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

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

363
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
363
Frequency-Domain Interpretation of PD Control01:24

Frequency-Domain Interpretation of PD Control

359
Proportional-Derivative (PD) controllers are widely used in fan control systems to improve stability and performance. A fan control system can be effectively represented using a Bode plot to illustrate the impact of a PD controller through its transfer function. The Bode plot visually conveys how PD control modifies the fan's response across various frequencies, providing a frequency domain interpretation of the controller's behavior.
The proportional control gain, combined with the...
359
Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

405
Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
405
Time and frequency -Domain Interpretation of Phase-lead Control01:24

Time and frequency -Domain Interpretation of Phase-lead Control

440
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...
440
Time and frequency -Domain Interpretation of Phase-lag Control01:21

Time and frequency -Domain Interpretation of Phase-lag Control

402
Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
402
The Sense of Self: Reflected Self-Appraisal and Social Comparison02:57

The Sense of Self: Reflected Self-Appraisal and Social Comparison

55.6K
According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
55.6K

You might also read

Related Articles

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

Sort by
Same author

Huang Qi Decoction Prevents BDL-Induced Liver Fibrosis Through Inhibition of Notch Signaling Activation.

The American journal of Chinese medicine·2017
Same author

Astragaloside IV Attenuates Podocyte Apoptosis Mediated by Endoplasmic Reticulum Stress through Upregulating Sarco/Endoplasmic Reticulum Ca<sup>2+</sup>-ATPase 2 Expression in Diabetic Nephropathy.

Frontiers in pharmacology·2017
Same author

A bio-chemical application of N-GQDs and g-C<sub>3</sub>N<sub>4</sub> QDs sensitized TiO<sub>2</sub> nanopillars for the quantitative detection of pcDNA3-HBV.

Biosensors & bioelectronics·2017
Same author

Clinical and imaging analysis of subclinical hemophilia combined with coxarthrosis: case report and literature review.

SpringerPlus·2016
Same author

On the summertime air quality and related photochemical processes in the megacity Shanghai, China.

The Science of the total environment·2016
Same author

Cuticular Wax Accumulation Is Associated with Drought Tolerance in Wheat Near-Isogenic Lines.

Frontiers in plant science·2016

Related Experiment Video

Updated: Jan 25, 2026

Luminescence Lifetime Imaging of O2 with a Frequency-Domain-Based Camera System
08:35

Luminescence Lifetime Imaging of O2 with a Frequency-Domain-Based Camera System

Published on: December 16, 2019

9.8K

Accelerating wavefront-sensing-based autofocusing using pixel reduction in spatial and frequency domains.

Jing Xu, Yan Kong, Zhilong Jiang

    Applied Optics
    |May 3, 2019
    PubMed
    Summary

    A new pixel-reduced wavefront-sensing-based autofocus (PRWSA) method speeds up image focusing. This technique reduces processing time by using fewer pixels for precise focal plane determination, enabling high-speed applications.

    More Related Videos

    The Frequency Domain Thermoreflectance Technique for Thermal Property Measurements
    09:10

    The Frequency Domain Thermoreflectance Technique for Thermal Property Measurements

    Published on: December 5, 2025

    630
    Fabrication of Carbon Nanotube High-Frequency Nanoelectronic Biosensor for Sensing in High Ionic Strength Solutions
    12:20

    Fabrication of Carbon Nanotube High-Frequency Nanoelectronic Biosensor for Sensing in High Ionic Strength Solutions

    Published on: July 22, 2013

    18.7K

    Related Experiment Videos

    Last Updated: Jan 25, 2026

    Luminescence Lifetime Imaging of O2 with a Frequency-Domain-Based Camera System
    08:35

    Luminescence Lifetime Imaging of O2 with a Frequency-Domain-Based Camera System

    Published on: December 16, 2019

    9.8K
    The Frequency Domain Thermoreflectance Technique for Thermal Property Measurements
    09:10

    The Frequency Domain Thermoreflectance Technique for Thermal Property Measurements

    Published on: December 5, 2025

    630
    Fabrication of Carbon Nanotube High-Frequency Nanoelectronic Biosensor for Sensing in High Ionic Strength Solutions
    12:20

    Fabrication of Carbon Nanotube High-Frequency Nanoelectronic Biosensor for Sensing in High Ionic Strength Solutions

    Published on: July 22, 2013

    18.7K

    Area of Science:

    • Optical microscopy
    • Image processing
    • Autofocusing technology

    Background:

    • Wavefront-sensing-based autofocusing offers precise focal plane determination with minimal images.
    • However, its computational demands (phase retrieval, wavefront propagation) limit high-speed applications.

    Purpose of the Study:

    • To accelerate the processing speed of wavefront-sensing-based autofocusing.
    • To introduce a novel pixel-reduced wavefront-sensing-based autofocus (PRWSA) method for faster, high-speed applications.

    Main Methods:

    • Proposed PRWSA method utilizes pixel reduction in both spatial (field of interest selection) and frequency (image compression) domains.
    • Simulation and experimental validation were performed to determine and test pixel reduction criteria.

    Main Results:

    • The PRWSA method significantly decreases processing time by employing a wavefront with fewer pixels.
    • Both simulations and experiments confirmed the effective implementation of PRWSA for various specimens.

    Conclusions:

    • The PRWSA method precisely locates the focal plane with a simple setup.
    • Its accelerated processing speed makes it a potential tool for high-speed autofocusing in microscopy and other optical systems.