Biomimetic spider web sensor designed with memristive oscillators for location-resolved disturbance detection
Bibliographic record
Abstract
This article introduces a memristor-coupled oscillatory network utilizing niobium dioxide (NbO2) memristors and a biomimetic spider web structure. It focuses on the dynamic behaviors of single oscillators and small-scale networks within this unique system, particularly emphasizing voltage, current, and frequency characteristics. By strategically applying step voltage signals on a 1 + 3 node single-layer bio-inspired spider network, a single disturbance or multiple disturbances were addressed under continuous external stimuli, with analyzing phase differences induced by disturbances at various locations within the network and systematically categorizing these phases to empower decision-making. These pattern differences enable precise location-resolved disturbance detection through eight encodable phase patterns and their corresponding phase-space trajectories, showcasing memristors' precision in dynamic control. Additionally, amplitude changes and phase relationships between oscillators can be visually represented through color-mapped voltage values. This work opens avenues for developing intelligent, adaptive systems, advancing neuromorphic computing, and intelligent system control, offering possibilities for artificial intelligence to process complex information.
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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 | 0.000 | 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 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".