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

Fast Fourier Transform01:10

Fast Fourier Transform

703
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
703
Parallel Processing01:20

Parallel Processing

482
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
482
Discrete Fourier Transform01:15

Discrete Fourier Transform

673
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
673
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

862
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
862
Parseval's Theorem for Fourier transform01:15

Parseval's Theorem for Fourier transform

1.8K
Parseval's theorem is a fundamental principle in signal processing that enables the calculation of a signal's energy in either the time domain or the frequency domain. This theorem is pivotal in demonstrating energy conservation between these two domains, ensuring that the computed energy value remains consistent regardless of the domain of analysis.
To understand Parseval's theorem, it is essential to first comprehend how signal energy is typically calculated. When considering a...
1.8K
Phasor Arithmetics01:13

Phasor Arithmetics

592
Phasors and their corresponding sinusoids are interrelated, offering unique insights into the behavior of alternating current (AC) circuits. One way to understand this relationship is through the operations of differentiation and integration in both the time and phasor domains.
When the derivative of a sinusoid is taken in the time domain, it transforms into its corresponding phasor multiplied by j-omega (jω) in the phasor domain, where j is the imaginary unit, and ω is the angular...
592

You might also read

Related Articles

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

Sort by
Same author

Hologram computation based on sparse matrix multiplication.

Optics express·2026
Same author

Perceptual quality assessment in digital pathology: Modeling diagnostic usability from expert opinions.

Computer methods and programs in biomedicine·2026
Same author

Multi-Path Interference Challenges and Suggested Solution for Correlation-Assisted Direct Time-of-Flight.

Sensors (Basel, Switzerland)·2026
Same author

Modified split-Lohmann holography: a shift- and ringing-free approach.

Optics letters·2026
Same author

Computer-generated holography using the generalized Van Cittert-Zernike Schell propagator.

Optics letters·2026
Same author

Sub-sampled single-step Fresnel diffraction for efficient computation of high-resolution holograms.

Optics letters·2026
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: Dec 7, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

12.6K

Dedicated processor for hologram calculation using sparse Fourier bases.

Daiki Yasuki, David Blinder, Tomoyoshi Shimobaba

    Applied Optics
    |September 25, 2020
    PubMed
    Summary
    This summary is machine-generated.

    A new processor speeds up hologram generation using the short-time Fourier transform (STFT) algorithm with fixed-point math and lookup tables. This dedicated hardware achieves faster calculations compared to traditional floating-point methods.

    More Related Videos

    Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display
    09:04

    Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display

    Published on: January 14, 2020

    10.1K
    Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization
    10:28

    Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization

    Published on: July 5, 2016

    10.6K

    Related Experiment Videos

    Last Updated: Dec 7, 2025

    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
    10:16

    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

    Published on: February 8, 2014

    12.6K
    Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display
    09:04

    Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display

    Published on: January 14, 2020

    10.1K
    Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization
    10:28

    Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization

    Published on: July 5, 2016

    10.6K

    Area of Science:

    • Digital Signal Processing
    • Holography
    • Computer Engineering

    Background:

    • The short-time Fourier transform (STFT) is a signal processing technique used for analyzing signals that change over time.
    • Hologram generation is computationally intensive, often requiring significant processing power.
    • Field-programmable gate arrays (FPGAs) offer a customizable hardware platform for accelerating complex algorithms.

    Purpose of the Study:

    • To design and implement a dedicated hardware processor for hologram generation using the STFT algorithm.
    • To optimize the STFT algorithm for fixed-point arithmetic and reduce computational cost through lookup tables (LUTs).
    • To evaluate the performance of the developed processor in terms of speed and efficiency.

    Main Methods:

    • Implementation of the STFT algorithm on a field-programmable gate array (FPGA).
    • Utilization of fixed-point arithmetic for all computational operations.
    • Integration of lookup tables (LUTs) for trigonometric and error functions to minimize calculations.
    • Development of a dedicated circuit architecture enabling parallel processing.

    Main Results:

    • A dedicated FPGA-based processor capable of executing the STFT algorithm was successfully developed.
    • The processor utilizes fixed-point arithmetic and LUTs, significantly reducing computational complexity.
    • Parallel operations were enabled through a specialized circuit architecture.
    • The STFT-based algorithm with fixed-point arithmetic and LUTs demonstrated higher hologram generation speeds compared to floating-point arithmetic.

    Conclusions:

    • The developed FPGA processor provides an efficient hardware solution for accelerating hologram generation using the STFT algorithm.
    • Fixed-point arithmetic and LUTs are effective strategies for optimizing computationally demanding algorithms on dedicated hardware.
    • The parallel architecture further enhances processing speed, making it suitable for real-time applications.