Methods of Sampling
1. Ideal Sampling
Concept: Ideal sampling, also known as impulse or Dirac sampling, is a theoretical notion in which samples of a continuous signal are taken at specific time intervals, often at the Dirac delta function impulse points.
Sampling Process: Each sample in perfect sampling is an impulse or delta function at the sampling instant. The sampled signal can be represented mathematically as the product of the continuous signal and the Dirac delta function.
Sampling Circuit
Reconstruction: The reconstruction of the original signal from ideal samples, we can use interpolation which uses the functions. Ideal sampling is a simple approach to express and analyze sampling theory, however it is not practical due to the requirement for infinite bandwidth.
Ideal Sampling
2. Natural Sampling
Concept: Natural sampling, also known as zero-order hold sampling, involves taking discrete interval samples of a continuous signal, similar to uniform sampling. The difference, though, is in how the samples are gathered.
Sampling Process: Each sample is taken in natural sampling by retaining the value of the continuous signal constant for the duration of the sampling period.
Sampling Circuit
Reconstruction: The reconstruction of the original signal from natural samples, it usually involves connecting the samples with flat line segments. This method simplifies the reconstruction process compared to ideal sampling.
Natural Sampling
3. Flat-Top Sampling
Concept: Flat-top sampling is a type of natural sampling in which each sample is obtained by maintaining the value of the continuous signal constant for a set period of time, resulting in a flat-top waveform.
Sampling Process: Instead of retaining the value for the whole sample interval, flat-top sampling holds it only for a portion of the interval while allowing it to change at the beginning and end.
Sampling Circuit
Reconstruction: The reconstruction of the original signal from flat-top samples, we can use interpolation techniques. Flat-top sampling is used in applications where it is desirable to minimize the effects of finite bandwidth and aliasing.
Flat Top Sampling
Sampling in Digital Communication
Sampling in digital communication is converting a continuous-time signal into a discrete-time signal. It can also be defined as the process of measuring the discrete instantaneous values of a continuous-time signal.
Digital signals are easier to store and have a higher chance of repressing noise. This makes sampling an important step in converting analog signals to digital signals with its primary purpose as representing analog signals in a discrete format.
- Sampling Process in Digital Communication
- Nyquist – Shannon Sampling Theorem
- Oversampling & Undersampling
- Aliasing
- Why Sampling is Required?
- Methods of Sampling
- Scope of Fourier Transform
- Solved Examples on Sampling