Usually, this problem is studied with the Hamming distance in mind.

This is related to the concept of Hamming distance in information theory.

Hamming distances between the individual genes are shown in Table 1.

The Hamming distance is the total number of misplaced tiles.

In this setting, the Hamming distance is the appropriate way to measure the bit error rate.

Thus first the Hamming distance (the number of differing bits) is calculated.

Given the booleans, this can be computed using Hamming distance.

It has minimal Hamming distance at least 3 and corrects up to 1 error.

The Hamming distance between the two messages is only two bits.

Hamming distance and Lee distance are used in coding theory.

Each valid character code has a Hamming distance of at least 2 from every other character.

This code has minimal Hamming distance 15 and corrects 7 errors.

It is thus equivalent to the Hamming distance from the all-zero string of the same length.

Output: A list of all codewords whose hamming distance from is at most .

From Hamming distance concept, a code with minimum distance can correct any errors.

Hamming distance between any two codewords in is .

The Hamming distance is also used in systematics as a measure of genetic distance.

Denote be the Hamming distance of two codewords and .

The horizontal axis is the Hamming distance d .

This approach works for the Hamming distance over d-dimensional vectors .

The Hamming distance is used as a metric for hard decision viterbi decoders.

This rule-to-rule distance is also called the Hamming distance.

Let be a code of length and minimum Hamming distance having maximal size:

We shall use the Venn diagram metric and the Hamming distance later.

Similarly, we can define the normalized Hamming distance between the two successor states at time t + .