TBCD-TDM: Novel Ultra-Low Energy Protocol for Implantable Wireless Body Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
The field of remote health monitoring now includes technologies such as home and mobile health monitoring, tele-retinal imaging, tele-radiology, remote cardiac monitoring, video conferencing and sensors for remote diagnosis and treatment to patients. In this regard, implantable wireless body sensor networks (IWBSNs) have recently emerged as an important and growing research area. These implantable sensors are required to be reliable, very small, battery-operated, and capable of collecting data, processing it, and transmitting it wirelessly and efficiently. Since these devices are required to run with limited resources (energy, processing, and memory), their utility protocols (collecting, processing, and communication) should be designed carefully, not only to work reliably but, more importantly, to be resource-efficient. The life time of the embedded batteries associated with these sensor nodes varies from a few days to a few weeks as was described in a previous work by the authors. In this paper, we propose a novel technique which allows the implanted sensor nodes to communicate with a base station located outside the body efficiently by consuming the minimum amount of energy. Our proposed protocol allows the battery to last significantly longer even for years with a gain of up to 100's times of power saving. This will improve the quality of patient life, and reduce risk of infection resulting from frequent chirurgical operations needed to replace such implantable batteries. Also, a new time synchronization algorithm is briefly introduced in this work that is especially applicable to our proposed communication protocol.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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