Realization of Public M-Health Service in License-Free Spectrum
Bibliographic record
Abstract
Public m-health is a new medical service under intensive development, which provides unobtrusive monitoring of peoples health conditions from anywhere at any time to enable detection of deteriorating health conditions before severe discomfort or disability occur. A key challenge in practical implementation of public m-health is the use of shared licensefree spectrum by Body Area Networks (BANs) to report sampled vital signs continuously and in real-time. A cognitive medium access control method called Centralized Body Area Network Access Scheme (CBAS) is proposed in this paper to reduce access delay in a BAN in the presence of coexistent systems. By opportunistic extraction of idle spaces from a pool of orthogonal channels, CBAS dynamically adjusts a BANs channel access pattern according to the current interference environment, and improves the BANs visibility among coexistent networks. Performance of a BAN under CBAS is analyzed by modeling the system as a preemptive-resume priority queue. Numerical and simulation results show that the queuing-delay and throughput of a BAN employing CBAS outperforms those of a BAN that utilizes a single channel statically, as channel access opportunities suffer less fragmentations and interruptions.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".