Angular domain optical imaging of structures within highly scattering material using silicon micromachined collimating arrays
Bibliographic record
Abstract
Optically Tomography within highly scattering material has focused on Coherence Domain and Time Domain methods: both detecting the shorting path photons over the dominant randomly scattered background light. Angular Domain Imaging instead uses collimators, small acceptance angle filters, to observe only those photons closely aligned to a laser light source. A linear collimating array was fabricated using silicon surface micromachining consisting of 51 μm wide by 10 mm long etched channels with 102 μm spacing very high aspect ration (200:1) 20 mm wide array. With careful array alignment to a laser source, restricted to a linear beam, the unscattered laser light passes directly through the channels to a CCD detector, and the channel walls absorb the scattered light at angles >0.29 degrees. With a computer controlled Z axis objects within a 1 cm thick scattering material were scanned quickly. High contrast 150 μm lines/spaces at the medium front were observed at scattered to ballistic photon ratios >5×105:1 with a 10 mm beam. Narrowing the beam to 130 μm width produces detectable images >3×108:1. Objects closer to the detector were more visible, and mid point objects were detectable >109:1. Smaller channels and longer arrays should enhance detection by factors of >100.
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 | 0.000 | 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".