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Thomas Fritsch
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Periodic functions $f(t)$ (with a time period $T$) can be approximated by a Fourier series, i.e. by summing harmonic oscillations with the discrete frequencies $0, \frac{2\pi}{T}, 2\frac{2\pi}{T}, 3\frac{2\pi}{T}, \ldots$ . $$f(t)=\sum_{n=-\infty}^{+\infty} F_n e^{in\frac{2\pi}{T}t}$$

But, as you already noticed, with a Fourier series you cannot build aperiodic functions (e.g. functions limited to a finite range). For approximating such functions you need a Fourier integral, i.e. summing harmonic oscillations with all frequencies from $0$ to $\infty$. $$f(t)=\int_{-\infty}^{+\infty}F(\omega)e^{i\omega t}\ d\omega$$


Example 1: Periodic function $\to$ discrete spectrum

As an example for $f(t)$ let's choose the rectangular waverectangular wave (with period $T$) extending from $t=-\infty$ to $t=+\infty$.
enter image description hereperiodic function
$f(t)$ is a perfectly periodic function, and therefore can be decomposed into a Fourier series. Its spectrum (the Fourier coefficients $F_n$) look like this:
enter image description herediscrete spectrum

Eaxample 2: Aperiodic function $\to$ continuous spectrum

Now as a second example for $f(t)$ let's choose the rectangular wave (again with period $T$) restricted to a certain range. Outside this range we define $f(t)=0$.
enter image description hereaperiodic (but nearly periodic) function
Obviously this function is quite similar to the first example. Therefore we expect its spectrum to be somehow similar to the spectrum of the first example.

Because $f(t)$ is aperiodic, it cannot be decomposed into a Fourier series. But it can be decomposed into a Fourier integral. Its Fourier spectrum (the function $F(\omega)$) looks like this:
enter image description herecontinuous (but nearly discrete) spectrum
(Function plots created with FooPlot)

This spectrum is continuous, but still quite similar to the discrete spectrum of the first eample. It has some pronounced peaks (at the same frequencies as in the first example). But there is also some spectral intensity outside of these peaks.

Periodic functions $f(t)$ (with a time period $T$) can be approximated by a Fourier series, i.e. by summing harmonic oscillations with the discrete frequencies $0, \frac{2\pi}{T}, 2\frac{2\pi}{T}, 3\frac{2\pi}{T}, \ldots$ . $$f(t)=\sum_{n=-\infty}^{+\infty} F_n e^{in\frac{2\pi}{T}t}$$

But, as you already noticed, with a Fourier series you cannot build aperiodic functions (e.g. functions limited to a finite range). For approximating such functions you need a Fourier integral, i.e. summing harmonic oscillations with all frequencies from $0$ to $\infty$. $$f(t)=\int_{-\infty}^{+\infty}F(\omega)e^{i\omega t}\ d\omega$$


Example 1: Periodic function $\to$ discrete spectrum

As an example for $f(t)$ let's choose the rectangular wave (with period $T$) extending from $t=-\infty$ to $t=+\infty$.
enter image description here
$f(t)$ is a perfectly periodic function, and therefore can be decomposed into a Fourier series. Its spectrum (the Fourier coefficients $F_n$) look like this:
enter image description here

Eaxample 2: Aperiodic function $\to$ continuous spectrum

Now as a second example for $f(t)$ let's choose the rectangular wave (again with period $T$) restricted to a certain range. Outside this range we define $f(t)=0$.
enter image description here
Obviously this function is quite similar to the first example. Therefore we expect its spectrum to be somehow similar to the spectrum of the first example.

Because $f(t)$ is aperiodic, it cannot be decomposed into a Fourier series. But it can be decomposed into a Fourier integral. Its Fourier spectrum (the function $F(\omega)$) looks like this:
enter image description here
(Function plots created with FooPlot)

This spectrum is continuous, but still quite similar to the discrete spectrum of the first eample. It has some pronounced peaks (at the same frequencies as in the first example). But there is also some spectral intensity outside of these peaks.

