Bibliographic record
Abstract
Channel equalization combats the effects of the imperfection of wire channels. This dissertation deals with adaptive decision feedback channel equalization. The dissertation starts with an in depth study of the challenges encountered in the design of adaptive DFE and techniques that address these challenges. Various 2-dimensional eye-opening monitors (EOMs) based adaptive DFE are proposed and implemented. A novel 2-dimensional hexagon EOM is proposed and its effectiveness is validated using simulation. A simplified and power efficient 2-dimensional hexagon EOM is also introduced. Both EOMs are capable of differentiating the severity of the violation of the minimum eye-opening so as to allow the DFE to take different actions adaptively and achieve desired eye-opening more rapidly. A maximum-jitter EOM-based adaptive DFE is also introduced to greatly reduce system complexity. The adaptive DFE is taped out in a 130nm 1.2V CMOS technology and finally an improved adaptive engine that outperforms DFE utilizing sign-sign least-mean-square is proposed.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".