Robust Prefix Encoding Using Lower (2,3) Number Representation

Anisimov, A. V.; Zavadsky, I. A.

doi:10.1007/s10559-014-9604-x

Cited by 5 publications

(13 citation statements)

References 4 publications

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“…(2,3) coding was first described in [1], and lower (2,3) coding, methods of its construction, and its noise-immune properties were investigated in [2]. We now give basic definitions.…”

Section: Internal Codingmentioning

confidence: 99%

“…The symbol G 1 or G 2 at the arrow of a transition means that the transition is carried out only if the current value of G x i ( )belongs to the set G 1 or G 2 , respectively (and/or other transition conditions are satisfied). Second, a condition can be as follows: the quantity 3 k i x must satisfy inequality (1) or (2). These conditions are denoted by (1) and (2) .…”

Section: Internal Codingmentioning

confidence: 99%

“…The conditions of transitions between states have been formulated with allowance for the probabilities of different values of D that are specified in [2] so that all transitions from each state are maximally equiprobable. This makes it possible to decrease the code length that, as a result, will exceed the length of the binary representation of a number by a factor of 1.25 on average.…”

Section: Internal Codingmentioning

confidence: 99%

“…If some bit of the code will be inverted by noises in the communication channel, then, during one of the transitions carried out soon after processing this bit, the value of the hash function G x i ( )can belong to the set G 1 though, according to coding, it must belong to the set G 2 , or vice versa. This situation makes it possible to detect errors in a code, which provides its definite noise immunity that is appreciably higher than the noise immunity of lower (2,3) codes described in [2]. …”

Section: Internal Codingmentioning

confidence: 99%

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Variable-Length Error-Correcting Codes Based on Finite Automata

Zavadskyi

2015

Cybern Syst Anal

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A new method for error-correcting coding is proposed. It is based on processing information messages by finite automata and using a two-base numeral system. The two-level structure of an encoder provides powerful error-correcting capabilities. On the first (internal) level, an input message is considered as a binary number represented as a lower (2,3) code that has some redundancy and error-correcting properties. The noise-resistant properties are strengthened on the external level where the code is processed by a special finite automaton. It is a variable-length code, i.e., the codeword length depends not only on the length of an input message but also on the message content. However, the average code rate equals 1/2.

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“…(2,3) coding was first described in [1], and lower (2,3) coding, methods of its construction, and its noise-immune properties were investigated in [2]. We now give basic definitions.…”

Section: Internal Codingmentioning

confidence: 99%

Section: Internal Codingmentioning

confidence: 99%

Section: Internal Codingmentioning

confidence: 99%

Section: Internal Codingmentioning

confidence: 99%

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Variable-Length Error-Correcting Codes Based on Finite Automata

Zavadskyi

2015

Cybern Syst Anal

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“…Construct the lower (2,3) code for this number, rewrite the sequence of its ( , ) D k -groups from right to left, and generate the corresponding noise-immune (2,3) code from it with the help of the internal automaton from [1, Fig. 1].…”

Section: Introductionmentioning

confidence: 99%

A Method for Decoding a Variable-Rate Error-Correcting Code Based on Finite-State Automata

Zavadskyi

2015

Cybern Syst Anal

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A decoding algorithm for a special error-correcting code is considered and its efficiency is estimated. This code is obtained as a result of processing information messages by finite automata and using a two-base numeral system. A general encoding algorithm is also considered. Both encoding or decoding are performed by a two-level system in which an input message is represented as a lower (2,3) code at the internal level, and the error correcting capabilities of this code are strengthened owing to its transformation with the help of a special finite automaton at the external level. In decoding, errors are first detected and corrected at the external level, and then possible remaining errors are eliminated by an internal automaton. The relationship between the external level of the system being considered and convolutional codes is investigated and the advantages of the proposed method are shown.

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Generating (2,3)-Codes

Anisimov

2020

Cybern Syst Anal

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Robust Prefix Encoding Using Lower (2,3) Number Representation

Cited by 5 publications

References 4 publications

Variable-Length Error-Correcting Codes Based on Finite Automata

Variable-Length Error-Correcting Codes Based on Finite Automata

A Method for Decoding a Variable-Rate Error-Correcting Code Based on Finite-State Automata

Generating (2,3)-Codes

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