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Vol. 1: An introduction to the bosonic string. ISBN 0-521-63303-6. Vol. 2: Superstring theory and beyond. ISBN 0-521-63304-4. Szabo, Richard J. (Reprinted 2007) An Introduction to String Theory and D-brane Dynamics. Imperial College Press. ISBN 978-1-86094-427-7. Zwiebach, Barton (2004) A First Course in String Theory. Cambridge University Press.
String-like objects in relativistic theories, such as the strings used in some models of interactions between quarks, or those used in the modern string theory, also possess tension. These strings are analyzed in terms of their world sheet, and the energy is then typically proportional to the length of the string. As a result, the tension in ...
In information theory, the Hamming distance between two strings or vectors of equal length is the number of positions at which the corresponding symbols are different. In other words, it measures the minimum number of substitutions required to change one string into the other, or equivalently, the minimum number of errors that could have transformed one string into the other.
If the tension on a string is ten lbs., it must be increased to 40 lbs. for a pitch an octave higher. [1] A string, tied at A , is kept in tension by W , a suspended weight, and two bridges, B and the movable bridge C , while D is a freely moving wheel; all allowing one to demonstrate Mersenne's laws regarding tension and length [ 1 ]
Vibration, standing waves in a string. The fundamental and the first 5 overtones in the harmonic series. A vibration in a string is a wave. Resonance causes a vibrating string to produce a sound with constant frequency, i.e. constant pitch. If the length or tension of the string is correctly adjusted, the sound produced is a musical tone.
The most widely known string metric is a rudimentary one called the Levenshtein distance (also known as edit distance). [2] It operates between two input strings, returning a number equivalent to the number of substitutions and deletions needed in order to transform one input string into another.
The Wagner–Fischer algorithm computes edit distance based on the observation that if we reserve a matrix to hold the edit distances between all prefixes of the first string and all prefixes of the second, then we can compute the values in the matrix by flood filling the matrix, and thus find the distance between the two full strings as the last value computed.
Various algorithms exist that solve problems beside the computation of distance between a pair of strings, to solve related types of problems. Hirschberg's algorithm computes the optimal alignment of two strings, where optimality is defined as minimizing edit distance. Approximate string matching can be formulated in terms of edit distance.