Analysis of Error Control Code Use in Utra-Low-Power Wireless Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
High-speed wireless sensor networks are currently being considered for a variety of communication application such as environmental, medical, industrial or security scenarios. For increased transmission rates given the limited embedded battery lifetime, ultra-low-power circuitry is needed in the sensor and processors. Much research is being undertaken in these different areas at the device, circuit, system and network levels Although using error control coding (ECC) potentially reduce the required transmit power for reliable communication, higher decoder complexity increases the required processing energy. The above tradeoff is explored in this paper to find when use of ECC results in more power-efficient systems. Several recently implemented decoders are analyzed, comparing both analog and digital implementations. The four most energy efficient decoders are analog decoders. The best analog decoder becomes energy-efficient at about 1/4 the distance of the best digital implementation.
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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 | 0.001 |
| 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