Signals & Comms: Unit step function - dirac

In summary, the Dirac delta function is a mathematical construct that can be thought of as a series of infinitely narrow and infinitely tall pulses, with the property that their areas are always equal to 1. When multiplied by a function, it "samples" the value of that function at a specific point, excluding all other points. In the real world, we deal with discrete-time signals and the Dirac delta function can be thought of as a narrow pulse that selects out a specific sample of the signal.
  • #1
thomas49th
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Homework Statement


The Dirac function (unit impulse) is defined as

[tex]\delta(t) = 0[/tex] where [tex]t \neq 0[/tex]

the integration of d(t) between -ve inf and +ve inf is 1.

Now I picture this as a rectangle with no width and infinite height. In fact I think of the width (along the x axis) as (1/inf = 0) and the height being inf. So the area (integral) is 1/inf * inf = 1

However if the width is 0, then why does the integral have limits between -inf and +inf?

Am I right in the thinking when a signal g(t) is multiplied by the Dirac function it just turns a signal "on" for an infintately small amount of time?

Can someone please explain how

[tex] \int_{-\infty}^{\infty} \delta(t-T)g(t)dt = g(T)[/tex]

I am having problems relating this to the real world

Thanks
Thomas
 
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  • #2
thomas49th said:
Now I picture this as a rectangle with no width and infinite height. In fact I think of the width (along the x axis) as (1/inf = 0) and the height being inf. So the area (integral) is 1/inf * inf = 1

This is not a good way to think about it, since ∞ is not a number, and 1/∞ ≠ 0. It is undefined.

However, you are correct that the Dirac delta can thought of as the limiting case of a series of rectangular pulses each of which is getting progressively narrower and taller, but in such a way that the products of their widths and heights (their areas) are always constant and equal to unity.

thomas49th said:
However if the width is 0, then why does the integral have limits between -inf and +inf?

You're right that any integral of δ(t-T) over the interval [a,b] will be equal to 1 provided that the point T is included within the bounds of integration (i.e. it lies somewhere between a and b). But the point is that if you extend the limits of integration extend all the way to infinity in either direction, it's guaranteed to be true that the integral is equal to 1 (for any T).

thomas49th said:
Am I right in the thinking when a signal g(t) is multiplied by the Dirac function it just turns a signal "on" for an infintately small amount of time?

Sort of. A good way to think about it is that doing this "samples" the value of the function at t = T, (since the Dirac delta is zero everywhere else except at this point of interest).

Imagine if you took one of our finite rectangular pulses of height h and width w such that homework = 1. "Sweep" this pulse over your signal of interest (with it centred at t = T) and take the integral over their product, and you have something like:

[tex] h\int_{T-w/2}^{T+w/2} g(t)\, dt [/tex]

Now, if we make w very small, we can sort of "pretend" (as an approximation to the integral) that g(t) is constant and equal to g(T), the value of the function in the middle of the integration range. In this case, the integral reduces to:

[tex] h g(T)\int_{T-w/2}^{T+w/2} dt = hwg(T) = g(T) [/tex]

since homework = 1. The smaller w is, the better this approximation is. It is in this way that integrating over a narrow pulse of unit area can "sample" the value of the signal at the point of interest, excluding all other points.

thomas49th said:
Can someone please explain how

[tex] \int_{-\infty}^{\infty} \delta(t-T)g(t)dt = g(T)[/tex]

The proof is simple enough. Since δ(t-T) is 0 everywhere except at t = T, we can replace g(t)δ(t-T) with g(T)δ(t-T) (again, we have "sampled" that particular value). Since g(T) is constant, it comes outside the integral, and we have:

[tex] g(T)\int_{-\infty}^{\infty} \delta(t-T)\,dt = g(T) (1)[/tex]

I hope that this result is easier to understand conceptually in light of my example with the finite pulse above. The properties of the Dirac delta function are such that using it is equivalent to taking the pulse example to the limiting case in which the approximation becomes exact.

thomas49th said:
I am having problems relating this to the real world

Ha, yeah, well in the real world we don't deal with signals that are continuous with time. Well, we do, but we can't store them for later manipulation in such a way that we can capture the value of that signal at any (and every) time value. We can really only record the value of the signal at a discrete set of (usually evenly-spaced) time steps. So what we end up is a set of numbers that are samples of the signal at regular intervals in time. In order to select out one of these samples it is sufficient to multiply the discretized signal by a pulse of height 1 that is narrow enough that it spans only one time step. Doing this is the equivalent for discrete-time signals of what convolving with the Dirac delta function does for continuous-time signals.
 
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1. What is a unit step function and how is it related to the dirac function?

A unit step function, also known as the Heaviside step function, is a mathematical function that is zero for negative inputs and one for positive inputs. The dirac function, also known as the delta function, is a mathematical function that is zero everywhere except at the origin, where it is infinite. The dirac function is related to the unit step function by taking the derivative of the unit step function.

2. How is the dirac function used in signal processing?

The dirac function is commonly used in signal processing to represent an impulse or spike in a signal. It can be used to model sudden changes or events in a signal, and is often used in the analysis and synthesis of signals.

3. Can the unit step function and dirac function be used in continuous-time and discrete-time systems?

Yes, both the unit step function and dirac function can be used in both continuous-time and discrete-time systems. In continuous-time systems, the unit step function is represented by the Heaviside function, while the dirac function is represented by the dirac delta function. In discrete-time systems, the unit step function is represented by the unit step sequence, while the dirac function is represented by the unit impulse sequence.

4. How are the unit step function and dirac function related to the concept of a unit impulse response?

The unit step function and dirac function are both used to represent a unit impulse response in a system. The unit impulse response is the output of a system when a dirac delta function is used as the input. The unit step function is often used to describe the behavior of a system over time, and the dirac function is used to describe the instantaneous response of a system.

5. Are there any real-world applications of the unit step function and dirac function?

Yes, there are many real-world applications of the unit step function and dirac function. They are commonly used in fields such as signal processing, control systems, and communication systems. For example, in communication systems, the dirac function is used to model the transmission of a single pulse or bit of information, while the unit step function is used to model the behavior of a system over time.

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