2 General typos
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Picture B is extremely wrong. It contains very sharp corners in the output signal. Very sharp corners equals very high frequencysfrequencies, a lot higher then the sample frequency.

In order to fulfill the Nyquist sample theorems, you need to low pass filter the reconstructed signal. After low pass filtering the signal B would look like the input signal, not like like a triangle (as all the sharp corners can not pass the low pass filter).

To be exaktexact you need to lowpass both the input signal and the output signal. The input signal needs to be low pass filtered to max half the sample frequency in order to not "fold" higher frequencysfrequencies.

Sadly, it is a common misrepresentation of how sampling sorksworks. A more correct description will use the sinc function for reconstruction (I recommend a search for sinc function).

In real world applications it is impossible to have a "perfect" low pass filter (passing all frequencies below and blocking all above). This means that you would normally sample with a frequencysfrequency at least 2.2 times the max frequency you want to reproduce (example: CD quality sampled at 44.1 kHz in order to allow a 20kHz max freqency). Even this difference would takemake it hard to create analog filters -- most real world applications "oversample" andas does the low pass filter partly in the digital area.

Picture B is extremely wrong. It contains very sharp corners in the output signal. Very sharp corners equals very high frequencys, a lot higher then the sample frequency.

In order to fulfill the Nyquist sample theorems, you need to low pass filter the reconstructed signal. After low pass filtering the signal B would look like the input signal, not like like a triangle (as all the sharp corners can not pass the low pass filter).

To be exakt you need to lowpass both the input signal and the output signal. The input signal needs to be low pass filtered to max half the sample frequency in order to not "fold" higher frequencys.

Sadly, it is a common misrepresentation of how sampling sorks. A more correct description will use the sinc function for reconstruction (I recommend a search for sinc function).

In real world applications it is impossible to have a "perfect" low pass filter (passing all frequencies below and blocking all above). This means that you would normally sample with a frequencys at least 2.2 times the max frequency you want to reproduce (example: CD quality sampled at 44.1 kHz in order to allow a 20kHz max freqency). Even this difference would take hard to create analog filters -- most real world applications "oversample" and does the low pass filter partly in the digital area.

Picture B is extremely wrong. It contains very sharp corners in the output signal. Very sharp corners equals very high frequencies, a lot higher then the sample frequency.

In order to fulfill the Nyquist sample theorems, you need to low pass filter the reconstructed signal. After low pass filtering the signal B would look like the input signal, not like like a triangle (as all the sharp corners can not pass the low pass filter).

To be exact you need to lowpass both the input signal and the output signal. The input signal needs to be low pass filtered to max half the sample frequency in order to not "fold" higher frequencies.

Sadly, it is a common misrepresentation of how sampling works. A more correct description will use the sinc function for reconstruction (I recommend a search for sinc function).

In real world applications it is impossible to have a "perfect" low pass filter (passing all frequencies below and blocking all above). This means that you would normally sample with a frequency at least 2.2 times the max frequency you want to reproduce (example: CD quality sampled at 44.1 kHz in order to allow a 20kHz max freqency). Even this difference would make it hard to create analog filters -- most real world applications "oversample" as does the low pass filter partly in the digital area.

1
source | link

Picture B is extremely wrong. It contains very sharp corners in the output signal. Very sharp corners equals very high frequencys, a lot higher then the sample frequency.

In order to fulfill the Nyquist sample theorems, you need to low pass filter the reconstructed signal. After low pass filtering the signal B would look like the input signal, not like like a triangle (as all the sharp corners can not pass the low pass filter).

To be exakt you need to lowpass both the input signal and the output signal. The input signal needs to be low pass filtered to max half the sample frequency in order to not "fold" higher frequencys.

Sadly, it is a common misrepresentation of how sampling sorks. A more correct description will use the sinc function for reconstruction (I recommend a search for sinc function).

In real world applications it is impossible to have a "perfect" low pass filter (passing all frequencies below and blocking all above). This means that you would normally sample with a frequencys at least 2.2 times the max frequency you want to reproduce (example: CD quality sampled at 44.1 kHz in order to allow a 20kHz max freqency). Even this difference would take hard to create analog filters -- most real world applications "oversample" and does the low pass filter partly in the digital area.