In information theory, sampling is the process of converting a continuous signal into a discrete signal.
Theoretical sampling
A theoretical/ideal sampler results from multiplying a continuous signal with a Dirac comb.
The resulting signal is then a scaled Dirac comb.
The discrete signal would then be the sequence of scale values
Realizable sampling
Realizable samplers are called analog to digital converters (A/D converter or ADC).
Distortion
Sampling distortion is introduced when the sampler is non-ideal.
Various types of distortion can occur, such as:
- Jitter: deviation from precisely-accurate sample timing intervals
- Integration effect: when a sampler has a non-zero width in which the sample is measured.
- Noise: thermal sensor noise, analog circuit noise, etc.
- Quantization error: round-off error introduced by representing each sample as an integer at the output of an A/D converter
- Slew rate limit error: error caused by an inability for an a/d converter output value to change sufficiently rapidly
- Clipping: caused when the input signal exceeds the range of values that an a/d converter can represent at its output
The integration effect is readily noticeable in photography when the exposure is too long and creates a blur in the image.
An ideal camera would have an exposure time of zero.
In a capacitor-based sample and hold circuit, the integration effect is introduced because the capacitor cannot instantly change voltage thus requiring the sample to have non-zero width.
Integration effects can be analyzed as a form of low-pass filtering.
Some of the other effects can often be analyzed by modeling them as random noise added to the sample values.
List of sampling topics
Sampling theory:
Definitions:
Sampling rates:
People: