Finding waveforms from Fourier Coefficient Values

In summary, you can find the waveforms x1(t) and x2(t) by using the given sets of Fourier coefficient values and the fundamental frequency of 20∏. You don't have to do any integrals, just write out the finite Fourier series and simplify to sines and cosines to obtain the functions.
  • #1
PJV9126
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Homework Statement


Find the waveforms x1(t) and x2(t) (expressed in a simple form) that are consistent with the sets of Fourier coefficient values provided below. Assume that the period T is equal to 1/10 seconds in both cases

(a-1): a(0) = 6, a(-1) = 3 + 4j, a(1) = 3 - 4j, and a(k) = 0 for k != -1,0,+1

(a-2): a(0) = 4, a(3) = 2e^(jPi/4), a(-3) = 2e^(-jPi/4), and a(k) = 0 for k != -3, 0 , +3


Homework Equations



x(t) = Sum[a_k*e^jkω_0*t]

a_k = 1/T integral x(t)*e^-jkω_0*t dt

The Attempt at a Solution



The fundamental frequency can be calculated using the period given. This would be ω = 2∏/T = 2*∏*10 = 20∏. From this I believe we can plug this into the second equation along with the value of k given and set it equal to whatever that value of k was. I am then running into the issue of solving it (assuming that is correct). I do not know what to do with the x(t) term, along with the integral bounds.

Sorry for the sloppy equations, I am new here and haven't had a chance to check out Latex yet fyi.
 
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  • #2
PJV9126 said:

Homework Statement


Find the waveforms x1(t) and x2(t) (expressed in a simple form) that are consistent with the sets of Fourier coefficient values provided below. Assume that the period T is equal to 1/10 seconds in both cases

(a-1): a(0) = 6, a(-1) = 3 + 4j, a(1) = 3 - 4j, and a(k) = 0 for k != -1,0,+1

(a-2): a(0) = 4, a(3) = 2e^(jPi/4), a(-3) = 2e^(-jPi/4), and a(k) = 0 for k != -3, 0 , +3


Homework Equations



x(t) = Sum[a_k*e^jkω_0*t]

a_k = 1/T integral x(t)*e^-jkω_0*t dt

The Attempt at a Solution



The fundamental frequency can be calculated using the period given. This would be ω = 2∏/T = 2*∏*10 = 20∏. From this I believe we can plug this into the second equation along with the value of k given and set it equal to whatever that value of k was. I am then running into the issue of solving it (assuming that is correct). I do not know what to do with the x(t) term, along with the integral bounds.

Sorry for the sloppy equations, I am new here and haven't had a chance to check out Latex yet fyi.

You don't have to do any integrals. These are finite Fourier series, and they each have only 3 terms. Just write them out and simplify to sines and cosines and you will have your functions.
 

1. What is the Fourier transform and how is it used to find waveforms?

The Fourier transform is a mathematical tool used to decompose a signal into its constituent frequencies. By taking the Fourier transform of a signal, we are able to separate out the different frequencies that make up that signal. This can then be used to find the specific waveform that corresponds to those frequencies.

2. Can the Fourier transform be used to find any waveform?

Yes, the Fourier transform can be used to find any waveform as long as it is a periodic signal. This means that the signal repeats itself over a specific time interval, making it possible to break it down into a series of frequency components.

3. How do you find the waveform from Fourier coefficient values?

To find the waveform from Fourier coefficient values, you first need to have the Fourier coefficients for the signal. These coefficients represent the amplitude and phase of each frequency component in the signal. Then, you can use the inverse Fourier transform to reconstruct the waveform from these coefficients.

4. Can the Fourier transform be used for non-periodic signals?

No, the Fourier transform is only applicable to periodic signals. For non-periodic signals, other techniques such as the Laplace transform or the short-time Fourier transform may be used.

5. What are some real-world applications of finding waveforms from Fourier coefficient values?

The use of Fourier transforms in finding waveforms has a wide range of applications, including audio and image processing, signal analysis in engineering, and even in medical imaging. It allows us to better understand and manipulate signals, which is essential in fields such as telecommunications, astronomy, and music production.

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