Find convolution sum of continuous signal [Signals&Systems]

In summary: Integral[1, t-3] ( (u(\tau) - 2*u(\tau-2) + u(\tau-5)) * exp(2*\tau) * u(1-\tau) ) d\tau + Integral[t-3, t] ( (u(\tau) - 2*u(\tau-2) + u(\tau-5)) * exp(2*\tau) * u(1-\tau) ) d\tauFor the fourth interval, t > 6, you will have to consider four cases: when t <=
  • #1
ColdStart
19
0

Homework Statement


x(t) = u(t) - 2*u(t-2) + u(t-5)
h(t) = exp(2*t) * u(1-t)


Homework Equations


Need to find convolution integrals for all possible intervals


The Attempt at a Solution


Ok, the first thing to do is to rewrite these equations in terms of [tex]\tau[/tex]. Now i have:
x([tex]\tau[/tex]) = u([tex]\tau[/tex]) - 2*u([tex]\tau[/tex]-2) + u([tex]\tau[/tex]-5)
h([tex]\tau[/tex]) = exp(2*[tex]\tau[/tex]) * u(1-[tex]\tau[/tex])

Now, i must derive h(t-[tex]\tau[/tex]), and i have:

h([tex]\tau[/tex]) = exp(2*(t-[tex]\tau[/tex])) * u(t + [tex]\tau[/tex] + 1)

Ok, now i am stuck, how to derive the needed intervals? And form the appropriate integral for each interval without graphing?

The answer says, that the intervals are: t <=1, 1 <= t <= 3, 3 <= t <= 6, 6 <= t

I have no clear idea now how did they derive these intervals WITHOUT GRAPHING!

What else i did is i rewrote x([tex]\tau[/tex]) and h(t-[tex]\tau[/tex]) in general term of convolution sum, and got:

y(t) = Integral[-Inf, Inf] ( (u([tex]\tau[/tex]) - 2*u([tex]\tau[/tex]-2) + u([tex]\tau[/tex]-5)) * exp(2*[tex]\tau[/tex]) * u(1-[tex]\tau[/tex]) ) d[tex]\tau[/tex]

Now, solving inequalilties inside above expression gives:
[tex]\tau[/tex] >= 0, [tex]\tau[/tex] >=2, [tex]\tau[/tex] >= 5, [tex]\tau[/tex] <= t, [tex]\tau[/tex] <= t+1

And now I am kind of stuck... how to derive now the correct integrals for bolded intervals above?

what is the clear logic to do this, and similar problems? I have several other problems too like this, and i think once i get the robust logic here, i will be able to solve others too.

thanks!
 
Physics news on Phys.org
  • #2




Thank you for your post. It seems like you are on the right track with your approach to solving this problem. The key to finding the intervals for the convolution integrals is to consider the different cases that can arise based on the given functions.

For the first interval, t <= 1, you can see that the function h(t-\tau) will only be non-zero when t <= 1 and \tau <= t. This gives you the following integral:

y(t) = Integral[0, t] ( (u(\tau) - 2*u(\tau-2) + u(\tau-5)) * exp(2*\tau) * u(1-\tau) ) d\tau

For the second interval, 1 <= t <= 3, you will have to consider two cases: when t <= 1 and t > 1. In the case when t <= 1, the integral will be the same as above. However, in the case when t > 1, the function h(t-\tau) will be non-zero for all values of \tau between 0 and t-1. This gives you the following integral:

y(t) = Integral[0, 1] ( (u(\tau) - 2*u(\tau-2) + u(\tau-5)) * exp(2*\tau) * u(1-\tau) ) d\tau + Integral[1, t] ( (u(\tau) - 2*u(\tau-2) + u(\tau-5)) * exp(2*\tau) * u(1-\tau) ) d\tau

For the third interval, 3 <= t <= 6, you will have to consider three cases: when t <= 1, 1 < t <= 3, and t > 3. In the case when t <= 1, the integral will be the same as above. In the case when 1 < t <= 3, the function h(t-\tau) will be non-zero for all values of \tau between t-3 and t-1. In the case when t > 3, the function h(t-\tau) will be non-zero for all values of \tau between 0 and t-1. This gives you the following integral:

y(t) = Integral[0, 1] ( (u(\tau) - 2
 

FAQ: Find convolution sum of continuous signal [Signals&Systems]

What is a convolution sum?

A convolution sum is a mathematical operation that combines two continuous signals to produce a third signal. It is often used in signal processing and is a fundamental concept in the study of signals and systems.

How is a convolution sum calculated?

The convolution sum is calculated by integrating the product of two signals over a certain range. This range is usually from negative infinity to positive infinity, but can also be limited to a specific interval depending on the problem at hand.

What is the significance of a convolution sum in signal processing?

The convolution sum is important in signal processing because it helps us understand how systems respond to inputs. It allows us to analyze the output of a system by considering the input and the characteristics of the system itself.

What are the properties of a convolution sum?

There are several properties of a convolution sum, including commutativity, distributivity, and associativity. These properties make it a useful tool for solving problems in signal processing.

Can a convolution sum be performed on any type of signal?

Yes, a convolution sum can be performed on any type of signal, as long as the signals are continuous. This includes signals that are deterministic or stochastic, periodic or aperiodic, and even signals with discontinuities.

Similar threads

Replies
4
Views
2K
Replies
4
Views
1K
Replies
8
Views
2K
Replies
1
Views
858
Replies
4
Views
2K
Replies
1
Views
2K
Replies
1
Views
1K
Replies
14
Views
3K
Back
Top