Capacity-approaching codes | Coding theory | Error detection and correction
In information theory, a low-density parity-check (LDPC) code is a linear error correcting code, a method of transmitting a message over a noisy transmission channel. An LDPC code is constructed using a sparse Tanner graph (subclass of the bipartite graph). LDPC codes are capacity-approaching codes, which means that practical constructions exist that allow the noise threshold to be set very close to the theoretical maximum (the Shannon limit) for a symmetric memoryless channel. The noise threshold defines an upper bound for the channel noise, up to which the probability of lost information can be made as small as desired. Using iterative belief propagation techniques, LDPC codes can be decoded in time linear to their block length. LDPC codes are finding increasing use in applications requiring reliable and highly efficient information transfer over bandwidth-constrained or return-channel-constrained links in the presence of corrupting noise. Implementation of LDPC codes has lagged behind that of other codes, notably turbo codes. The fundamental patent for turbo codes expired on August 29, 2013. LDPC codes are also known as Gallager codes, in honor of Robert G. Gallager, who developed the LDPC concept in his doctoral dissertation at the Massachusetts Institute of Technology in 1960. LDPC codes have also been shown to have ideal combinatorial properties. In his dissertation, Gallager showed that LDPC codes achieve the Gilbert–Varshamov bound for linear codes over binary fields with high probability. In 2020 it was shown that Gallager's LDPC codes achieve list decoding capacity and also achieve the Gilbert–Varshamov bound for linear codes over general fields. (Wikipedia).
Finding the Class Limits, Width, Midpoints, and Boundaries from a Frequency Table
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From playlist Statistics
Computing z-scores(standard scores) and comparing them
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Computing z-scores(standard scores) and comparing them
From playlist Statistics
Maximum and Minimum Values (Closed interval method)
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From playlist 241Fall13Ex3
Lec 22 | MIT 6.451 Principles of Digital Communication II
Lattice and Trellis Codes View the complete course: http://ocw.mit.edu/6-451S05 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.451 Principles of Digital Communication II
practice finding the range of a given data set
From playlist Common Core Standards - 7th Grade
calculus, math, closed interval method, maximum, minimum, optimization
From playlist ptest2 fall2011 mat241
Lec 23 | MIT 6.451 Principles of Digital Communication II
Lattice and Trellis Codes View the complete course: http://ocw.mit.edu/6-451S05 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.451 Principles of Digital Communication II
(IC 1.3) Applications of Error-correcting codes
Applications of Information theory and Coding. Compression algorithms: Huffman codes, Arithmetic coding, Lempel-Ziv Error-correction algorithms: Hamming codes, Reed-Solomon codes, Turbo codes, Gallager (LDPC) codes Attribution for images: Hummingbird http://en.wikipedia.org/wiki/File
From playlist Information theory and Coding
Nexus trimester - Henry Pfister (Duke University) 1/2
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From playlist 2016-T1 - Nexus of Information and Computation Theory - CEB Trimester
Lec 18 | MIT 6.451 Principles of Digital Communication II
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From playlist MIT 6.451 Principles of Digital Communication II
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From playlist Stability and Testability
MagLab Theory Winter School 2018: Steven Girvin - Entanglement Entropy
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From playlist 2018 Theory Winter School
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From playlist Number
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From playlist All about decimals
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The National MagLab held it's sixth Theory Winter School in Tallahassee, FL from January 8th - 13th, 2018.
From playlist 2018 Theory Winter School
Statistics - How to find outliers
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From playlist Statistics
MIT 6.02 Introduction to EECS II: Digital Communication Systems, Fall 2012 View the complete course: http://ocw.mit.edu/6-02F12 Instructor: George Verghese This lecture starts with historical applications of error control and convolutional codes in space programs. Convolutional codes are
From playlist MIT 6.02 Introduction to EECS II: Digital Communication Systems, Fall 2012