Generic Architecture for a Self-Powered Smart Sensor Interface in Avionic Application
Bibliographic record
Abstract
In this paper, we present a universal architecture for a reliable self-powered Smart Sensor Interface (SSI) in avionic applications. The SSI module consists of data acquisition and signal excitation paths. The power recovery unit harvests energy from data field bus to power up the SSI module entirely. Using integrated CMOS technologies, the interface is flexible and configurable to be integrated with and fully controlled by Transducer Interface Module (TIM) introduced in IEEE1451 standard. Employing data converters within the signal paths makes the SSI well suited for full digital control over specifications of the excitation signal and data processing algorithms. The interface can be used along with various types of position sensors including legacy R/LVDT, MEMS-based and optical ones. The analog parts of the SSI are implemented using IBM 0.13 μm CMOS process while its digital modules are realized in FPGAs. The Power Conversion Chain (PCC) of the SSI is also presented and its complex components are modeled in Verilog-A using a top-down modeling approach. The models make it possible to study over power transfer and distribution throughout the SSI. Simulation results prove that the proposed power recovery scheme could procure and deliver significant amount of power to SSI which makes the structure self-powered.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".