Bibliographic record
Abstract
This dissertation explores methods of reducing the oversampling ratio (OSR) of both delta-sigma (∆Σ) modulators and incremental data converters. The first reduced-OSR architec-ture is the high-order cascaded ∆Σ modulator. These ∆Σ modulators are shown to reduce the in-band noise sufficiently at OSRs as low as 3 while providing power savings. The second low OSR architecture is the high-order cascaded incremental data converter which possesses signal-to-quantization noise ratio (SQNR) advantages over equivalent ∆Σ modu-lators at low OSRs. The final architecture is the time-interleaved incremental data converter where two designs are identified as potential methods of increasing the throughput of low OSR incremental data converters. A prototype chip is designed in 0.18 µm CMOS technol-ogy which can operate in three modes by simply changing the resetting clock phases. It can operate as an 8-stage pipeline analog-to-digital (A/D) converter, an 8th-order cascaded ∆Σ modulator, and an 8th-order cascaded incremental data converter with an OSR of 3. ii Acknowledgements This dissertation has only come to completion with the help and support of numerous indi-viduals. First I must thank my supervisor Prof. David Johns for his invaluable advice and guidance throughout the course of this degree. I thank Prof. Ken Martin, Ahmed Gharbiya and Richard Schreier who provided countless suggestions throughout my research. I also
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".