More information

Question 1: The magnetic case

Question 1: The magnetic case September 6, 018 Corell Uiversity, Departmet of Physics PHYS 337, Advace E&M, HW # 4, due: 9/19/018, 11:15 AM Questio 1: The magetic case I class, we skipped over some details, so here you are asked to

More information

2 f(x) dx = 1, 0. 2f(x 1) dx d) 1 4t t6 t. t 2 dt i)

2 f(x) dx = 1, 0. 2f(x 1) dx d) 1 4t t6 t. t 2 dt i) Math PracTest Be sure to review Lab (ad all labs) There are lots of good questios o it a) State the Mea Value Theorem ad draw a graph that illustrates b) Name a importat theorem where the Mea Value Theorem

More information

Properties and Tests of Zeros of Polynomial Functions

Properties and Tests of Zeros of Polynomial Functions Properties ad Tests of Zeros of Polyomial Fuctios The Remaider ad Factor Theorems: Sythetic divisio ca be used to fid the values of polyomials i a sometimes easier way tha substitutio. This is show by

More information

Exponential Moving Average Pieter P

Exponential Moving Average Pieter P Expoetial Movig Average Pieter P Differece equatio The Differece equatio of a expoetial movig average lter is very simple: y[] x[] + (1 )y[ 1] I this equatio, y[] is the curret output, y[ 1] is the previous

More information

Voltage controlled oscillator (VCO)

Voltage controlled oscillator (VCO) Voltage cotrolled oscillator (VO) Oscillatio frequecy jl Z L(V) jl[ L(V)] [L L (V)] L L (V) T VO gai / Logf Log 4 L (V) f f 4 L(V) Logf / L(V) f 4 L (V) f (V) 3 Lf 3 VO gai / (V) j V / V Bi (V) / V Bi

More information

WHAT ARE THE BERNOULLI NUMBERS? 1. Introduction

WHAT ARE THE BERNOULLI NUMBERS? 1. Introduction WHAT ARE THE BERNOULLI NUMBERS? C. D. BUENGER Abstract. For the "What is?" semiar today we will be ivestigatig the Beroulli umbers. This surprisig sequece of umbers has may applicatios icludig summig powers

More information

Dynamic Response of Linear Systems

Dynamic Response of Linear Systems Dyamic Respose of Liear Systems Liear System Respose Superpositio Priciple Resposes to Specific Iputs Dyamic Respose of st Order Systems Characteristic Equatio - Free Respose Stable st Order System Respose

More information

Problem 4: Evaluate ( k ) by negating (actually un-negating) its upper index. Binomial coefficient

Problem 4: Evaluate ( k ) by negating (actually un-negating) its upper index. Binomial coefficient Problem 4: Evaluate by egatig actually u-egatig its upper idex We ow that Biomial coefficiet r { where r is a real umber, is a iteger The above defiitio ca be recast i terms of factorials i the commo case

More information

1 1 2 = show that: over variables x and y. [2 marks] Write down necessary conditions involving first and second-order partial derivatives for ( x0, y

1 1 2 = show that: over variables x and y. [2 marks] Write down necessary conditions involving first and second-order partial derivatives for ( x0, y Questio (a) A square matrix A= A is called positive defiite if the quadratic form waw > 0 for every o-zero vector w [Note: Here (.) deotes the traspose of a matrix or a vector]. Let 0 A = 0 = show that:

More information

ELEG 4603/5173L Digital Signal Processing Ch. 1 Discrete-Time Signals and Systems

ELEG 4603/5173L Digital Signal Processing Ch. 1 Discrete-Time Signals and Systems Departmet of Electrical Egieerig Uiversity of Arasas ELEG 4603/5173L Digital Sigal Processig Ch. 1 Discrete-Time Sigals ad Systems Dr. Jigxia Wu wuj@uar.edu OUTLINE 2 Classificatios of discrete-time sigals

More information

Generalizing the DTFT. The z Transform. Complex Exponential Excitation. The Transfer Function. Systems Described by Difference Equations

Generalizing the DTFT. The z Transform. Complex Exponential Excitation. The Transfer Function. Systems Described by Difference Equations Geeraliig the DTFT The Trasform M. J. Roberts - All Rights Reserved. Edited by Dr. Robert Akl 1 The forward DTFT is defied by X e jω = x e jω i which = Ω is discrete-time radia frequecy, a real variable.

