Contents
What is sampling and reconstruction in control system?
Sampling: Conversion of a continuous-time signal (usu- ally not quantized) to a discrete-time signal (usually quantized). Page 8. ee 424 #1: sampling and reconstruction 8. Reconstruction: Conversion of a discrete-time signal (usually quantized) to a continuous-time signal.
What is sampling and reconstruction?
In signal processing, sampling is the reduction of a continuous-time signal to a discrete-time signal. The original signal is retrievable from a sequence of samples, up to the Nyquist limit, by passing the sequence of samples through a type of low pass filter called a reconstruction filter.
How is the sampling theorem used in signal reconstruction?
In reconstructing a signal from its samples, there is another practical difficulty. The sampling theorem was proved on the assumption that the signal x (t) is bandlimited. All practical signals are time limited, i.e., they are of finite duration. As a signal cannot be timelimited and bandlimited simultaneously.
What was the assumption of the sampling theorem?
The sampling theorem was proved on the assumption that the signal x (t) is bandlimited. All practical signals are time limited, i.e., they are of finite duration. As a signal cannot be timelimited and bandlimited simultaneously.
Why is the Nyquist-Shannon sampling theorem important?
With the introduction of the concept of signal sampling, which produces a discrete time signal by selecting the values of the continuous time signal at evenly spaced points in time, it is now possible to discuss one of the most important results in signal processing, the Nyquist-Shannon sampling theorem.
Can a signal be reconstructed from a sample?
The condition in which this is possible is known as Nyquist sampling theorem and is derived below. A real signal whose spectrum is bandlimited to D Hz [X (f) = 0 for | f |>D] can be reconstructed from its samples taken uniformly at a rate fs > 2D samples/sec.