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Page 1: EE 314 Signal and Linear System Analysis

EE 314 Signal and Linear System Analysis

Graphical Convolution

Lecture 8 EE 314 Signal and Linear System Analysis Slide 1 of 14

Page 2: EE 314 Signal and Linear System Analysis

Summary of Last Lecture

Lecture 8 EE 314 Signal and Linear System Analysis

β€’ Applying a causal input (π‘₯π‘₯(𝑑𝑑)) to a causal LTI systems with impulse response β„Ž(𝑑𝑑) gives rise to a causal output 𝑦𝑦 𝑑𝑑 :

0

( ) ( ) ( )t

y t x h t dΟ„ Ο„ Ο„= βˆ’βˆ«

( )h t

0

( ) ( )t

h x t dΟ„ Ο„ Ο„= βˆ’βˆ«

Slide 2 of 14

Page 3: EE 314 Signal and Linear System Analysis

Graphical Convolution

Lecture 8 EE 314 Signal and Linear System Analysis

β€’ Revisiting the prior RC example Let 𝑅𝑅𝑅𝑅 = 1/2 = πœπœπ‘π‘, hence,

Input: 𝑣𝑣𝑖𝑖𝑖𝑖 𝑑𝑑 = 𝑒𝑒 𝑑𝑑 βˆ’ 𝑒𝑒(𝑑𝑑 βˆ’ 1)β€’ Analytical soln is: For, 0 < t < 1

2( ) 2 ( )th t e u tβˆ’=

0

( ) ( ) ( )t

out inv t v h t dΟ„ Ο„ Ο„= βˆ’βˆ« ( ) ( )2

0

1 2t

te dΟ„ Ο„βˆ’ βˆ’= ∫ 2 2

0

2t

te e dΟ„ Ο„βˆ’= ∫

2 2

0

tte e Ο„βˆ’ = ( )2 2 1t te eβˆ’= βˆ’ 21 teβˆ’= βˆ’( )( )21 ( ) ( 1)te u t u tβˆ’= βˆ’ βˆ’ βˆ’

Slide 3 of 14

Page 4: EE 314 Signal and Linear System Analysis

Graphical Convolution

Lecture 8 EE 314 Signal and Linear System Analysis

For, t > 1

Final result

1

0 1

( ) ( ) ( ) ( ) ( )t

out in inv t v h t d v h t dΟ„ Ο„ Ο„ Ο„ Ο„ Ο„= βˆ’ + βˆ’βˆ« ∫1

0

( ) 0h t dΟ„ Ο„= βˆ’ +∫12 2

0

te e Ο„βˆ’ =

( )2 2 1te eβˆ’= βˆ’ 2( 1) 2t te eβˆ’ βˆ’ βˆ’= βˆ’( )2( 1) 2 ( 1)t te e u tβˆ’ βˆ’ βˆ’= βˆ’ βˆ’

( )( ) ( )2 2( 1) 2( ) 1 ( ) ( 1) ( 1)t t toutv t e u t u t e e u tβˆ’ βˆ’ βˆ’ βˆ’= βˆ’ βˆ’ βˆ’ + βˆ’ βˆ’

( ) ( )2 2( 1)1 ( ) 1 ( 1)t te u t e u tβˆ’ βˆ’ βˆ’= βˆ’ + βˆ’ βˆ’

Slide 4 of 14

Page 5: EE 314 Signal and Linear System Analysis

Graphical Convolution

Lecture 8 EE 314 Signal and Linear System Analysis

( ) ( )2 2( 1)( ) 1 ( ) 1 ( 1)t toutv t e u t e u tβˆ’ βˆ’ βˆ’= βˆ’ + βˆ’ βˆ’

Slide 5 of 14

Page 6: EE 314 Signal and Linear System Analysis

β„Ž βˆ’πœπœ + 𝑑𝑑 => Shift then flipβ„Ž(βˆ’(𝜏𝜏 βˆ’ 𝑑𝑑)) => Flip then shift

Graphical Convolution

Lecture 8 EE 314 Signal and Linear System Analysis

β€’ Let’s evaluate the convolution graphically

Overlay a plot of 𝑣𝑣𝑖𝑖𝑖𝑖(𝜏𝜏) with a plot of β„Ž(𝑑𝑑 βˆ’ 𝜏𝜏), compute the area under the product (for 0 ≀ 𝜏𝜏 ≀ 𝑑𝑑), then vary 𝑑𝑑.

0

( ) ( ) ( )t

out inv t v h t dΟ„ Ο„ Ο„= βˆ’βˆ«

( )inv t( )h t

We need 𝑣𝑣𝑖𝑖𝑖𝑖(𝜏𝜏)

We need β„Ž(𝑑𝑑 βˆ’ 𝜏𝜏)

Flip then shift by 𝑑𝑑

The area under the product!!