More information

Frequency Response of FIR Filters

Frequency Response of FIR Filters EEL335: Discrete-Time Sigals ad Systems. Itroductio I this set of otes, we itroduce the idea of the frequecy respose of LTI systems, ad focus specifically o the frequecy respose of FIR filters.. Steady-state

More information

FIR Filter Design: Part II

FIR Filter Design: Part II EEL335: Discrete-Time Sigals ad Systems. Itroductio I this set of otes, we cosider how we might go about desigig FIR filters with arbitrary frequecy resposes, through compositio of multiple sigle-peak

More information

Introduction to Signals and Systems, Part V: Lecture Summary

Introduction to Signals and Systems, Part V: Lecture Summary EEL33: Discrete-Time Sigals ad Systems Itroductio to Sigals ad Systems, Part V: Lecture Summary Itroductio to Sigals ad Systems, Part V: Lecture Summary So far we have oly looked at examples of o-recursive

More information

2.004 Dynamics and Control II Spring 2008

2.004 Dynamics and Control II Spring 2008 MIT OpeCourseWare http://ocw.mit.edu 2.004 Dyamics ad Cotrol II Sprig 2008 For iformatio about citig these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. Massachusetts Istitute of Techology

More information

Q-BINOMIALS AND THE GREATEST COMMON DIVISOR. Keith R. Slavin 8474 SW Chevy Place, Beaverton, Oregon 97008, USA.

Q-BINOMIALS AND THE GREATEST COMMON DIVISOR. Keith R. Slavin 8474 SW Chevy Place, Beaverton, Oregon 97008, USA. INTEGERS: ELECTRONIC JOURNAL OF COMBINATORIAL NUMBER THEORY 8 2008, #A05 Q-BINOMIALS AND THE GREATEST COMMON DIVISOR Keith R. Slavi 8474 SW Chevy Place, Beaverto, Orego 97008, USA slavi@dsl-oly.et Received:

More information

The picture in figure 1.1 helps us to see that the area represents the distance traveled. Figure 1: Area represents distance travelled

The picture in figure 1.1 helps us to see that the area represents the distance traveled. Figure 1: Area represents distance travelled 1 Lecture : Area Area ad distace traveled Approximatig area by rectagles Summatio The area uder a parabola 1.1 Area ad distace Suppose we have the followig iformatio about the velocity of a particle, how

More information

1 T T 1 = T ν 1 = ν fundamental or 1 st harmonic. 2 T T 2 = T/2 ν 2 = 2 ν 2 nd harmonic. 3 T T 3 = T/3 ν 3 = 3 ν 3 rd harmonic

1 T T 1 = T ν 1 = ν fundamental or 1 st harmonic. 2 T T 2 = T/2 ν 2 = 2 ν 2 nd harmonic. 3 T T 3 = T/3 ν 3 = 3 ν 3 rd harmonic PAR 4 Fourier Series ad Fourier rasform By the use of the famous Fourier Series, a periodic fuctio is expressed as a sum of harmoics. I the case of o-periodic fuctios a geeralisatio of the Fourier series

More information

Physics 116A Solutions to Homework Set #1 Winter Boas, problem Use equation 1.8 to find a fraction describing

Physics 116A Solutions to Homework Set #1 Winter Boas, problem Use equation 1.8 to find a fraction describing Physics 6A Solutios to Homework Set # Witer 0. Boas, problem. 8 Use equatio.8 to fid a fractio describig 0.694444444... Start with the formula S = a, ad otice that we ca remove ay umber of r fiite decimals

