Channels with Synchronization/Substitution Errorsand Computation of Error Control Codes

Channels with Synchronization/Substitution Errors and Computation of Error Control Codes

Abstract

We introduce the concept of an -maximal error-detecting block code, for some parameter between 0 and 1, in order to formalize the situation where a block code is close to maximal with respect to being error-detecting. Our motivation for this is that constructing a maximal error-detecting code is a computationally hard problem. We present a randomized algorithm that takes as input two positive integers , a probability value , and a specification of the errors permitted in some application, and generates an error-detecting, or error-correcting, block code having up to codewords of length . If the algorithm finds less than codewords, then those codewords constitute a code that is -maximal with high probability. The error specification (also called channel) is modelled as a transducer, which allows one to model any rational combination of substitution and synchronization errors. We also present some elements of our implementation of various error-detecting properties and their associated methods. Then, we show several tests of the implemented randomized algorithm on various channels. A methodological contribution is the presentation of how various desirable error combinations can be expressed formally and processed algorithmically.

I Introduction

We consider block codes , that is, sets of words of the same length , for some integer . The elements of are called codewords or -words. We use to denote the alphabet used for making words and

Our typical alphabet will be the binary one . We shall use the variables to denote words over (not necessarily in ). The empty word is denoted by .

We also consider error specifications , which we call combinatorial channels, or simply channels. A channel specifies, for each allowed input word , the set of all possible output words. We assume that error-free communication is always possible, so . On the other hand, if and then the channel introduces errors into . Informally, a block code is -detecting if the channel cannot turn a given -word into a different -word. It is -correcting if the channel cannot turn two different -words into the same word.

In Section II, we make the above concepts mathematically precise, and show how known examples of combinatorial channels can be defined formally so that they can be used as input to algorithms. In Section III, we present two randomized algorithms: the first one decides (up to a certain degree of confidence) whether a given block code is maximal -detecting for a given channel . The second algorithm is given a channel , an -detecting block code (which could be empty), and integer , and attempts to add to new words of length resulting into a new -detecting code. If less than words get added then either the new code is 95%-maximal or the chance that a randomly chosen word can be added is less than 5%. Our motivation for considering a randomized algorithm is that embedding a given -detecting block code into a maximal -detecting block code is a computationally hard problem—this is shown in Section IV. In Section V, we discuss briefly some capabilities of the new module codes.py in the open source software package FAdo [4, 1] and we discuss some tests of the randomized algorithms on various channels. In Section VI, we discuss a few more points on channel modelling and conclude with directions for future research.

We note that, while there are various algorithms for computing error-control codes, to our knowledge these work for specific channels and implementations are generally not open source.

Ii Channels and Error Control Codes

We need a mathematical model for channels that is useful for answering algorithmic questions pertaining to error control codes. While many models of channels and codes for substitution-type errors use a rich set of mathematical structures, this is not the case for channels involving synchronization errors [13]. We believe the appropriate model for our purposes is that of a transducer. We note that transducers have been defined as early as in [18], and are a powerful computational tool for processing sets of words—see [2] and pg 41–110 of [17].

Definition 1.

A transducer is a 5-tuple1 such that is the alphabet, is the finite set of states, is the set of initial states, is the set of final states, and is the finite set of transitions. Each transition is a 4-tuple , where and are words over . The word is the input label and the word is the output label of the transition. For two words we write to mean that is a possible output of when is used as input. More precisely, there is a sequence

of transitions such that , , and . The relation realized by is the set of word pairs such that . A relation is called rational if it is realized by a transducer. If every input and every output label of is in , then we say that is in standard form. The domain of the transducer is the set of words such that . The transducer is called input-preserving if , for all words in the domain of . The inverse of , denoted by , is the transducer that is simply obtained by making a copy of and changing each transition to . Then

We note that every transducer can be converted (in linear time) to one in standard form realizing the same relation.