Periodic functions $f(t)$ (with a time period $T$) can be approximated by a Fourier series, i.e. by summing harmonic oscillations with the discrete frequencies $0, \frac{2\pi}{T}, 2\frac{2\pi}{T}, 3\frac{2\pi}{T}, \ldots$ . $$f(t)=\sum_{n=-\infty}^{+\infty} F_n e^{in\frac{2\pi}{T}t}$$

But, as you already noticed, with a Fourier series you cannot build aperiodic functions (e.g. functions limited to a finite range). For approximating such functions you need a Fourier integral, i.e. summing harmonic oscillations with all frequencies from $0$ to $\infty$. $$f(t)=\int_{-\infty}^{+\infty}F(\omega)e^{i\omega t}\ d\omega$$


Example 1: Periodic function $\to$ discrete spectrum

As an example for $f(t)$ let's choose the rectangular wave (with period $T$) extending from $t=-\infty$ to $t=+\infty$.
periodic function
$f(t)$ is a perfectly periodic function, and therefore can be decomposed into a Fourier series. Its spectrum (the Fourier coefficients $F_n$) look like this:
discrete spectrum

Eaxample 2: Aperiodic function $\to$ continuous spectrum

Now as a second example for $f(t)$ let's choose the rectangular wave (again with period $T$) restricted to a certain range. Outside this range we define $f(t)=0$.
aperiodic (but nearly periodic) function
Obviously this function is quite similar to the first example. Therefore we expect its spectrum to be somehow similar to the spectrum of the first example.

Because $f(t)$ is aperiodic, it cannot be decomposed into a Fourier series. But it can be decomposed into a Fourier integral. Its Fourier spectrum (the function $F(\omega)$) looks like this:
continuous (but nearly discrete) spectrum
(Function plots created with FooPlot)

This spectrum is continuous, but still quite similar to the discrete spectrum of the first eample. It has some pronounced peaks (at the same frequencies as in the first example). But there is also some spectral intensity outside of these peaks.

added examples; deleted 1 character in body
Source Link
Thomas Fritsch
  • 41k
  • 13
  • 75
  • 144

Periodic functions $f(t)$ (with a time period $T$) can be approximated by a Fourier series, i.e. by summing harmonic oscillations with the discrete frequencies $0, \frac{2\pi}{T}, 2\frac{2\pi}{T}, 3\frac{2\pi}{T}, \ldots$ . $$f(t)=\sum_{n=-\infty}^{+\infty} F_n e^{in\frac{2\pi}{T}t}$$

But, as you already noticed, with a Fourier series you cannot build aperiodic functions (e.g. functions limited to a finite range). For approximating such functions you need a Fourier integral, i.e. summing harmonic oscillations with all frequencies from $0$ to $\infty$. $$f(t)=\int_{-\infty}^{+\infty}F(\omega)e^{i\omega t}\ d\omega$$


Example 1: Periodic function $\to$ discrete spectrum

As an example for $f(t)$ let's choose the rectangular wave (with period $T$) extending from $t=-\infty$ to $t=+\infty$.
enter image description here
$f(t)$ is a perfectly periodic function, and therefore can be decomposed into a Fourier series. Its spectrum (the Fourier coefficients $F_n$) look like this:
enter image description here

Eaxample 2: Aperiodic function $\to$ continuous spectrum

Now as a second example for $f(t)$ let's choose the rectangular wave (again with period $T$) restricted to a certain range. Outside this range we define $f(t)=0$.
enter image description here
Obviously this function is quite similar to the first example. Therefore we expect its spectrum to be somehow similar to the spectrum of the first example.

Because $f(t)$ is aperiodic, it cannot be decomposed into a Fourier series. But it can be decomposed into a Fourier integral. Its Fourier spectrum (the function $F(\omega)$) looks like this:
enter image description here
This(Function plots created with FooPlot)

This spectrum is continuous, but still quite similar to the discrete spectrum of the first eample. It has some pronounced peaks (at the same frequencies as in the first example). But there is also some spectral intensity outside of these peaks.

