Distributed joint source-channel coding of correlated binary sources in wireless sensor networks
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Bibliographic record
Abstract
In this paper, we present a distributed joint source-channel (DJSC) coding approach for a pair of correlated binary sources transmitted over independent binary symmetric channels. This problem is of interest in wireless sensor network applications, where encoders with low complexity and delay may be required. In the proposed method, a judiciously chosen fraction of information bits and a fraction of parity bits obtained by puncturing the output of a systematic channel code are transmitted for each source. We obtain the achievable rate region for the proposed coding scheme and show that it coincides with the Slepian-Wolf lower bound as the channel error probability approaches zero. Experimental results obtained with a practical implementation based on LDPC codes are also presented which demonstrate that for short coding block lengths (or low delay coding), the proposed DJSC coding method outperforms separate distributed source coding and channel coding.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it