Difference between revisions of "Information Theory"
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{{BlueBox|TEXT=From the earliest beginnings of message transmission as an engineering discipline, it has been the endeavour of many engineers and mathematicians to find a quantitative measure for the | {{BlueBox|TEXT=From the earliest beginnings of message transmission as an engineering discipline, it has been the endeavour of many engineers and mathematicians to find a quantitative measure for the | ||
| − | *contained $\rm information$ $($quite generally: | + | *contained $\rm information$ $($quite generally: »the knowledge about something«$)$ |
| − | *in a $\rm message$ $($here we mean | + | *in a $\rm message$ $($here we mean »a collection of symbols and/or states»$)$. |
The $($abstract$)$ information is communicated by the $($concrete$)$ message and can be conceived as the interpretation of a message. | The $($abstract$)$ information is communicated by the $($concrete$)$ message and can be conceived as the interpretation of a message. | ||
| − | [https:// | + | [https://en.wikipedia.org/wiki/Claude_Shannon '''Claude Elwood Shannon'''] succeeded in 1948, in establishing a consistent theory about the information content of messages, which was revolutionary in its time and created a new, still highly topical field of science: »'''Shannon's information theory«''' named after him. |
| − | This is what the fourth book in the $\rm | + | This is what the fourth book in the $\rm LNTwww$ series deals with, in particular: |
| − | # Entropy of discrete-value sources with and | + | # Entropy of discrete-value sources with and without memory, as well as natural message sources: Definition, meaning and computational possibilities. |
| − | # Source coding and data compression, especially the | + | # Source coding and data compression, especially the »Lempel–Ziv–Welch method« and »Huffman's entropy encoding«. |
# Various entropies of two-dimensional discrete-value random quantities. Mutual information and channel capacity. Application to digital signal transmission. | # Various entropies of two-dimensional discrete-value random quantities. Mutual information and channel capacity. Application to digital signal transmission. | ||
# Discrete-value information theory. Differential entropy. AWGN channel capacity with continuous-valued as well as discrete-valued input. | # Discrete-value information theory. Differential entropy. AWGN channel capacity with continuous-valued as well as discrete-valued input. | ||
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| + | ===Exercises and multimedia=== | ||
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$(2)$ [[LNTwww:Learning_videos_to_"Information_Theory"|$\text{Learning videos}$]] | $(2)$ [[LNTwww:Learning_videos_to_"Information_Theory"|$\text{Learning videos}$]] | ||
| − | $(3)$ [[Applets_to_"Information_Theory"|$\text{Applets}$]] }} | + | $(3)$ [[LNTwww:Applets_to_"Information_Theory"|$\text{Applets}$]] }} |
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===Further links=== | ===Further links=== | ||
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| − | $(4)$ [[LNTwww:Bibliography_to_" | + | $(4)$ [[LNTwww:Bibliography_to_"Information_Theory"|$\text{Bibliography}$]] |
| − | $(5)$ [[LNTwww:Imprint_for_the_book_" | + | $(5)$ [[LNTwww:Imprint_for_the_book_"Information_Theory"|$\text{Impressum}$]]}} |
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Latest revision as of 18:50, 31 December 2023
Brief summary
From the earliest beginnings of message transmission as an engineering discipline, it has been the endeavour of many engineers and mathematicians to find a quantitative measure for the
- contained $\rm information$ $($quite generally: »the knowledge about something«$)$
- in a $\rm message$ $($here we mean »a collection of symbols and/or states»$)$.
The $($abstract$)$ information is communicated by the $($concrete$)$ message and can be conceived as the interpretation of a message.
Claude Elwood Shannon succeeded in 1948, in establishing a consistent theory about the information content of messages, which was revolutionary in its time and created a new, still highly topical field of science: »Shannon's information theory« named after him.
This is what the fourth book in the $\rm LNTwww$ series deals with, in particular:
- Entropy of discrete-value sources with and without memory, as well as natural message sources: Definition, meaning and computational possibilities.
- Source coding and data compression, especially the »Lempel–Ziv–Welch method« and »Huffman's entropy encoding«.
- Various entropies of two-dimensional discrete-value random quantities. Mutual information and channel capacity. Application to digital signal transmission.
- Discrete-value information theory. Differential entropy. AWGN channel capacity with continuous-valued as well as discrete-valued input.
⇒ First a »content overview« on the basis of the »four main chapters« with a total of »13 individual chapters« and »106 sections«:
Content
Exercises and multimedia
In addition to these theory pages, we also offer exercises and multimedia modules on this topic, which could help to clarify the teaching material:
$(1)$ $\text{Exercises}$
$(2)$ $\text{Learning videos}$
$(3)$ $\text{Applets}$
Further links
