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

Downsampling01:20

Downsampling

771
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
771
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

733
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
733
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

610
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
610
Fast Fourier Transform01:10

Fast Fourier Transform

1.2K
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...
1.2K
Reducing Line Loss01:18

Reducing Line Loss

442
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
442
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

430
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....
430

You might also read

Related Articles

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

Sort by
Same author

Mechanobiology of the tumor microenvironment: a review of therapeutic interactions and in vitro elasticity measurement techniques.

Journal of biomedical science·2026
Same author

Mapping Microvascular Flow via Radon Transform Ultrasound: Technical Advances and Pilot Application.

BME frontiers·2026
Same author

Self-supervised Deep Learning for Denoising in Ultrasound Microvascular Imaging.

Biomedical signal processing and control·2026
Same author

Fast 3-D Ultrasound Localization Microscopy via Projection-Based Processing Framework.

IEEE transactions on medical imaging·2026
Same author

High sensitivity ultrasound microvessel imaging for the assessment of placental health.

Placenta·2026
Same author

Super-Resolution Posterior Ocular Microvascular Imaging Using 3-D Ultrasound Localization Microscopy With a 32X32 Matrix Array.

ArXiv·2025

Related Experiment Video

Updated: Mar 26, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

16.1K

Transform-Based Channel-Data Compression to Improve the Performance of a Real-Time GPU-Based Software Beamformer.

U-Wai Lok, Pai-Chi Li

    IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
    |January 23, 2016
    PubMed
    Summary

    A new nearly lossless compression method enhances ultrasound data transfer rates for graphics processing unit (GPU) beamforming. This improves real-time imaging performance without sacrificing image quality, enabling faster data acquisition.

    More Related Videos

    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

    10.4K
    Quasi-light Storage for Optical Data Packets
    07:45

    Quasi-light Storage for Optical Data Packets

    Published on: February 6, 2014

    11.4K

    Related Experiment Videos

    Last Updated: Mar 26, 2026

    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
    11:34

    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

    Published on: December 3, 2013

    16.1K
    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

    10.4K
    Quasi-light Storage for Optical Data Packets
    07:45

    Quasi-light Storage for Optical Data Packets

    Published on: February 6, 2014

    11.4K

    Area of Science:

    • Medical Imaging
    • Ultrasound Technology
    • Signal Processing

    Background:

    • Graphics processing unit (GPU)-based software beamforming offers programmability and faster design cycles for ultrasound imaging.
    • High data rates for ultrasound radio-frequency (RF) data transfer between hardware and software back ends limit real-time performance.
    • Existing decompression methods are often inefficient on GPUs, creating a bottleneck, and lossless compression is crucial to maintain image quality.

    Purpose of the Study:

    • To analyze factors limiting lossless compression efficiency in GPU-based ultrasound beamforming.
    • To propose a nearly lossless compression method to enhance compression efficiency and data acquisition rates.
    • To evaluate the performance and feasibility of the proposed method in a real-time system.

    Main Methods:

    • Analysis of lossless compression algorithms to identify limitations.
    • Development of a nearly lossless compression technique using transformation coding to suppress amplitude data.
    • Implementation and testing of the proposed method on a 64-channel ultrasound system with USB 3.0 data transfer.

    Main Results:

    • The proposed nearly lossless compression method improved the compression ratio from 1.8 to 2.5.
    • Decompression of a single frame on a GPU took only milliseconds, with negligible impact on spatial and contrast resolutions.
    • The 64-channel system demonstrated feasible real-time data transfer using USB 3.0 for practical imaging applications.

    Conclusions:

    • The developed nearly lossless compression algorithm significantly enhances data acquisition rates for GPU-based ultrasound beamforming.
    • The method achieves high compression ratios with minimal impact on image quality, overcoming previous GPU decompression bottlenecks.
    • The successful implementation in a 64-channel system validates its practical applicability in real-time ultrasound imaging.