More information

MAT 271 Project: Partial Fractions for certain rational functions

MAT 271 Project: Partial Fractions for certain rational functions MAT 7 Project: Partial Fractios for certai ratioal fuctios Prerequisite kowledge: partial fractios from MAT 7, a very good commad of factorig ad complex umbers from Precalculus. To complete this project,

More information

TR/46 OCTOBER THE ZEROS OF PARTIAL SUMS OF A MACLAURIN EXPANSION A. TALBOT

TR/46 OCTOBER THE ZEROS OF PARTIAL SUMS OF A MACLAURIN EXPANSION A. TALBOT TR/46 OCTOBER 974 THE ZEROS OF PARTIAL SUMS OF A MACLAURIN EXPANSION by A. TALBOT .. Itroductio. A problem i approximatio theory o which I have recetly worked [] required for its solutio a proof that the

More information

EECE 301 Signals & Systems

EECE 301 Signals & Systems EECE 301 Sigals & Systems Prof. Mark Fowler Note Set #8 D-T Covolutio: The Tool for Fidig the Zero-State Respose Readig Assigmet: Sectio 2.1-2.2 of Kame ad Heck 1/14 Course Flow Diagram The arrows here

More information

Review of Discrete-time Signals. ELEC 635 Prof. Siripong Potisuk

Review of Discrete-time Signals. ELEC 635 Prof. Siripong Potisuk Review of Discrete-time Sigals ELEC 635 Prof. Siripog Potisuk 1 Discrete-time Sigals Discrete-time, cotiuous-valued amplitude (sampled-data sigal) Discrete-time, discrete-valued amplitude (digital sigal)

More information

0 otherwise. sin( nx)sin( kx) 0 otherwise. cos( nx) sin( kx) dx 0 for all integers n, k.

0 otherwise. sin( nx)sin( kx) 0 otherwise. cos( nx) sin( kx) dx 0 for all integers n, k. . Computtio of Fourier Series I this sectio, we compute the Fourier coefficiets, f ( x) cos( x) b si( x) d b, i the Fourier series To do this, we eed the followig result o the orthogolity of the trigoometric

More information

732 Appendix E: Previous EEE480 Exams. Rules: One sheet permitted, calculators permitted. GWC 352,

732 Appendix E: Previous EEE480 Exams. Rules: One sheet permitted, calculators permitted. GWC 352, 732 Aedix E: Previous EEE0 Exams EEE0 Exam 2, Srig 2008 A.A. Rodriguez Rules: Oe 8. sheet ermitted, calculators ermitted. GWC 32, 9-372 Problem Aalysis of a Feedback System Cosider the feedback system

More information

A. Basics of Discrete Fourier Transform

A. Basics of Discrete Fourier Transform A. Basics of Discrete Fourier Trasform A.1. Defiitio of Discrete Fourier Trasform (8.5) A.2. Properties of Discrete Fourier Trasform (8.6) A.3. Spectral Aalysis of Cotiuous-Time Sigals Usig Discrete Fourier

More information

Ma 4121: Introduction to Lebesgue Integration Solutions to Homework Assignment 5

Ma 4121: Introduction to Lebesgue Integration Solutions to Homework Assignment 5 Ma 42: Itroductio to Lebesgue Itegratio Solutios to Homework Assigmet 5 Prof. Wickerhauser Due Thursday, April th, 23 Please retur your solutios to the istructor by the ed of class o the due date. You

More information

Lecture #20. n ( x p i )1/p = max

Lecture #20. n ( x p i )1/p = max COMPSCI 632: Approximatio Algorithms November 8, 2017 Lecturer: Debmalya Paigrahi Lecture #20 Scribe: Yua Deg 1 Overview Today, we cotiue to discuss about metric embeddigs techique. Specifically, we apply

More information

Math 234 Test 1, Tuesday 27 September 2005, 4 pages, 30 points, 75 minutes.

