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Related Concept Videos

Line Loss01:10

Line Loss

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The different configurations of source-load connections include wye (star) and delta connections. The relationship between line and phase voltages and currents varies depending on the configuration. When the source is supplying power, it is transmitted through the wires to the load, and during this transmission, some power is absorbed by the wires, leading to line loss.
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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.
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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
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Major Losses in Pipes01:28

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Concrete exhibits specific behaviors under different compressive loads. Understanding this is crucial for understanding its structural integrity. When concrete undergoes uniaxial compression, it tends to develop cracks that run parallel to the direction of the force. These parallel cracks stem from localized tensile stresses that occur perpendicular to the compression direction. Additionally, angled cracks may appear due to the formation of shear planes.
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A framework for constraining image SNR loss due to MR raw data compression.

Matthew C Restivo1, Adrienne E Campbell-Washburn2, Peter Kellman2

  • 1Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Rm B1D47, 10 Center Dr, Bethesda, MD, 20814, USA. Matthew.restivo@nih.gov.

Magma (New York, N.Y.)
|October 27, 2018
PubMed
Summary
This summary is machine-generated.

Data compression for Magnetic Resonance Imaging (MRI) enables faster transmission during online reconstructions. This technique reduces network bottlenecks, allowing for real-time streaming and quicker cloud uploads.

Keywords:
CloudCompressionGadgetronReal timeSoftware

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Area of Science:

  • Medical Imaging
  • Data Compression
  • Network Engineering

Background:

  • Online Magnetic Resonance Imaging (MRI) reconstruction relies on high-performance computing, but network bandwidth limitations create bottlenecks.
  • Data acquisition rates in MRI can exceed available network resources, hindering real-time processing.
  • Data compression is essential for efficient transmission during online MRI exams.

Purpose of the Study:

  • To develop and evaluate a framework for data compression in online MRI reconstructions.
  • To constrain signal-to-noise ratio (SNR) loss during compression.
  • To alleviate network transmission bottlenecks in real-time MRI data streaming.

Main Methods:

  • Statistical analysis was used to determine the added noise variance from two compression libraries (custom and generic).
  • Compression error variance was limited relative to thermal noise to constrain SNR loss.
  • The framework was implemented to enable data compression for online reconstructions.

Main Results:

  • Achievable compression ratios depend on image SNR, SNR loss tolerance, and acquisition type.
  • A 1% SNR reduction resulted in approximately four to ninefold compression ratios.
  • Streaming bandwidth for free-breathing cine data was reduced from 37 MB/s to 6.1 MB/s, resolving the bottleneck.

Conclusions:

  • The developed framework facilitates data compression for online MRI reconstructions.
  • The system allows for user-defined constraints on SNR loss.
  • This tool enables real-time data streaming and accelerates cloud upload times by over fourfold.