Automotive Sensors & Sensor Interfaces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div class="htmlview paragraph">The increasing legal requirements for safety, emission reduction, fuel economy and onboard diagnosis systems push the market for more innovative solutions with rapidly increasing complexity. Hence, the embedded systems that will have to control the automobiles have been developed at such an extent that they are now equivalent in scale and complexity to the most sophisticated avionics systems.</div> <div class="htmlview paragraph">This paper will demonstrate the key elements to provide a powerful, scalable and configurable solution that offers a migration pass to evolution and even revolution of automotive Sensors and Sensor interfaces. The document will explore different architectures and partitioning. Sensor technologies such as magnetic field sensors based on the hall effect as well as bulk and surface silicon micro machined sensors will be mapped to automotive applications by examples. Functions such as self-test, self-calibration and self-repair will be developed. Possible migration to lower voltage (5V to 3,3V) will be investigated. In this context the document will also propose sensors interfaces to ease the signal conditioning inside the ECU. An insight of sensor busses will be performed to provide a picture of sensor networks.</div> <div class="htmlview paragraph"> <figure id="F1" class="figure"> <div class="graphic-wrapper"><img class="article-figure figure" src="2004-01-0210_fig0001.jpg" alt="No Caption Available"/></div> </figure> </div>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it