Periodic functions $f(t)$ (with a time period $T$) can be approximated by a Fourier series, i.e. by summing harmonic oscillations with the discrete frequencies $0, \frac{2\pi}{T}, 2\frac{2\pi}{T}, 3\frac{2\pi}{T}, \ldots$ . $$f(t)=\sum_{n=-\infty}^{+\infty} F_n e^{in\frac{2\pi}{T}t}$$

But, as you already noticed, with a Fourier series you cannot build aperiodic functions (e.g. functions limited to a finite range). For approximating such functions you need a Fourier integral, i.e. summing harmonic oscillations with all frequencies from $0$ to $\infty$. $$f(t)=\int_{-\infty}^{+\infty}F(\omega)e^{i\omega t}\ d\omega$$


Example 1: Periodic function $\to$ discrete spectrum

As an example for $f(t)$ let's choose the rectangular wave (with period $T$) extending from $t=-\infty$ to $t=+\infty$.
enter image description here
$f(t)$ is a perfectly periodic function, and therefore can be decomposed into a Fourier series. Its spectrum (the Fourier coefficients $F_n$) look like this:
enter image description here

Eaxample 2: Aperiodic function $\to$ continuous spectrum

Now as a second example for $f(t)$ let's choose the rectangular wave (again with period $T$) restricted to a certain range. Outside this range we define $f(t)=0$.
enter image description here
Obviously this function is quite similar to the first example. Therefore we expect its spectrum to be somehow similar to the spectrum of the first example.

Because $f(t)$ is aperiodic, it cannot be decomposed into a Fourier series. But it can be decomposed into a Fourier integral. Its Fourier spectrum (the function $F(\omega)$) looks like this:
enter image description here
This spectrum is continuous, but still quite similar to the discrete spectrum of the first eample. It has some pronounced peaks (at the same frequencies as in the first example). But there is also some spectral intensity outside of these peaks.

Periodic functions $f(t)$ (with a time period $T$) can be approximated by a Fourier series, i.e. by summing harmonic oscillations with the discrete frequencies $0, \frac{2\pi}{T}, 2\frac{2\pi}{T}, 3\frac{2\pi}{T}, \ldots$ . $$f(t)=\sum_{n=-\infty}^{+\infty} F_n e^{in\frac{2\pi}{T}t}$$

But, as you already noticed, with a Fourier series you cannot build aperiodic functions (e.g. functions limited to a finite range). For approximating such functions you need a Fourier integral, i.e. summing harmonic oscillations with all frequencies from $0$ to $\infty$. $$f(t)=\int_{-\infty}^{+\infty}F(\omega)e^{i\omega t}\ d\omega$$


Example 1: Periodic function $\to$ discrete spectrum

As an example for $f(t)$ let's choose the rectangular wave (with period $T$) extending from $t=-\infty$ to $t=+\infty$.
enter image description here
$f(t)$ is a perfectly periodic function, and therefore can be decomposed into a Fourier series. Its spectrum (the Fourier coefficients $F_n$) look like this:
enter image description here

Eaxample 2: Aperiodic function $\to$ continuous spectrum

Now as a second example for $f(t)$ let's choose the rectangular wave (again with period $T$) restricted to a certain range. Outside this range we define $f(t)=0$.
enter image description here
Obviously this function is quite similar to the first example. Therefore we expect its spectrum to be somehow similar to the spectrum of the first example.

Because $f(t)$ is aperiodic, it cannot be decomposed into a Fourier series. But it can be decomposed into a Fourier integral. Its Fourier spectrum (the function $F(\omega)$) looks like this:
enter image description here
(Function plots created with FooPlot)

This spectrum is continuous, but still quite similar to the discrete spectrum of the first eample. It has some pronounced peaks (at the same frequencies as in the first example). But there is also some spectral intensity outside of these peaks.

added examples
Source Link
Thomas Fritsch
  • 41k
  • 13
  • 75
  • 144

Periodic functions $f(t)$ (with a time period $T$) can be approximated by a Fourier series, i.e. by summing harmonic oscillations with the discrete frequencies $0, \frac{2\pi}{T}, 2\frac{2\pi}{T}, 3\frac{2\pi}{T}, \ldots$ . $$f(t)=\sum_{n=-\infty}^{+\infty} F_n e^{in\frac{2\pi}{T}t}$$

