MicroFluID
Bibliographic record
Abstract
RFID has been widely used for activity and gesture recognition in emerging interaction paradigms given its low cost, lightweight, and pervasiveness. However, current learning-based approaches on RFID sensing require significant efforts in data collection, feature extraction, and model training. To save data processing effort, we present MicroFluID, a novel RFID artifact based on a multiple-chip structure and microfluidic switches, which informs the input state by directly reading variable ID information instead of retrieving primitive signals. Fabricated on flexible substrates, four types of microfluidic switch circuits are designed to respond to external physical events, including pressure, bend, temperature, and gravity. By default, chips are disconnected into the circuit owing to the reserved gaps in transmission line. While external input or status change occurs, conductive liquid floating in the microfluidics channels will fill the gap(s), creating a connection to certain chip(s). In prototyping the device, we conducted a series of simulations and experiments to explore the feasibility of the multi-chip tag design, key fabrication parameters, interaction performance, and users' perceptions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.210 | 0.120 |
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".