ORβ„Ž(𝑑𝑑 βˆ’ 𝜏𝜏) = β„Ž(βˆ’(𝜏𝜏 βˆ’ 𝑑𝑑))

Shift by 𝑑𝑑 then flip

Slide 6 of 14

Page 7: EE 314 Signal and Linear System Analysis

Graphical Convolution

Lecture 8 EE 314 Signal and Linear System Analysis

β€’ Start with 𝑑𝑑 < 0

0

( ) ( ) ( )t

y t x h t dΟ„ Ο„ Ο„= βˆ’βˆ«

( ( ))h tΟ„βˆ’ βˆ’ ( )x τ𝑑𝑑 = βˆ’0.5 𝑠𝑠𝑠𝑠𝑠𝑠

0=

( ( 0.5))h Ο„βˆ’ βˆ’

The area under the product?

0.5

0

( 0.5) ( ) ( )y x h t dΟ„ Ο„ Ο„βˆ’

βˆ’ = βˆ’βˆ«

Slide 7 of 14

Page 8: EE 314 Signal and Linear System Analysis

Graphical Convolution

Lecture 8 EE 314 Signal and Linear System Analysis

β€’ Now, consider 0 ≀ 𝑑𝑑 < 1

0

( ) ( ) ( )t

y t x h t dΟ„ Ο„ Ο„= βˆ’βˆ«

( ( ))h tΟ„βˆ’ βˆ’ ( )x τ𝑑𝑑 = +0.6 𝑠𝑠𝑠𝑠𝑠𝑠

2( )

0

2t

te dΟ„ Ο„βˆ’ βˆ’= ∫

2( ) 2 ( )th t e u tβˆ’=

( ) ( ( ))x h tΟ„ Ο„βˆ’ βˆ’

Slide 8 of 14

Page 9: EE 314 Signal and Linear System Analysis

Graphical Convolution

Lecture 8 EE 314 Signal and Linear System Analysis

β€’ Now, consider 𝑑𝑑 > 1

0

( ) ( ) ( )t

y t x h t dΟ„ Ο„ Ο„= βˆ’βˆ«

( ( ))h tΟ„βˆ’ βˆ’

( )x Ο„

𝑑𝑑 = +1.5 𝑠𝑠𝑠𝑠𝑠𝑠

12( )

0

2 te dΟ„ Ο„βˆ’ βˆ’= ∫

Slide 9 of 14

Page 10: EE 314 Signal and Linear System Analysis

Graphical Convolution

Lecture 8 EE 314 Signal and Linear System Analysis

MATLAB code

Slide 10 of 14

Page 11: EE 314 Signal and Linear System Analysis

Graphical Convolution

Lecture 8 EE 314 Signal and Linear System Analysis

β€’ More Convolution Examples

MATLAB code

Slide 11 of 14

Page 12: EE 314 Signal and Linear System Analysis

Graphical Convolution

Lecture 8 EE 314 Signal and Linear System Analysis

β€’ Systems connected in series

β€’ Systems connected in parallel

( )z t1( ) ( )* ( )z t x t h t=

2( ) ( )* ( )y t z t h t=

( )1 2( )* ( ) * ( )x t h t h t=

( )1 2( )** )( ()x t h t h t=

1( ) ( )* ( ) ( )* ( )Ny t x t h t x t h t= + +

[ ]1( )* ( ) ( )Nh t tx t h+ +=

Slide 12 of 14

Page 13: EE 314 Signal and Linear System Analysis

Causal LTI System

Lecture 8 EE 314 Signal and Linear System Analysis

β€’ Causality A LTI system is causal if it does NOT rely on future inputs in

order to determine the current output.o All real/physical systems are causal – They can not anticipate

future inputs!!

o i.e., A causal system has an impulse response that is a causal function.

( ) 0 for all 0h t t⇒ = <

( ) ( ) ( )y t x h t dΟ„ Ο„ Ο„βˆž

βˆ’βˆž

= βˆ’βˆ« Consider 𝜏𝜏 > 𝑑𝑑 β€Ό

Would use future values of π‘₯π‘₯ 𝑑𝑑to determine 𝑦𝑦(𝑑𝑑)!!

Does this system have memory?

Give an example of a system that does NOT have memory?

( )h t

0

( ) ( )t

x h t dΟ„ Ο„ Ο„= βˆ’βˆ«

Slide 13 of 14

Page 14: EE 314 Signal and Linear System Analysis

Next Lecture

Lecture 8 EE 314 Signal and Linear System Analysis

β€’ LTI Sinusoidal Response

β€’ Reading Assignment: Chap. 2.7

Slide 14 of 14