Sparse Data Blocks and Multi-User Channels Edward A. Ratzer Cavendish Laboratory, Madingley Road, Cambridge CB3 0HE, UK Phone: +Email: October 25, 2002

Source URL: www.inference.phy.cam.ac.uk

• Coding theory
• Error detection and correction
• Information theory
• Discrete mathematics
• Mathematics
• Low-density parity-check code
• Forward error correction
• Binary symmetric channel
• Noisy-channel coding theorem
• Linear code
• Prefix code
• Huffman coding

Convolutional code performance as a function of decoding delay Edward A. Ratzer Cavendish Laboratory, Madingley Road, Cambridge CB3 0HE Phone: Email:

Source URL: www.inference.phy.cam.ac.uk

• Error detection and correction
• Coding theory
• Information theory
• Discrete mathematics
• Mathematics
• Convolutional code
• Systematic code
• Turbo code
• Sequential decoding
• Binary symmetric channel
• Linear code
• Viterbi algorithm

Error-correction on non-standard communication channels Edward A. Ratzer Christ’s College Cambridge

Source URL: www.inference.phy.cam.ac.uk

• Error detection and correction
• Coding theory
• Low-density parity-check code
• Forward error correction
• Convolutional code
• Turbo code
• EXIT chart
• Noisy-channel coding theorem
• Linear code
• Code
• Block code
• Binary symmetric channel

2748 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 58, NO. 5, MAY 2012 Three-Receiver Broadcast Channels With Common and Confidential Messages

Source URL: isl.stanford.edu

• Information theory
• Information
• Theoretical computer science
• Telecommunications engineering
• Information-theoretic security
• Channel capacity
• Channel
• Code
• Noisy-channel coding theorem
• Binary symmetric channel

1998 Paper 8 Question 11 Information Theory and Coding Consider a binary symmetric communication channel, having source alphabet X = {0, 1} with probabilities {0.5, 0.5}. Its output alphabet is Y = {0, 1} and its channe

Source URL: www.cl.cam.ac.uk

2004 Paper 8 Question 10 Information Theory and Coding (a) Consider a binary symmetric communication channel, whose input source is the alphabet X = {0, 1} with probabilities {0.5, 0.5}; whose output alphabet is Y = {0,

Source URL: www.cl.cam.ac.uk

1 Asymmetric Error Correction and Flash-Memory Rewriting using Polar Codes Eyal En Gad, Yue Li, Joerg Kliewer, Michael Langberg, Anxiao (Andrew) Jiang and Jehoshua Bruck

Source URL: arxiv.org

• Telecommunications engineering
• Mathematics
• Channel capacity
• Mutual information
• Binary symmetric channel
• Entropy
• Group action
• Distributed source coding
• Additive white Gaussian noise
• Information theory
• Coding theory
• Information

Approaching the Shannon Limit: A Progress Report Robert J. McEliece California Institute of Technology +

Source URL: www.systems.caltech.edu

• Telecommunications engineering
• Discrete mathematics
• Convolutional code
• Reed–Solomon error correction
• Hamming code
• Hamming
• Bit error rate
• Noisy-channel coding theorem
• Binary symmetric channel
• Coding theory
• Error detection and correction
• Information theory

1 A Study of Polar Codes for MLC NAND Flash Memories Yue Li1,2 , Hakim Alhussien3 , Erich F. Haratsch3 , and Anxiao (Andrew) Jiang1 1 Texas A&M University, College Station, TX 77843, USA

Source URL: paradise.caltech.edu

• Information
• Mathematics
• Low-density parity-check code
• List decoding
• Forward error correction
• Additive white Gaussian noise
• Binary symmetric channel
• BCH code
• Channel code
• Coding theory
• Error detection and correction
• Discrete mathematics

1 Asymmetric Error Correction and Flash-Memory Rewriting using Polar Codes Eyal En Gad, Yue Li, Joerg Kliewer, Michael Langberg, Anxiao (Andrew) Jiang and Jehoshua Bruck

Source URL: www.paradise.caltech.edu

• Telecommunications engineering
• Mathematics
• Channel capacity
• Binary symmetric channel
• Mutual information
• Entropy
• Group action
• Distributed source coding
• Additive white Gaussian noise
• Information theory
• Coding theory
• Information