New channel model for wireless body area network with compressed sensing theory
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.
Bibliographic record
Abstract
Wireless body area networks (WBANs) consist of small intelligent wireless sensors attached on or implanted in the body to collect vital biomedical data for providing a Continuous Health Monitoring System for diagnostic and therapeutic purposes. To fully exploit the benefits of WBANs the power consumption and sampling rate should be restricted to a minimum. The power usage can be minimised by optimising the features of multipath fading channels (MFCs) such as the number of arrival paths. That is why an improving of MFCs as well as a simple and generic channel model is inevitably required. With this in mind, compressed sensing (CS) theory, as a new sampling procedure, is employed to MFCs. Advance WBANs with the authors new model for MFCs based on CS theory will be able to deliver healthcare not only to patients in hospital and medical centres; but also in their homes and workplaces thus offering cost saving, and improving the quality of life. The authors simulation results illustrate 20% reduction for path loss and 10% for bit‐error rate at gate way (GW). The simulation results also confirm that signal amplitude at GW increases by 25%, which will result in an increase, in the distance, between transmitter and receiver sensors.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 it