3D heterogeneous sensor system on a chip for defense and security applications
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
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.
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 | 0.000 | 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) | 0.000 | 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 it