Robust access for wireless body area networks in public m-health
Bibliographic record
Abstract
Recent advances in very-low-power wireless communications have stimulated great interest in the development and application of wireless technology in biomedical applications, including wireless body area networks (BANs). A BAN consists of multiple sensor nodes capable of sampling, processing, and communicating one or more vital signs (e.g., heart rate, brain activity, blood pressure, oxygen saturation) and/or environmental parameters (location, temperature, humidity, light) over extended periods via wireless transmissions over short distances. Low cost implementation and ubiquitous deployment calls for the use of license-exempt ISM bands, in which coexistence of other license-exempt devices, particular WiFi radios, negatively impacts on the robustness of BANs. We present proposals to increase the robustness of wireless access in BANs by identifying and taking advantages of spectrum holes that are unused by co-existing devices. Simulation and experimental results are presented to show the effective of our proposals in increasing the robustness of channel access in BANs.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".