Smoothing Data with Average Calculation

AI Thread Summary
The discussion focuses on smoothing data using average calculations and ensuring causality in the transfer function. The formula y(n) = x((n-1) + x(n) + x(n+1))/3 is analyzed, emphasizing the need to position the transfer function in the right half-plane for n>=0. By substituting n with m-1, the function is effectively shifted in time, resulting in y(m) = x((m-2) + x(m-1) + x(m))/3. The conversation highlights the challenge of determining causality without knowing the specific form of x(n), as it evaluates values at multiple indices. A system is deemed causal if it is Linear Time Invariant and h(n)=0 for t<0.
rjunior
Messages
4
Reaction score
0
How to make it causal:

y (n) = x ((n-1) + x(n) + x(n+1))/3
 
Physics news on Phys.org
you need to move the transfer function such that it is located in the right half plane, n>=0

replace n=m-1, this will move the function in time

y(m)=x((m-2)+x(m-1)+x(m))/3
 
Jaynte said:
you need to move the transfer function such that it is located in the right half plane, n>=0

replace n=m-1, this will move the function in time

y(m)=x((m-2)+x(m-1)+x(m))/3

So why does y magically become indexed at m instead of m - 1?

It also seems like you can never know if that system is causal (though you can know it is non-causal) without knowing what x(n) looks like since he is evaluating x at x(n-1) + x(n) + x(n+1).
 
Sorry for the index.

If the system is Linear Time Invariant it is casual if h(n)=0 for t<0
 

Similar threads

Replies
8
Views
1K
Replies
2
Views
3K
Replies
3
Views
1K
Replies
1
Views
2K
Replies
2
Views
2K
Replies
3
Views
1K
Replies
7
Views
1K
Back
Top