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Related Concept Videos

Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...

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Related Experiment Video

Updated: May 15, 2026

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

Digital methods to quantify sensor output uncertainty in real time.

Orestis Kaparounakis1, Phillip Stanley-Marbell2,3

  • 1Physical Computation Laboratory, Department of Engineering, University of Cambridge, Cambridge, UK. ok302@cam.ac.uk.

Communications Engineering
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a real-time method for quantifying sensor output uncertainty on devices. It improves decision-making in data-driven applications by accurately tracking sensor reliability.

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Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
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Related Experiment Videos

Last Updated: May 15, 2026

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Area of Science:

  • Sensor technology
  • Embedded systems
  • Data science

Background:

  • Real-time decision-making applications rely on sensors with pre-stored calibration data.
  • Accurate characterization of sensor output uncertainty is crucial for reliable data interpretation.

Purpose of the Study:

  • To present a method for real-time, on-device dynamic uncertainty quantification for sensor outputs.
  • To demonstrate how calibration parameter quantization affects sensor output uncertainty.

Main Methods:

  • Developed a method for real-time uncertainty quantification on embedded systems.
  • Prototyped the method on commercial uncertainty-tracking hardware platforms.
  • Evaluated performance against Monte Carlo methods and analyzed calibration data storage scenarios.

Main Results:

  • Achieved significant speedups (42.9× and 94.4×) compared to Monte Carlo simulations.
  • Demonstrated improved accuracy (4.97 pp) and precision (40.25 pp) in an edge-detection application.
  • Showed that increased memory (48%) can yield substantial reductions (75%) in uncertainty metrics.

Conclusions:

  • The proposed method is practical for embedded sensor systems, enabling dynamic uncertainty quantification.
  • This approach enhances decision-making in critical applications requiring reliable sensor data.
  • Optimizing calibration data storage can significantly reduce output uncertainty metrics.