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Related Concept Videos

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...
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...
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first column of the Routh...
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
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Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
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Updated: Jun 14, 2026

Quantifying Microorganisms at Low Concentrations Using Digital Holographic Microscopy (DHM)
07:27

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Published on: November 1, 2017

Representation-related errors in binary digital holograms: a unified analysis.

J P Allebach

    Applied Optics
    |March 24, 2010
    PubMed
    Summary
    This summary is machine-generated.

    Binary digital holograms produce image errors from their binary representation. This study analyzes these errors, showing that sampling methods significantly impact reconstruction quality, with optimized sampling reducing false images.

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    Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects
    10:16

    Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

    Published on: February 8, 2014

    Area of Science:

    • Optics and Photonics
    • Digital Imaging
    • Holography

    Background:

    • Binary digital holograms suffer image degradation due to the binary representation of complex-valued object spectra and computational limitations.
    • These errors manifest as false images in both desired and adjacent diffraction orders, impacting reconstruction fidelity.

    Purpose of the Study:

    • To analyze representation-related errors in binary digital holograms.
    • To investigate the impact of object spectrum sampling and spectral sample-to-binary transmittance mapping on reconstruction quality.
    • To categorize digital holograms based on sampling strategies and evaluate their effectiveness in reducing false images.

    Main Methods:

    • Analysis of false image formation in binary digital holograms.
    • Categorization of digital holograms into three types based on object spectrum sampling methods (center of cell, center of aperture, each resolvable spot).
    • Comparison of reconstruction quality across different sampling categories.

    Main Results:

    • False images in binary hologram reconstructions are highly dependent on object spectrum sampling and mapping to binary transmittance.
    • Three categories of digital holograms were identified based on sampling strategies.
    • Reconstruction quality improves with successive categories, with the third category (sampling at each resolvable spot) showing significantly fewer false images.

    Conclusions:

    • Optimized sampling strategies for binary digital holograms can substantially reduce reconstruction errors caused by binary representation.
    • Sampling the object spectrum at each resolvable spot minimizes false images in the desired reconstruction order and limits them in adjacent orders.
    • Further methods within this category focus on suppressing the remaining false images for enhanced reconstruction accuracy.