EEE 503 Digital Signal Processing Lecture #2 : Discrete-Time Signals & Systems

1. EEE 503 Digital Signal Processing Lecture #2 : Discrete-Time Signals & Systems Dr. Panuthat Boonpramuk Department of Control System & Instrumentation Engineering KMUTT

2. Analog Signal & Digital Signal Analog Signal x(t) Discrete Time Signal x(nT), x(n) Sampling Quantization Digital Signal X(n)

3. Analog Signal & Digital Signal

4. Discrete-Time Signals : Sequences Discrete-time (Digital) signals are represented mathematically as sequences of numbers. x={x[n]}, −∞ < n < ∞ where n is an integer. In practice, such sequences can often arise from periodic sampling of an analog signal. x[n] =xa[nT], −∞ < n < ∞ where T is called the sampling period, and fs=1/T is the sampling rate.

5. Basic Sequences (1)

6. Basic Sequences (2)

7. Basic Sequences (3)

8. Basic Sequences (4)

9. Periodicity of Sinusoidal Sequences andComplex Exponential Sequence

10. Sinusoidal Sequences with Different Frequencies

11. Discrete-Time Systems (1) • A discrete-time system is defined mathematically as a transformation or operator that maps an input sequence x[n] into a unique output sequence y[n].

12. Discrete-Time Systems (2) • The ideal delay system • Memoryless systems A system is referred as memoryless system if the output y[n] at every value of n only depends on the input x[n] at the same value of n. For example,

13. Discrete-Time Systems (3) • Linear systems The class of linear systems is defined by the principle of superposition. If and a is an arbitrary constant, then the system is linear if and only if (additivity property) (homogeneity or scaling property)

14. Discrete-Time Systems (4) • Time-invariant systems A time-invariant system is a system for which a time shift or delay of the input sequence causes a corresponding in the output sequence.

15. Discrete-Time Systems (5) • Causality A system is causal if, for every choice of n0, the output sequence value at the index n=n0 depends only on the input sequence value for n ≤n0. • Examples Forward difference system – not causal y[n] = x[n+1] - x[n] Backward difference system - causal y[n] = x[n] - x[n-1]

16. Discrete-Time Systems (6) • Stability A system is in the bounded input, bounded output (BIBO) sense if and only if every bounded input sequence produces a bounded output sequence. The input x[n] is bounded if there exists a fixed positive finite value Bx such that |x[n]| ≤Bx<∞ for all n. Stability requires that, for every bounded input, there exists a fixed positive finite value By such that |y[n]| ≤By<∞ for all n.

17. Linear Time-Invariant Systems • A particularly important class of systems consists of those that are linear and time-invariant (LTI). Impulse response If Time-invariant Linear

18. Convolution Sum (1) • Convolution Sum Example

19. Convolution Sum (2) • Method 1 1. For each k for which x[k] has a nonzero value, evaluate x[k] h[n–k] corresponding to the specific x[k]. It equals to the waveform of h[n] multiplied by x[k] and timeshifted by k (shift toward right if k>0, and shift toward left if k<0). 2. Add the resultant sequence values for all k’s to obtain the convolution sum corresponding to the full input sequence x[n].

20. Convolution Sum (3) • Method 1

21. Convolution Sum (4) • Method 2 1. For each value n (see *), producing h[n – k]. This is the mirror image of h[k] about the vertical axis shifted by n (shift toward right if n>0, and shift toward left if n<0). 2. Multiply this shifted sequence h[n–k] and the input sequence x[k], and add the resultant sequence values to obtain the value of the convolution at n. 3. Repeat steps 1-2 for different value of n. [* Note the range of n: if x[n] has its nonzero value between x1 and x2, and h[n] has nonzero values between h1 and h2, then x[n]*h[n] has nonzero value between x1+h1 and x2 +h2.]

22. Convolution Sum (5) • Method 2

23. Properties of LTI Systems (1) The impulse response is a complete characterization of the properties of a specific LTI system. • Convolution operation is commutative x[n]*h[n] = h[n]*x[n] • Parallel combination of LTI systems x[n]*(h1[n]+h2[n]) = x[n]*h1[n]+x[n]*h2[n])

24. Properties of LTI Systems (2) • Cascade connection of LTI systems h[n]= h1[n]*h2[n]

25. Properties of LTI Systems (3) • Stability LTI systems are stable if and only if the impulse response is absolutely summable, i.e, if • Causality LTI systems are causal if and only if

26. Impulse Responses of Some LTI Systems • Ideal delay (stable, causal when nd ≥ 0) • Accumulator (unstable, causal) • Forward difference system (stable, noncausal) • Backward difference system (stable, causal)

27. Inverse System If a LTI system has impulse response h[n], then its inverse system, if exists, has impulse response hi[n] defined by the relation • Example

28. Linear Constant-Coefficient Difference Equations (1) • An important subclass of LTI systems consists of those systems for which the input x[n] and the output y[n] satisfy an Nth-order linear constant-coefficient difference equation of the form present & past inputs If a0=1, then present output past outputs present & past inputs

29. Linear Constant-Coefficient Difference Equations (2) • Recursive filter At least one ak≠0 (k = 1, …, N). h[n] has infinite support. Also known as infinite impulse response (IIR) filter. • Non-recursive filter a1, …, aN=0 (no feedback). h[n] has finite support. Also known as finite impulse response (FIR) filter. • Example - accumulator

30. Recursive Computation of Difference Equations (1)

31. Recursive Computation of Difference Equations (2)

32. Recursive Computation of Difference Equations (3) • For a system defined by an Nth-order linear constant-coefficient difference equation, the output for a given input is not uniquely specified. Auxiliary information or conditions are required. • If the auxiliary information is in the form of N sequential values of the output, then the output of the system is uniquely specified. • Linearity, time-invariance, and causality of the system depend on the auxiliary conditions. If an additional condition is that the system is initially at rest, then the system will be LTI and causal.

33. Frequency-Domain Representation of Discrete-Time Signals and Systems (1)

34. Frequency-Domain Representation of Discrete-Time Signals and Systems (2) Eigenfunction and eigenvalue A signal for which the system output is just a (possibly complex) constant times the input is referred to as an eigenfunction of the system, and the constant factor is referred to as the eigenvalue. • Consider the cases that the input signals are complex exponential sequences. Complex exponential sequences are eigenfunctions of LTI systems. The response to a complex exponential sequence input is complex exponential sequence with the same frequency as the input and with amplitude and phase determined by the system.

35. Frequency-Domain Representation of Discrete-Time Signals and Systems (3)

36. Frequency-Domain Representation of Discrete-Time Signals and Systems (4)

37. Frequency-Domain Representation of Discrete-Time Signals and Systems (5)

38. Frequency-Domain Representation of Discrete-Time Signals and Systems (6)

39. Fourier Representation • Fourier transform • Inverse Fourier transform • X(ejω) is in general a complex function of ω.

40. Fourier Representation – Examples (1) • Fourier transform (real and imaginary parts)

41. Fourier Representation – Examples (2) • Fourier transform (magnitude and phase)

42. Fourier Representation – Examples (3) • Fourier transform using normalized frequency (fs=22 kHz)

43. Fourier Representation – Examples (4) • Fourier transform using actual frequency (fs=22 kHz)

44. Fourier Representation – Examples (5) • Fourier transform using normalized frequency

45. Fourier Representation – Examples (6) • Fourier transform using actual frequency