Bibliographic record
Abstract
This dissertation concerns the investigation of current problems associated with the analysis and design of tunable continuous time bandpass (CT BP) sigma-delta (CA) modulators. This specific modulator group is particularly promising within the context of softwaredefined radios. However, due to the nonlinear sampling element within a closed-loop sdomain system, the high level of analytical complexity makes current CT BP CA modulators difficult to implement. Specific problems addressed in this research were the fundamental principles of the CA modulation process, analytical and design methodology, loop delay compensation techniques, monolithic implementation and possible application areas. Theoretical general closed form solutions for the center frequency tunable CT BP CA modulator with fractional delays were derived, defining a new sub-class of fractional CT BP CA modulators. The developed subclass offers numerous possible solutions to existing problems in CA modulator based circuits, such as loop delay compensation and signal upconversion. A theoretical CT BP CA design methodology was then modified to be suitable for mixed-signal integrated circuit (IC) design flow, currently used in both industrial and academic environments. In order to experimentally demonstrate these new analytical concepts, an IC prototype of the proposed fractional CA modulator was designed, manufactured in SiGe technology, and tested. This research showed that the developed fractional CT BP CA modulator concept is a feasible option for future wireless networks; thus providing the crucial element required for software-defined radios.
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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 | 0.000 |
| 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".