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Record W1539832159 · doi:10.1109/icc.1999.767980

Joint source-channel decoding of entropy coded Markov sources over binary symmetric channels

2003· article· en· W1539832159 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDecoding methodsSoft-decision decoderBinary numberComputer scienceAlgorithmMarkov processEntropy (arrow of time)Channel (broadcasting)Binary symmetric channelMarkov chainJoint (building)Maximum a posteriori estimationSynchronization (alternating current)MathematicsChannel codeMaximum likelihoodTelecommunicationsStatisticsArithmeticEngineering

Abstract

fetched live from OpenAlex

This paper proposes an optimal joint source-channel, maximum a posteriori, decoder for entropy coded Markov sources transmitted over noisy channels. We introduce the concept of incomplete and complete states to deal with the problem of variable length source codes in the decoder. The proposed decoder is sequential, thereby making the expected delay finite. When compared to the traditional decoder, the proposed decoder shows a maximum improvement, of about 4 dB in a modified signal to noise ratio and an improvement of 21.95% in percentage of bits that are received in an out of synchronization condition, for a simple test source.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.892

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.232
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it