Injection-lock and reconfigurable charge-domain sampling mixers/filters for data communications over wireless channels.
Bibliographic record
Abstract
This thesis provides a theoretical and experimental study of injection locking and reconfigurable charge-domain sampling mixers and filters for data communications over wireless channels. On injection-locking, the intrinsic relation between the characteristics of injection signals such as sinusoidal or square, single-tone or multi-tone, the type of oscillators under injection such as harmonic oscillators (passive or active LC oscillators) or non-harmonic oscillators (ring or relaxation oscillators), and the lock range of the oscillators under injection was investigated. For the very first time, we discovered the intrinsic relation between the lock range and the phase of multiple injections of harmonic oscillators. In addition, we obtained the closed-form expression of the lock range of harmonic oscillators with square-wave injections. Moreover, we obtained the distinct characteristics of the lock range of harmonic and non-harmonic oscillators and that of different types of non-harmonic oscillators. These theoretical findings were not known before and were validated using simulation results. On reconfigurable charge-domain sampling mixers and filters for software-defined radio, a novel quadrature charge-domain down-conversion sampling mixer with embedded finite-impulse-response (FIR), infinite-impulse-response (IIR), and 4-path bandpass filters was developed. An in-depth investigation of the principles of periodic impulse sampling, periodic windowed sampling, and periodic N-path windowed sampling was presented and a detailed mathematical treatment of charge-domain windowed samplers with built-in sinc, FIR and IIR filters was provided. The proposed quadrature charge-domain sampler with embedded FIR, IIR, and 4-path band-pass filters was implemented in IBM 130 nm 1.2V CMOS technology and its performance was validated both using simulation results and on-wafer measurement.
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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 | 0.000 |
| 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) | 0.001 | 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 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".