Search

# Sampling (information theory)

In information theory, sampling is the process of converting a continuous signal into a discrete signal.

 Contents

## 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:

Last updated: 05-10-2005 04:19:48
The contents of this article are licensed from Wikipedia.org under the GNU Free Documentation License. How to see transparent copy

 Fact Archive.com, 2005. Legal info