Spatial Polarization Modulation for Terahertz Single-Pixel Imaging
Bibliographic record
Abstract
Terahertz (THz) technology has been developed to meet advancements in spatial light modulation of frequencies in the THz regime, where fast modulation techniques designed for optical wavelengths perform poorly. Applications of THz frequencies in nondestructive imaging and quality testing have been thoroughly explored—specifically, polarization-resolved measurements can be scanned to image fiber anisotropy and birefringence in wood products and 3D printed materials and strains in plastics. There is a need to explore faster image acquisition techniques, such as single pixel imaging (SPI) via spatial light modulation, to realize fast polarization-resolved THz imagers. Such spatial light modulators are vital for patterning the THz imaging beam before interacting with the sample to reconstruct the response of the sample with a single detector via compressive sensing algorithms. In this work, a spatial polarization modulation scheme is employed using wire grid polarizer (WGP) mask patterns to acquire polarization-resolved images of polarizing samples at 0.1 THz. A laser ablation technique is used to fabricate 8 × 8 WGP masks with randomly vertical and horizontal wire grids to pattern the imaging beam and enable orthogonal polarization images. With various sampling amounts, 8 × 8 polarization images are successfully reconstructed for polarizing samples of low and high spatial frequencies.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".