Help with convolution bounds

In summary, the equation for y when x is the impulse function is y(t) = ∫h(τ)x(t-τ)dτ [-∞,∞]. When x is shifted by t so that t-5 is included under the graph h(τ), the equation is y(t) = ∫h(τ)x(t-5-τ)dτ [-∞,∞].
  • #1
izelkay
115
3

Homework Statement


avKHkBD.jpg

Mainly concerned with part (a). Here's the answer:

zOob5lf.jpg

I understand where the answers inside the bracket came from, but I don't understand how they got their bounds (-infinity to 3, 3 to 5, and 5 to infinity)

Homework Equations


x is the impulse function here so y(t) = ∫h(τ)x(t-τ)dτ [-∞,∞]

The Attempt at a Solution


Since x was chosen to be the impulse function, I start by reflecting it across the y-axis and then shifting it by t to get the graph x(t-τ):
tnByHNQ.png

(minor mistake in my graph here: that τ-5 should be t-5 and the τ-3 should be t-3)

I then start "sliding" this graph into h(τ):

1aDAcaD.png

And this is where my confusion starts. If I perform the integration it'll be:

∫e^(-3τ)dτ [0, t-3]

and I get the answer they have for when 3 < t ≤ 5

So basically my question is why is it 3 < t ≤ 5 here?
 
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  • #2
I don't understand your method of 'sliding'.
I wrote out the integrand as ##(u(t-3-\tau)-u(t-5-\tau))u(\tau)e^{-3\tau}.d\tau##. It is clear that this vanishes outside ##t-3>\tau>t-5##. Write the resulting integral.
 
  • #3
haruspex said:
I don't understand your method of 'sliding'.
I wrote out the integrand as ##(u(t-3-\tau)-u(t-5-\tau))u(\tau)e^{-3\tau}.d\tau##. It is clear that this vanishes outside ##t-3>\tau>t-5##. Write the resulting integral.
I'm using the "flip and shift method" like here: http://www-rohan.sdsu.edu/~jiracek/DAGSAW/4.2.html

I think I may understand the bounds now though.
In the picture I have illustrated where the integrand is from 0 to t-3, it's only valid when t-5 < 0 and t - 3> 0. In other words, 3 < t ≤ 5.

When I slide it in some more so that t-5 is now included under the graph h(τ), the integrand would be from t-5 to t-3, and it's then valid from t = 5 to infinity since h(τ) goes on forever
 

1. What is convolution and why is it important in scientific research?

Convolution is a mathematical operation that combines two signals to produce a third signal. It is important in scientific research because it allows us to analyze and understand complex systems by breaking them down into simpler components.

2. How can convolution bounds help with my research?

Convolution bounds provide a mathematical framework for analyzing the behavior of convolutions. They can help you determine the range of values that the convolved signal can take, and thus provide insights into the behavior of the system being studied.

3. Are there different types of convolution bounds?

Yes, there are different types of convolution bounds depending on the specific properties of the signals being convolved. These include time-domain and frequency-domain bounds, as well as bounds for different types of signals such as continuous or discrete signals.

4. How can I calculate convolution bounds?

Calculating convolution bounds involves using mathematical techniques such as Fourier transforms and the Cauchy-Schwarz inequality. It is recommended to consult with a mathematician or use specialized software to accurately calculate these bounds.

5. Can convolution bounds be applied to real-world problems?

Yes, convolution bounds can be applied to a wide range of real-world problems in various fields such as engineering, physics, and biology. They provide a powerful tool for analyzing and understanding complex systems and can help researchers make predictions and design more efficient systems.

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