The sun moves east to west in the sky, with 24 hours between sunrises. If one were to take a picture of the sun in the sky every 23 hours and write down the precise date and time on each picture, the sun would appear to move west to east, and the period between each apparent "sunrise" on the horizon in the west would be 24 pictures, which is 552 hours (24x23), that is, 23 real days. If the observations are made every 25 hours, the sun now moves apparently east to west, and it will still take 24 images for the sun to complete a revolution, but now each image will have lapsed 25 hours. The apparent period of the sun is then 600 hours (24x25), which is 25 real days. A similar temporal aliasing effect may occur filming a spoked wheel. This effect can be used to cause a repetitive action to appear to slow down by using a strobe light.

The term "aliasing" derives from the usage in radio engineering, where a radio signal could be picked up at two different positions on the radio dial in a superheterodyne radio: one where the local oscillator was above the radio frequency, and one where it was below. This is analogous to the frequency-space "wrapround" that is one way of understanding aliasing.

The qualitative effects of aliasing can be heard in the following audio demonstration. Four sawtooth waves are played in succession, with the first two sawtooths having a fundamental frequency of 220 Hz (A2) and the second two having fundamental frequency of 440 Hz (A3). The sawooths alternate between bandlimited (non-aliased) sawtooths and aliased sawtooths. Note that the audio file has been coded using Ogg's Vorbis codec, and as such the audio is somewhat degraded.

- {220 Hz bandlimited, 220 Hz aliased, 440 Hz bandlimited, 440 Hz aliased}

In engineering, the method introduced in the third section is called sampling, while a method such as that introduced in the fifth section is called filtering. This discussion may be viewed as a theoretical introduction to the ideas of anti-aliasing.

A *signal* is usually a real or complex valued function whose domain could be the real line (which we might understand as a time variable), the plane (for computer graphics) or some other set. More specific details depend on the nature of the signals we're studying, but an example of a mathematically precise space of interest might be

- ,

These signals are in fact not necessarily continuous functions; the adjective "continuous" refers to the domain as opposed to a discrete or even finite set.

The signal could arise from a variety of physical processes. For instance, one could measure the seismic movement of the ground with a seismograph. The output of a seismograph is a strip of paper known as a seismogram. This strip of paper can be interpreted as the graph of a function. This function will be in *L*^{2} as defined above, and thus we obtain a mathematical signal from a physical process.

We need to find a way of judging whether two such signals are similar. We will quantify this, usually with a norm. Such a simple system may not be appropriate; for an example of a more sophisticated approach, see the psychoacoustic model. For the sake of discussion, we will use the root mean square norm (see L_{p} spacess for some details).

Note that is a linear map: if *f* and *g* are any two functions for which and are defined, and if *a* is any scalar, then . The domain of includes at least all continuous functions of . On the other hand, for technical reasons, it is not clear how to extend to all of . In particular (and perhaps more telling) is that is not continuous as a function on .

Indeed, define by

Therefore, the sampling function very poorly represents our notion of closeness in our signal space .

We continue with but now we will use its Hilbert space structure. Let *F* be any sampling method (a linear map from to .)

In our example, the vector space of sampled signals is *n*-dimensional complex space. Any proposed inverse *R* of *F* (*reconstruction formula*, in the lingo) would have to map to some subset of . We could choose this subset arbitrarily, but if we're going to want a reconstruction formula *R* that is also a linear map, then we have to choose an *n*-dimensional linear subspace of .

This fact that the dimensions have to agree is related to the Nyquist-Shannon sampling theorem.

The elementary linear algebra approach works here. Let (all entries zero, except for the *k*th entry, which is a one) or some other basis of . To define an inverse for *F*, simply choose, for each *k*, an so that . This uniquely defines the (pseudo-)inverse of *F*.

Of course, one can choose some reconstruction formula first, then either compute some sampling algorithm from the reconstruction formula, or analyze the behavior of a given sampling algorithm with respect to the given formula.

Then we can define a linear map *R* by

The choice of range is somewhat arbitrary, although it satisfies the dimensionality requirement and reflects the usual notion that the most important information is contained in the low frequencies. In some cases, this is incorrect, so a different reconstruction formula needs to be chosen.

A similar approach can be obtained by using wavelets instead of Hilbert bases. For many applications, the best approach is still not clear today.

We note here that there is an efficient algorithm, known as the Fast Fourier transform to convert vectors between the canonical basis of and the Fourier basis . This algorithm is significantly faster than the matrix multiplication required in the general case of change of basis. On the other hand, wavelets are often defined so that the change of basis matrix is sparse, and so again the change of basis algorithm is efficient.

In computer science, **aliasing** is the phenomenon of multiple names referring to a single object. Aliasing, in that sense, is an important consideration in both compiler and CPU design.