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  2. Concatenated error correction code - Wikipedia

    en.wikipedia.org/wiki/Concatenated_error...

    Turbo codes, as described first in 1993, implemented a parallel concatenation of two convolutional codes, with an interleaver between the two codes and an iterative decoder that passes information forth and back between the codes. [6] This design has a better performance than any previously conceived concatenated codes.

  3. Error correction code - Wikipedia

    en.wikipedia.org/wiki/Error_correction_code

    A convolutional code that is terminated is also a 'block code' in that it encodes a block of input data, but the block size of a convolutional code is generally arbitrary, while block codes have a fixed size dictated by their algebraic characteristics. Types of termination for convolutional codes include "tail-biting" and "bit-flushing".

  4. Coding theory - Wikipedia

    en.wikipedia.org/wiki/Coding_theory

    Linear block codes; Convolutional codes; It analyzes the following three properties of a code – mainly: [citation needed] Code word length; Total number of valid code words; The minimum distance between two valid code words, using mainly the Hamming distance, sometimes also other distances like the Lee distance

  5. Reed–Solomon error correction - Wikipedia

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

    The Reed–Solomon code is actually a family of codes, where every code is characterised by three parameters: an alphabet size , a block length, and a message length, with <. The set of alphabet symbols is interpreted as the finite field F {\displaystyle F} of order q {\displaystyle q} , and thus, q {\displaystyle q} must be a prime power .

  6. BCJR algorithm - Wikipedia

    en.wikipedia.org/wiki/BCJR_algorithm

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Pages for logged out editors learn more

  7. Neural network (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Neural_network_(machine...

    For instance, deep convolutional neural networks (CNNs) have been important in handwritten digit recognition, achieving state-of-the-art performance. [244] This demonstrates the ability of ANNs to effectively process and interpret complex visual information, leading to advancements in fields ranging from automated surveillance to medical imaging.

  8. Viterbi decoder - Wikipedia

    en.wikipedia.org/wiki/Viterbi_decoder

    The commonly used rule of thumb of a truncation depth of five times the memory (constraint length K-1) of a convolutional code is accurate only for rate 1/2 codes. For an arbitrary rate, an accurate rule of thumb is 2.5(K - 1)/(1−r) where r is the code rate. [1]

  9. Reed–Muller code - Wikipedia

    en.wikipedia.org/wiki/Reed–Muller_code

    The Reed–Muller RM(r, m) code of order r and length N = 2 m is the code generated by v 0 and the wedge products of up to r of the v i, 1 ≤ i ≤ m (where by convention a wedge product of fewer than one vector is the identity for the operation).