Lecture Notes on Continuous-Time and Discrete-Time Sinusoids, Study notes of Signals and Systems

Lecture notes on continuous-time and discrete-time sinusoids, covering topics such as their definition, period, frequency, dc component, rms value, sum of multiple sinusoids, and elementary signal operations like translation. It also discusses the difference between continuous-time and discrete-time sinusoids.

Typology: Study notes

Pre 2010

Uploaded on 09/02/2009

koofers-user-ac3
koofers-user-ac3 🇺🇸

10 documents

1 / 2

Toggle sidebar

This page cannot be seen from the preview

Don't miss anything!

bg1
EECS 216 LECTURE NOTES
CONTINUOUS-TIME AND DISCRETE-TIME SINUSOIDS
DEF: Asinusoidal signal has the standard form x(t) = Acos(ωot+φ).
Where: A0=amplitude;ωo0=frequency;|φ| π=phase (shift).
Freq: fo=ωo/(2π)=circular or cyclic frequency in Hertz=cycles per second.
Note: Sinusoid x(t) = Acos(2πfot+φ) has cyclic frequency foHertz.
Period: T=1
fo=2π
ωox(t) = x(t+T)for all tusing cos(x) = cos(x+ 2).
DC: DC (constant) signals: ωo=fo= 0; T=(low-frequency limit).
RMS(x)= q1
TRT
0A2cos2(ωot+φ)dt =q1
TRT
0
A2
2(1 + cos(2ωot+ 2φ))dt =A
2
using cos2(x) = 1
2(1 + cos(2x)) and M(cos(t)) = 0 (by inspection).
DC: ωo= 0 x(t) = Acos φRMS(x) = A|cos φ| 0 (note A0).
Note: Can convert other forms to this standard form. Some examples:
1. x(t) = 3 cos(7t+0.1) = 3 cos(7t±π+0.1) using cos(x) = cos(x±π).
2. x(t) = 3 sin(7t+ 0.1) = 3 cos(7tπ
2+ 0.1) using sin(x) = cos(xπ
2).
3. x(t) = 3 cos(7t+ 0.1) = 3 cos(7t±π0.1) using cos(x) = cos(x).
x(t): Continuous-time sinusoids are periodic with period T=1
fo=2π
ωo.
x[n]: Discrete-time sinusoids are NOT periodic UNLESS fois rational.
ωo= 2πK
Tx[n] has period T, provided K
Tis reduced to lowest terms.
Why? Acos(ωon+φ) = Acos(ωo(n+T) + φ)ωoT= 2πk fo=ωo
2π=k
T.
EX: ωo= 2πT= 1; ωo= (2.02)πT= 100; ω0= 2 nonperiodic.
Freq.: Acos(ωon+φ) = Acos([ωo+ 2]n+φ)ωoitself is periodic.
As ωoincreases from 0 to π, oscillation rate increases.
As ωoincreases from πto 2π, oscillation rate decreases.
Huh? Frequency range πωo2π πωo0 since ωoperiodic.
WLOG: Restrict discrete-time frequencies to range πωoπ.
Note: ωo= 0 cos(ωon) = 1; ωo=±πcos(ωon) = (1)n.
SUM OF MULTIPLE SINUSOIDS
If: x(t) has period Txand freq. fx;y(t) has period Tyand freq. fy;
And: Tx/Tyis a rational number (otherwise x(t) + y(t) not periodic);
Then: x(t) + y(t) has period Tx+y=Least Common Multiple of Txand Ty;
Then: x(t) + y(t) has freq. fx+y=Greatest Common Divisor of fxand fy.
Note: Always works for sinusoids; rarely fails for general periodic signals.
How? (1) Reduce Tx
Ty=M
Nto lowest terms. (2) Tx+y=N Tx=MTy.
Why? Tx
Ty=M
Nx(t+Tx+y) + y(t+Tx+y) = x(t+NTx) + y(t+M Ty).
EX: x(t) = cos(0.3πt) + cos( 2
3πt)fo=3
20 ,1
3fx+y=1
60 Tx+y= 60.
pf2

Partial preview of the text

Download Lecture Notes on Continuous-Time and Discrete-Time Sinusoids and more Study notes Signals and Systems in PDF only on Docsity!

EECS 216 LECTURE NOTES

CONTINUOUS-TIME AND DISCRETE-TIME SINUSOIDS

DEF: A sinusoidal signal has the standard form x(t) = A cos(ωot + φ). Where: A ≥ 0=amplitude; ωo ≥ 0=frequency; |φ| ≤ π=phase (shift). Freq: fo = ωo/(2π)=circular or cyclic frequency in Hertz=cycles per second. Note: Sinusoid x(t) = A cos(2πfot + φ) has cyclic frequency fo Hertz. Period: T = (^) f^1 o = (^2) ωπo → x(t) = x(t + T ) for all t using cos(x) = cos(x + 2kπ). DC: DC (constant) signals: ωo = fo = 0; T = ∞ (low-frequency limit).

RMS(x)=

1 T

∫ T

0 A

(^2) cos (^2) (ωot + φ)dt =

1 T

∫ T

0

A^2 2 (1 + cos(2ωot^ + 2φ))dt^ =^ √A 2 using cos^2 (x) = 12 (1 + cos(2x)) and M (cos(t)) = 0 (by inspection). DC: ωo = 0 → x(t) = A cos φ → RM S(x) = A| cos φ| ≥ 0 (note A ≥ 0).

