Calculating Convolution Integrals with Unit Step Functions

In summary: Likewise u(-a) is zero whenever the argument, a>0. so u(\ -(\tau-(t+3))\) is zero whenever \tau-(t+3)>0 or \tau>t+3 . hmmm. so it looks like you did the integral right, at least for the case when the upper limit t+3 is greater than the lower limit of 0.in the case that the upper limit t+3 is less than the lower limit of 0, then you have to recognize that for all \tau, at least one of those unit step functions are zero, so you are integrating something that is zero for all \tau, so your
  • #1
dashkin111
47
0

Homework Statement


Compute the following

[tex]y(t)=e^{-3t}u(t)\ast u(t+3)[/tex]


Homework Equations


u(t) is the unit step function.


The Attempt at a Solution



I get confused with these for some reason...

[tex]y(t)= \int^{+\infty}_{-\infty}e^{-3 \tau}u(\tau)u(t-\tau+3)d\tau[/tex]

This is where I have my first problem, trying to eliminate the step functions. I tried

[tex]y(t)= \int^{t+3}_{0}e^{-3 \tau}d\tau[/tex]

Does that look right?

Finally integrating that with my limits gave:

[tex][1/3 - e^{-3(t+3)}]u(t+3)[/tex]

Now the step function I added at the end, and I'm not quite sure why... My main problem is dealing with the step functions, any suggestions/ confirmations I did it wrong or right?
 
Physics news on Phys.org
  • #2
dashkin111 said:
[tex]y(t)= \int^{t+3}_{0}e^{-3 \tau}d\tau[/tex]

Does that look right?

Finally integrating that with my limits gave:

[tex][1/3 - e^{-3(t+3)}]u(t+3)[/tex]

Now the step function I added at the end, and I'm not quite sure why... My main problem is dealing with the step functions, any suggestions/ confirmations I did it wrong or right?

the fact that the symbol you use for the (heaviside) unit step function is u(t) not "h(t)", which is what is used by a lot of folks in the mathematics discipline (and you didn't use the term "heaviside function") makes me think that this is an EE course. likely the first Linear System Theory course (or whatever your school calls it). no?

now, i think i see one error. let's break it up a little. let's say it's:

[tex] y(t) = h(t) \ * \ x(t) [/tex]

or, maybe a better notation is:

[tex] y(t) = (h \ * \ x)(t) [/tex]where

[tex] h(t) = e^{-3 t}u(t) [/tex]
and
[tex] x(t) = u(t+3) [/tex]

Convolution of h(t) against x(t) is

[tex] y(t) = (h \ * \ x)(t) = \int_{-\infty}^{+\infty} h(\tau) x(t-tau) \ d\tau = \int_{-\infty}^{+\infty} x(\tau) h(t-tau) \ d\tau [/tex]

you can pick either integral. but be careful. it looks to me that you intended to pick

[tex] y(t) = (h \ * \ x)(t) = \int_{-\infty}^{+\infty} h(\tau) x(t-tau) \ d\tau [/tex]

or

[tex] y(t) = (h \ * \ x)(t) = \int_{-\infty}^{+\infty} e^{-3 \tau}u(\tau) u((t-\tau)+3) \ d\tau [/tex]

now remember that it is [itex]\tau[/itex] that is your independent variable inside the integral, not [itex]t[/itex]. so you might want to express it as

[tex] y(t) = (h \ * \ x)(t) = \int_{-\infty}^{+\infty} e^{-3 \tau}u(\tau) u( \ -(\tau-(t+3))\ ) \ d\tau [/tex]

you are correct that the bottom limit is always zero because of [itex]u(\tau)[/itex]. but what is the top limit? [itex]u(a)[/itex] is zero whenever the argument, a<0 . likewise [itex]u(-a)[/itex] is zero whenever the argument, a>0. so [itex]u(\ -(\tau-(t+3))\)[/itex] is zero whenever [itex] \tau-(t+3)>0 [/itex] or [itex] \tau>t+3 [/itex]. hmmm. so it looks like you did the integral right, at least for the case when the upper limit t+3 is greater than the lower limit of 0.

in the case that the upper limit t+3 is less than the lower limit of 0, then you have to recognize that for all [itex]\tau[/itex], at least one of those unit step functions are zero, so you are integrating something that is zero for all [itex]\tau[/itex], so your integral is zero. this happens whenever t+3 < 0 or t<-3. but your integral

[tex]y(t)= \int^{t+3}_{0}e^{-3 \tau}d\tau[/tex]

still evaluates to something that is not necessarily zero when t<-3, so you have to fix the result so it is

[tex]y(t) = \frac{1}{3} \left(1 - e^{-3(t+3)} \right) [/tex]

when t>-3 and

[tex]y(t) = 0 [/tex]

when t<-3 . how're you going to do that?
 
