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Related Concept Videos

Downsampling01:20

Downsampling

231
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...
231
Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
236
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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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...
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Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

401
A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

440
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Related Experiment Video

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Visualizing Visual Adaptation
04:43

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Context-aware adaptation of mobile video decoding resolution.

Octavian Machidon1, Jani Asprov1, Tine Fajfar1

  • 1Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.

Multimedia Tools and Applications
|October 10, 2022
PubMed
Summary
This summary is machine-generated.

Mobile app energy use is high, but users accept lower video quality based on activity and personality. This research optimizes mobile energy by adapting playback resolution to user context.

Keywords:
Approximate computingContext inferenceMobile computingSpatial informationTemporal informationVideo decoding

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Area of Science:

  • Mobile computing
  • Human-computer interaction
  • Energy efficiency

Background:

  • Mobile computing growth is limited by battery technology.
  • Increasing energy demands of mobile applications strain current battery capabilities.
  • User perception and individual needs raise questions about delivering maximum quality in all scenarios.

Purpose of the Study:

  • Investigate the interplay of user physical activity, video characteristics, and personality traits.
  • Determine how these factors influence the minimum acceptable video playback resolution.
  • Explore opportunities for energy saving in mobile devices through context-aware adjustments.

Main Methods:

  • Conducted two studies involving 45 participants.
  • Analyzed the influence of physical activity, spatial/temporal video properties, and user personality.
  • Developed predictive models for minimal acceptable playback resolution.

Main Results:

  • Minimal acceptable video resolution significantly varies based on contextual factors.
  • User's physical activity, video properties, and personality traits jointly affect perceived quality thresholds.
  • Lower playback resolutions were measured to reduce power consumption.

Conclusions:

  • Context-adaptable approximate computing can save mobile energy.
  • Adjusting video playback resolution based on user context offers a viable energy-saving strategy.
  • Future mobile systems can leverage these findings for improved energy efficiency.