Math 234 Test 1, Tuesday 27 September 2005, 4 pages, 30 points, 75 minutes. Math 34 Test 1, Tuesday 7 September 5, 4 pages, 3 poits, 75 miutes. The high score was 9 poits out of 3, achieved by two studets. The class average is 3.5 poits out of 3, or 77.5%, which ordiarily would

More information

1. Hydrogen Atom: 3p State

1. Hydrogen Atom: 3p State 7633A QUANTUM MECHANICS I - solutio set - autum. Hydroge Atom: 3p State Let us assume that a hydroge atom is i a 3p state. Show that the radial part of its wave fuctio is r u 3(r) = 4 8 6 e r 3 r(6 r).

More information

Carleton College, Winter 2017 Math 121, Practice Final Prof. Jones. Note: the exam will have a section of true-false questions, like the one below.

Carleton College, Winter 2017 Math 121, Practice Final Prof. Jones. Note: the exam will have a section of true-false questions, like the one below. Carleto College, Witer 207 Math 2, Practice Fial Prof. Joes Note: the exam will have a sectio of true-false questios, like the oe below.. True or False. Briefly explai your aswer. A icorrectly justified

More information

Definition of z-transform.

Definition of z-transform. - Trasforms Frequecy domai represetatios of discretetime sigals ad LTI discrete-time systems are made possible with the use of DTFT. However ot all discrete-time sigals e.g. uit step sequece are guarateed

More information

The Binomial Multi-Section Transformer

The Binomial Multi-Section Transformer 4/15/2010 The Bioial Multisectio Matchig Trasforer preset.doc 1/24 The Bioial Multi-Sectio Trasforer Recall that a ulti-sectio atchig etwork ca be described usig the theory of sall reflectios as: where:

More information

MATH 10550, EXAM 3 SOLUTIONS

MATH 10550, EXAM 3 SOLUTIONS MATH 155, EXAM 3 SOLUTIONS 1. I fidig a approximate solutio to the equatio x 3 +x 4 = usig Newto s method with iitial approximatio x 1 = 1, what is x? Solutio. Recall that x +1 = x f(x ) f (x ). Hece,

More information

Fall 2011, EE123 Digital Signal Processing

Fall 2011, EE123 Digital Signal Processing Lecture 5 Miki Lustig, UCB September 14, 211 Miki Lustig, UCB Motivatios for Discrete Fourier Trasform Sampled represetatio i time ad frequecy umerical Fourier aalysis requires a Fourier represetatio that

More information

6.003: Signal Processing

6.003: Signal Processing 6.003: Sigal Processig Discrete-Time Fourier Series orthogoality of harmoically related DT siusoids DT Fourier series relatios differeces betwee CT ad DT Fourier series properties of DT Fourier series

More information

Riemann Sums y = f (x)

Riemann Sums y = f (x) Riema Sums Recall that we have previously discussed the area problem I its simplest form we ca state it this way: The Area Problem Let f be a cotiuous, o-egative fuctio o the closed iterval [a, b] Fid

More information

MAT1026 Calculus II Basic Convergence Tests for Series

MAT1026 Calculus II Basic Convergence Tests for Series MAT026 Calculus II Basic Covergece Tests for Series Egi MERMUT 202.03.08 Dokuz Eylül Uiversity Faculty of Sciece Departmet of Mathematics İzmir/TURKEY Cotets Mootoe Covergece Theorem 2 2 Series of Real

More information

CHAPTER 10 INFINITE SEQUENCES AND SERIES

CHAPTER 10 INFINITE SEQUENCES AND SERIES CHAPTER 10 INFINITE SEQUENCES AND SERIES 10.1 Sequeces 10.2 Ifiite Series 10.3 The Itegral Tests 10.4 Compariso Tests 10.5 The Ratio ad Root Tests 10.6 Alteratig Series: Absolute ad Coditioal Covergece