In our objective to model channels as transducers, we require that a transducer is a channel if it allows error-free communication, that is, is input-preserving.

Definition 2.

An error specification is an input-preserving transducer. The (combinatorial) channel specified by is , that is, the relation realized by . For the purposes of this paper, however, we simply identify the concept of channel with that of error specification.

A piece of notation that is useful in this work is the following, where is any set of words,

(1)

Thus, is the set of all possible outputs of when the input is any word from . For example, if = the channel that allows up to 1 symbol to be deleted or inserted in the input word, then

Fig. 4 considers examples of channels that have been defined in past research when designing error control codes. Here these channels are shown as transducers, which can be used as inputs to algorithms for computing error control codes. For the channel , we have because on input 00000, the channel can read the first two input 0’s at state and output 0, 0; then, still at state , read the 3rd 0 and output 1 and go to state ; etc.

0/0, 1/1

0/0, 1/1

Figure 1: Examples of (combinatorial) channels: . Notation: A short arrow with no label points to an initial state (e.g., state ), and a double line indicates a final state (e.g., state ). An arrow with label represents multiple transitions, each with label , for ; and similarly for an arrow with label —recall, = empty word. Two or more labels on one arrow from some state to some state represent multiple transitions between and having these labels. Channel : uses the binary alphabet . On input , outputs , or any word that results by performing one or two substitutions in . The latter case is when takes the transition or , corresponding to one error, and then possibly or , corresponding to a second error. A block code is -detecting iff the min. Hamming distance of is . Channel : alphabet not specified. On input , outputs a word that results by inserting and/or deleting at most 2 symbols in . A block code is -detecting iff the min. Levenshtein distance of is [8]. Channels : considered in [15], here alphabet not specified. On input , outputs either , or any word that results by deleting exactly one symbol in and then inserting a symbol at the end of .

The concepts of error-detection and -correction mentioned in the introduction are phrased below more rigorously.

Definition 3.

Let be a block code and let be a channel. We say that is -detecting if

We say that is -correcting if

An -detecting block code is called maximal -detecting if is not -detecting for any word of length that is not in . The concept of a maximal -correcting code is similar.

From a logical point of view (see Lemma 4 below) error-detection subsumes the concept of error-correction. This connection is stated already in [7] but without making use of it there. Here we add the fact that maximal error-detection subsumes maximal error-correction. Due to this observation, in this paper we focus only on error-detecting codes.

Note: The operation ’’ between two transducers and is called composition and returns a new transducer such that

Lemma 4.

Let be a block code and be a channel. Then is -correcting if and only if it is -detecting. Moreover, is maximal -correcting if and only if it is maximal -detecting.

Proof.

The first statement is already in [7]. For the second statement, first assume that is maximal -correcting and consider any word . If were -detecting then would also be -correcting and, hence, would be non-maximal; a contradiction. Thus, must be maximal -detecting. The converse can be shown analogously. ∎

The operation ’’ between any two transdcucers and is obtained by simply taking the union of their five corresponding components (states, alphabet, initial states, transitions, final states) after a renaming, if necessary, of the states such that the two transdcucers have no states in common. Then

Let be a channel, let be an -detecting block code, and let . In [3], the authors show that

is -detecting iff . (2)
Definition 5.

Let be an -detecting block code. We say that a word can be added into if .

Statement (2) above implies that

is maximal -detecting  iff  . (3)
Definition 6.

The maximality index of a block code w. r. t. a channel is the quantity

Let be a real number in . An -detecting block code is called -maximal -detecting if .

The maximality index of is the proportion of the ‘used up’ words of length over all words of length . One can verify the following useful lemma.

Lemma 7.

Let be a channel and let be an -detecting block code.

  1. if and only if is maximal -detecting.

  2. Assuming that words are chosen uniformly at random from , the maximality index is the probability that a randomly chosen word of length cannot be added into preserving its being -detecting, that is,

Proof.

