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The quantity is called the relative redundancy and gives the maximum possible data compression ratio, when expressed as the percentage by which a file size can be decreased. (When expressed as a ratio of original file size to compressed file size, the quantity R : r {\displaystyle R:r} gives the maximum compression ratio that can be achieved.)
A success tree may then be converted to a fault tree or vice versa by applying de Morgan's theorem. To evaluate an RBD, closed form solutions are available when blocks or components have statistical independence. When statistical independence is not satisfied, specific formalisms and solution tools such as dynamic RBD have to be considered. [4]
"Don't repeat yourself" (DRY), also known as "duplication is evil", is a principle of software development aimed at reducing repetition of information which is likely to change, replacing it with abstractions that are less likely to change, or using data normalization which avoids redundancy in the first place.
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 ...
[4] [5] The redundancy allows the receiver not only to detect errors that may occur anywhere in the message, but often to correct a limited number of errors. Therefore a reverse channel to request re-transmission may not be needed. The cost is a fixed, higher forward channel bandwidth.
A man found 4-month-old and 5-month-old baby girls in a ditch outside his Indianapolis home after they were kidnapped in a vehicle earlier in the day. Man getting packages finds missing babies in ...
The producer price index (PPI) for final demand rose 0.2% on a monthly basis in October, in line with forecasts, though the annual rise of 2.4% was a touch higher than expectations.
In the field of data compression, Shannon coding, named after its creator, Claude Shannon, is a lossless data compression technique for constructing a prefix code based on a set of symbols and their probabilities (estimated or measured).