Solving Integration Problem: Convolution of Two Identical Square Functions

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In summary, the conversation discusses finding the convolution of two identical square functions, represented by F(t). The process involves finding the intersection of the intervals where F(u) and F(t-u) are equal to 1, with the resulting convolution being a hat function. The final result is a piecewise function that is nonzero on the interval 0 < t < 2. This process has practical applications in fields such as wavelets and splines, where it can produce smoother functions with larger time scales.
  • #1
CrusaderSean
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I have to find the convolution of two identical square functions.
[tex]
F(t)=\left\{\begin{array}{cc}1,&\mbox{ if } 0\leq t < 1\\0, & \mbox otherwise\end{array}\right.
[/tex]

I need to calculate F * F
[tex](F * F)(t) = \int_{\infty}^{-\infty} F(u)F(t-u) du[/tex]

The first thing I did was change integration limit to [0,1] and F(u) to 1. My main problem is F(t-u).

F(t-u) = 1 when
0 < t-u < 1
-1 < u-t < 0
t-1 < u < t

I think thus far I haven't made any mistakes. Now what do I do to substitue F(t-u)? I think my options are to either redefine the integration limit (but it should be [0,1]) or somehow write F(t-u) as a algebraic function. I don't see how I can do the latter. If someone can point me in the right direction I would appreciate it.

oh and the resulting convolution is a hat function.
 
Last edited:
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  • #2
Would this not just be

(F * F)(t) = 1 - |t - 1| for 0 < t < 2, 0 otherwise?

The integrand will have a value of 1 where both F(u) and F(t - u) take on the value of 1, which is when their arguments both take on values in [0, 1); the integrand is zero elsewhere. If you can characterize the interval on which both F(u) and F(t - u) are 1, then the length of that interval will be the value of the integral. That interval is then where both t-1 < u < t as you found and where 0 < u < 1. It is the intersection of (t-1, t] and [0, 1). You can see that there will be a non-empty intersection only if t > 0 and t-1 < 1, i.e. t < 2, so the integrand will only take on non-zero values ever if 0 < t < 2. Otherwise, it will be 0 everywhere, and the integral will be zero everywhere. In fact, when t = 0, we get:

[tex]\int _{-\infty} ^{\infty}F(u)F(t - u)du = \int _{-\infty} ^{\infty}F(u)F(0 - u)du = \int _0 ^01du = 0[/tex]

So we can even say that F*F(t) = 0 unless 0 < t < 2, as I did near the top of my post. To figure out what F*F will do on that interval, you need to figure out the length of the intersection of [0,1) and (t-1,t] for the values of t in (0,2). This should be pretty simple, if not, pick a few values of t and do it yourself to find out how to define F*F for these values of t. It will look like a triangle with values of 0 at 0 and 2, coming up to a point in the middle with a value of 1 at t=1 (I suppose this shape is the "hat" you're talking about).
 
  • #3
thanks for help

I didn't think about defining the intersection of the intervals that way but it really makes sense. However to define what F*F looks like in general for arbitrary functions of F (not given in algebraic form), I just need to calculate a few values (or graph) and find an equation that fits?

edit:
I decided to define it piecewise since I need to calculate the convolution of F with (F*F). It sure gets messy but I think I got it.

F*F is nonzero on interval 0<t<2
so

0<t<1
[tex](F * F)(t) = \int_{t}^{0} du = t[/tex]

1<t<2
[tex](F * F)(t) = \int_{1}^{t-1} du = 2-t[/tex]


If anyone is wondering what all this is for, this is an assignment from my intro to wavelets and splines class. The original F (box function) is a local averaging linear functional (operates on functions). F*F produces a hat function and F*(F*F) produces a bell curve like function (called quadratic B-spline). So more applications of this averaging function F will produce even smoother functions I believe. I would say this is due to the fact that the "time" scale of F is rather large compare to the functions it operates on. It's pretty neat I think. Once again thanks AKG for your help.
 
Last edited:

What is the purpose of solving integration problem: convolution of two identical square functions?

The purpose of solving integration problem: convolution of two identical square functions is to find the integral of the product of two identical square functions over a specified interval. This is a common problem in mathematics and physics, and it has many practical applications in fields such as signal processing, image processing, and engineering.

What are the basic steps for solving integration problem: convolution of two identical square functions?

The basic steps for solving integration problem: convolution of two identical square functions are as follows:

  • 1. Set up the integral by writing the product of the two functions as a sum.
  • 2. Use algebraic manipulation and the properties of integrals to simplify the integral.
  • 3. Apply the appropriate integration techniques, such as substitution or integration by parts, to evaluate the integral.
  • 4. Use the limits of integration to determine the final answer.

What are some common challenges when solving integration problem: convolution of two identical square functions?

Some common challenges when solving integration problem: convolution of two identical square functions include:

  • 1. Identifying the correct limits of integration.
  • 2. Simplifying the integrand to make the problem more manageable.
  • 3. Knowing which integration techniques to apply.
  • 4. Checking for errors in calculation.

What are some tips for solving integration problem: convolution of two identical square functions?

Here are a few tips for solving integration problem: convolution of two identical square functions:

  • 1. Practice identifying the limits of integration for different types of functions.
  • 2. Familiarize yourself with various integration techniques and when to use them.
  • 3. Double check your calculations and make sure to simplify as much as possible before integrating.
  • 4. If you're stuck, try breaking the integral into smaller parts and solving them separately.

How can solving integration problem: convolution of two identical square functions be applied in real life?

Solving integration problem: convolution of two identical square functions has many practical applications in real life. For example, it can be used in signal processing to analyze and filter signals, in image processing to blur or sharpen images, and in engineering to model and simulate physical systems. It can also be applied in physics to calculate the total energy of a system or in probability to calculate the probability densities of random variables.

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