Lecture Notes on Basic System Properties and Convolution in Discrete Systems, Study notes of Signals and Systems

Lecture notes on the basic properties of systems, focusing on linearity, time-invariance, causality, and stability. It also covers the concept of convolution and impulse response in discrete systems. The notes include examples and proofs to help understand these concepts.

Typology: Study notes

Pre 2010

Uploaded on 09/02/2009

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EECS 216 LECTURE NOTES
BASIC SYSTEM PROPERTIES
What: Input x[n] |SYSTEM| y[n] output.
Why: Design the system to filter input x[n].
DEF: A system is LINEAR if these two properties hold:
1. Scaling: If x[n] |SYS| y[n], then ax[n] |SYS| ay[n]
for: any constant a. NOT true if avaries with time (i.e., a[n]).
2. Superposition: If x1[n] |SYS| y1[n] and x2[n] |SYS| y2[n],
Then: (ax1[n] + bx2[n]) |SYS| (ay1[n] + by2[n])
for: any constants a, b. NOT true if aor bvary with time (i.e., a[n], b[n]).
EX: y[n] = 3x[n2]; y[n] = x[n+1]nx[n]+2x[n1]; y[n] = sin(n)x[n].
NOT: y[n] = x2[n]; y[n] = sin(x[n]); y[n] = |x[n]|;y[n] = x[n]/x[n1].
NOT: y[n] = x[n] + 1 (try it). This is called an affine system.
HOW: If any nonlinear function of x[n], not linear. Nonlinear of just nOK.
DEF: A system is TIME-INVARIANT if this property holds:
If x[n] |SYS| y[n], then x[nN] |SYS| y[nN]
for: any integer time delay N. NOT true if Nvaries with time (e.g., N(n)).
EX: y[n] = 3x[n2]; y[n] = sin(x[n]); y[n] = x[n]/x[n1].
NOT: y[n] = nx[n]; y[n] = x[n2]; y[n] = x[2n]; y[n] = x[n].
HOW: If nappears anywhere other than in x[n], not time-invariant. Else OK.
DEF: A system is CAUSAL if it has this form for some function F(·):
y[n] = F(x[n], x[n1], x[n2] . . .) (present and past input only).
Note: Physical systems must be causal. But DSP filters need not be causal!
DEF: A system is MEMORYLESS if y[n] = F(x[n]) (present input only).
DEF: A system is (BIBO) STABLE iff: Let x[n] |SYSTEM| y[n].
If |x[n]|< M for some constant M, then |y[n]|< N for some N.
i.e.: “Every bounded input (BI) produces a bounded output (BO).”
HOW: BIBO stable P
n=−∞ |h[n]|< L for some constant L
where: Impulse δ[n] |SYS| h[n]=impulse response.
EX: A time-invariant system is observed to have these two responses:
{0,0,3} |SYS| {0,1,0,2}and {0,0,0,1} |SYS| {1,2,1}.
Prove: The system is nonlinear.
Proof: By contradiction. Suppose the system is linear. But then:
{0,0,0,1} |SYS| {1,2,1}implies {0,0,3} |SYS| {3,6,3}
since we know it is time-invariant. Then {0,0,3}produces two outputs!
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BASIC SYSTEM PROPERTIES

What: Input x[n] → |SYSTEM| → y[n] output. Why: Design the system to filter input x[n]. DEF: A system is LINEAR if these two properties hold:

  1. Scaling: If x[n] → |SYS| → y[n], then ax[n] → |SYS| → ay[n] for: any constant a. NOT true if a varies with time (i.e., a[n]).
  2. Superposition: If x 1 [n] → |SYS| → y 1 [n] and x 2 [n] → |SYS| → y 2 [n], Then: (ax 1 [n] + bx 2 [n]) → |SYS| → (ay 1 [n] + by 2 [n]) for: any constants a, b. NOT true if a or b vary with time (i.e., a[n], b[n]). EX: y[n] = 3x[n−2]; y[n] = x[n+1]−nx[n]+2x[n−1]; y[n] = sin(n)x[n]. NOT: y[n] = x^2 [n]; y[n] = sin(x[n]); y[n] = |x[n]|; y[n] = x[n]/x[n − 1]. NOT: y[n] = x[n] + 1 (try it). This is called an affine system. HOW: If any nonlinear function of x[n], not linear. Nonlinear of just n OK. DEF: A system is TIME-INVARIANT if this property holds: If x[n] → |SYS| → y[n], then x[n − N ] → |SYS| → y[n − N ] for: any integer time delay N. NOT true if N varies with time (e.g., N (n)). EX: y[n] = 3x[n − 2]; y[n] = sin(x[n]); y[n] = x[n]/x[n − 1]. NOT: y[n] = nx[n]; y[n] = x[n^2 ]; y[n] = x[2n]; y[n] = x[−n]. HOW: If n appears anywhere other than in x[n], not time-invariant. Else OK. DEF: A system is CAUSAL if it has this form for some function F (·): y[n] = F (x[n], x[n − 1], x[n − 2].. .) (present and past input only). Note: Physical systems must be causal. But DSP filters need not be causal! DEF: A system is MEMORYLESS if y[n] = F (x[n]) (present input only). DEF: A system is (BIBO) STABLE iff: Let x[n] → |SYSTEM| → y[n]. If |x[n]| < M for some constant M , then |y[n]| < N for some N. i.e.: “Every bounded input (BI) produces a bounded output (BO).” HOW: BIBO stable ⇔