Last edited:
  • #3
rbj said:
the fact that the symbol you use for the (heaviside) unit step function is u(t) not "h(t)", which is what is used by a lot of folks in the mathematics discipline (and you didn't use the term "heaviside function") makes me think that this is an EE course. likely the first Linear System Theory course (or whatever your school calls it). no?

now, i think i see one error. let's break it up a little. let's say it's:

[tex] y(t) = h(t) \ * \ x(t) [/tex]

or, maybe a better notation is:

[tex] y(t) = (h \ * \ x)(t) [/tex]


where

[tex] h(t) = e^{-3 t}u(t) [/tex]
and
[tex] x(t) = u(t+3) [/tex]

Convolution of h(t) against x(t) is

[tex] y(t) = (h \ * \ x)(t) = \int_{-\infty}^{+\infty} h(\tau) x(t-tau) \ d\tau = \int_{-\infty}^{+\infty} x(\tau) h(t-tau) \ d\tau [/tex]

you can pick either integral. but be careful. it looks to me that you intended to pick

[tex] y(t) = (h \ * \ x)(t) = \int_{-\infty}^{+\infty} h(\tau) x(t-tau) \ d\tau [/tex]

or

[tex] y(t) = (h \ * \ x)(t) = \int_{-\infty}^{+\infty} e^{-3 \tau}u(\tau) u((t-\tau)+3) \ d\tau [/tex]

now remember that it is [itex]\tau[/itex] that is your independent variable inside the integral, not [itex]t[/itex]. so you might want to express it as

[tex] y(t) = (h \ * \ x)(t) = \int_{-\infty}^{+\infty} e^{-3 \tau}u(\tau) u( \ -(\tau-(t+3))\ ) \ d\tau [/tex]

you are correct that the bottom limit is always zero because of [itex]u(\tau)[/itex]. but what is the top limit? [itex]u(a)[/itex] is zero whenever the argument, a<0 . likewise [itex]u(-a)[/itex] is zero whenever the argument, a>0. so [itex]u(\ -(\tau-(t+3))\)[/itex] is zero whenever [itex] \tau-(t+3)>0 [/itex] or [itex] \tau>t+3 [/itex]. hmmm. so it looks like you did the integral right, at least for the case when the upper limit t+3 is greater than the lower limit of 0.

in the case that the upper limit t+3 is less than the lower limit of 0, then you have to recognize that for all [itex]\tau[/itex], at least one of those unit step functions are zero, so you are integrating something that is zero for all [itex]\tau[/itex], so your integral is zero. this happens whenever t+3 < 0 or t<-3. but your integral

[tex]y(t)= \int^{t+3}_{0}e^{-3 \tau}d\tau[/tex]

still evaluates to something that is not necessarily zero when t<-3, so you have to fix the result so it is

[tex]y(t) = \frac{1}{3} \left(1 - e^{-3(t+3)} \right) [/tex]

when t>-3 and

[tex]y(t) = 0 [/tex]

when t<-3 . how're you going to do that?

Wouldn't the y(t) = 0 when t <-3 be taken care of by just multiplying that whole thing by a step function u(t+3)


[tex]y(t) = 1/3[1-e^{-3(t+3)}]u(t+3)[/tex]
?
 
  • #4
dashkin111 said:
Wouldn't the y(t) = 0 when t <-3 be taken care of by just multiplying that whole thing by a step function u(t+3)


[tex]y(t) = 1/3[1-e^{-3(t+3)}]u(t+3)[/tex]
?

yes.

thus ends the lesson. :smile:
 

FAQ: Calculating Convolution Integrals with Unit Step Functions

What is a convolution integral?

A convolution integral is a mathematical operation that involves taking two functions, multiplying them together, and then integrating over their common domain. It is commonly used in signal processing and image processing to combine two signals or images in a meaningful way.

How is a convolution integral calculated?

A convolution integral can be calculated using the formula:

f*g(t) = ∫ f(τ)g(t-τ)dτ

where f and g are the two functions being convolved, t is the variable of integration, and τ is a dummy variable. This formula can be evaluated using numerical methods or by hand using basic integration techniques.

What is the significance of a convolution integral?

The convolution integral is significant in many areas of science and engineering, as it allows for the manipulation and analysis of complex signals and images. It is used in fields such as image and audio processing, physics, and statistics to extract meaningful information from data.

What are some common applications of convolution integrals?

Convolution integrals have a wide range of applications, including image blurring and sharpening, noise reduction, edge detection, and pattern recognition. They are also used in solving differential equations and modeling physical systems in engineering and physics.

Are there any limitations to using convolution integrals?

While convolution integrals are a powerful tool, they do have some limitations. They can be computationally intensive, especially for large data sets, and may require specialized software or hardware to perform efficiently. Additionally, the accuracy of the results can be affected by the choice of the functions being convolved and the numerical methods used to evaluate the integral.

Similar threads

Replies
1
Views
848
Replies
1
Views
1K
Replies
14
Views
2K
Replies
2
Views
1K
Replies
23
Views
3K
Replies
11
Views
6K
Replies
19
Views
28K
Replies
2
Views
3K
Back
Top