Note: Can convert other forms to this standard form. Some examples:

  1. x(t) = −3 cos(7t+0.1) = 3 cos(7t±π +0.1) using − cos(x) = cos(x±π).
  2. x(t) = 3 sin(7t + 0.1) = 3 cos(7t − π 2 + 0.1) using sin(x) = cos(x − π 2 ).
  3. x(t) = −3 cos(− 7 t + 0.1) = 3 cos(7t ± π − 0 .1) using cos(−x) = cos(x).

x(t): Continuous-time sinusoids are periodic with period T = (^) f^1 o = (^2) ωπo. x[n]: Discrete-time sinusoids are NOT periodic UNLESS fo is rational. ωo = 2π KT ⇔ x[n] has period T , provided KT is reduced to lowest terms. Why? A cos(ωon + φ) = A cos(ωo(n + T ) + φ) ⇔ ωoT = 2πk ⇔ fo = ω 2 πo = (^) Tk. EX: ωo = 2π → T = 1; ωo = (2.02)π → T = 100; ω 0 = 2 →nonperiodic. Freq.: A cos(ωon + φ) = A cos([ωo + 2kπ]n + φ) → ωo itself is periodic. As ωo increases from 0 to π, oscillation rate increases. As ωo increases from π to 2π, oscillation rate decreases. Huh? Frequency range π ≤ ωo ≤ 2 π ⇔ −π ≤ ωo ≤ 0 since ωo periodic. WLOG: Restrict discrete-time frequencies to range −π ≤ ωo ≤ π. Note: ωo = 0 → cos(ωon) = 1; ωo = ±π → cos(ωon) = (−1)n. SUM OF MULTIPLE SINUSOIDS If: x(t) has period Tx and freq. fx; y(t) has period Ty and freq. fy ; And: Tx/Ty is a rational number (otherwise x(t) + y(t) not periodic); Then: x(t) + y(t) has period Tx+y =Least Common Multiple of Tx and Ty ; Then: x(t) + y(t) has freq. fx+y =Greatest Common Divisor of fx and fy. Note: Always works for sinusoids; rarely fails for general periodic signals. How? (1) Reduce T Txy = MN to lowest terms. (2) Tx+y = N Tx = M Ty. Why? T Txy = MN → x(t + Tx+y ) + y(t + Tx+y ) = x(t + N Tx) + y(t + M Ty ). EX: x(t) = cos(0. 3 πt) + cos( 23 πt) → fo = 203 , 13 → fx+y = 601 → Tx+y = 60.

EECS 216 LECTURE NOTES

ELEMENTARY SIGNAL OPERATIONS: TRANSLATION

Value: y(t) = x(t) + c shifts plot of x(t) up by c if c > 0 (down if c < 0). Value: y(t) = cx(t) scales vertical axis of plot by factor c.

Time: y(t) = x(t − d) shifts plot of x(t) right by d if d > 0 (left if d < 0). Time: y(t) = x(dt) scales horizontal axis of plot by factor d. Time: y(t) = x(−t) reverses: flips plot about t = 0 axis. Note: For discrete time signals x[n], d must be an integer!

EX #1: y(t) = x(t − 2) delays x(t) by 2: y(2) = x(0), y(3) = x(1). EX #2: y(t) = x(2t) shrinks x(t) by 2: y(1) = x(2), y(2) = x(4). Means: If x(t) = cos(ωot), this doubles frequency & halves period. EX #3: y(t) = x(−t): If x(t) audio, then y(t) is x(t) backwards! Play Beatles records backwards to find hidden messages? Doppler. COMBINING SIGNAL OPERATIONS

  • One of the trickiest topics in EECS 216! Be very careful.
  • Two methods are available. Pick one and stick with it. Why? See lecture notes. Reread at least three times.
  • First Method: Shift then Scale
  1. Put problem into the form y(t) = x(at − b).
  2. Shift x(t) by b. b > 0 →shift right. b < 0 →shift left.
  3. Scale result of #2 in time by a. a > 1 →compress. 0 < a < 1 →expand. a < 0 →time reversal: flip plot around t = 0 axis.
  • Second Method: Scale then Shift
  1. Put problem into the form y(t) = x(c(t − d)).
  2. Scale x(t) in t by c. c > 1 →compress in time. 0 < c < 1 →expand. c < 0 →time reversal: flip plot around t = 0 axis.
  3. Shift result of #2 in time by d. d > 0 →shift right. d < 0 →shift left. EX: x(t) = 5 cos(3t + 2). y(t) = 4x(2t − 1). y(3) = 4x(6 − 1) = 20 cos(17). Result: y(t) = 20 cos(3(2t − 1) + 2) = 20 cos(6t − 1). Frequency doubles. DEF: Linear combination z(t) of x(t) and y(t): z(t) = ax(t) + by(t) for two constants a and b. NOT: tx(t) + 3y(t) is NOT a linear combination of x(t) and y(t). EX: Audio mixing; summing Fourier series (later in EECS 216). DEF: Concatenation z(t) of finite-support x(t) and y(t): x(t) has support [0, Tx] → z(t) = x(t) for 0 ≤ t ≤ Tx. y(t) has support [0, Ty ] → z(t) = y(t − Tx) for Tx ≤ t ≤ (Tx + Ty ). z(t) has support [0, Tx + Ty ] ⇔ z(t) = 0 for t < 0 or t > (Tx + Ty ).