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Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

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...
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

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.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
Line Loss01:10

Line Loss

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.
Line loss impacts power delivery efficiency in a balanced three-phase circuit. The symmetry in such a circuit simplifies the...
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

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...
Downsampling01:20

Downsampling

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...

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Related Experiment Videos

Network-based H.264/AVC whole frame loss visibility model and frame dropping methods.

Yueh-Lun Chang, Ting-Lan Lin, Pamela C Cosman

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 29, 2012
    PubMed
    Summary
    This summary is machine-generated.

    Many B frame losses in H.264/AVC videos go unnoticed by viewers. Predictive models can identify visually impactful frame losses for intelligent network congestion management.

    Related Experiment Videos

    Area of Science:

    • Video compression and transmission
    • Human-computer interaction
    • Network engineering

    Background:

    • Whole frame loss in H.264/AVC compressed videos is a common issue.
    • Decoders use frame copy or interpolation for concealment, affecting visual quality.
    • Understanding the perceptual impact of frame loss is crucial for network optimization.

    Purpose of the Study:

    • To evaluate the visual impact of whole frame losses using different decoding concealment methods.
    • To develop predictive models for B frame loss visibility without original video access.
    • To demonstrate the effectiveness of intelligent frame dropping for network congestion relief.

    Main Methods:

    • H.264/AVC compressed videos with introduced whole frame losses were decoded using frame copy and interpolation.
    • Human observers viewed videos and reported perceived glitches.
    • Predictive models were trained using network-node-calculable features to forecast frame loss visibility.

    Main Results:

    • Approximately 39% of whole B frame losses were not perceived by any observer.
    • Over 58% of B frame losses were perceived by 20% or fewer observers.
    • Predictive models accurately estimated B frame loss visibility.
    • Intelligent frame dropping based on visual scores outperformed random dropping.

    Conclusions:

    • A significant portion of B frame losses have minimal visual impact.
    • Predictive models enable network nodes to assess and manage frame loss visibility.
    • Visually-aware frame dropping is a superior strategy for mitigating network congestion.