- #1
linki
- 5
- 0
I have a digital transmitter from which I collect and save values from. How do I know if I must apply this theorem or not? My values seems fine..
anorlunda said:If your values are fine, why do you need any theorem? The purpose of your question is unclear.
linki said:I have a digital transmitter from which I collect and save values from. How do I know if I must apply this theorem or not? My values seems fine..
Actually, the instant at which the decision is made about the digital value of each symbol totally involves 'sampling'. The analogue value of any digital signal at any time can consist of contributions of many other symbols - hence the principle of sampling in the middle of an 'Eye'. That technique has long been superseded by modern signalling systems which use some very fancy filtering over many symbols.anorlunda said:There's a contradiction. Normally we don't sample digital signals such as Ethernet.
anorlunda said:Welcome to PF.
If your values are fine, why do you need any theorem? The purpose of your question is unclear.
But if I guess, you are asking when sampling rate becomes significant. It is significant whenever the analog signal has frequency components higher than roughly 0.1 of the sampling frequency. It causes seemingly false readings because of aliasing. To prevent that, we typically use an analog low-pass filter before sampling and call that the anti-aliasing filter. Are you familiar with those concepts?
cabrera said:You need to add a anti-aliasing filter with a roll-off at half of the sampling frequency of the ADC
Depending on the actual spectrum of the data (I'm thinking of comb spectra), you can be even more cheeky than that - as long as the artefacts can be guaranteed to lay between the elements of that comb spectrum. Early digital coding of good old PAL colour TV did just that and allowed some useful sub Nyquist sampling by choosing to make the alias components lie between the comb of line frequency harmonics. Perfect for stationary pictures but the artefacts would start to show when there was enough motion in the picture.the_emi_guy said:you actually need a bandpass filter whose bandwidth is <1/2 sampling freq.
The Nyquist theorem is a fundamental concept in the field of digital signal processing. It states that in order to accurately sample and reconstruct an analog signal, the sampling rate must be at least twice the highest frequency present in the signal.
The Nyquist theorem is important because it ensures the fidelity of the digital representation of an analog signal. If the sampling rate is too low, it can result in aliasing and distortion of the signal, leading to inaccurate and unreliable digital values.
In practical situations, the Nyquist theorem is used to determine the appropriate sampling rate for a given analog signal. This is especially important in fields such as telecommunications and audio engineering, where accurate representation of analog signals is crucial.
Yes, the Nyquist theorem is based on the assumption that the analog signal being sampled is band-limited, meaning it has a finite highest frequency. In reality, many signals are not band-limited and can contain frequencies above the Nyquist limit, which can result in errors even with a sampling rate that meets the Nyquist criteria.
The Nyquist theorem is essential in digital signal processing because it sets the minimum sampling rate required to accurately represent an analog signal in digital form. It is used in various applications such as audio and video compression, digital filtering, and data acquisition systems.