Chapter6 sampling

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Chapter6 sampling

  1. 1. Signals & Systems Chapter 6 SamplingINC212 Signals and Systems : 2 / 2554
  2. 2. Overview  Sampling theorem  Signal reconstruction  Interpolation formula  AliasingINC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling
  3. 3. The sampling theoremINC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling
  4. 4. The sampling theoremINC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling
  5. 5. The sampling theoremINC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling
  6. 6. The sampling theorem F (ω ) = 0 for ω > 2πB bandlimited Fs ≥ 2B Hz f (t ) = f (t )δ T (t ) = ∑ f (nT )δ (t − nT ) n 1 ∞ 2π F (ω ) = ∑ F (ω − nω s ); ω s = = 2πFs T n = −∞ TINC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling
  7. 7. The sampling theorem ∞ T 2 1 x(t ) = ∑ ck e jkω s t , − ∞ < t < ∞ ; ck = ∫ x(t )e − jkω s t dt k = −∞ T −T 2 ∞ Trigonometric form δ T (t ) = ∑ ck e jkω s t , − ∞ < t < ∞ k = −∞ 1 1 T 2 T 2 1 1 c0 = ck = ∫ δ T (t )e − jkω s t dt = T −T 2 ∫ δ T (t )(1)dt = T −T 2 T T 2 Ak = 2 ck = , k = 1,2,3, 1 ∞ jkω s t 2π T δ T (t ) = ∑ e , ω s = T k = −∞ T θk = 0 δ T (t ) = 1 [1 + 2( cos ωs t + cos 2ωst + cos ωst + ) ], ωs = 2π = 2πFs T TINC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling
  8. 8. The sampling theorem f (t ) = f (t )δ T (t ) 1 f (t ) = [ f (t ) + 2 f (t ) cos ω s t + 2 f (t ) cos 2ω s t + 2 f (t ) cos ω s t + ] T F 2 f (t ) cos ω s t ↔ F (ω − ω s ) + F (ω + ω s ) F 2 f (t ) cos 2ω s t ↔ F (ω − 2ω s ) + F (ω + 2ω s ) 1 ∞ F (ω ) = ∑ F (ω − nω s ) T n = −∞INC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling
  9. 9. Effect of undersampling and oversampling f (t ) = sinc 2 (5πt )  ω  F (ω ) = 0.2 tri   20π INC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling
  10. 10. Effect of undersampling and oversampling  ω  f (t ) = sinc (5πt ) 2 F (ω ) = 0.2 tri   20π  Fs = 10 Hz → T = 0.1 secINC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling
  11. 11. Effect of undersampling and oversampling  ω  f (t ) = sinc (5πt ) 2 F (ω ) = 0.2 tri   20π  Fs = 5 Hz → T = 0.2 secINC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling
  12. 12. Effect of undersampling and oversampling  ω  f (t ) = sinc (5πt ) 2 F (ω ) = 0.2 tri   20π  Fs = 20 Hz → T = 0.05 secINC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling
  13. 13. Effect of undersampling and oversampling  ω  f (t ) = sinc (5πt ) 2 F (ω ) = 0.2 tri   20π  F (ω ) = 0 for ω > 10π ω s ≥ 20π ; Fs ≥ 10 Hz; T ≤ 0.1INC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling
  14. 14. Effect of undersampling and oversampling F (ω ) = 0 for ω > 2πB ⇒ Bandlimited to B Hz Fs ≥ 2 B Hz The minimum sampling rate = 2B The Nyquist rate The sampling interval = 1/2B The Nyquist intervalINC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling
  15. 15. Signal Reconstruction  Zero-order holdINC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling
  16. 16. Signal Reconstruction  The Interpolation FormulaINC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling
  17. 17. Signal Reconstruction  The Interpolation Formula h(t ) = 2 BT sinc( 2πBt )  ω  H (ω ) = T rect  Assuming the Nyquist rate; 2 BT = 1  4πB  h(t ) = sinc( 2πBt ) f (t ) = h(t ) * f (t ) ∞ = h(t ) * ∑ f (nT )δ (t − nT ) n = −∞ ∞ = ∑ f (nT )h(t − nT ) n = −∞ ∞ = ∑ f (nT ) sinc(2πB(t − nT )) n = −∞ ∞ ∴ f (t ) = ∑ f (nT ) sinc(2πBt − nπ ) n = −∞INC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling
  18. 18. AliasingINC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling
  19. 19. Aliasing  Amplitude spectrum of time-limited signal not be bandlimited ωs = 2 BINC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling
  20. 20. Aliasing  Anti-aliasing x (t) x [n] Lowpass Sampling filter  The sampling frequency may be as large as 10 or 20 times B.INC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling
  21. 21. INC212 Signals and Systems : 2 / 2554 Chapter 6 Sampling

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