The first statement follows from Definition 6 and condition (3). The second statement follows when we note that the event that a randomly chosen word from cannot be added into is the same as the event that . ∎

Iii Generating Error Control Codes

We turn now our attention to algorithms processing channels and sets of words. A set of words is called a language, with a block code being a particular example of language. A powerful method of representing languages is via finite automata [17]. A (finite) automaton is a 5-tuple as in the case of a channel, but each transition has only an input label, that is, it is of the form with being one alphabet symbol or the empty word . The language accepted by is denoted by and consists of all words formed by concatenating the labels in any path from an initial to a final state. The automaton is called deterministic, or DFA for short, if consists of a single state, there are no transitions with label , and there are no two distinct transitions with same labels going out of the same state. Special cases of automata are constraint systems in which normally all states are final (pg 1635–1764 of [16]), and trellises. A trellis is an automaton accepting a block code, and has one initial and one final state (pg 1989–2117 of [16]). In the case of a trellis we talk about the code represented by , and we denote it as , which is equal to .

For computational complexity considerations, the size of a finite state machine (automaton or transducer) is the number of states plus the sum of the sizes of the transitions. The size of a transition is 1 plus the length of the label(s) on the transition. We assume that the alphabet is small so we do not include its size in our estimates.

An important operation between an automaton and a transducer , here denoted by ’’, returns an automaton that accepts the set of all possible outputs of when the input is any word from , that is,

Remark 8.

We recall here the construction of from given and , where we assume that contains no transition with label . First, if necessary, we convert to standard form. Second, if contains any transition whose input label is , then we add into transitions , for all states . Let denote now the updated set of transitions. Then, we construct the automaton

such that , exactly when there are transitions and . The above construction can be done in time and the size of is . The required automaton is the trim version of , which can be computed in time . (The trim version of an automaton is the automaton resulting when we remove any states of that do not occur in some path from an initial to a final state of .)

nonMax  ()

  := ;

:= 1 + ;

:= the length of the words in ;

tr := 1;

while (tr ):

:= ;

if ( not in ) return ;

tr := tr+1;

return None;

Figure 2: Algorithm nonMax—see Theorem 9.

Next we present our randomized algorithms—we use [14] as reference for basic concepts. We assume that we have available to use in our algorithms an ideal method that chooses uniformly at random a word in . A randomized algorithm with specific values for its parameters can be viewed as a random variable whose value is whatever value is returned by executing on the specific values.

Theorem 9.

Consider the algorithm nonMax  in Fig. 2, which takes as input a channel , a trellis accepting an -detecting code, and two numbers .

  1. The algorithm either returns a word such that the code is -detecting, or it returns None.

  2. If is not -maximal -detecting, then

  3. The time complexity of nonMax  is

Proof.

The first statement follows from statement (2) in the previous section, as any returned by the algorithm is not in . For the second statement, suppose that the code is not -maximal -detecting. Let   be the random variable whose value is the value of tr 1 at the end of execution of the randomized algorithm nonMax. Then,   counts the number of words that are in out of randomly chosen words . Thus   is binomial: the number of successes (words in ) in trials. So , where . By the definition of in nonMax, we get . Now consider the Chebyshev inequality, where is arbitrary and is the variance of some random variable . For the variance is , and we get

where we used and the fact that .

Using Lemma 7 and the assumption that is not -maximal, we have that , which implies ; hence, . Then

as required.

For the third statement, we use standard results from automaton theory, [17], and Remark 8. In particular, computing can be done in time such that . Testing whether can be done in time . Thus, the algorithm works in time

Remark 10.

We mention the important observation that one can modify the algorithm nonMax  by removing the construction of   and replacing the ‘if’ line in the loop with

if ( is -detecting) return ;

While with this change the output would still be correct, the time complexity of the algorithm would increase to . This is because testing whether is -detecting, for any given automaton and channel , can be done in time , and in practice is much larger than .

