Machine Learning for Sensing Applications: A Tutorial
Bibliographic record
Abstract
The developments in microsensor fabrication over the past few decades have contributed to the availability of a wide range of sensors with varying degrees of performance and cost. Many of the recent waves of technological developments such as the Internet-of-Things or wearables rely on such sensors. With the increasing availability of on-board and remote computing power, the trend is to go beyond the simple quantification of events and (re)create context from sensor data using statistical signal processing, or as commonly known, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">machine learning</i> . Within the scope of this tutorial, we highlight the applications of machine learning in sensing and introduce the fundamental stages for creating data-driven models based on simple machine learning algorithms. We focus on algorithms that are simple to implement, provide accurate results, and yet remain <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">understandable</i> to the human developer. The ability to follow how a data-driven model functions is essential in many engineering applications where a trade-off between accuracy and reliability is often acceptable. We provide case studies that utilize the presented material to solve different real-life applications. These examples demonstrate the importance of choosing appropriate features, selecting algorithms, and finally, a study on figuring out the environmental conditions from sensor data.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".