user3502: I read this:
To reproduce a given frequency, the sample rate must be at least twice that frequency.
Why is this so?

user37307: Because you need at least 3 samples per signal period, to uniquely interpolate the original signal. (with regard to aperiodic signals, ‘period’ refers to the time period over which it has been time-limited)The reason for this is as follows:
When you sample a signal, there are three variables(degrees of freedom) involved:

The signal frequency/frequency spectrum. This determines the
magnitude variation between the sample points.
The sampling frequency. This determines the spacing between the
sample points.
The phase of the sampled signal. This determines the relative
orientation of the sample points. For example, a cosine wave sampled
at half its frequency, starting from the zero degree point, will
have a peak followed by a trough, while a sampled sine wave will
have a trough followed by a peak.

To uniquely determine the original wave from three variables, we need three relations(constraints) between them. If the number of constraints is less than three, then the solution is not unique.
Let’s illustrate this, by considering a sinusoid of frequency  f  sampled at a frequency  fs  :
Case 1:  fs≤f
If the sampling frequency is less than or equal to the max signal frequency, then there would be at most one sample in a period. This one sample constrains only one variable - the sampling frequency(the spacing between the samples). The other two variables - the signal frequency and the phase can be freely chosen - meaning that any signal, with any phase, sampled at the given frequency, can pass through those points.

Case 2:  fs=2f
In this case, there are two samples per signal period. Now, consider two sinusoids, one of which has a frequency three times the other, as shown below:

As can be seen, in this case, there are constraints on two variables - the signal frequency(the magnitude variation between the points) and the sampling frequency(spacing between the points), but the third variable - phase can be freely chosen.
As a result, a sine wave of 3 times the frequency, but phase-shifted by 270 degrees, can perfectly pass through the points generated by sampling the original wave.
Case 3:  fs>2f
In this case, there are at least three constraints on the sample points and only a signal, that:

Has the required spacing between the sample points.(Sampling frequency)

Has the required magnitude variation between the sample points(Signal frequency)

Has the required relative orientation of sample points(Signal phase),


will pass through the generated sample points, enabling us to determine the original signal uniquely.

user3503: That's Nyquist-Shannon sampling theorem. Think about it in the other way: imagine you are given a set of samples (the black dots in the image).

The most intuitive way to recover the original analog signal would be by joining these samples. But you can easily notice that there is not a unique way of joining them. In fact, there are infinite possible signals that would contain the whole set of samples. Obviously, given the sample set, you would join them the easiest way, i.e. the blue signal.
Now, what about sampling? In the image you also have a red signal (which frequency is 1 Hz) that has been sampled too slowly, resulting in the black dot sample set. To have a clue about the real signal, we should have sampled it at, at least, 2 Hz. That way, we also would have samples (dots) at 0.5, 1.5, 2.5, etc seconds. And, as you can draw in Paint, the easiest path to join all the samples would be, again, the red signal. So we would have recovered our original signal successfully.

3502: <p>I read this:
To reproduce a given frequency, the sample rate must be at least twice that frequency.
Why is this so?</p>
 37307: <p>Because you need at least 3 samples per signal period, to uniquely interpolate the original signal. (with regard to aperiodic signals, ‘period’ refers to the time period over which it has been time-limited)The reason for this is as follows:</p>
<p>When you sample a signal, there are three variables(degrees of freedom) involved:</p>
<ol>
<li>The signal frequency/frequency spectrum. This determines the
magnitude variation between the sample points.</li>
<li>The sampling frequency. This determines the spacing between the
sample points.</li>
<li>The phase of the sampled signal. This determines the relative
orientation of the sample points. For example, a cosine wave sampled
at half its frequency, starting from the zero degree point, will
have a peak followed by a trough, while a sampled sine wave will
have a trough followed by a peak.</li>
</ol>
<p>To uniquely determine the original wave from three variables, we need three relations(constraints) between them. If the number of constraints is less than three, then the solution is not unique.</p>
<p>Let’s illustrate this, by considering a sinusoid of frequency  <em><strong>f</strong></em>  sampled at a frequency  <em><strong>fs</strong></em>  :</p>
<p><em><strong>Case 1:  fs≤f</strong></em></p>
<p>If the sampling frequency is less than or equal to the max signal frequency, then there would be at most one sample in a period. This one sample constrains only one variable - the sampling frequency(the spacing between the samples). The other two variables - the signal frequency and the phase can be freely chosen - meaning that any signal, with any phase, sampled at the given frequency, can pass through those points.
<a href="https://i.stack.imgur.com/OXfQG.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/OXfQG.png" alt="enter image description here" /></a></p>
<p><em><strong>Case 2:  fs=2f</strong></em></p>
<p>In this case, there are two samples per signal period. Now, consider two sinusoids, one of which has a frequency three times the other, as shown below:
<a href="https://i.stack.imgur.com/lbtAN.gif" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/lbtAN.gif" alt="enter image description here" /></a></p>
<p>As can be seen, in this case, there are constraints on two variables - the signal frequency(the magnitude variation between the points) and the sampling frequency(spacing between the points), but the third variable - phase can be freely chosen.</p>
<p>As a result, a sine wave of 3 times the frequency, but phase-shifted by 270 degrees, can perfectly pass through the points generated by sampling the original wave.</p>
<p><em><strong>Case 3:  fs&gt;2f</strong></em></p>
<p>In this case, there are at least three constraints on the sample points and only a signal, that:</p>
<ol>
<li><p>Has the required spacing between the sample points.(Sampling frequency)</p>
</li>
<li><p>Has the required magnitude variation between the sample points(Signal frequency)</p>
</li>
<li><p>Has the required relative orientation of sample points(Signal phase),</p>
</li>
</ol>
<p>will pass through the generated sample points, enabling us to determine the original signal uniquely.</p>
 3503: <p>That's Nyquist-Shannon sampling theorem. Think about it in the other way: imagine you are given a set of samples (the black dots in the image).</p>

<p><img src="https://i.stack.imgur.com/05b9o.png" alt="http://i.stack.imgur.com/ld5VI.png"></p>

<p>The most intuitive way to recover the original analog signal would be by joining these samples. But you can easily notice that there is not a unique way of joining them. In fact, there are infinite possible signals that would contain the whole set of samples. Obviously, given the sample set, you would join them the easiest way, i.e. the blue signal.</p>

<p>Now, what about sampling? In the image you also have a red signal (which frequency is 1 Hz) that has been sampled too slowly, resulting in the black dot sample set. To have a clue about the real signal, we should have sampled it at, at least, 2 Hz. That way, we also would have samples (dots) at 0.5, 1.5, 2.5, etc seconds. And, as you can draw in Paint, the easiest path to join all the samples would be, again, the red signal. So we would have recovered our original signal successfully.</p>
