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  2. Kraft–McMillan inequality - Wikipedia

    en.wikipedia.org/wiki/Kraft–McMillan_inequality

    Let each source symbol from the alphabet = {,, …,} be encoded into a uniquely decodable code over an alphabet of size with codeword lengths ,, …,. Then = Conversely, for a given set of natural numbers ,, …, satisfying the above inequality, there exists a uniquely decodable code over an alphabet of size with those codeword lengths.

  3. Variable-length code - Wikipedia

    en.wikipedia.org/wiki/Variable-length_code

    A code is uniquely decodable if its extension is § non-singular.Whether a given code is uniquely decodable can be decided with the Sardinas–Patterson algorithm.. The mapping = {,,} is uniquely decodable (this can be demonstrated by looking at the follow-set after each target bit string in the map, because each bitstring is terminated as soon as we see a 0 bit which cannot follow any ...

  4. Sardinas–Patterson algorithm - Wikipedia

    en.wikipedia.org/wiki/Sardinas–Patterson_algorithm

    This code, which is based on an example by Berstel, [3] is an example of a code which is not uniquely decodable, since the string 011101110011. can be interpreted as the sequence of codewords 01110 – 1110 – 011, but also as the sequence of codewords 011 – 1 – 011 – 10011.

  5. Prefix code - Wikipedia

    en.wikipedia.org/wiki/Prefix_code

    For example, a code with code {9, 55} has the prefix property; a code consisting of {9, 5, 59, 55} does not, because "5" is a prefix of "59" and also of "55". A prefix code is a uniquely decodable code: given a complete and accurate sequence, a receiver can identify each word without requiring a special marker between words. However, there are ...

  6. Shannon's source coding theorem - Wikipedia

    en.wikipedia.org/wiki/Shannon's_source_coding...

    In information theory, the source coding theorem (Shannon 1948) [2] informally states that (MacKay 2003, pg. 81, [3] Cover 2006, Chapter 5 [4]): N i.i.d. random variables each with entropy H(X) can be compressed into more than N H(X) bits with negligible risk of information loss, as N → ∞; but conversely, if they are compressed into fewer than N H(X) bits it is virtually certain that ...

  7. Huffman coding - Wikipedia

    en.wikipedia.org/wiki/Huffman_coding

    Huffman tree generated from the exact frequencies of the text "this is an example of a huffman tree". Encoding the sentence with this code requires 135 (or 147) bits, as opposed to 288 (or 180) bits if 36 characters of 8 (or 5) bits were used (This assumes that the code tree structure is known to the decoder and thus does not need to be counted as part of the transmitted information).

  8. List decoding - Wikipedia

    en.wikipedia.org/wiki/List_decoding

    This is quite significant because it proves the existence of (,)-list-decodable codes of good rate with a list-decoding radius much larger than . In other words, the Johnson bound rules out the possibility of having a large number of codewords in a Hamming ball of radius slightly greater than d 2 {\displaystyle {\tfrac {d}{2}}} which means that ...

  9. Serial concatenated convolutional codes - Wikipedia

    en.wikipedia.org/wiki/Serial_concatenated...

    Fig 1 is an example of a SCCC. Fig. 1. SCCC Encoder. The example encoder is composed of a 16-state outer convolutional code and a 2-state inner convolutional code linked by an interleaver. The natural code rate of the configuration shown is 1/4, however, the inner and/or outer codes may be punctured to achieve higher code rates as needed.