Two-dimensional binary halftoned optical intensity channels
Bibliographic record
Abstract
The capacity of two-dimensional (2D) optical intensity channels in which transmit images are constrained to be binary-level has been considered. Examples of such links exist in holographic storage, page-oriented memories, optical interconnects, 2D barcodes as well as multiple-input/multiple-output wireless optical links. Data are transmitted by sending a series of time-varying binary-level optical intensity images from transmitter to receiver. Neither strict spatial alignment between transmitter and receiver nor independence among the spatial channels is required. The approach combines spatial discrete multitone modulation developed for spatially frequency selective channels with halftoning to produce a binary-level output image. Data are modulated in spatial frequency domain as dictated by a water pouring spectrum over the optical transfer function as well as channel and quantisation noise. A binary-level output image is produced by exploiting the excess spatial bandwidth available at the transmitter to shape quantisation noise out of band. A general mathematical framework has been presented, in which such systems can be analysed and designed. In a pixelated wireless optical channel application, halftoning achieves 99.8% of the capacity of an equivalent unconstrained continuous amplitude channel using 1megapixel arrays.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".