Smelling on the Edge: Using Fuzzy Logic in Edge Computing to Control an Olfactory Display in a Video Game
Bibliographic record
Abstract
This paper presents a 3D video game project that incorporates smell generated by an olfactory display (an ultrasonic humidifier and a PC fan) controlled by fuzzy logic. In order to improve the olfactory display efficiency, we apply Edge computing by running the fuzzy logic control software on the microcontroller itself and not on the video game computer or a network server. Our video game activates the olfactory display by sending a wireless signal using MQTT data communication protocol to the microcontroller board connected to a local wireless network. The video game objective is to find a virtual lemon in less than 15 seconds, hidden behind many virtual crates. The olfactory display generates a lemon smell when the player is close to the virtual lemon. The fuzzy logic controls the fan speed according to the distance between the virtual lemon and the player’s main game view. An early test showed that the fuzzy logic and the MQTT protocol ran efficiently on the microcontroller board. This demonstrates that Edge computing can be useful in simple olfactory display applications.
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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) | 0.001 | 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".