3D heterogeneous sensor system on a chip for defense and security applications
Bibliographic record
Abstract
This paper describes a new concept for ultra-small, ultra-compact, unattended multi-phenomenological sensor systems for rapid deployment, with integrated classification-and-decision-information extraction capability from a sensed environment. We discuss a unique approach, namely a 3-D Heterogeneous System on a Chip (HSoC) in order to achieve a minimum 10X reduction in weight, volume, and power and a 10X or greater increase in capability and reliability -- over the alternative planar approaches. These gains will accrue from (a) the avoidance of long on-chip interconnects and chip-to-chip bonding wires, and (b) the cohabitation of sensors, preprocessing analog circuitry, digital logic and signal processing, and RF devices in the same compact volume. A specific scenario is discussed in detail wherein a set of four types of sensors, namely an array of acoustic and seismic sensors, an active pixel sensor array, and an uncooled IR imaging array are placed on a common sensor plane. The other planes include an analog plane consisting of transductors and A/D converters. The digital processing planes provide the necessary processing and intelligence capability. The remaining planes provide for wireless communications/networking capability. When appropriate, this processing and decision-making will be accomplished on a collaborative basis among the distributed sensor nodes through a wireless network.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".