Visualizing the Fourier transform using the center of mass concept

In summary, the conversation discusses a video on YouTube where the concept of Fourier transform using the center of mass is explained. However, there is a disagreement on whether the math in the video is correct. The conversation delves into the difference between the center of mass and the time average, and how they lead to different integrals in the limiting process. Ultimately, it is concluded that the math in the video is correct, but the concept of center of mass is not fully explained.
  • #1
person_random_normal
164
8
I found this video on youtube which is trying to explain Fourier transform using the center of mass concept


At 15:20 the expression of the x coordinate is given in the video. I believe it is wrong, and it should be:

##\frac{{\int g(t)e^{(-2 \pi ift)}.g(t).2 \pi f.dt}} { \int g(t).2 \pi f.dt}##

Because the wire is assumed to have- uniform mass distribution
Can someone please check?
 
Last edited by a moderator:
Physics news on Phys.org
  • #2
Shreyas Samudra said:
At 15:20 the expression of the x coordinate is given in the video. I believe it is wrong, and it should be:

##\frac{{\int g(t)e^{(-2 \pi ift)}.g(t).2 \pi f.dt}} { \int g(t).2 \pi f.dt}##

Because the wire is assumed to have- uniform mass distribution
Can someone please check?
The math in the video is correct. He doesn't really explain the centre of mass concept (he is giving all sample points the same mass) and he leaves out some steps. He goes from:

$$\hat{x}_{com} = \frac{1}{N} \sum_{k=1}^N g(f)e^{-2\pi ift}$$

directly to:

$$\hat{x}_{com} = \frac{1}{(T_2 - T_1)}\int_{T_1}^{T_2} g(f)e^{-2\pi ift}dt$$

without explanation.

The intermediate steps should be something like:

$$\hat{x}_{com} = \frac{\Delta t}{(T_2-T_1)}\sum_{k=1}^N g(f)e^{-2\pi ift}$$

where ##\Delta t = (T_2-T_1)/N## i.e. ##\Delta t## is the time interval between equally spaced sample points so: ##N = (T_2-T_1)/\Delta t##

It follows that:

$$\hat{x}_{com} = \frac{1}{(T_2-T_1)}\sum_{k=1}^N g(f)e^{-2\pi ift}\Delta t$$

and in the limit where ##\Delta t \rightarrow 0##:

$$\hat{x}_{com} = \frac{1}{(T_2 - T_1)}\int_{T_1}^{T_2} g(f)e^{-2\pi ift}dt$$
AM
 
  • #3
I am confused about this as well. Here is my take on the problem. I think instead of center of mass what Grant defined is actually the center of time or the time average. Let's compare the limiting process for the center of mass and the time average to go from the discrete case to the continuous case.

For the center of mass we have:
1631764266603.png

In the limiting process we take smaller and smaller mass pieces. This will lead to the integral you derived with a g square integral on the top and a g integral at the denominator.

What Grant did was to keep the mass of each piece constant and take more and more pieces at smaller time intervals.
1631764222801.png

1631764186747.png

This limiting process leads to finding the time average of the position of the winding curve in 2D. Notice that compared to the center of mass integral dm is just replaced with dt. The process of finding the center of mass for more and more particles of constant mass leads to the time average. This will lead to the g integral on top and a constant integral at the bottom.
 
Last edited:

1. What is the Fourier transform?

The Fourier transform is a mathematical operation that decomposes a signal into its constituent frequencies. It is commonly used in signal processing and data analysis to analyze the frequency components of a signal.

2. How does the center of mass concept relate to the Fourier transform?

The center of mass concept is a physical analogy used to explain the Fourier transform. Just as the center of mass of an object represents its overall distribution of mass, the center of mass of a signal represents its overall distribution of frequencies.

3. What does it mean to visualize the Fourier transform using the center of mass?

Visualizing the Fourier transform using the center of mass means representing the frequency components of a signal as a point on a graph, with the x-coordinate representing the frequency and the y-coordinate representing the magnitude of that frequency component. The center of mass of these points can then be used to understand the overall distribution of frequencies in the signal.

4. How can the center of mass concept help in understanding the Fourier transform?

The center of mass concept provides an intuitive way to understand the frequency components of a signal and how they contribute to the overall signal. By visualizing the Fourier transform using the center of mass, one can easily identify dominant frequencies and their relative strengths in a signal.

5. Are there any limitations to using the center of mass concept to visualize the Fourier transform?

While the center of mass concept can be a helpful tool in understanding the Fourier transform, it is important to note that it is just one way of visualizing the transform. It may not always accurately represent the complex frequency components of a signal and should be used in conjunction with other methods for a more comprehensive understanding.

Similar threads

Replies
3
Views
1K
  • Calculus and Beyond Homework Help
Replies
3
Views
768
Replies
3
Views
2K
  • Calculus and Beyond Homework Help
Replies
6
Views
2K
  • Calculus and Beyond Homework Help
Replies
1
Views
790
Replies
11
Views
862
Replies
4
Views
302
  • Calculus
Replies
11
Views
2K
  • Calculus and Beyond Homework Help
Replies
16
Views
569
  • Calculus
Replies
8
Views
4K
Back
Top