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What does the sampling theorem say?
The sampling theorem essentially says that a signal has to be sampled at least with twice the frequency of the original signal. Since signals and their respective speed can be easier expressed by frequencies, most explanations of artifacts are based on their representation in the frequency domain.
How often you must sample in order not to lose any information?
4 Sampling Theorem (sometimes also known as the Shannon Theorem or the Nyquist Theorem) provides the answer. It states that if the original signal has a MAXIMUM frequency component at fmax, then we MUST sample at 2 x fmax or higher in order NOT to loose information.
What are the results of the sampling theorem?
These results are important in determining appropriate sample rates and bandwidths for conversion between analog and digital signals. Applying the sampling theorem to speech signals that are limited to 4000 Hz, we find that they need to be sampled 8000 times/sec to be completely specified.
Why is the Nyquist-Shannon sampling theorem important?
The Nyquist–Shannon sampling theorem is a theorem in the field of signal processing which serves as a fundamental bridge between continuous-time signals and discrete-time signals. It establishes a sufficient condition for a sample rate that permits a discrete sequence of samples to capture all the information from a continuous-time signal
How is the sampling theorem used in low pass signals?
Sampling Theorem for Low Pass Signals The low pass signals having the low range frequency and whenever this type of low-frequency signals need to convert to discrete then the sampling frequency should be double than these low-frequency signals to avoid the distortion in the output discrete signal.
How is the number of samples represented in the sampled signal?
The number of samples is represented in the sampled signal is indicated by the sampling rate.