In Fig. 3, we present the main algorithm for adding new words into a given deterministic trellis .

makeCode  ()

:= empty list; :=

cnt := 0;  more := True;

while (cnt and  more)

:= nonMax ();

if ( is None) more := False;

else {add to   and to ;   cnt := cnt+1;}

return ,  ;

Figure 3: Algorithm makeCode—see Theorem 12. The trellis can be omitted so that the algorithm would start with an empty set of codewords. In this case, however, the algorithm would require as extra input the codeword length and the desired alphabet . We used the fixed values 0.95 and 0.05, as they seem to work well in practical testing.
Remark 11.

In some sense, algorithm makeCode  generalizes to arbitrary channels the idea used in the proof of the well-known Gilbert-Varshamov bound [12] for the largest possible block code that is -correcting, for some number of substitution errors. In that proof, a word can be added into the code if the word is outside of the union of the “balls” , for all . In that case, we have that and . The present algorithm adds new words to the constructed trellis such that each new word is outside of the “union-ball” .

Theorem 12.

Algorithm makeCode  in Fig. 3 takes as input a channel , a deterministic trellis of some length , and an integer such that the code is -detecting, and returns a deterministic trellis and a list of words such that the following statements hold true:

  1. and is -detecting,

  2. If has less than words, then either or the probability that a randomly chosen word from can be added in is .

  3. The algorithm runs in time .

Proof.

Let be the value of the trellis at the end of the -th iteration of the while loop. The first statement follows from Theorem 9: any word returned by nonMax  is such that is -detecting. For the second statement, assume that, at the end of execution, has words and is not 95%-maximal. By the previous theorem, this means that the random process returns None  with probability , as required. For the third statement, as the loop in the algorithm nonMax  performs a fixed number of iterations (=2 000), we have that the cost of nonMax  is . The cost of adding a new word of length to is and increases its size by , so each is of size . Thus, the cost of the -th iteration of the while loop in makeCode  is . As there are up to iterations the total cost is

Remark 13.

In the algorithm makeCode, attempting to add only one word into (case of ), requires time , which is of polynomial magnitude. This case is equivalent to testing whether is maximal -detecting, which is shown to be a hard decision problem in Theorem 15.

Remark 14.

In the version of the algorithm makeCode  where the initial trellis is omitted, the time complexity is . We also note that the algorithm would work with the same time complexity if the given trellis is not deterministic. In this case, however, the resulting trellis would not be (in general) deterministic either.

Iv Why not Use a Deterministic Algorithm

Our motivation for considering randomized algorithms is that the embedding problem is computationally hard: given a deterministic trellis and a channel , compute (using a deterministic algorithm) a trellis that represents a maximal -detecting code containing . By computationally hard, we mean that a decision version of the embedding problem is coNP-hard. This is shown next.

Theorem 15.

The following decision problem is coNP-hard.

Instance:

deterministic trellis and channel .

Answer:

whether is maximal -detecting.

Proof.

Let us call the decision problem in question , and let be the problem of deciding whether a given trellis over the alphabet with no -labeled transitions accepts , for some . The statement is a logical consequence of the following claims.

Claim 1: is coNP-complete.
Claim 2: is polynomially reducible to .

The first claim follows from the proof of the following fact on page 329 of [10]: Deciding whether two given star-free regular expressions over are inequivalent is an NP-complete problem. Indeed, in that proof the first regular expression can be arbitrary, but the second regular expression represents the language , for some positive integer . Moreover, converting a star-free regular expression to an acyclic automaton with no -labeled transitions is a polynomial time problem.

For the second claim, consider any trellis with no -labeled transitions in . We need to construct in polynomial time an instance of such that accepts if and only if is a maximal -detecting block code of length . The rest of the proof consists of 5 parts: construction of deterministic trellis accepting words of length , construction of , facts about and , proving that is -detecting, proving that accepts if and only if is maximal -detecting.

