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 Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Lines in Space01:29

Lines in Space

In three-dimensional analytic geometry, a line can be fully described using vector equations when both a point on the line and its direction are known. This approach has practical applications in fields such as engineering and surveying, where precise spatial modeling is essential. For instance, a laser beam from a surveying instrument directed across a construction site can be modeled mathematically as a line using vectors.Let the laser beam originate from a known point P₀, represented by the...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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.
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
Aliasing01:18

Aliasing

Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...

You might also read

Related Articles

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

Sort by
Same author

Aspirin therapy after heart transplantation is associated with higher myocardial perfusion reserve assessed by cardiovascular magnetic resonance imaging.

JHLT open·2026
Same author

Cardiovascular magnetic resonance to differentiate veteran athlete's heart with cavity dilatation and mild dilated cardiomyopathy.

European heart journal. Cardiovascular Imaging·2025
Same author

Quantitative cardiovascular magnetic resonance myocardial perfusion can discriminate significant cardiac allograft vasculopathy: a multi-centre study.

European heart journal. Cardiovascular Imaging·2025
Same author

Roadmap on digital holography [Invited].

Optics express·2021
Same author

Myocardial late gadolinium enhancement: a head-to-head comparison of motion-corrected balanced steady-state free precession with segmented turbo fast low angle shot.

Clinical radiology·2018
Same author

Response to correspondence by Johan Øvrevik.

Indoor air·2014

Related Experiment Video

Updated: Jun 16, 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

Linear space-variant optical processing of 1-D signals.

J W Goodman, P Kellman, E W Hansen

    Applied Optics
    |February 20, 2010
    PubMed
    Summary

    This study introduces three novel optical methods for linear space-variant filtering, overcoming limitations of traditional space-invariant systems. These techniques enable advanced optical data processing for complex applications.

    Area of Science:

    • Optics
    • Optical Data Processing
    • Signal Processing

    Background:

    • Traditional optical data processing systems are limited to linear space-invariant operations.
    • Many advanced data processing tasks require linear space-variant filtering.

    Purpose of the Study:

    • To describe three methods for performing linear space-variant processing of one-dimensional (1-D) inputs using optical systems.
    • To present experimental results for these methods.
    • To discuss the relative advantages and disadvantages of each system.

    Main Methods:

    • Development and implementation of three distinct optical systems for linear space-variant filtering.
    • Processing of one-dimensional (1-D) input data.

    Main Results:

    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

    Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy (oSLO) and Optical Coherence Tomography (OCT)
    12:22

    Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy (oSLO) and Optical Coherence Tomography (OCT)

    Published on: August 4, 2018

    Related Experiment Videos

    Last Updated: Jun 16, 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

    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

    Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy (oSLO) and Optical Coherence Tomography (OCT)
    12:22

    Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy (oSLO) and Optical Coherence Tomography (OCT)

    Published on: August 4, 2018

    • Successful demonstration of linear space-variant processing using the developed optical systems.
    • Presentation of experimental data validating the performance of each method.

    Conclusions:

    • The presented optical methods offer viable solutions for linear space-variant processing.
    • Each system has unique advantages and disadvantages, providing flexibility for different applications.