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

Time and frequency -Domain Interpretation of Phase-lead Control01:24

Time and frequency -Domain Interpretation of Phase-lead Control

76
Phase-lead controllers are commonly used in various control systems to enhance response speed and stability. Adjusting the brightness on a television screen offers a practical example of phase-lead control. When contrast is enhanced, a phase-lead controller is employed. Mathematically, phase-lead control is identified when the first parameter is smaller than the second.
The design of phase-lead control involves the strategic placement of poles and zeros to balance steady-state error and system...
76
Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

7.5K
Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
7.5K

You might also read

Related Articles

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

Sort by
Same author

CRISPR/Cas9 in locusts: Successful establishment of an olfactory deficiency line by targeting the mutagenesis of an odorant receptor co-receptor (Orco).

Insect biochemistry and molecular biology·2016
Same author

To Be or Not To Be Humorous? Cross Cultural Perspectives on Humor.

Frontiers in psychology·2016
Same author

Armadillo Repeat-Containing Protein 8 (ARMC8) Silencing Inhibits Proliferation and Invasion in Osteosarcoma Cells.

Oncology research·2016
Same author

Knockdown of DDX46 Inhibits the Invasion and Tumorigenesis in Osteosarcoma Cells.

Oncology research·2016
Same author

Corrigendum: The Associations of Dyadic Coping and Relationship Satisfaction Vary between and within Nations: A 35-Nation Study.

Frontiers in psychology·2016
Same author

Changes in c-Kit expression levels during the course of radiation therapy for nasopharyngeal carcinoma.

Biomedical reports·2016

Related Experiment Video

Updated: Jun 7, 2025

Preparation of Liquid Crystal Networks for Macroscopic Oscillatory Motion Induced by Light
07:56

Preparation of Liquid Crystal Networks for Macroscopic Oscillatory Motion Induced by Light

Published on: September 20, 2017

11.5K

Complex phase modulation of liquid crystal devices with deep learning.

Qian Chen, Weiping Ding, Feng Jiang

    Optics Express
    |November 14, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a deep learning method for precise phase modulation in liquid crystal (LC) devices. It overcomes challenges in single and multi-electrode designs, improving accuracy for complex phase distributions.

    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

    9.7K
    An Electrochemical Cholesteric Liquid Crystalline Device for Quick and Low-Voltage Color Modulation
    10:33

    An Electrochemical Cholesteric Liquid Crystalline Device for Quick and Low-Voltage Color Modulation

    Published on: February 27, 2019

    8.4K

    Related Experiment Videos

    Last Updated: Jun 7, 2025

    Preparation of Liquid Crystal Networks for Macroscopic Oscillatory Motion Induced by Light
    07:56

    Preparation of Liquid Crystal Networks for Macroscopic Oscillatory Motion Induced by Light

    Published on: September 20, 2017

    11.5K
    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

    9.7K
    An Electrochemical Cholesteric Liquid Crystalline Device for Quick and Low-Voltage Color Modulation
    10:33

    An Electrochemical Cholesteric Liquid Crystalline Device for Quick and Low-Voltage Color Modulation

    Published on: February 27, 2019

    8.4K

    Area of Science:

    • Optoelectronics
    • Materials Science
    • Artificial Intelligence

    Background:

    • Achieving complex phase distributions in single-electrode liquid crystal (LC) devices is difficult.
    • Multi-electrode LC devices face modulation accuracy and complexity issues with increasing pixel resolution and decreasing electrode size.
    • Fringe field effects complicate precise phase modulation in LC devices.

    Purpose of the Study:

    • To demonstrate a deep learning-based phase modulation method for liquid crystal (LC) devices.
    • To address the challenges associated with phase modulation in both single and multi-electrode LC configurations.
    • To enable accurate and precise phase modulation distributions by mitigating fringe field effects.

    Main Methods:

    • Developed a deep learning model to map phase retardation distribution to electric field distribution in LC devices.
    • Utilized the concept of electric field modulation for phase control in LC devices.
    • Validated the method's effectiveness in mitigating fringe field effects.

    Main Results:

    • Successfully demonstrated a deep learning-based phase modulation technique for LC devices.
    • The proposed method effectively mitigates phase modulation issues caused by fringe fields.
    • Achieved accurate and precise phase modulation distributions, overcoming limitations of conventional methods.

    Conclusions:

    • The deep learning approach offers an effective solution for precise phase modulation in LC devices.
    • This method enhances the feasibility of complex phase distributions in LC device applications.
    • The study highlights the potential of AI in advancing optoelectronic device performance.