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Titlebook: General Theory of Information Transfer and Combinatorics; Rudolf Ahlswede,Lars Bäumer,Haik Mashurian Book 2006 Springer-Verlag Berlin Heid

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Large Families of Pseudorandom Sequences of , Symbols and Their Complexity – Part IIer the .–complexity (extended now to .–ary alphabets) . of a family .. It serves again as a performance parameter of key spaces in cryptography. We give a lower bound for the .–complexity for a family of the type constructed in Part I. In the last sections we explain what can be said about the theor
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Density Functional Calculations,s an answer to the question “What is the actual message?” In the identification problem we deal with the question how many possible messages the receiver of a noisy channel can identify? Identification means there is an answer to the question “Is the actual message .?” Here . can be any member of the set of possible messages.
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https://doi.org/10.1007/978-3-031-37499-9ede. This and other directions of research can be found also in [2]. Well known is Shannon’s theory of transmission of messages over a noisy channel ([15]). Using the framework of Shannon’s channel model a new concept of information transfer – called identification – was introduced by Ahlswede and Dueck in [1].
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Computational Commutative Algebra 2tian’s result on multiple descriptions [13], we refine our previous proof on successive refinability conditions reported in [7] and restate the result by Tuncel and Rose [17]. In particular, it is noted that the successive refinement in “purely” reliability sense is always possible.
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,Theodorsen’s Integral Equation,,...,.. We are interested in codes . with the property that, given any descendant .., one can always identify at least one of the ‘parent’ codewords in .. We study bounds on .(.,.), the maximal cardinality of a code . with this property, which we call the .. Such codes play a role in schemes that protect against piracy of software.
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