A Multitasking Flexible Sensor via Reservoir Computing
Bibliographic record
Abstract
Natural disasters are reported globally, and one source of severe damage to cities is flooding caused by locally heavy rain. Sharing of local weather information can save lives. However, it is difficult to collect local weather information in real-time because such data collection requires bulky, expensive sensors. For local, real-time monitoring of heavy rain and wind, a sensor system should be simple and low-cost so that it can be attached to a variety of surfaces, including roofs, vehicles, and umbrellas. To develop simple, low-cost multitasking sensors located on nonplanar surfaces, a flexible rain sensor to monitor waterdrop volume and wind velocity is devised. To monitor both simultaneously, a laser-induced graphene-based superhydrophobic conductive film is introduced. Using the superhydrophobic surface, water dynamics are measured when waterdrops collide with the sensor surface, and obtained time-series data are processed using "reservoir computing" to extract the volume and velocity from a single sensor as multitasking electronics. As a proof-of-concept, it is shown that the sensor measures continuous, long-term volume and wind-change dynamics. The results demonstrate feasibility of multitasking electronics with reservoir computing to reduce sensor integration complexity with low power consumption for both sensor and signal processing.
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.
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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".