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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2509.21311 |
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| _version_ | 1866908869659197440 |
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| author | Kaparounakis, Orestis Stanley-Marbell, Phillip |
| author_facet | Kaparounakis, Orestis Stanley-Marbell, Phillip |
| contents | Modern data-driven applications that make real-time decisions increasingly depend on advanced sensors which use pre-stored calibration data. In such applications, accurate characterization of sensor output uncertainty is important for reliable data interpretation. Here, we present a method for real-time on-device dynamic uncertainty quantification for sensor outputs which depend on pre-stored calibration data. We show how sensor calibration compensation equations (essential in advanced sensing systems) propagate uncertainties resulting from the quantization of calibration parameters to the sensor output. We use a low-cost thermal sensor as a motivating example and show these ideas are practical and possible on actual embedded sensor systems by prototyping them on two commercially-available uncertainty tracking hardware platforms. One has average power dissipation 16.7 mW and achieves 42.9x speedup compared to the equal-accuracy Monte Carlo computation (the status quo), and the other 147.15 mW and achieves 94.4x speedup. We present a proof-of-usefulness application using the quantified uncertainty in edge detection over ten test scenes where we show accuracy and precision average improvement by 4.97 and 40.25 percentage points, respectively, trading off sensitivity. Another application example examines uncertainty quantification for four different calibration-data storage scenarios and compute that a 48% increase in memory yields 75% smaller uncertainty metrics over the baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21311 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Digital Methods to Quantify Sensor Output Uncertainty in Real Time Kaparounakis, Orestis Stanley-Marbell, Phillip Signal Processing I.4.1; G.3; E.4; H.3 Modern data-driven applications that make real-time decisions increasingly depend on advanced sensors which use pre-stored calibration data. In such applications, accurate characterization of sensor output uncertainty is important for reliable data interpretation. Here, we present a method for real-time on-device dynamic uncertainty quantification for sensor outputs which depend on pre-stored calibration data. We show how sensor calibration compensation equations (essential in advanced sensing systems) propagate uncertainties resulting from the quantization of calibration parameters to the sensor output. We use a low-cost thermal sensor as a motivating example and show these ideas are practical and possible on actual embedded sensor systems by prototyping them on two commercially-available uncertainty tracking hardware platforms. One has average power dissipation 16.7 mW and achieves 42.9x speedup compared to the equal-accuracy Monte Carlo computation (the status quo), and the other 147.15 mW and achieves 94.4x speedup. We present a proof-of-usefulness application using the quantified uncertainty in edge detection over ten test scenes where we show accuracy and precision average improvement by 4.97 and 40.25 percentage points, respectively, trading off sensitivity. Another application example examines uncertainty quantification for four different calibration-data storage scenarios and compute that a 48% increase in memory yields 75% smaller uncertainty metrics over the baseline. |
| title | Digital Methods to Quantify Sensor Output Uncertainty in Real Time |
| topic | Signal Processing I.4.1; G.3; E.4; H.3 |
| url | https://arxiv.org/abs/2509.21311 |