Bibliographic record
Abstract
The rapid growth of technology these years has brought up the demands on LiDAR.Research on LiDAR has shown a higher detecting range, resolution, and detection speed than microwave radar.As the essential component of LiDAR, the optical phased array plays a fundamental role in the beamforming for the LiDAR.Unlike microwave phased array antennas, optical phased array antennas naturally have grating lobes in the radiation pattern due to the large element spacing in the array.The grating lobes are undesirable since they dissipate energy from the main lobe, reducing the power efficiency of the optical phased array.Eliminating and reducing the grating lobes then become a pressing problem to solve.This thesis presents three types of optical phased arrays: the rectangular, circular, and randomly distributed configurations, to achieve the maximum grating lobes suppression.Using a specific antenna design as the base element, we design and optimize three types of the arrays by implementing the genetic algorithm.We conclude that the rectangular phased arrays have a minor performance on sidelobe suppression compared to the other two types of phased arrays.The randomly distributed phased arrays will have a similar performance on sidelobe suppression compared to the circular phased arrays when over 400 antennas are placed, but it utilizes less device footprint.The circular phased arrays utilize the most device footprint, but it has the narrowest 3 dB beamwidth.Firstly, I would like to deeply thank my thesis supervisor, Professor Winnie Ye, for her invaluable support throughout my master's studies.I greatly appreciate her for leading me to the correct path once I had no clue about my thesis research.I am grateful for all the discussions and help from NRC team, which guided me in many ways.I also sincerely appreciate all the help and productive discussions
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".