Bibliographic record
Abstract
This thesis presents the design of a miniaturized delay-line discriminator (DLD) array for electronic warfare (EW) applications.Over very wide bandwidths, delay-line discriminators can provide the high accuracy, low latency measurements desired in many EW systems.The drawback to these circuits is that they can be quite bulky due to the long time delays they require.To miniaturize the discriminator's delay line, the design employed a slow-wave transmission line that allowed for a length reduction of approximately 60 %.An array consisting of four discriminators with delay ratios of 1, 2, 4, and 16 was fabricated on a 4-layer printed circuit board.The design obtained a measurement range of over 2 GHz with better than 4 MHz RMS accuracy.Additional processing of the discriminators' outputs is required to further improve this accuracy.Measurements of pulses with phase and frequency modulation present have demonstrated the potential for the identification of these modulation schemes.Firstly, I would like to thank my thesis supervisors, Professors Jim Wight and Langis Roy, for the invaluable guidance they've given me throughout my Master's degree.Thanks to the technical and administrative support staff at the Department of Electronics.Whether it was computer issues or paperwork, they were always ready to lend a helping hand.A big thank you to my friends and peers in the Blue Room.Your advice was always appreciated and you guys made this entire process much more fun and enjoyable.I want extend a big thank you to my boss, Scott McDonald, for his support and patience over the course of this thesis.If it wasn't for him, I wouldn't have been in a position to undertake this venture.To Garrett
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".