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  2. Error detection and correction - Wikipedia

    en.wikipedia.org/wiki/Error_detection_and_correction

    A checksum of a message is a modular arithmetic sum of message code words of a fixed word length (e.g., byte values). The sum may be negated by means of a ones'-complement operation prior to transmission to detect unintentional all-zero messages.

  3. Cross-interleaved Reed–Solomon coding - Wikipedia

    en.wikipedia.org/wiki/Cross-interleaved_Reed...

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  4. Reed–Solomon error correction - Wikipedia

    en.wikipedia.org/wiki/Reed–Solomon_error...

    A Reed–Solomon code (like any MDS code) is able to correct twice as many erasures as errors, and any combination of errors and erasures can be corrected as long as the relation 2E + S ≤ n − k is satisfied, where is the number of errors and is the number of erasures in the block.

  5. Coding theory - Wikipedia

    en.wikipedia.org/wiki/Coding_theory

    It is an error-correcting code capable of correcting up to three errors in each 24-bit word, and detecting a fourth. Richard Hamming won the Turing Award in 1968 for his work at Bell Labs in numerical methods, automatic coding systems, and error-detecting and error-correcting codes.

  6. Error correction code - Wikipedia

    en.wikipedia.org/wiki/Error_correction_code

    Low-density parity-check (LDPC) codes are a class of highly efficient linear block codes made from many single parity check (SPC) codes. They can provide performance very close to the channel capacity (the theoretical maximum) using an iterated soft-decision decoding approach, at linear time complexity in terms of their block length.

  7. Redundancy (information theory) - Wikipedia

    en.wikipedia.org/wiki/Redundancy_(information...

    In describing the redundancy of raw data, the rate of a source of information is the average entropy per symbol. For memoryless sources, this is merely the entropy of each symbol, while, in the most general case of a stochastic process, it is

  8. Type I and type II errors - Wikipedia

    en.wikipedia.org/wiki/Type_I_and_type_II_errors

    In statistical hypothesis testing, a type I error, or a false positive, is the rejection of the null hypothesis when it is actually true. A type II error, or a false negative, is the failure to reject a null hypothesis that is actually false. [1] Type I error: an innocent person may be convicted. Type II error: a guilty person may be not convicted.

  9. Damm algorithm - Wikipedia

    en.wikipedia.org/wiki/Damm_algorithm

    The Damm algorithm is similar to the Verhoeff algorithm.It too will detect all occurrences of the two most frequently appearing types of transcription errors, namely altering a single digit or transposing two adjacent digits (including the transposition of the trailing check digit and the preceding digit).