Приклади вживання Shannon's Англійська мовою та їх переклад на Українською
{-}
-
Colloquial
-
Ecclesiastic
-
Computer
All proceeds will go to Shannon's Olympic dream.
This fact, which is truly fundamental for the entire theory of information transmission, is called Shannon's theorem.
If we restrict Shannon's information to one of the five aspects of information, then we do obtain a scientifically sound solution[G5].
Even if the information content could be calculated according to Shannon's theory, the real nature of information is still ignored.
Shannon's definition of information entails only one minor aspect of the nature of information, as we will discuss at length.
It should now be clear that certain characteristics of a language maybe described in terms of values derived from Shannon's theory of information.
Shannon's concept: His definition of information is based on a communications problem, namely to determine the optimal transmission speed.
When the second law of thermodynamics, which is also known as the entropy theorem,is flippantly applied to Shannon's information concept, it only causes confusion.
Shannon's entropy measures the information contained in a message as opposed to the portion of the message that is determined(or predictable).
However, a well-designed SP network with several alternatingrounds of S-boxes and P-boxes already satisfies Shannon's confusion and diffusion properties:.
Theorem 5: Shannon's definition of information exclusively concerns the statistical properties of sequences of symbols; meaning is completely ignored.
Since the German Bible contains more letters than the English one,its information content is then larger in terms of Shannon's theory, although the actual contents are the same as regards their meaning.
Shannon's articles"Mathematical Theory of Communication" and"Theory of Communication in Secret Systems" are considered fundamental to information theory and cryptography.
However, if a"data generating mechanism" does exist in reality,then according to Shannon's source coding theorem it provides the MDL description of the data, on average and asymptotically.
Shannon's“theory of information is suitable for describing the statistical aspects of information, e.g. those quantitative properties of languages which depend on frequencies.
The method was the first of its type,the technique was used to prove Shannon's noiseless coding theorem in his 1948 article"A Mathematical Theory of Communication", and is therefore a centerpiece of the information age.
Shannon's definition of entropy, when applied to an information source, can determine the minimum channel capacity required to reliably transmit the source as encoded binary digits.
Beginning with the language of Africa's talking drums and weaving his way through the written alphabet, cyphers in the First and Second World Wars,and Claude Shannon's Information Theory, Gleick looks at exactly what information is and how it affects our lives.
Message: In Shannon's theory, a message is not necessarily meaningful, but it refers to a symbol(e.g., a letter) or a sequence of symbols(e.g., a word).
The only other woman associated with the profession of photography who was listed in local publications anddirectories of the 1850s was a colorist for one of Shannon's competitors, making it likely that Shannon was the only woman pursuing the profession of daguerrotypist in the city at the time.[1] The second documented woman photographer in the city was Mrs. Amanda M. Genung of Stockton, a daguerreotypist who set up shop in San Francisco in 1860.
Shannon's experiments with human predictors show an information rate between 0.6 and 1.3 bits per character in English; the PPM compression algorithm can achieve a compression ratio of 1.5 bits per character in English text.
According to Shannon's definition, the information content of a single message(whether it is one symbol, one syllable, or one word) is a measure of the uncertainty of its reception.
According to Shannon's definition, the information content of a single message(which could be one symbol, one sign, one syllable, or a single word) is a measure of the probability of its being received correctly.
According to Shannon's definition, the information content of a single message(whether it is one symbol, one syllable, or one word) is a measure of the uncertainty of its reception.
According to Shannon's definition, the information content of a single message(which could be one symbol, one sign, one syllable, or a single word) is a measure of the probability of its being received correctly.
The ban pre-dates Shannon's use of bit as a unit of information by at least eight years, and remains in use in the early 21st Century.[6] In the International System of Quantities it is replaced by the hartley.
Shannon's definition of entropy, when applied to an information source, can determine the minimum channel capacity required to reliably transmit the source as encoded binary digits(see caveat below in italics).
It should now be clear that Shannon's information theory is very important for evaluating transmission processes of messages, but, as far as the message itself is concerned, it can only say something about its statistical properties, and nothing about the essential nature of information.
Shannon's source coding theorem states a lossless compression scheme cannot compress messages, on average, to have more than one bit of information per bit of message, but that any value less than one bit of information per bit of message can be attained by employing a suitable coding scheme.