Construction of : Let be the alphabet , where is the set of transitions of . The required deterministic trellis is any deterministic trellis accepting , that is,

This can be constructed, for instance, by making deterministic trellises and accepting, respectively, and , and then intersecting with the complement of . Note that any word in contains at least one symbol in .

Construction of : This is of the form as follows. The transducer has only one state and transitions , for all , and realizes the identity relation . Thus, we have that , for all words . The transducer is such that consists of exactly the transitions for which is a transition of .

Facts about and : The following facts are helpful in the rest of the proof. Some of these facts refer to the deterministic trellis resulting by omitting the output parts of the transition labels of , that is, exactly when . Then, .

F0: .
F1: The domain of is , a subset of .
F2: If then and .
F3: .
F4: .

For fact F0, note that the product construction described in Remark 8 produces in exactly the transitions , where is a transition in , by matching any transition of only with the transition of . Fact F1 follows by the construction of and the definition of : in any accepting computation of , the input labels appear in an accepting computation of that uses the same sequence of states. F3 is shown as follows: As the domain of is and , we have that , which is by F0. Fact F4 follows by noting that the domain of is a subset of but contains no words in .

is -detecting: Let such that . We need to show that , that is, to show that . Indeed, if then , which contradicts .

accepts if and only if is maximal -detecting: By statement (3) we have that is maximal -detecting, if and only if . We have:

Thus, is maximal -detecting, if and only if , as required. ∎

V Implementation and Use

All main algorithmic tools have been implemented over the years in the Python package FAdo [4, 1, 6]. Many aspects of the new module FAdo.codes are presented in [6]. Here we present methods of that module pertaining to generating codes.

Assume that the string d1 contains a description of the transducer in FAdo format. In particular, d1 begins with the type of FAdo object being described, the final states, and the initial states (after the character *). Then, d1 contains the list of transitions, with each one of the form “ \n”, where ’\n’ is the new-line character. This shown in the following Python script.

    import FAdo.codes as codes
    d1 = ’@Transducer 0 2 * 0\n’
      ’0 0 0 0\n0 1 1 0\n0 0 @epsilon 1\n’
      ’0 1 @epsilon 1\n1 0 0 1\n1 1 1 1\n’
      ’1 @epsilon 0 2\n1 @epsilon 1 2\n’
    pd1 = codes.buildErrorDetectPropS(d1)
    a = pd1.makeCode(100, 8, 2)
    print pd1.notSatisfiesW(a)
    print pd1.nonMaximalW(a, m)
    s2 = ...string for transducer sub_2
    ps2 = codes.buildErrorDetectPropS(s2)
    pd1s2 = pd1 & ps2
    b = pd1s2.makeCode(100, 8, 2)

The above script uses the string d1 to create the object pd1 representing the -detection property over the alphabet {0,1}. Then, it constructs an automaton a representing a -detecting block code of length with up to words over the 2-symbol alphabet {0,1}. The method notSatisfiesW(a) tests whether the code is -detecting and returns a witness of non-error-detection (= pair of codewords with ), or (None, None)—of course, in the above example it would return (None, None). The method nonMaximalW(a, m) tests whether the code is maximal -detecting and returns either a word such that is -detecting, or None if is already maximal. The object m is any automaton—here it is the trellis representing . This method is used only for small codes, as in general the maximality problem is algorithmically hard (recall Theorem 15), which motivated us to consider the randomized version nonMax  in this paper. For any channel and trellis a, the method notSatisfiesW(a) can be made to work in time , which is of polynomial complexity. The operation ’&’ combines error-detection properties. Thus, the second call to makeCode  constructs a code that is -detecting and -detecting (=-correcting).

Vi More on Channel Modelling, Testing

In this section, we consider further examples of channels and show how operations on channels can result in new ones. We also show the results of testing our codes generation algorithm for several different channels.

Remark 16.

