An Efficient RF Rectifier for Energy Harvesting Systems with Applications to Wireless Dosimetry
Bibliographic record
Abstract
In the medical industry, there exists a great need for donated blood, which must first be sterilized before being used in transfusions.A growing number of facilities have begun to use X-ray irradiation for blood sterilization, along with a tag employing a radiation-sensitive chemical to measure the applied dose.Such an approach is prone to measurement error and inaccuracy, leading to the wastage of blood, time, and expense.A wireless RFID sensor tag has been proposed by researchers at Carleton University.This work presents both a system-level overview of the X-ray dosimeter tag and a design of the energy harvesting module.The wireless X-ray dosimeter tag is estimated to consume 263.1 µW of power and is designed to operate at a distance of one metre away from a 2.45 GHz intentional RF power source.This source is harnessed by the energy harvesting module, which consists of a: dipole antenna, matching network, Dynamic V th Cancellation (DVC) rectifier, Smart Voltage Regulator (SVR), and off-chip ceramic capacitor.As part of the energy harvesting module, an RF rectifier employing DVC was designed and implemented in a commercial 0.13 µm CMOS process.Experimental measurements demonstrate that the design achieves a peak power conversion efficiency (PCE) of 49.7% at a power level of -12.0 dBm, an operational frequency of 2.45 GHz, and an output loading of 25 kΩ.I would first like to extend my foremost gratitude to my thesis supervisor, Professor Langis Roy, in recognition of his extraordinary commitment to my work.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".