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Introduction to LSI/LTI systems.

linear shift invariant system
A linear shift invariant system is one that is both:
  • linear
  • shift invariant
Also known as LSI, LTI ( ).

All systems

LSI systems are the bread 'n' budduh of DSP ( ).

Characterizing lsi systems

1.

Since the system is linear , it can be represented as a matrix multiply ( ).

x N , y N , and is an N N complex matrix.

2.

Since is shift invariant , it cannot be just any old matrix. Its values are highly constrained .

In particular we know that

C - m C m
Let's understand this...

Understanding conditions on matrixℋ for shiftInvariance

Recall .

y n ( row n of | x ) ( h n r | x )
y x
·
where the· is in the n th row.

Now shift x down circularly m units. If the system is SI then y will also shift down circularly m units. i.e. : C m y C m x

·
where the· is in the n th row, the▪ is in the n m th row, and the↓ has been shifted down circularly m units.

Key: we want the value▪ in C m y to equal the· value in y .

This implies that the rows of must circularly shift as we shift x and y . i.e. : row n m of is equal to the circular shift right of row n of by m . i.e. : h n + m r h n r C m i.e. : all rows of are circular shifts of each other.

y 0 y 1 y 2 1 4 7 2 5 8 3 6 9 x 0 x 1 x 2 LSI also needs m 1 : y 2 y 0 y 1 1 4 7 2 5 8 3 6 9 x 2 x 0 x 1 and m 2 : y 1 y 2 y 0 1 4 7 2 5 8 3 6 9 x 1 x 2 x 0 i.e. : 1 5 9 2 6 7 3 4 8 1 3 2 2 1 3 3 2 1

is called a circulant matrix .

  • each row is a circulary shifted version of the row above (right).
  • each column is a circularly shifted version of the column to the left (down).
1 3 2 2 1 3 3 2 1 h 0 r h 0 r C 1 h 0 r C N - 1 h 0 c C 1 h 0 c C N - 1 h 0 c
which implies that either the first row or first column are all you need to know to know all of .
Circulant matrices are a special case of Toeplitz matrices , which are constant along diagonals. e.g. : T 1 3 5 6 2 1 3 5 4 2 1 3 7 4 2 1 T n k t n k

3-point smoother ?

Edge detector ?

Also, row n , column k element of is

n k h n k N
where 0 n N 1 and 0 k N 1 and h is the signal corresponding to the first ( i.e. the zeroth!) column of .

N 2 , h 0 c -1 7 3

h 0 0 N h 0 1 N h 0 2 N h 1 0 N h 1 1 N h 1 2 N h 2 0 N h 2 1 N h 2 2 N h 0 h 2 h 1 h 1 h 0 h 2 h 2 h 1 h 0

Apply a 3-point moving average smoother to a signal x -2 -1 0 1 2 1 0 -1 .

8 .
In ,
y n 1 x n 1 N 2 x n 3 x n 1 N
y 2 3 0 0 0 0 0 1 1 2 3 0 0 0 0 0 0 1 2 3 0 0 0 0 0 0 1 2 3 0 0 0 0 0 0 1 2 3 0 0 0 0 0 0 1 2 3 0 0 0 0 0 0 1 2 3 3 0 0 0 0 0 1 2 -2 -1 0 1 2 1 0 -1 -8 -4 2 8 8 4 -2 -8

The relationship between rows and columns of :

n k h n k N
where n is the row, k is the column, and 0 n N 1 and 0 k N 1 .
Rows and columns run time in reverse order !!!

4

y 0 y 1 y 2 y 3 2 3 0 1 1 2 3 0 0 1 2 3 3 0 1 2 x 0 x 1 x 2 x 3
where the zeroth column, 2 1 0 3 , is the impulse response , h n ( ).
Reponse to impulse at n 0 : 2 comes out, then 1, then 0,….
In , the zeroth row, 2 3 0 1 , is the time-reversed impulse response , h k .

Upshot for lsi systems

The impulse response; the zeroth column of .
The shifted impulse response. Here, δ 1 is a shifted impulse.
The m-shifted impulse response. Here, C m δ 0 is an m-shifted impulse.
i.e. : if we input and measure output h 0 c , we can place it in the zeroth column of and then replicate it with circular shifts to build the entire !!

Summary: lsi systems and imuplse response

Given an LSI system ( ), we can characterize it by the impulse response , h ( ).

and build up with circular shifts of the zeroth column, h :
h C 1 h C N - 1 h
Then we can compute y for any input x N through
y x

How to get the impulse response?

4-point edge detector for 8-point signals in complex space ( ).

h =

=

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Source:  OpenStax, Ece 454 and ece 554 supplemental reading. OpenStax CNX. Apr 02, 2012 Download for free at http://cnx.org/content/col11416/1.1
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