Bibliographic record
Abstract
Optical controllers exploit lightwave technologies to implement different control strategies. It is possible to replace many of the electrical and mechanical components found in traditional linear and nonlinear controllers with optical analogues that increase the speed of signal processing or enhance system sensitivity. These optical sensors, switches, communication links, and actuators are largely immune from electromagnetic interference, exhibit low signal attenuation, provide secure flow of information, and are safe in hazardous or explosive environments. In addition, the energy in the light beam is one of the easiest forms of energy to shape and transmit through free space. The fundamental characteristics of several "control-by-light" systems are discussed in this paper. The proposed control system utilizes an acousto-optic deflector (AOD) to change the direction of the reshaped laser beam in response to the feedback error signal. The deflected beam strikes an array of photodetectors where each discrete detector represents a specific control action. One- and two-dimensional detector array configurations are explored for control. Although the controller designs can be implemented on optical breadboards using off-the-shelf optical devices, recent advances in nanotechnology would allow similar micro-scale optical controller to be fabricated at low cost.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".