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

Methods of Medium Optimization01:28

Methods of Medium Optimization

63
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
63
Wave Parameters01:10

Wave Parameters

9.9K
The simplest mechanical waves are associated with simple harmonic motion and repeat themselves for several cycles. These simple harmonic waves can be modeled using a combination of sine and cosine functions. Consider a simplified surface water wave that moves across the water's surface. Unlike complex ocean waves, in surface water waves, water moves vertically, oscillating up and down, whereas the disturbance of the wave moves horizontally through the medium. If a seagull is floating on the...
9.9K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

410
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
410

You might also read

Related Articles

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

Sort by
Same author

Two-Color Fluorescence Thermometry Using Lock-in Amplifiers for Background Suppression.

Sensors (Basel, Switzerland)·2025
Same author

Multi-frame x-ray radiography and image tracking for quantification of expansion in laser-driven tin ejecta microjets.

The Review of scientific instruments·2024
Same author

Subsurface Spectroscopy of Thermal Degradation Inside an Inert Plastic Bonded Explosive (PBX) Simulant Using Feedback-Assisted Wavefront Shaping.

Applied spectroscopy·2024
Same author

Single-Shot Standoff Hyperspectral Raman Imaging of a Chemical Warfare Agent Simulant.

Applied spectroscopy·2024
Same author

Absorption-invariant focusing efficiency for wavefront-shaping controlled reflection from absorbing disordered media.

Journal of the Optical Society of America. A, Optics, image science, and vision·2024
Same author

Nursing Home Residents' Use of Radiography in New Brunswick: A Case for Mobile Radiography?

Healthcare policy = Politiques de sante·2023

Related Experiment Video

Updated: Apr 12, 2026

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
09:43

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

10.4K

Microgenetic optimization algorithm for optimal wavefront shaping.

Benjamin R Anderson, Patrick Price, Ray Gunawidjaja

    Applied Optics
    |May 14, 2015
    PubMed
    Summary
    This summary is machine-generated.

    A new microgenetic optimization algorithm (μGA) significantly speeds up wavefront shaping for imaging and authentication. This faster method also shows improved resistance to noise and sample decoherence compared to existing techniques.

    More Related Videos

    Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator
    08:39

    Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator

    Published on: January 28, 2019

    10.5K
    Bringing the Visible Universe into Focus with Robo-AO
    10:35

    Bringing the Visible Universe into Focus with Robo-AO

    Published on: February 12, 2013

    20.3K

    Related Experiment Videos

    Last Updated: Apr 12, 2026

    Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
    09:43

    Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

    Published on: March 20, 2017

    10.4K
    Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator
    08:39

    Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator

    Published on: January 28, 2019

    10.5K
    Bringing the Visible Universe into Focus with Robo-AO
    10:35

    Bringing the Visible Universe into Focus with Robo-AO

    Published on: February 12, 2013

    20.3K

    Area of Science:

    • Optics and Photonics
    • Computational Science
    • Algorithm Development

    Background:

    • Optimal wavefront shaping is crucial for advanced imaging and authentication.
    • Current optimization algorithms limit the speed and applicability of wavefront shaping.
    • There is a need for faster and more robust optimization methods.

    Purpose of the Study:

    • To develop a novel, faster optimization algorithm for wavefront shaping.
    • To compare the performance of the new algorithm against existing methods.
    • To evaluate the robustness of the new algorithm to noise and sample decoherence.

    Main Methods:

    • Development of a microgenetic optimization algorithm (μGA).
    • Experimental optimization of light transmission through an opaque medium.
    • Comparative analysis of μGA, iterative, and simple-genetic algorithms.

    Main Results:

    • The μGA demonstrated superior speed compared to iterative and simple-genetic algorithms.
    • Both genetic algorithms (μGA and simple-genetic) exhibited greater resistance to noise.
    • The μGA and simple-genetic algorithms showed enhanced resilience to sample decoherence.

    Conclusions:

    • The microgenetic optimization algorithm (μGA) offers a significant speed improvement for wavefront shaping.
    • μGA provides a more robust solution for wavefront shaping in challenging environments.
    • This advancement has potential implications for real-time imaging and secure authentication systems.