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

Basic Operations on Signals01:22

Basic Operations on Signals

Basic signal operations include time reversal, time scaling, time shifting, and amplitude transformations. These operations are fundamental in signal processing and analysis.
Time Reversal mirrors a continuous-time signal about the vertical axis at t=0. This is achieved by substituting t with −t. For example, if a signal x(t) is considered, the time-reversed signal is x(−t). This operation can be graphically represented, showing the mirrored signal.
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...
Properties of the z-Transform I01:17

Properties of the z-Transform I

The z-transform is a fundamental tool in digital signal processing, enabling the analysis of discrete-time systems through its various properties. It is an invaluable tool for analyzing discrete-time systems, offering a range of properties that simplify complex signal manipulations. One fundamental property is linearity. For any two discrete-time signals, the z-transform of their linear combination equals the same linear combination of their individual z-transforms. This property is essential...
Properties of Fourier Transform II01:24

Properties of Fourier Transform II

The Fourier Transform (FT) is an essential mathematical tool in signal processing, transforming a time-domain signal into its frequency-domain representation. This transformation elucidates the relationship between time and frequency domains through several properties, each revealing unique aspects of signal behavior.
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Basic signals of Fourier Transform01:07

Basic signals of Fourier Transform

The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at zero. It...

You might also read

Related Articles

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

Sort by
Same author

Diffractive-phase-element design that implements several optical functions.

Applied optics·2010
Same author

Iterative optimization of diffractive phase elements simultaneously implementing several optical functions.

Applied optics·2010
Same author

Multistage parallel algorithm for diffraction tomography.

Applied optics·2010
Same author

Gerchberg-Saxton and Yang-Gu algorithms for phase retrieval in a nonunitary transform system: a comparison.

Applied optics·2010
Same author

Iterative interlacing error diffusion for synthesis of computer-generated holograms.

Applied optics·2010
Same author

Iterative interlacing approach for synthesis of computer-generated holograms.

Applied optics·2010
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
See all related articles

Related Experiment Video

Updated: Jun 13, 2026

High-Throughput Analysis of Optical Mapping Data Using ElectroMap
07:36

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

Electrooptical processing of signal transforms.

O K Ersoy

    Applied Optics
    |May 11, 2010
    PubMed
    Summary
    This summary is machine-generated.

    New optical computing architectures enable efficient signal transform implementation. These designs utilize matrix factorization for simplified preprocessing and circular convolution operations.

    More Related Videos

    An Integrated Method for Crafting Flexible and Convenient Electrophysiological Optrodes for Multi-Region In Vivo Recording
    06:55

    An Integrated Method for Crafting Flexible and Convenient Electrophysiological Optrodes for Multi-Region In Vivo Recording

    Published on: November 21, 2024

    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

    Related Experiment Videos

    Last Updated: Jun 13, 2026

    High-Throughput Analysis of Optical Mapping Data Using ElectroMap
    07:36

    High-Throughput Analysis of Optical Mapping Data Using ElectroMap

    Published on: June 4, 2019

    An Integrated Method for Crafting Flexible and Convenient Electrophysiological Optrodes for Multi-Region In Vivo Recording
    06:55

    An Integrated Method for Crafting Flexible and Convenient Electrophysiological Optrodes for Multi-Region In Vivo Recording

    Published on: November 21, 2024

    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

    Area of Science:

    • Optics
    • Computer Science
    • Signal Processing

    Background:

    • Discrete trigonometric transforms are fundamental in signal processing.
    • Implementing these transforms efficiently is crucial for computational performance.

    Purpose of the Study:

    • To introduce novel architectures for signal transform implementation.
    • To leverage optical computing and signal processing techniques for enhanced efficiency.

    Main Methods:

    • Developing new architectures based on matrix factorization.
    • Decomposing discrete trigonometric transform matrices into preprocessing and circular convolution components.

    Main Results:

    • The proposed architectures offer a new approach to signal transform implementation.
    • The factorization method simplifies the computational structure.

    Conclusions:

    • The new architectures provide a promising direction for optical signal processing.
    • This approach can lead to more efficient computation of signal transforms.