But, as you already noticed, with a Fourier series you cannot build aperiodic functions (e.g. functions limited to a finite range). For approximating such functions you need a Fourier integral, i.e. summing harmonic oscillations with all frequencies from $0$ to $\infty$. $$f(t)=\int_{-\infty}^{+\infty}F(\omega)e^{i\omega t}\ d\omega$$


Example 1: Periodic function $\to$ discrete spectrum

As an example for $f(t)$ let's choose the rectangular wave (with period $T$) extending from $t=-\infty$ to $t=+\infty$.
enter image description here
$f(t)$ is a perfectly periodic function, and therefore can be decomposed into a Fourier series. Its spectrum (the Fourier coefficients $F_n$) look like this:
enter image description here

Eaxample 2: Aperiodic function $\to$ continuous spectrum

Now as a second example for $f(t)$ let's choose the rectangular wave (again with period $T$) restricted to a certain range. Outside this range we define $f(t)=0$.
enter image description here
Obviously this function is quite similar to the first example. Therefore we expect its spectrum to be somehow similar to the spectrum of the first example.

Because $f(t)$ is aperiodic, it cannot be decomposed into a Fourier series. But it can be decomposed into a Fourier integral. Its Fourier spectrum (the function $F(\omega)$) looks like this:
enter image description here
This spectrum is continuous, but still quite similar to the discrete spectrum of the first eample. It has some pronounced peaks (at the same frequencies as in the first example). But there is also some spectral intensity outside of these peaks.

Periodic functions $f(t)$ (with a time period $T$) can be approximated by a Fourier series, i.e. by summing harmonic oscillations with the discrete frequencies $0, \frac{2\pi}{T}, 2\frac{2\pi}{T}, 3\frac{2\pi}{T}, \ldots$ . $$f(t)=\sum_{n=-\infty}^{+\infty} F_n e^{in\frac{2\pi}{T}t}$$

But, as you already noticed, with a Fourier series you cannot build aperiodic functions (e.g. functions limited to a finite range). For approximating such functions you need a Fourier integral, i.e. summing harmonic oscillations with all frequencies from $0$ to $\infty$. $$f(t)=\int_{-\infty}^{+\infty}F(\omega)e^{i\omega t}\ d\omega$$

Periodic functions $f(t)$ (with a time period $T$) can be approximated by a Fourier series, i.e. by summing harmonic oscillations with the discrete frequencies $0, \frac{2\pi}{T}, 2\frac{2\pi}{T}, 3\frac{2\pi}{T}, \ldots$ . $$f(t)=\sum_{n=-\infty}^{+\infty} F_n e^{in\frac{2\pi}{T}t}$$

But, as you already noticed, with a Fourier series you cannot build aperiodic functions (e.g. functions limited to a finite range). For approximating such functions you need a Fourier integral, i.e. summing harmonic oscillations with all frequencies from $0$ to $\infty$. $$f(t)=\int_{-\infty}^{+\infty}F(\omega)e^{i\omega t}\ d\omega$$


Example 1: Periodic function $\to$ discrete spectrum

As an example for $f(t)$ let's choose the rectangular wave (with period $T$) extending from $t=-\infty$ to $t=+\infty$.
enter image description here
$f(t)$ is a perfectly periodic function, and therefore can be decomposed into a Fourier series. Its spectrum (the Fourier coefficients $F_n$) look like this:
enter image description here

Eaxample 2: Aperiodic function $\to$ continuous spectrum

Now as a second example for $f(t)$ let's choose the rectangular wave (again with period $T$) restricted to a certain range. Outside this range we define $f(t)=0$.
enter image description here
Obviously this function is quite similar to the first example. Therefore we expect its spectrum to be somehow similar to the spectrum of the first example.

Because $f(t)$ is aperiodic, it cannot be decomposed into a Fourier series. But it can be decomposed into a Fourier integral. Its Fourier spectrum (the function $F(\omega)$) looks like this:
enter image description here
This spectrum is continuous, but still quite similar to the discrete spectrum of the first eample. It has some pronounced peaks (at the same frequencies as in the first example). But there is also some spectral intensity outside of these peaks.

added 5 characters in body
Source Link
Thomas Fritsch
  • 41k
  • 13
  • 75
  • 144
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Source Link
Thomas Fritsch
  • 41k
  • 13
  • 75
  • 144
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