Cost-Effective 3-D-Printable Polarization Reconfigurable Antenna for Millimeter-Wave Smart IoT Applications
Bibliographic record
Abstract
Polarization reconfigurable antennas (PRAs) benefit Internet of Things (IoT) applications as they can adapt their polarization to enhance signal strength, minimize interference, and ensure efficient communication in diverse environments. However, most PRAs in existing literature utilize microstrip technology and PIN diodes for polarization reconfiguration, which perform well at lower frequencies. When adapting these designs for millimeter-wave (mm-wave) frequencies, several challenges arise. Therefore, a novel ferromagnetic-based PRA is introduced in the mm-wave spectrum for smart IoT applications for the first time. Circular polarization (CP) is achieved by creating two perpendicular electric fields (Ex11andEy11) by the incidence of the linear polarized fields rotated at an angle of 45° relative to the dual-image-dielectric-guide (DIDG). The antenna incorporates a DIDG supporting bothEx11andEy11modes with identical cut-off frequencies, two ferrite pillboxes (FPs), a matching taper for impedance matching, and a tapered dielectric rod for radiation. The FPs facilitate the polarization control. Each FP interacts with the corresponding propagating mode along its axis, enabling the antenna to reconfigure itself between CP and linear polarization (horizontal or vertical polarization). The degree of interaction depends on the magnetic bias and the distance of the pillbox from the dielectric rod. The proposed technique combines the peculiar attributes of the DIDGs with the ferrite characteristics to develop a novel PRA. The PRA requires a small magnetic bias due to its dependency on the applied magnetic field direction. Moreover, the antenna can be manufactured using only a 3-D printer and copper tape, resulting in a cost-effective production process. Owing to its simple structure, lower cost, and control over polarization, the proposed PRA could be an attractive option for future smart mm-wave IoT systems.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".