Optimization of a Sun-Sensor Illumination Pattern using Genetic Algorithms
Bibliographic record
Abstract
Changes to the illumination pattern in a digital sun-sensor can dramatically improve the resolution of the device. In this study we use genetic algorithms (GAs) as a heuristic to optimize the illumination pattern for a single-axis digital sun-sensor. The main objective is to determine an illumination pattern that resolves the sun-angle to better than one pixel. A linear-phase super resolution technique is proposed, to evaluate the effective resolution of the illumination pattern determined using GA. Our finding show that patterns with multiple, narrow peaks provide sub-pixel accuracy in resolving the sun-angle. Performance of the proposed GA estimator displayed the evolution of high-fitness solutions. We contend that multiple peak patterns can greatly improve the performance of the sun-sensor when coupled with parametric methods of displacement estimation. The optimal illumination pattern can be implemented by fabricating a replacement aperture mask for the sensor - a change that can be made at minimal cost
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".