SST: Software Sonic Thermometer on Acoustic-Enabled IoT Devices
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
Temperature is an important data source for weather forecasting, agriculture irrigation, anomaly detection, etc. While temperature measurement can be achieved via low-cost yet standalone hardware with reasonable accuracy, integrating thermal sensing into ubiquitous computing devices is highly non-trivial due to the design requirement for specific heat isolation and proper device layout. In this paper, we present the first integrated thermometer using commercial-off-the-shelf acoustic-enabled devices. Our software sonic thermometer (SST) utilizes on-board dual microphones on commodity mobile devices to estimate sound speed, which has a known relation with temperature. To precisely measure temperature via sound speed, we propose a chirp mixing approach to circumvent low sampling rates on commodity hardware and design a pipeline of signal processing blocks to handle channel distortions. SST, for the first time, empowers ubiquitous computing devices with thermal sensing capability. It is portable and cost-effective, making it competitive with current thermometers using dedicated hardware. SST is potential to facilitate many interesting applications such as large-scale distributed thermal sensing, yielding high temporal/spatial resolutions with unimaginable low costs. We implement SST on a commodity platform and results show that SST achieves a median accuracy of <inline-formula><tex-math notation="LaTeX">${0.5^\circ \mathrm{C}}$</tex-math></inline-formula> even at varying humidity levels.
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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 | 0.001 |
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