Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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...
Numerical Calculations01:24

Numerical Calculations

In engineering applications, the representation of the numerical value is critical. Presenting or reporting the answer is one of the essential parts of engineering practices. Numerical calculations are performed using handheld calculators or computers since numerically accurate answers are always preferred.
The solution to a problem is obtained using different methods. While manually solving algebraic symbols is one of the most common methods, the graphical method is often preferred. Computers...
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
Critical Numbers and the Closed Interval Method01:21

Critical Numbers and the Closed Interval Method

Understanding the maximum and minimum values of a function is essential for analyzing its overall behavior. These values, often referred to as extrema, provide insight into how a function behaves across its domain. In mathematical terms, extrema can be either local—representing peaks and valleys within a limited region—or absolute, indicating the highest or lowest points over an entire interval.A function’s extrema occur at critical numbers, which are values in the domain where the derivative...
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...
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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Combination of 3D and 2D Small and Wide Angle X-Ray Scattering Imaging Reveals Diminished Bone Quality in the Superior Human Femoral Neck Cortex.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

In situ ptychographic x-ray nanotomography of temperature-controlled crystallization processes.

Nature communications·2026
Same author

In situ ptychographic nanotomography captures activation, mobility, and deactivation of supported catalysts.

Nature communications·2026
Same author

Impact of Microporous Layer Composition on the Water Content in the Membrane Electrode Assembly of Polymer Electrolyte Fuel Cells.

ACS applied materials & interfaces·2026
Same author

Nondestructive X-ray tomography of brain tissue ultrastructure.

Nature methods·2025
Same author

Experimental comparison of X-ray ptychographic and holographic nanotomography of metal-stained neuronal tissue.

Optics express·2025

Related Experiment Video

Updated: Jun 8, 2026

Development of a Quantitative Recombinase Polymerase Amplification Assay with an Internal Positive Control
08:37

Development of a Quantitative Recombinase Polymerase Amplification Assay with an Internal Positive Control

Published on: March 30, 2015

Validation of quantitative Ronchi test through numerical propagation.

Sukmock Lee1, Manuel Guizar-Sicairos

  • 1Department of Physics, Inha University, Incheon, 402-751, Republic of Korea. smlee@inha.ac.kr

Optics Express
|October 14, 2010
PubMed
Summary
This summary is machine-generated.

Quantitative Ronchigram analysis accurately retrieves wavefront aberrations and beam parameters. Numerical simulations confirm the technique

More Related Videos

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
06:16

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

Conducting Respiratory Oscillometry in an Outpatient Setting
14:49

Conducting Respiratory Oscillometry in an Outpatient Setting

Published on: April 8, 2022

Related Experiment Videos

Last Updated: Jun 8, 2026

Development of a Quantitative Recombinase Polymerase Amplification Assay with an Internal Positive Control
08:37

Development of a Quantitative Recombinase Polymerase Amplification Assay with an Internal Positive Control

Published on: March 30, 2015

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
06:16

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

Conducting Respiratory Oscillometry in an Outpatient Setting
14:49

Conducting Respiratory Oscillometry in an Outpatient Setting

Published on: April 8, 2022

Area of Science:

  • Optical metrology
  • Wavefront sensing

Background:

  • Ronchigrams are used for wavefront sensing.
  • Quantitative analysis of Ronchigrams can determine wavefront aberrations, beam F/#, and ruling distance.

Purpose of the Study:

  • To validate the quantitative analysis of Ronchigrams for wavefront sensing.
  • To assess the accuracy and robustness of this wavefront measurement technique.

Main Methods:

  • Detailed numerical simulations.
  • Analysis of experimental Ronchigrams to retrieve wavefront aberrations, F/#, and ruling distance.
  • Numerical simulation of Ronchigrams using retrieved parameters.

Main Results:

  • Retrieved parameters from experimental Ronchigrams showed excellent agreement with numerical simulations.
  • The quantitative analysis accurately determined wavefront aberrations, F/#, and ruling distance.

Conclusions:

  • The quantitative analysis of Ronchigrams is a validated and accurate method for wavefront sensing.
  • This technique provides a reliable tool for examining wavefront measurement accuracy and robustness.