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  2. Shannon M. Kent - Wikipedia

    en.wikipedia.org/wiki/Shannon_M._Kent

    Shannon Mary Kent (née Smith, May 11, 1983 – January 16, 2019) was a United States Navy cryptologic technician and member of JSOC's Intelligence Support Activity ...

  3. Entropy (information theory) - Wikipedia

    en.wikipedia.org/wiki/Entropy_(information_theory)

    The concept of information entropy was introduced by Claude Shannon in his 1948 paper "A Mathematical Theory of Communication", [2] [3] and is also referred to as Shannon entropy. Shannon's theory defines a data communication system composed of three elements: a source of data, a communication channel , and a receiver.

  4. Joe Kent - Wikipedia

    en.wikipedia.org/wiki/Joe_Kent

    Shannon Kent was the wife of Joe Kent and was killed in the 2019 Manbij bombing.. Kent was born in Sweet Home, Oregon, and raised in Portland. [11]Kent enlisted in the US Army at age 18 as an infantryman, having applied shortly before the September 11 attacks, and served 11 combat deployments.

  5. Joint entropy - Wikipedia

    en.wikipedia.org/wiki/Joint_entropy

    A misleading [1] Venn diagram showing additive, and subtractive relationships between various information measures associated with correlated variables X and Y. The area contained by both circles is the joint entropy H(X,Y).

  6. Network entropy - Wikipedia

    en.wikipedia.org/wiki/Network_entropy

    The limitations of the random walker Shannon entropy can be overcome by adapting it to use a Kolmogorov–Sinai entropy. In this context, network entropy is the entropy of a stochastic matrix associated with the graph adjacency matrix ( A i j ) {\displaystyle (A_{ij})} and the random walker Shannon entropy is called the dynamic entropy of the ...

  7. Channel capacity - Wikipedia

    en.wikipedia.org/wiki/Channel_capacity

    Information-theoretic analysis of communication systems that incorporate feedback is more complicated and challenging than without feedback. Possibly, this was the reason C.E. Shannon chose feedback as the subject of the first Shannon Lecture, delivered at the 1973 IEEE International Symposium on Information Theory in Ashkelon, Israel.

  8. Shannon's source coding theorem - Wikipedia

    en.wikipedia.org/wiki/Shannon's_source_coding...

    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 ...

  9. Shannon–Hartley theorem - Wikipedia

    en.wikipedia.org/wiki/Shannon–Hartley_theorem

    It connects Hartley's result with Shannon's channel capacity theorem in a form that is equivalent to specifying the M in Hartley's line rate formula in terms of a signal-to-noise ratio, but achieving reliability through error-correction coding rather than through reliably distinguishable pulse levels.