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Related Concept Videos

Reducing Line Loss01:18

Reducing 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.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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Linear Approximation in Frequency Domain01:26

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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....
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Boundary Conditions: Lossless Lines01:21

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Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
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Linear Approximation in Time Domain01:21

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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,...
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Transformation of Plane Strain01:12

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When analyzing elongated structures like bars subjected to uniformly distributed loads, it is essential to understand the transformation of plane strain when coordinate axes are rotated. This transformation helps to assess how material deformation characteristics vary with orientation, which is crucial in materials science and structural engineering.
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Lossy Lines and Overvoltages01:22

Lossy Lines and Overvoltages

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Transmission-line series resistance and shunt conductance cause three primary effects: attenuation, distortion, and power losses.
Attenuation
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A case study on entropy-aware block-based linear transforms for lossless image compression.

Borut Žalik1, David Podgorelec2, Ivana Kolingerová3

  • 1Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000, Maribor, Slovenia. borut.zalik@um.si.

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Summary
This summary is machine-generated.

A new block optimization framework significantly reduces image information entropy for lossless image compression. This method outperforms JPEG LS and CALIC, achieving smaller file sizes despite added block information.

Keywords:
Computer scienceInformation entropyInverse distance transformPredictionString transformations

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Area of Science:

  • Computer Science
  • Image Processing
  • Data Compression

Background:

  • Data compression is vital for managing data-intensive images.
  • Lossless image compression is particularly challenging, often relying on prediction and pixel transformations.
  • Reducing information entropy is key to effective image compression.

Purpose of the Study:

  • Introduce a novel block optimization programming framework for raster image compression experiments.
  • Evaluate the framework's effectiveness in reducing information entropy compared to existing methods.
  • Assess the overall efficiency, including file size, of the proposed framework.

Main Methods:

  • Implemented eleven diverse compression methods within the block optimization framework.
  • Included prediction, string transformation, and inverse distance weighting (interpolation) techniques.
  • Conducted experiments on thirty-two greyscale raster images of varying resolutions and content.

Main Results:

  • The block optimization framework demonstrated superior information entropy reduction compared to JPEG LS and CALIC predictors.
  • Analysis confirmed that the framework achieves smaller estimated file sizes than JPEG LS and CALIC.
  • The additional information cost per block was evaluated and found to be offset by compression gains.

Conclusions:

  • The proposed block optimization framework offers a more effective approach to lossless image compression.
  • It surpasses established methods like JPEG LS and CALIC in reducing information entropy and file size.
  • The framework provides a versatile platform for further research in image data compression.