We note that the definition of error-detecting (or error-correcting) block code is trivially extended to any language , that is, one replaces in Definition 3 ’block code ’ with ’language ’. Let be channels. By Definition 3 and using standard logical arguments, it follows that

  1. is -detecting and -detecting, if and only if is -detecting;

  2. is -detecting, if and only if it is -detecting, if and only if it is -detecting.

The inverse of is and is shown in Fig. 4, where recall it results by simply exchanging the order of the two words in all the labels in . By statement 2 of the above remark, the -detecting codes are the same as the -detecting ones, and the same as the -detecting ones—this is shown in [15] as well. The method of using transducers to model channels is quite general and one can give many more examples of past channels as transducers, as well as channels not studied before. Some further examples are shown in the next figures, Fig. 4-6.

One can go beyond the classical error control properties and define certain synchronization properties via transducers. Let be the set of all overlap-free words, that is, all words such that a proper and nonempty prefix of cannot be a suffix of . A block code is a solid code if any proper and nonempty prefix of a -word cannot be a suffix of a -word. For example, {0100, 1001} is not a block solid code, as 01 is a prefix and a suffix of some codewords and 01 is nonempty and a proper prefix (shorter than the codewords). Solid codes can also be non-block codes by extending appropriately the above definition [19] (they are also called codes without overlaps in [9]). The transducer in Fig. 6 is such that any block code is a solid code, if and only if is an ’-detecting’ block code. We note that solid codes have instantaneous synchronization capability (in particular all solid codes are comma-free codes) as well as synchronization in the presence of noise [5].

Figure 4: The channel specified by allows up to two errors in the input word. Each of these errors can be a deletion, an insertion, or a bit shift: a 10 becomes 01, or a 01 becomes 10. The alphabet is {0, 1}.

to state

to state

Figure 5: Transducer for the segmented deletion channel of  [11] with parameter . In each of the length consecutive segments of the input word, at most one deletion error occurs. The length of the input word is a multiple of . By Lemma 4, -correction is equivalent to -detection.

Figure 6: This input-preserving transducer deletes a prefix of the input word (a possibly empty prefix) and then inserts a possibly empty suffix at the end of the input word.

For and , the value of in nonMax  is 2 000. We performed several executions of the algorithm makeCode  on various channels using , no initial trellis, and alphabet .

, , end=
1
01

In the above table, the first column gives the values of and , and if present and nonempty, the pattern that all codewords should end with (1 or 01). For each entry in an ’’ row, we executed makeCode  21 times and reported smallest, median, and largest sizes of the 21 generated codes. For = 500, we reported the same figures by executing the algorithm 5 times. For example, the entry 37,42,51 corresponds to executing makeCode  21 times for , , end = . The entry 64,64,64 corresponds to the systematic code of [15] whose codewords end with 01, and any of the 6-bit words can be used in positions 1–6. The entry for ’, end = , ’ corresponds to 2-substitution error-detection which is equivalent to 1-substitution error-correction. Here the Hamming code of length 7 with codewords has a maximum number of codewords for this length. Similarly, the entry for ’, ’ corresponds to 2-synchronization error-detection which is equivalent to 1-synchronization error-correction. Here the Levenshtein code [8] of length 8 has 30 codewords. We recall that a maximal code is not necessarily maximum, that is, having the largest possible number of codewords, for given and . It seems maximum codes are rare, but there are many random maximal ones having lower rates. The -detecting code of [15] has higher rate than all the random ones generated here.

For the case of block solid codes (last column of the table), we note that the function pickFrom  in the algorithm nonMax  has to be modified as the randomly chosen word should be in .

