Design of 900 MHz RadioFrequency Energy Harvesting Circuit for the Internet of Things Applications
Bibliographic record
Abstract
Rectifying Antenna, (more commonly known as rectenna) due to its power density, is more and more considered as an autonomous power supply for wireless sensor nodes dedicated to the Internet of Things (IoT) applications. However, harvestable power fluctuates a lot over time. In this work, a design method, which considers the fluctuations in the harvestable power, is proposed. The method consists of a measure of the harvestable RF power density followed by the design of a rectenna optimized to achieve the best performance in the harvestable RF power range. The measurements are made at 900 MHz, and an average power density of -7.8 dBm is obtained. The proposed rectenna is based on a Villard voltage doubler using the Schottky HSMS 285B diode. Analysis results in 3 stage voltage doubler offering the best performance in terms of DC output voltage and RF/DC conversion efficiency. By associating the rectifier with a low pass filter L, a conversion efficiency of 40.74% and a voltage of 2.3 V are achieved at 4 dBm of incident power.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".