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Related Concept Videos

Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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...
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...
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...
The Phase Rule01:20

The Phase Rule

The phase rule describes the relationship between the variance (degrees of freedom), the number of components, and the number of phases in a system at equilibrium.Variance is a concept that denotes the number of independent intensive properties (properties are those that do not depend on the amount of material in the system), such as temperature, pressure, and composition, that can be altered without impacting the number of phases in equilibrium.In a single-component system, such as pure water,...
Time and frequency -Domain Interpretation of Phase-lag Control01:21

Time and frequency -Domain Interpretation of Phase-lag Control

Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any finite,...

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Related Experiment Video

Updated: Jun 8, 2026

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies
05:59

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

Published on: October 6, 2023

Effects of systematic phase errors on phase-only correlation.

R W Cohn, J L Horner

    Applied Optics
    |October 12, 2010
    PubMed
    Summary
    This summary is machine-generated.

    Phase errors in optical correlators reduce performance. A new model predicts correlation peak amplitude based on phase error, aligning with computer simulations for improved optical system design.

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    Sample Drift Correction Following 4D Confocal Time-lapse Imaging
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    Sample Drift Correction Following 4D Confocal Time-lapse Imaging

    Published on: April 12, 2014

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    Last Updated: Jun 8, 2026

    New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies
    05:59

    New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

    Published on: October 6, 2023

    Sample Drift Correction Following 4D Confocal Time-lapse Imaging
    10:04

    Sample Drift Correction Following 4D Confocal Time-lapse Imaging

    Published on: April 12, 2014

    Area of Science:

    • Optics and Photonics
    • Information Processing

    Background:

    • Phase-only optical correlators are susceptible to performance degradation.
    • Deviations from ideal matched filter phase, caused by spatial light modulators and interface circuits, introduce systematic phase errors.

    Purpose of the Study:

    • To model the impact of systematic phase errors on the correlation-peak amplitude in phase-only optical correlators.
    • To develop a predictive model for correlation performance under non-ideal phase conditions.

    Main Methods:

    • Development of a mathematical model relating correlation-peak amplitude to phase error.
    • Approximation of correlation peak as a product of error phasors and average filter plane amplitude.
    • Validation of the model using computer simulations with gray-scale images.

    Main Results:

    • The correlation peak amplitude is reasonably approximated by the developed model.
    • The model accurately predicts trends observed in computer simulations.
    • Systematic phase errors significantly impact correlator performance.

    Conclusions:

    • The proposed model provides a valuable tool for understanding and predicting the effects of phase errors in optical correlators.
    • The findings can guide the design of more robust optical processing systems.
    • Minimizing phase errors is crucial for optimizing the performance of phase-only optical correlators.