Vii Conclusions

We have presented a unified method for generating error control codes, for any rational combination of errors. The method cannot of course replace innovative code design, but should be helpful in computing various examples of codes. The implementation codes.py is available to anyone for download and use [4]. In the implementation for generating codes, we allow one to specify that generated words only come from a certain desirable subset of , which is represented by a deterministic trellis. This requires changing the function pickFrom  in nonMax  so that it chooses randomly words from . There are a few directions for future research. One is to work on the efficiency of the implementations, possibly allowing parallel processing, so as to allow generation of block codes having longer block length. Another direction is to somehow find a way to specify that the set of generated codewords is a ‘systematic’ code so as to allow efficient encoding of information. A third direction is to do a systematic study on how one can map a stochastic channel , like the binary symmetric channel or one with memory, to a channel (representing a combinatorial channel), so as the available algorithms on have a useful meaning on as well.

Footnotes

  1. The general definition of transducer allows two alphabets: the input and the output alphabet. Here, however, we assume that both alphabets are the same.

References

  1. André Almeida, Marco Almeida, José Alves, Nelma Moreira, and Rogério Reis. FAdo and GUItar: Tools for automata manipulation and visualization. In Proceedings of CIAA 2009, Sydney, Australia, volume 5642 of Lecture Notes in Computer Science, pages 65–74, 2009.
  2. Jean Berstel. Transductions and Context-Free Languages. B.G. Teubner, Stuttgart, 1979.
  3. Krystian Dudzinski and Stavros Konstantinidis. Formal descriptions of code properties: decidability, complexity, implementation. International Journal of Foundations of Computer Science, 23:1:67–85, 2012.
  4. FAdo. Tools for formal languages manipulation. Accessed in Jan. 2016. URL: http://fado.dcc.fc.up.pt/.
  5. Helmut Jürgenesen and S. S. Yu. Solid codes. Elektron. Informationsverarbeit. Kybernetik., 26:563–574, 1990.
  6. Stavros Konstantinidis, Casey Meijer, Nelma Moreira, and Rogério Reis. Implementation of code properties via transducers. In Yo-Sub Han and Kai Salomaa, editors, Proceedings of CIAA 2016, number 9705 in Lecture Notes in Computer Science, pages 189–201, 2016. ArXiv version: Symbolic manipulation of code properties. arXiv:1504.04715v1, 2015.
  7. Stavros Konstantinidis and Pedro V. Silva. Maximal error-detecting capabilities of formal languages. J. Automata, Languages and Combinatorics, 13(1):55–71, 2008.
  8. Vladimir I. Levenshtein. Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Dokl., 10:707–710, 1966.
  9. Vladimir I. Levenshtein. Maximum number of words in codes without overlaps. Probl. Inform. Transmission, 6(4):355–357, 1973.
  10. H. Lewis and C.H. Papadimitriou. Elements of the Theory of Computation, 2nd ed. Prentice Hall, 1998.
  11. Zhenming Liu and Michael Mitzenmacher. Codes for deletion and insertion channels with segmented errors. In Proceedings of ISIT, Nice, France, 2007, pages 846–849, 2007.
  12. F. J. MacWilliams and N. J. A. Sloane. The Theory of Error-Correcting Codes. Amsterdam, 1977.
  13. Hugues Mercier, Vijay Bhargava, and Vahid Tarokh. A survey of error-correcting codes for channels with symbol synchronization errors. IEEE Communic. Surveys & Tutorials, 12(1):87–96, 2010.
  14. Michael Mitzenmacher and Eli Upfal. Probability and Computing. Cambridge Univ. Press, 2005.
  15. Filip Paluncic, Khaled Abdel-Ghaffar, and Hendrik Ferreira. Insertion/deletion detecting codes and the boundary problem. IEEE Trans. Information Theory, 59(9):5935–5943, 2013.
  16. V. S. Pless and W. C. Huffman, editors. Handbook of Coding Theory. Elsevier, 1998.
  17. Grzegorz Rozenberg and Arto Salomaa, editors. Handbook of Formal Languages, Vol. I. Springer-Verlag, Berlin, 1997.
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  19. H. J. Shyr. Free Monoids and Languages. Hon Min Book Company, Taichung, second edition, 1991.
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