A triple band rectenna for RF energy harvesting in smart city applications
Bibliographic record
Abstract
Radio Frequency Energy Harvesting is found to be the best alternative to conventional batteries for providing endless power to the sensor networks connected to perform various applications in the smart city. The electromagnetic energy density in the ambient environment is very low and not uniform, which demands a high gain multi-band rectenna system operating at mostly available energy bands and a suitable rectifier circuit that offers large PCE performance. This paper introduces a triple-band monopole rectenna with enhanced performance for smart city applications. A defective ground structure (DGS) has been investigated for a significant increase in gain with an increasing number of resonant frequencies. The proposed antenna operates over a band of frequencies that lies within 1.25–3 GHz, which covers 1.8/2.1/2.45 GHz frequencies. The antenna offers the gain values of 4.16/6.54/10.2 dB at 1.8/2.1/2.45 GHz frequencies, respectively. A single diode series-connected rectifier has been opted to efficiently operate over a wide range of input power levels from −10 dBm to 5 dBm and offers a minimum conversion efficiency of 60% within the operating bands at 0 dBm input power. The rectenna system is experimentally measured and the measured output voltage at 2.45 GHz frequency is 1.123 V.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".