Built-in-test for integrating analog-to-digital converters that utilize a phase-sensitive detector
Bibliographic record
Abstract
Integrating analog-to-digital converters that utilize a phase-sensitive detector (PSADCs) are frequently used in high precision instrumentation and measurement systems. As any technical object, a PSADC is subject to faults. These faults must be detected promptly and accurately by built-in low complexity hardware. In the present work, this objective is achieved by the adoption of error-control codes. Off-line and on-line test methods are explored. We demonstrate how to perform compaction in digital and analog domains. We design a compactor of analog signals on the basis of a PSADC and explain how to utilize coding redundancy for on-line testing. We also explain how to use a PSADC for generating multiple residues which can further be processed by a computing device operating in a residue number system (RNS). Compared to existing techniques, the proposed approach is characterized by higher accuracy and lower latency. The proposed device can be used for analog circuits testing in much the same way as a conventional signature analyzer is used for digital circuits testing. The test process involves measuring signatures of analog signals, which ultimately appear in digital form. The signatures are then verified to make a pass/fail decision.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".