Wireless Body Area Networks with compressed sensing theory
Bibliographic record
Abstract
With the rapid advancements of Wireless Sensor Networks (WSNs), wireless communication, and electronic technologies the area of wireless networks has grown significantly supporting a range of applications of Wireless Body Area Networks (WBANs) including Electronic Health (EH) and Mobile health (MH). Wireless Body Area Networks (WBANs) consist of small intelligent wireless sensors attached on or implanted in the body. These wireless sensors are responsible for collecting, processing, and transmitting vital information such as: blood pressure, heart rate, respiration rate, electrocardiographic (ECG), electroencephalography (EEG), oxygenation signals, respiratory rate, and temperature to provide continuous health monitoring with real-time feedback to the users and medical centers. In order to fully exploit the benefits of WBANs for important applications such as EH, MH, and Ambulatory Health Monitoring (AHM), the power consumption must be minimized. Each Wireless Node (WN) in WBANs must be designed to manage its local power supply in order to maximize total network lifetime. With this in mind, we want to employ Compressed Sensing (CS) to WBANs theory as a new sampling procedure to reduce load of sampling rate and minimize power consumption. Our simulation results show that sampling rate can be reduced to 30% of Nyquist Rate (NR) and power consumption to 40% in ECG signals without sacrificing reliability and availability by employing the CS theory to WBANs. This paper presents a novel sampling approach to WBANs using compressive sensing methods to WBANs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".