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

Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
Color Vision01:24

Color Vision

Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

You might also read

Related Articles

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

Sort by
Same journal

Multifunctional reconfigurable terahertz metasurface based on vanadium dioxide phase transition: achieving broadband absorption and efficient polarization conversion.

Applied optics·2026
Same journal

High-Q-factor electromagnetically induced transparency utilizing quasi-bound states in the continuum in an all-dielectric terahertz metasurface.

Applied optics·2026
Same journal

Automated stitching interferometry for high-precision metrology of X-ray mirrors.

Applied optics·2026
Same journal

Experimental demonstration of an approach to designing a metal-dielectric DBR resonant cavity structure.

Applied optics·2026
Same journal

High-precision wavefront reconstruction from a single-shot interferogram using a physics-driven hybrid feature calibration network.

Applied optics·2026
Same journal

Ultra-high-Q Fano resonance based on coupled topological corner states in Kagome photonic crystals.

Applied optics·2026

Related Experiment Video

Updated: Jun 17, 2026

Measuring the Behavioral Effects of Intraocular Scatter
05:10

Measuring the Behavioral Effects of Intraocular Scatter

Published on: February 18, 2021

Backscatter effects in active night vision systems.

H Steingold, R E Strauch

    Applied Optics
    |January 15, 2010
    PubMed
    Summary
    This summary is machine-generated.

    Atmospheric backscatter limits artificial night vision. This study models active night vision systems, deriving formulas for target signal and atmospheric backscatter to analyze system performance using signal-to-noise ratio.

    More Related Videos

    Scanning Light Scattering Profiler (SLPS) Based Methodology to Quantitatively Evaluate Forward and Backward Light Scattering from Intraocular Lenses
    06:55

    Scanning Light Scattering Profiler (SLPS) Based Methodology to Quantitatively Evaluate Forward and Backward Light Scattering from Intraocular Lenses

    Published on: June 6, 2017

    Simultaneous Evaluation of Cerebral Hemodynamics and Light Scattering Properties of the In Vivo Rat Brain Using Multispectral Diffuse Reflectance Imaging
    07:06

    Simultaneous Evaluation of Cerebral Hemodynamics and Light Scattering Properties of the In Vivo Rat Brain Using Multispectral Diffuse Reflectance Imaging

    Published on: May 7, 2017

    Related Experiment Videos

    Last Updated: Jun 17, 2026

    Measuring the Behavioral Effects of Intraocular Scatter
    05:10

    Measuring the Behavioral Effects of Intraocular Scatter

    Published on: February 18, 2021

    Scanning Light Scattering Profiler (SLPS) Based Methodology to Quantitatively Evaluate Forward and Backward Light Scattering from Intraocular Lenses
    06:55

    Scanning Light Scattering Profiler (SLPS) Based Methodology to Quantitatively Evaluate Forward and Backward Light Scattering from Intraocular Lenses

    Published on: June 6, 2017

    Simultaneous Evaluation of Cerebral Hemodynamics and Light Scattering Properties of the In Vivo Rat Brain Using Multispectral Diffuse Reflectance Imaging
    07:06

    Simultaneous Evaluation of Cerebral Hemodynamics and Light Scattering Properties of the In Vivo Rat Brain Using Multispectral Diffuse Reflectance Imaging

    Published on: May 7, 2017

    Area of Science:

    • Optics and Photonics
    • Remote Sensing
    • Systems Engineering

    Background:

    • Artificial illumination is crucial for night vision applications.
    • Atmospheric backscatter from illumination sources is a significant performance limitation.
    • Understanding and quantifying backscatter is essential for effective night vision system design.

    Purpose of the Study:

    • To investigate the fundamental limitation of atmospheric backscatter in artificial night vision.
    • To develop a mathematical model for active night vision systems.
    • To analyze system performance based on signal-to-noise ratio.

    Main Methods:

    • Constructed a mathematical model of an active night vision system.
    • Derived formulas for received signal from a target and atmospheric backscatter.
    • Defined signal-to-noise ratio (SNR) as a performance metric.
    • Presented performance graphs for hypothetical systems.

    Main Results:

    • Quantified the impact of atmospheric backscatter on night vision performance.
    • Established SNR as a key performance indicator.
    • Demonstrated performance variations across different hypothetical night vision systems.
    • Visualized SNR as a function of range.

    Conclusions:

    • Atmospheric backscatter is a critical factor affecting active night vision system performance.
    • The developed model provides a framework for analyzing and optimizing night vision systems.
    • Signal-to-noise ratio is a valuable metric for evaluating system effectiveness at various ranges.