An Energy Harvesting Receiver Utilizing Microstrip Filter Technology for IoT devices in 5G Network
Bibliographic record
Abstract
Energy scavenging from radiofrequency waves is getting more attention to energize low-power IoT sensors as the fifth-generation (5G) of wireless technology becomes mainstream. To efficiently extract energy from electromagnetic waves, the receiver antenna has to be properly matched with a rectifier to provide a steady dc output. A common solution is to design a matched bandpass filter with lumped LC components. However, in high frequencies using a conventional LC filter is not practical due to parasitic effects. In this paper, a 3rdorder Chebyshev bandpass filter and a 5thorder Elliptic function microstrip lowpass filter have been designed using Advance Design System (ADS). RO4003C material with a dielectric constant of 3.55 and height of 0.508 mm is utilized for the substrate. The Chebyshev filter shows perfect matching at 12-17 GHz. The Elliptic filter shapes the output of the rectifier, and the energy harvester provides 25 mW output with input power of -10 dBm and overall efficiency of 86%.
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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".