Delay calculation and error compensation in TBCD-TDM communication protocol for Wireless Body Sensor Networks
Bibliographic record
Abstract
Implantable sensor network becomes nowadays an important area of research. Sensors are implanted inside the patient body to measure its physiological changes and send them wirelessly to an external close-by device. Such network is called IWBSN (Implantable Wireless Body Sensor Network). The life time of the embedded batteries associated with these tiny sensor nodes is too short. Hence power consumption is the most important design metrics associated with these sensors. A novel ultra low energy communication protocol has been proposed to address the life time of the battery within the sensor. Our proposed protocol is based on time synchronization between all sensor nodes and the base station, where the delay of the wireless signal propagation is very critical. Tracking of the time synchronization using the standard algorithms is very costly energy-wise. In this paper, we propose a technique which automatically calculates the delay (sensor-node to Base-station) and compensates for any discrepancies. This correction is done while saving the tiny energy resources inside the sensor nodes. Our proposed solution has been tested wirelessly through transceiver boards using a single FPGA board. The experimental results have shown the correctness of our protocol along with the delay correction technique.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".