n=−∞ |h[n]|^ < L^ for some constant^ L where: Impulse δ[n] → |SYS| → h[n]=impulse response.

EX: A time-invariant system is observed to have these two responses: { 0 , 0 , 3 } → |SYS| → { 0 , 1 , 0 , 2 } and { 0 , 0 , 0 , 1 } → |SYS| → { 1 , 2 , 1 }. Prove: The system is nonlinear. Proof: By contradiction. Suppose the system is linear. But then: { 0 , 0 , 0 , 1 } → |SYS| → { 1 , 2 , 1 } implies { 0 , 0 , 3 } → |SYS| → { 3 , 6 , 3 } since we know it is time-invariant. Then { 0 , 0 , 3 } produces two outputs!

CONVOLUTION AND IMPULSE RESPONSE

x[n] = { 3 , 1 , 4 , 6 } ⇔ x[n] = 3δ[n + 1] + 1δ[n] + 4δ[n − 1] + 6δ[n − 2]. Note: x[n] =

i x[i]δ[n^ −^ i] =^ x[n]^ ∗^ δ[n] (sifting property of impulse). Delay: x[n − D] is x[n] shifted right (later) if D > 0; left (earlier) if D < 0. Fold: x[−n] is x[n] flipped/folded/reversed around n = 0. Both: x[N − n] is x[−n] shifted right if N > 0 (since x[0] is now at n = N ). FOR LINEAR TIME-INVARIANT (LTI) SYSTEMS:

  1. δ[n] → |LT I| → h[n] Definition of Impulse response h[n].
  2. δ[n − i] → |LT I| → h[n − i] Time invariant: delay by i.
  3. x[i]δ[n − i] → |LT I| → x[i]h[n − i] Linear: scale by x[i].

i x[i]δ[n^ −^ i]^ → |LT I| →^

i x[i]h[n^ −^ i]^ Linear:^ superposition.

  1. x[n] → |LT I| → y[n] =

i x[i]h[n^ −^ i] =^ h[n]^ ∗^ x[n]^ Convolution. Input x[n] into LTI system with no initial stored energy→output y[n]. PROPERTIES OF DISCRETE CONVOLUTION

  1. y[n] = h[n] ∗ x[n] = x[n] ∗ h[n] =

i h[i]x[n^ −^ i] =^

h[n − i]x[i].

  1. h[n], x[n] both causal (h[n] = 0 for n < 0 and x[n] = 0 for n < 0) → y[n] = ∑ni=0 h[i]x[n − i] = ∑ni=0 h[n − i]x[i] also causal.
  2. Suppose h[n] 6 = 0 only for 0 ≤ n ≤ L (h[n] has length L + 1). Suppose x[n] 6 = 0 only for 0 ≤ n ≤ M (x[n] has length M + 1). Then y[n] 6 = 0 only for 0 ≤ n ≤ L + M (y[n] has length L + M + 1). Note: Length[y[n]]=Length[h[n]]+Length[x[n]]-1. Note: y[0] = h[0]x[0]; y[L + M ] = h[L]x[M ]; x[n] ∗ δ[n − D] = x[n − D].
  3. x[n] → |h 1 [n]| → |h 2 [n]| → y[n] (cascade connection) Equivalent to: x[n] → |h 1 [n] ∗ h 2 [n]| → y[n].
  4. x[n] → ↗↘→|→|hh^12 [[nn]]|→|→↘↗

→ y[n] (parallel connection) Equivalent to: x[n] → |h 1 [n] + h 2 [n]| → y[n]. MA: y[n] = b 0 x[n] + b 1 x[n − 1] +... + bq x[n − q] (Moving Average) Huh? Present output=weighted average of q most recent inputs. Note: Equivalent to y[n] = b[n] ∗ x[n] where b[k] = bk, 0 ≤ k ≤ q. FIR: Finite Impulse Response ⇔ h[n] has finite duration. EX: Any MA system is also an FIR system, and vice-versa. IIR: Infinite Impulse Response ⇔ h[n] not finite duration. EX: h[n] = anu[n] = an^ for n ≥ 0 and |a| < 1 is stable and IIR.