More information

1. Nature of Impulse Response - Pole on Real Axis. z y(n) = r n. z r

1. Nature of Impulse Response - Pole on Real Axis. z y(n) = r n. z r . Nature of Impulse Respose - Pole o Real Axis Causal system trasfer fuctio: Hz) = z yz) = z r z z r y) = r r > : the respose grows mootoically > r > : y decays to zero mootoically r > : oscillatory, decayig

More information

University of California at Berkeley College of Engineering Department of Electrical Engineering and Computer Sciences

University of California at Berkeley College of Engineering Department of Electrical Engineering and Computer Sciences A Uiversity of Califoria at Berkeley College of Egieerig Departmet of Electrical Egieerig ad Computer Scieces U N I V E R S T H E I T Y O F LE T TH E R E B E LI G H T C A L I F O R N 8 6 8 I A EECS : Sigals

More information

Fourier Series and their Applications

Fourier Series and their Applications Fourier Series ad their Applicatios The fuctios, cos x, si x, cos x, si x, are orthogoal over (, ). m cos mx cos xdx = m = m = = cos mx si xdx = for all m, { m si mx si xdx = m = I fact the fuctios satisfy

More information

TECHNIQUES OF INTEGRATION

TECHNIQUES OF INTEGRATION 7 TECHNIQUES OF INTEGRATION Simpso s Rule estimates itegrals b approimatig graphs with parabolas. Because of the Fudametal Theorem of Calculus, we ca itegrate a fuctio if we kow a atiderivative, that is,

More information

SIGNAL PROCESSING & SIMULATION NEWSLETTER

SIGNAL PROCESSING & SIMULATION NEWSLETTER SIGNAL PROCESSING & SIMULAION NEWSLEER Fourier aalysis made Easy Part Jea Baptiste Joseph, Baro de Fourier, 768-83 While studyig heat coductio i materials, Baro Fourier (a title give to him by Napoleo)

More information

Analytic Continuation

Analytic Continuation Aalytic Cotiuatio The stadard example of this is give by Example Let h (z) = 1 + z + z 2 + z 3 +... kow to coverge oly for z < 1. I fact h (z) = 1/ (1 z) for such z. Yet H (z) = 1/ (1 z) is defied for

More information

3. Z Transform. Recall that the Fourier transform (FT) of a DT signal xn [ ] is ( ) [ ] = In order for the FT to exist in the finite magnitude sense,

3. Z Transform. Recall that the Fourier transform (FT) of a DT signal xn [ ] is ( ) [ ] = In order for the FT to exist in the finite magnitude sense, 3. Z Trasform Referece: Etire Chapter 3 of text. Recall that the Fourier trasform (FT) of a DT sigal x [ ] is ω ( ) [ ] X e = j jω k = xe I order for the FT to exist i the fiite magitude sese, S = x [

More information

The Bilateral Laplace Transform of the Positive Even Functions and a Proof of Riemann Hypothesis

The Bilateral Laplace Transform of the Positive Even Functions and a Proof of Riemann Hypothesis The Bilateral Laplace Trasform of the Positive Eve Fuctios ad a Proof of Riema Hypothesis Seog Wo Cha Ph.D. swcha@dgu.edu Abstract We show that some iterestig properties of the bilateral Laplace trasform

More information

f(x) dx as we do. 2x dx x also diverges. Solution: We compute 2x dx lim

f(x) dx as we do. 2x dx x also diverges. Solution: We compute 2x dx lim Math 3, Sectio 2. (25 poits) Why we defie f(x) dx as we do. (a) Show that the improper itegral diverges. Hece the improper itegral x 2 + x 2 + b also diverges. Solutio: We compute x 2 + = lim b x 2 + =

More information

PH 411/511 ECE B(k) Sin k (x) dk (1)

