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 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.046 | 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".