Information theory Information theory A Mathematical Theory of Communication, published in 1948 by mathematician and computer scientist Claude E. Shannon, was one of the founding works of the field of information theory. It developed the concepts of information entropy and redundancy. ...more on Wikipedia about "A Mathematical Theory of Communication"
In his 1937 MIT master's thesis, A Symbolic Analysis of Relay and Switching Circuits, Claude Elwood Shannon proved that Boolean algebra and binary arithmetic could be used to simplify the arrangement of the electromechanical relays then used in telephone routing switches, then turned the concept upside down and also proved that it should be possible to use arrangements of relays to solve Boolean algebra problems. This concept, of utilizing the properties of electrical switches to do logic, is the basic concept that underlies all electronic digital computers, and the thesis became the foundation of practical digital circuit design when it became widely known among the electrical engineering community during and after World War II. Contemporaneous methods to design logic circuits at the time were ad hoc and lacked the theoretical rigor that Shannon's paper supplied to later projects. ...more on Wikipedia about "A Symbolic Analysis of Relay and Switching Circuits"
Vannevar Bush suggested that Claude Elwood Shannon work on his dissertation at Cold Spring Harbor Laboratory, funded by the Carnegie Institution headed by Bush, to develop similar mathematical relationships for Mendelian genetics, which resulted in Shannon's 1940 PhD thesis at MIT, An Algebra for Theoretical Genetics. Shannon then joined Bell Labs to work on fire-control systems and cryptography during World War II. He returned to MIT to hold an endowed chair in 1956. ...more on Wikipedia about "An Algebra for Theoretical Genetics"
The asymptotic equipartition property (AEP) is general property used extensively in information theory concerning the output samples of a stochastic source. It is fundamental to the concept of typical set used in theories of compression. ...more on Wikipedia about "Asymptotic equipartition property"
Bandwidth is a measure of frequency range, measured in hertz, of a function of a frequency variable. ...more on Wikipedia about "Bandwidth"
In information theory, the bar product of two linear codes is defined as ...more on Wikipedia about "Bar product (coding theory)"
A block code is the primary type of channel coding which was used in earlier mobile communication systems. Simply it adds redundancy in order that at the receiver, one can decode with (theoretical) probability of zero errors, provided that the information rate (amount of transported information in bits per sec) would not exceed the channel capacity. ...more on Wikipedia about "Block code" Enjoy http://www.shortopedia.com.
Channel capacity, is the amount ...more on Wikipedia about "Channel capacity"
This article is a comparison of latency and throughput in telecommunications. A common misunderstanding of communication is that having more throughput means a "faster" (lower- latency) connection. But, in many cases, the reverse is true, depending on context and needs. ...more on Wikipedia about "Comparison of latency and throughput"
Differential entropy is a concept in information theory which extends the idea of entropy, a measure of average surprisal of a random variable, to continuous probability distributions. ...more on Wikipedia about "Differential entropy"
In statistics and information theory, the Fisher information (denoted ), named in honor of the geneticist and statistician Ronald Fisher, is the variance of the score. ...more on Wikipedia about "Fisher information"
(Gibbs' inequality) for all i. ...more on Wikipedia about "Gibbs' inequality"
Harry Nyquist ( February 7, 1889 - April 4, 1976) was an important contributor to information theory. ...more on Wikipedia about "Harry Nyquist"
The Hartley function is a measure of uncertainty, introduced by Hartley in 1928. If we pick a sample from a finite set A uniformly at random, the information revealed after we know the outcome is given by the Hartley function ...more on Wikipedia about "Hartley function"
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In information theory, the Hirschman uncertainty is defined as the product of the temporal and spectral Shannon entropies. ...more on Wikipedia about "Hirchman uncertainty"
The IEEE Transactions on Information Theory is a scientific journal published by the Institute of Electrical and Electronic Engineers (IEEE) It is dedicated to the study of information theory, the mathematics of communications. ...more on Wikipedia about "IEEE Transactions on Information Theory"
Entropy is a concept in thermodynamics (see thermodynamic entropy), statistical mechanics and information theory. The concepts of information and entropy have deep links with one another, although it took many years for the development of the theories of statistical mechanics and information theory to make this apparent. This article is about information entropy, the information-theoretic formulation of entropy. Information entropy is occasionally called Shannon's entropy in honor of Claude E. Shannon. ...more on Wikipedia about "Information entropy"
Information flow in an information theoretical context from a variable h to a variable l in a given process p is defined as the uncertainty before the process started minus the uncertainty after the process has terminated. This can be quantified as ...more on Wikipedia about "Information flow (information theory)"
Information theory is the mathematical theory of data communication and storage, generally considered to have been founded in 1948 by Claude E. Shannon. The central paradigm of classic information theory is the engineering problem of the transmission of information over a noisy channel. The most fundamental results of this theory are Shannon's source coding theorem, which establishes that on average the number of bits needed to represent the result of an uncertain event which is given by the entropy; and Shannon's noisy-channel coding theorem, which states that reliable communication is possible over noisy channels provided that the rate of communication is below a certain threshold called the channel capacity. The channel capacity is achieved with appropriate encoding and decoding systems. ...more on Wikipedia about "Information theory"
In computer science, the Kolmogorov complexity (also known as descriptive complexity, Kolmogorov-Chaitin complexity, stochastic complexity, algorithmic entropy, or program-size complexity) of an object such as a piece of text is a measure of the computational resources needed to specify the object. For example consider the following two strings of length 100 ...more on Wikipedia about "Kolmogorov complexity"
This is a list of information theory topics, by Wikipedia page. ...more on Wikipedia about "List of information theory topics"
In physics the MaxEnt school of thermodynamics, initiated with two papers published in the Physical Review by Edwin T. Jaynes in 1957, views statistical mechanics as an inference process: a specific application of inference techniques rooted in information theory, which relate not just to equilibrium thermodynamics, but are general to all problems requiring prediction from incomplete or insufficient data (such as for example image reconstruction, spectral analysis, or inverse problems). ...more on Wikipedia about "MaxEnt thermodynamics"
Metcalfe's law states that the value of a network equals approximately the square of the number of users ...more on Wikipedia about "Metcalfe's law"
Multiple-input multiple-output, or MIMO (pronounced MY-moh), is an abstract mathematical model for some communications systems. In radio communications if multiple antennas are employed, the MIMO model naturally arises. MIMO exploits phenomena such as multipath propagation to increase throughput, or reduce bit error rates, rather than attempting to eliminate effects of multipath. ...more on Wikipedia about "Multiple-input multiple-output"
In probability theory and, in particular, information theory, the mutual information, or transinformation, of two random variables is a quantity that measures the mutual dependence of the two variables. The most common unit of measurement of mutual information is the bit, in which case the logarithms below should be taken to the base 2. ...more on Wikipedia about "Mutual information"
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