PH 411/511 ECE B(k) Sin k (x) dk (1) Fall-26 PH 4/5 ECE 598 A. La Rosa Homework-2 Due -3-26 The Homework is iteded to gai a uderstadig o the Heiseberg priciple, based o a compariso betwee the width of a pulse ad the width of its spectral

More information

Appendix F: Complex Numbers

Appendix F: Complex Numbers Appedix F Complex Numbers F1 Appedix F: Complex Numbers Use the imagiary uit i to write complex umbers, ad to add, subtract, ad multiply complex umbers. Fid complex solutios of quadratic equatios. Write

More information

Numerical Methods in Fourier Series Applications

Numerical Methods in Fourier Series Applications Numerical Methods i Fourier Series Applicatios Recall that the basic relatios i usig the Trigoometric Fourier Series represetatio were give by f ( x) a o ( a x cos b x si ) () where the Fourier coefficiets

More information

Lecture 25 (Dec. 6, 2017)

Lecture 25 (Dec. 6, 2017) Lecture 5 8.31 Quatum Theory I, Fall 017 106 Lecture 5 (Dec. 6, 017) 5.1 Degeerate Perturbatio Theory Previously, whe discussig perturbatio theory, we restricted ourselves to the case where the uperturbed

More information

Describing Function: An Approximate Analysis Method

Describing Function: An Approximate Analysis Method Describig Fuctio: A Approximate Aalysis Method his chapter presets a method for approximately aalyzig oliear dyamical systems A closed-form aalytical solutio of a oliear dyamical system (eg, a oliear differetial

More information

Time-Domain Representations of LTI Systems

Time-Domain Representations of LTI Systems 2.1 Itroductio Objectives: 1. Impulse resposes of LTI systems 2. Liear costat-coefficiets differetial or differece equatios of LTI systems 3. Bloc diagram represetatios of LTI systems 4. State-variable

More information

Fundamental Theorem of Algebra. Yvonne Lai March 2010

Fundamental Theorem of Algebra. Yvonne Lai March 2010 Fudametal Theorem of Algebra Yvoe Lai March 010 We prove the Fudametal Theorem of Algebra: Fudametal Theorem of Algebra. Let f be a o-costat polyomial with real coefficiets. The f has at least oe complex

More information

Chapter 2 Feedback Control Theory Continued

Chapter 2 Feedback Control Theory Continued Chapter Feedback Cotrol Theor Cotiued. Itroductio I the previous chapter, the respose characteristic of simple first ad secod order trasfer fuctios were studied. It was show that first order trasfer fuctio,

More information

INFINITE SEQUENCES AND SERIES

INFINITE SEQUENCES AND SERIES 11 INFINITE SEQUENCES AND SERIES INFINITE SEQUENCES AND SERIES 11.4 The Compariso Tests I this sectio, we will lear: How to fid the value of a series by comparig it with a kow series. COMPARISON TESTS

More information

CALCULUS BASIC SUMMER REVIEW

CALCULUS BASIC SUMMER REVIEW CALCULUS BASIC SUMMER REVIEW NAME rise y y y Slope of a o vertical lie: m ru Poit Slope Equatio: y y m( ) The slope is m ad a poit o your lie is, ). ( y Slope-Itercept Equatio: y m b slope= m y-itercept=

More information

Chapter 2 Systems and Signals

Chapter 2 Systems and Signals Chapter 2 Systems ad Sigals 1 Itroductio Discrete-Time Sigals: Sequeces Discrete-Time Systems Properties of Liear Time-Ivariat Systems Liear Costat-Coefficiet Differece Equatios Frequecy-Domai Represetatio

More information

Chapter 7 z-transform

Chapter 7 z-transform Chapter 7 -Trasform Itroductio Trasform Uilateral Trasform Properties Uilateral Trasform Iversio of Uilateral Trasform Determiig the Frequecy Respose from Poles ad Zeros Itroductio Role i Discrete-Time

More information

Lecture 11: A Fourier Transform Primer

Lecture 11: A Fourier Transform Primer PHYS 34 Fall 1 ecture 11: A Fourier Trasform Primer Ro Reifeberger Birck aotechology Ceter Purdue Uiversity ecture 11 1 f() I may edeavors, we ecouter sigals that eriodically reeat f(t) T t Such reeatig

More information

It is often useful to approximate complicated functions using simpler ones. We consider the task of approximating a function by a polynomial.

