Dirac Delta Function: Scaling and Shifting

In summary, the Dirac delta function can be shifted by multiplying the argument by a and can be scaled by dividing the function by a. This means that for discrete time, the function would be written as 2*diracdelta[n] to double the value at n=0, or as diracdelta[2n] to halve the value at n=0. This works the same way as scaling other functions.
  • #1
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Just have a question about the dirac delta function. I understand how you would write it if you want to shift it but how would you scale it assuming we are using discrete time. Would you write 2*diracdelta[n] or diracdelta[2n]. Also, would that increase it or reduce it by 2 meaning that instead of it being 1 at n=0, it would be 2 instead or would it be 1/2. Does it work the same way as scaling other functions in other words? Thank you!
 
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  • #2
Remember the defining property of the Dirac delta:

[tex] \int_{-\infty}^{\infty} \delta(x)dx = 1[/tex]

Thus

[tex] \int_{-\infty}^{\infty} \delta(ax)dx = \frac{1}{a} \int_{-\infty}^{\infty} \delta(y)dy = \frac{1}{a}[/tex]

So we can think of multipling the argument by a as being the same thing as dividing the function by a:

[tex]\delta(ax) = \frac{1}{a}\delta(x)[/tex]
 
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  • #3


Hello,

Thank you for your question about the Dirac delta function and how it can be scaled and shifted in discrete time.

To answer your question, let's first review the definition of the Dirac delta function. The Dirac delta function, denoted by δ(t), is a mathematical function that is defined to be 0 for all values of t except for t=0, where it is infinity (or undefined). In discrete time, the Dirac delta function is often denoted as δ[n] and is defined to be 0 for all values of n except for n=0, where it is equal to 1.

Now, to scale the Dirac delta function, you would use the same principles as scaling any other function. In other words, if you want to scale the function by a factor of 2, you would write 2*δ[n] or δ[2n]. This means that the function will now have a value of 2 at n=0 instead of 1. Similarly, if you want to scale the function by a factor of 1/2, you would write 1/2*δ[n] or δ[n/2]. This means that the function will now have a value of 1/2 at n=0 instead of 1.

It is important to note that scaling the Dirac delta function does not change its essential properties. It will still be 0 for all values of n except for n=0, where it will have a value of 1 (or any scaled value). This is because the Dirac delta function is a distribution, not a regular function, and thus cannot be scaled in the same way as other functions.

I hope this answers your question. Please let me know if you have any further inquiries.

Best regards,
 

1. What is the Dirac Delta Function?

The Dirac Delta Function, also known as the impulse function, is a mathematical concept that represents an infinitely narrow and tall spike at a specific point on a graph. It is often used in physics and engineering to model point sources or instantaneous events.

2. What is the purpose of scaling and shifting the Dirac Delta Function?

Scaling and shifting the Dirac Delta Function allows us to manipulate its position and amplitude on a graph. This is useful in applications where the function needs to be adjusted to fit a specific scenario or to simplify calculations.

3. How is the Dirac Delta Function scaled?

The Dirac Delta Function can be scaled by multiplying it by a constant value. This will change the height of the spike without affecting its position. For example, multiplying the function by 2 will result in a spike that is twice as tall as the original.

4. How is the Dirac Delta Function shifted?

The Dirac Delta Function can be shifted by adding or subtracting a constant value from the independent variable inside the function. This will change the position of the spike without affecting its height. For example, adding 3 to the variable will shift the spike 3 units to the right on the graph.

5. What are some real-life applications of the Dirac Delta Function?

The Dirac Delta Function has various applications in physics, engineering, and mathematics. It is commonly used to model point sources in fields such as electromagnetism and fluid mechanics. It is also used in signal processing and control systems to represent instantaneous events. Additionally, it is used in the study of distributions and Fourier analysis.

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