It is often useful to approximate complicated functions using simpler ones. We consider the task of approximating a function by a polynomial. Taylor Polyomials ad Taylor Series It is ofte useful to approximate complicated fuctios usig simpler oes We cosider the task of approximatig a fuctio by a polyomial If f is at least -times differetiable

More information

Math 113 Exam 3 Practice

Math 113 Exam 3 Practice Math Exam Practice Exam 4 will cover.-., 0. ad 0.. Note that eve though. was tested i exam, questios from that sectios may also be o this exam. For practice problems o., refer to the last review. This

More information

ECEN 644 HOMEWORK #5 SOLUTION SET

ECEN 644 HOMEWORK #5 SOLUTION SET ECE 644 HOMEWORK #5 SOUTIO SET 7. x is a real valued sequece. The first five poits of its 8-poit DFT are: {0.5, 0.5 - j 0.308, 0, 0.5 - j 0.058, 0} To compute the 3 remaiig poits, we ca use the followig

More information

Butterworth LC Filter Designer

Butterworth LC Filter Designer Butterworth LC Filter Desiger R S = g g 4 g - V S g g 3 g R L = Fig. : LC filter used for odd-order aalysis g R S = g 4 g V S g g 3 g - R L = useful fuctios ad idetities Uits Costats Table of Cotets I.

More information

Kinetics of Complex Reactions

Kinetics of Complex Reactions Kietics of Complex Reactios by Flick Colema Departmet of Chemistry Wellesley College Wellesley MA 28 wcolema@wellesley.edu Copyright Flick Colema 996. All rights reserved. You are welcome to use this documet

More information

Solution of EECS 315 Final Examination F09

Solution of EECS 315 Final Examination F09 Solutio of EECS 315 Fial Examiatio F9 1. Fid the umerical value of δ ( t + 4ramp( tdt. δ ( t + 4ramp( tdt. Fid the umerical sigal eergy of x E x = x[ ] = δ 3 = 11 = ( = ramp( ( 4 = ramp( 8 = 8 [ ] = (

More information

MAS160: Signals, Systems & Information for Media Technology. Problem Set 5. DUE: November 3, (a) Plot of u[n] (b) Plot of x[n]=(0.

MAS160: Signals, Systems & Information for Media Technology. Problem Set 5. DUE: November 3, (a) Plot of u[n] (b) Plot of x[n]=(0. MAS6: Sigals, Systems & Iformatio for Media Techology Problem Set 5 DUE: November 3, 3 Istructors: V. Michael Bove, Jr. ad Rosalid Picard T.A. Jim McBride Problem : Uit-step ad ruig average (DSP First

More information

ECE 301: Signals and Systems Homework Assignment #4

ECE 301: Signals and Systems Homework Assignment #4 ECE 301: Sigals ad Systems Homework Assigmet #4 Due o October 28, 2015 Professor: Aly El Gamal TA: Xiaglu Mao 1 Aly El Gamal ECE 301: Sigals ad Systems Homework Assigmet #4 Problem 1 Problem 1 Let x[]

More information

1.3 Convergence Theorems of Fourier Series. k k k k. N N k 1. With this in mind, we state (without proof) the convergence of Fourier series.

1.3 Convergence Theorems of Fourier Series. k k k k. N N k 1. With this in mind, we state (without proof) the convergence of Fourier series. .3 Covergece Theorems of Fourier Series I this sectio, we preset the covergece of Fourier series. A ifiite sum is, by defiitio, a limit of partial sums, that is, a cos( kx) b si( kx) lim a cos( kx) b si(

More information